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    "# How many times is too many - and how long does an audience stay worth targeting?\n",
    "\n",
    "Two questions about the same people. You have an audience - say, everyone who visited your\n",
    "site this month - and you are paying to show them an ad. How many times should one person\n",
    "see it before the next impression stops paying for itself? And as the weeks pass and the\n",
    "people in that audience get older, when does the audience as a whole stop being worth the\n",
    "spend?\n",
    "\n",
    "Both questions have a curve behind them, and both curves come from reports you already have.\n",
    "The first is the **frequency report**: how many people saw the ad once, twice, ten times,\n",
    "and how many of each group bought. The second is the **audience's own history**: when each\n",
    "person joined it, and whether and when they bought. This notebook builds both curves the\n",
    "way the platform's own methods do, reads the two answers off them - a frequency cap and an\n",
    "audience half-life - and then does the thing most write-ups skip: it checks the answers\n",
    "against a truth it built in, on hundreds of audiences, so every claim below is scored rather\n",
    "than asserted.\n",
    "\n",
    "Seven things are worth knowing before the working.\n",
    "\n",
    "**Reading the frequency report by eye sets no cap at all, or the wrong one.** On the worked\n",
    "audience the buying rate keeps rising with frequency and never turns down, so \"cap where it\n",
    "stops improving\" says no cap - and across five hundred audiences it says that on about a\n",
    "quarter. Run the same audience with buyers dropped once they buy and the rate falls with\n",
    "frequency for a reason that has nothing to do with saturation; the same reading then says\n",
    "cap at one. Both are measured here.\n",
    "\n",
    "**The cap is an economic number, not a statistical one.** The curve says how much the next\n",
    "impression adds; what an impression costs and what a sale is worth say whether that is\n",
    "enough. The same audience gets a different cap at a different cost per thousand, and no\n",
    "cap at all costs the worked audience about eight per cent of its spend.\n",
    "\n",
    "**The curve you can see is not the curve you want.** The people who saw the ad twelve times\n",
    "are not the people who saw it twice shown ten more ads - the platform chose who to show it\n",
    "to. On the worked audience the report's cap lands three impressions past the within-person\n",
    "truth. Across audiences, a lean toward people who are merely online more pulls the report's\n",
    "cap about an impression early; a lean toward keen people pushes it late, and increasingly\n",
    "off the end of the data altogether, where the method can only say \"at least the top\n",
    "bucket\". The report cannot tell you which way its lean runs. Only an experiment settles it.\n",
    "\n",
    "**A rule of thumb is right when it happens to be right.** Twelve a month is as close as the\n",
    "curve where the best cap happens to sit near twelve, and costs about a quarter of the\n",
    "spend where the best cap is under five. The curve earns its keep on the audiences the rule\n",
    "cannot see: those that need a low cap, and those that need none.\n",
    "\n",
    "**The half-life is read off the people who were going to buy, not off everyone.** Most of an\n",
    "audience never buys. The rate-by-age table halves at about twice the true half-life; a curve\n",
    "that forgets the never-buyers stretches the half-life past a year; one that throws away the\n",
    "not-yet-bought shortens it. The method lands within twenty per cent on six audiences in\n",
    "seven.\n",
    "\n",
    "**You cannot read a half-life off an audience you have not watched long enough.** Three\n",
    "half-lives of history is what the fit needs before nine runs in ten land within twenty per\n",
    "cent, and a week-old audience returns a number that means nothing.\n",
    "\n",
    "**The data in this notebook is synthetic.** It is generated here with the answer built in.\n",
    "Run it on your own figures in the last section; nothing leaves your machine."
   ]
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     "text": [
      "seed 20260914\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import warnings\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.optimize import minimize\n",
    "from scipy.stats import chi2\n",
    "\n",
    "SEED = 20260914\n",
    "rng = np.random.default_rng(SEED)\n",
    "\n",
    "PAPER = \"#fdfcf9\"\n",
    "INK = \"#1f2124\"\n",
    "MUTED = \"#565c66\"\n",
    "LINE = \"#e6e2da\"\n",
    "SIGNAL = \"#0f6e56\"\n",
    "FLAG = \"#b06a2c\"\n",
    "NAIVE = \"#2a78d6\"\n",
    "BAND = \"#a9d6c5\"\n",
    "\n",
    "plt.rcParams.update({\n",
    "    \"figure.facecolor\": PAPER, \"axes.facecolor\": PAPER, \"savefig.facecolor\": PAPER,\n",
    "    \"axes.edgecolor\": LINE, \"axes.labelcolor\": INK, \"text.color\": INK,\n",
    "    \"xtick.color\": MUTED, \"ytick.color\": MUTED, \"axes.grid\": True,\n",
    "    \"grid.color\": LINE, \"grid.linewidth\": 0.8, \"axes.spines.top\": False,\n",
    "    \"axes.spines.right\": False, \"font.size\": 11, \"figure.dpi\": 140,\n",
    "})\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "HERE = Path.cwd()\n",
    "FIGURES = HERE / \"figures\"\n",
    "DATA = HERE / \"data\"\n",
    "FIGURES.mkdir(exist_ok=True)\n",
    "DATA.mkdir(exist_ok=True)\n",
    "\n",
    "print(f\"seed {SEED}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1cc1274",
   "metadata": {},
   "source": [
    "## Part one - how many times is too many\n",
    "\n",
    "### The audience I build, and the truth I hide from the report\n",
    "\n",
    "One online store, one retargeting audience: everyone who visited the site, shown the ad\n",
    "for twenty-eight days. The generator gives the audience three properties real audiences\n",
    "have and toy examples usually do not.\n",
    "\n",
    "**People see the ad different numbers of times.** Each person has their own rate of\n",
    "exposure - how often they are online where the ad runs - and the month's frequency is the\n",
    "sum of twenty-eight days of that. Most people see it a handful of times; a long tail sees\n",
    "it twenty or thirty.\n",
    "\n",
    "**Each extra impression adds less than the one before.** The chance a person buys rises\n",
    "with the number of times they have seen the ad along a curve that flattens - a Hill curve,\n",
    "the same shape the platform's marketing-mix model uses for spend. This is the **within-person\n",
    "truth**: what showing one more ad to one person actually does. A holdout experiment could\n",
    "measure it; a report cannot, for the reason in the next point.\n",
    "\n",
    "**The people who see it most are not a random sample.** The platform's delivery decides who\n",
    "sees the ad more, and it leans toward the people it thinks are likelier to buy. So the\n",
    "high-frequency buckets hold a keener sub-audience than the low-frequency ones, and the\n",
    "conversion rate in those buckets is partly frequency working and partly who is in them. The\n",
    "generator builds this in as a dial - how strongly exposure follows keenness - so it can be\n",
    "turned off, up, and even reversed later.\n",
    "\n",
    "Each person converts at most once, at whichever impression first tips them over, and keeps\n",
    "seeing the ad afterwards; the case where buyers are dropped from the audience is run\n",
    "separately below, because it changes the report in a way that matters."
   ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113,591 people reached over 28 days; 943 converted (0.83%)\n",
      "median person saw the ad 4 times; mean 5.5; one in ten saw it 11+ times\n",
      "an impression costs 1.20 cents; a conversion is worth 25 euros;\n",
      "so an extra impression pays when it adds more than 0.00048 to the chance of a sale (0.048 points)\n",
      "the lean built in: people who saw the ad twelve times are 1.24 times as keen as those who saw it twice\n",
      "\n",
      "label  reach  conversions      cvr\n",
      "    1  12641           29 0.002294\n",
      "    2  15986           78 0.004879\n",
      "    3  15805          101 0.006390\n",
      "    4  14206          111 0.007814\n",
      "    5  12072          103 0.008532\n",
      "    6   9621           94 0.009770\n",
      "    7   7503           78 0.010396\n",
      "    8   5922           78 0.013171\n",
      "    9   4438           63 0.014196\n",
      "   10   3358           41 0.012210\n",
      "   11   2613           35 0.013395\n",
      "   12   1991           25 0.012557\n",
      "   13   1592           21 0.013191\n",
      "   14   1234           17 0.013776\n",
      "  15+   4609           69 0.014971\n"
     ]
    }
   ],
   "source": [
    "WINDOW_DAYS = 28\n",
    "N_USERS = 120_000\n",
    "\n",
    "TRUE_CVR_MAX = 0.013        # the within-person curve's ceiling\n",
    "TRUE_K = 3.0                # half-saturation frequency\n",
    "TRUE_N = 1.5                # shape: above one, the first impression does little\n",
    "RATE_LOG_MEAN = np.log(4.2 / WINDOW_DAYS)   # median person sees about four ads a month\n",
    "RATE_LOG_SD = 0.65          # spread of exposure rates across people (log scale)\n",
    "KEEN_SD = 0.4               # spread of keenness across people (log scale)\n",
    "CONFOUND = 0.3              # how strongly exposure follows keenness (correlation of the logs)\n",
    "\n",
    "CPM_EUR = 12.0              # cost per thousand impressions\n",
    "VALUE_EUR = 25.0            # what one conversion is worth (margin, not revenue)\n",
    "COST_PER_IMPRESSION = CPM_EUR / 1000.0\n",
    "TAU = COST_PER_IMPRESSION / VALUE_EUR   # the extra conversion rate an impression must buy to pay\n",
    "\n",
    "\n",
    "def hill(f, cvr_max, k, n):\n",
    "    \"\"\"Chance of having converted after f impressions: cvr_max * f^n / (k^n + f^n).\"\"\"\n",
    "    f = np.asarray(f, dtype=float)\n",
    "    return cvr_max * f ** n / (k ** n + f ** n)\n",
    "\n",
    "\n",
    "def hill_slope(f, cvr_max, k, n):\n",
    "    \"\"\"How much the next impression adds: the derivative of the curve at f.\"\"\"\n",
    "    f = np.asarray(f, dtype=float)\n",
    "    return cvr_max * n * k ** n * f ** (n - 1) / (k ** n + f ** n) ** 2\n",
    "\n",
    "\n",
    "def draw_people(n_users, generator, rate_log_mean=RATE_LOG_MEAN, rate_log_sd=RATE_LOG_SD,\n",
    "                keen_sd=KEEN_SD, confound=CONFOUND):\n",
    "    \"\"\"Each person's exposure rate and keenness, correlated by `confound`.\n",
    "\n",
    "    Keenness is a multiplier on the within-person curve with mean one, so the average person\n",
    "    IS the within-person truth; the correlation decides who ends up in the heavy buckets.\n",
    "    \"\"\"\n",
    "    a = generator.normal(size=n_users)\n",
    "    b = generator.normal(size=n_users)\n",
    "    rate = np.exp(rate_log_mean + rate_log_sd * a)\n",
    "    keen = np.exp(keen_sd * (confound * a + np.sqrt(1 - confound ** 2) * b) - keen_sd ** 2 / 2)\n",
    "    return rate, keen\n",
    "\n",
    "\n",
    "def tipping_impression(keen, generator, cvr_max=TRUE_CVR_MAX, k=TRUE_K, n=TRUE_N):\n",
    "    \"\"\"The impression at which each person converts, or 0 for never.\n",
    "\n",
    "    A person with keenness m has converted after f impressions with chance m*hill(f). Draw one\n",
    "    uniform number per person and invert the curve: the first f where m*hill(f) reaches it.\n",
    "    \"\"\"\n",
    "    u = generator.uniform(size=len(keen))\n",
    "    v = u / keen\n",
    "    never = v >= cvr_max\n",
    "    with np.errstate(divide=\"ignore\", invalid=\"ignore\"):\n",
    "        f_star = k * (v / (cvr_max - v)) ** (1.0 / n)\n",
    "    tip = np.where(never, 0, np.ceil(f_star)).astype(int)\n",
    "    tip = np.where((~never) & (tip < 1), 1, tip)\n",
    "    return tip\n",
    "\n",
    "\n",
    "def simulate_month(n_users, generator, drop_buyers=False, **people_kwargs):\n",
    "    \"\"\"Twenty-eight days of delivery. Returns per-person (impressions, converted, keenness, tip).\n",
    "\n",
    "    With `drop_buyers`, a person leaves the audience the day after converting, so their\n",
    "    frequency in the report stops there - the way a retargeting audience that excludes\n",
    "    purchasers behaves.\n",
    "    \"\"\"\n",
    "    rate, keen = draw_people(n_users, generator, **people_kwargs)\n",
    "    tip = tipping_impression(keen, generator)\n",
    "    daily = generator.poisson(rate[:, None], size=(n_users, WINDOW_DAYS))\n",
    "    cumulative = np.cumsum(daily, axis=1)\n",
    "    reached_tip = (tip > 0)[:, None] & (cumulative >= tip[:, None])\n",
    "    converted = reached_tip.any(axis=1)\n",
    "    conv_day = np.where(converted, reached_tip.argmax(axis=1), -1)\n",
    "    if drop_buyers:\n",
    "        day_index = np.arange(WINDOW_DAYS)[None, :]\n",
    "        keep = (conv_day[:, None] < 0) | (day_index <= conv_day[:, None])\n",
    "        impressions = (daily * keep).sum(axis=1)\n",
    "    else:\n",
    "        impressions = cumulative[:, -1]\n",
    "    reached = impressions >= 1\n",
    "    return pd.DataFrame({\n",
    "        \"impressions\": impressions[reached],\n",
    "        \"converted\": converted[reached],\n",
    "        \"keen\": keen[reached],\n",
    "        \"tip\": tip[reached],\n",
    "        \"conv_day\": conv_day[reached],\n",
    "    })\n",
    "\n",
    "\n",
    "TOP_BUCKET = 15\n",
    "\n",
    "\n",
    "def frequency_report(people, top=TOP_BUCKET):\n",
    "    \"\"\"The report the ad platform hands you: reach and conversions by frequency, top bucket open.\"\"\"\n",
    "    f = np.minimum(people.impressions.to_numpy(), top)\n",
    "    frame = pd.DataFrame({\"frequency\": f, \"converted\": people.converted.to_numpy()})\n",
    "    rep = frame.groupby(\"frequency\").agg(reach=(\"converted\", \"size\"), conversions=(\"converted\", \"sum\"))\n",
    "    rep = rep.reindex(range(1, top + 1), fill_value=0).reset_index()\n",
    "    rep[\"cvr\"] = np.where(rep.reach > 0, rep.conversions / rep.reach.clip(lower=1), np.nan)\n",
    "    rep[\"label\"] = [str(v) if v < top else f\"{top}+\" for v in rep.frequency]\n",
    "    # the open top bucket is fitted at the mean frequency of the people inside it\n",
    "    mean_top = float(people.impressions[people.impressions >= top].mean()) if (people.impressions >= top).any() else float(top)\n",
    "    rep[\"f_fit\"] = np.where(rep.frequency < top, rep.frequency.astype(float), mean_top)\n",
    "    return rep\n",
    "\n",
    "\n",
    "people = simulate_month(N_USERS, np.random.default_rng(SEED))\n",
    "report = frequency_report(people)\n",
    "\n",
    "print(f\"{len(people):,} people reached over {WINDOW_DAYS} days; \"\n",
    "      f\"{int(people.converted.sum()):,} converted ({people.converted.mean():.2%})\")\n",
    "print(f\"median person saw the ad {int(people.impressions.median())} times; \"\n",
    "      f\"mean {people.impressions.mean():.1f}; one in ten saw it {int(people.impressions.quantile(0.9))}+ times\")\n",
    "print(f\"an impression costs {COST_PER_IMPRESSION * 100:.2f} cents; a conversion is worth {VALUE_EUR:.0f} euros;\")\n",
    "print(f\"so an extra impression pays when it adds more than {TAU:.5f} to the chance of a sale ({TAU * 100:.3f} points)\")\n",
    "keen_by_f = people.groupby(np.minimum(people.impressions, TOP_BUCKET)).keen.mean()\n",
    "keen_ratio_12_vs_2 = float(keen_by_f[12] / keen_by_f[2])\n",
    "print(f\"the lean built in: people who saw the ad twelve times are {keen_ratio_12_vs_2:.2f} times as keen as those who saw it twice\")\n",
    "print()\n",
    "print(report[[\"label\", \"reach\", \"conversions\", \"cvr\"]].to_string(index=False))"
   ]
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",
      "text/plain": [
       "<Figure size 1400x644 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 4.6))\n",
    "ax.bar(report.frequency, report.reach, color=BAND, width=0.8, label=\"people reached (left axis)\")\n",
    "ax.set_xlabel(\"times one person saw the ad in the month\")\n",
    "ax.set_ylabel(\"people\")\n",
    "ax.set_xticks(report.frequency)\n",
    "ax.set_xticklabels(report.label)\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(report.frequency, report.cvr * 100, color=INK, lw=1.8, marker=\"o\", ms=4,\n",
    "         label=\"share who bought (right axis)\")\n",
    "ax2.set_ylabel(\"share who bought, %\")\n",
    "ax2.set_ylim(0, None)\n",
    "ax2.grid(False)\n",
    "ax.set_title(\"The frequency report: most people saw the ad a few times, and the buying rate keeps rising\")\n",
    "h1, l1 = ax.get_legend_handles_labels()\n",
    "h2, l2 = ax2.get_legend_handles_labels()\n",
    "ax.legend(h1 + h2, l1 + l2, frameon=False, loc=\"upper right\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_report.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5905e37",
   "metadata": {},
   "source": [
    "### What a reader does today\n",
    "\n",
    "Two readings cover most of what happens in practice.\n",
    "\n",
    "- **Cap where the rate stops improving.** Scan the report for the frequency where the share\n",
    "  who bought peaks, and cap there. On this report the share never stops rising, so the\n",
    "  reading is \"no cap\".\n",
    "- **A rule of thumb.** Three exposures a purchase cycle is the number the effective-frequency\n",
    "  literature made famous, and \"about three a week\" is what it has become in practice. Over a\n",
    "  twenty-eight-day month that is twelve.\n",
    "\n",
    "Both produce a number. Neither says what the next impression is worth, so neither can say\n",
    "whether it pays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "707f27da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:17:49.137973Z",
     "iopub.status.busy": "2026-10-20T07:17:49.137415Z",
     "iopub.status.idle": "2026-10-20T07:17:49.147607Z",
     "shell.execute_reply": "2026-10-20T07:17:49.146128Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  where the rate peaks   -> no cap - the share who bought is highest in the top bucket\n",
      "  three a week           -> cap at 12 over 28 days\n"
     ]
    }
   ],
   "source": [
    "def read_peak(rep):\n",
    "    \"\"\"The frequency where the observed share who bought is highest.\"\"\"\n",
    "    valid = rep[rep.reach > 0]\n",
    "    return int(valid.loc[valid.cvr.idxmax(), \"frequency\"])\n",
    "\n",
    "\n",
    "RULE_OF_THUMB_PER_WEEK = 3\n",
    "rule_of_thumb_cap = RULE_OF_THUMB_PER_WEEK * WINDOW_DAYS // 7\n",
    "\n",
    "peak_cap = read_peak(report)\n",
    "peak_reading = \"no cap - the share who bought is highest in the top bucket\" if peak_cap >= TOP_BUCKET \\\n",
    "    else f\"cap at {peak_cap}\"\n",
    "print(f\"  where the rate peaks   -> {peak_reading}\")\n",
    "print(f\"  three a week           -> cap at {rule_of_thumb_cap} over {WINDOW_DAYS} days\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c70b40f",
   "metadata": {},
   "source": [
    "### The method, in one formula\n",
    "\n",
    "The curve fitted to the report is the **Hill curve** - three numbers describing a rise that\n",
    "flattens:\n",
    "\n",
    "$$\\mathrm{CVR}(f) = \\mathrm{CVR}_{\\max}\\,\\frac{f^{\\,n}}{K^{\\,n} + f^{\\,n}}$$\n",
    "\n",
    "- $f$ is how many times a person has seen the ad.\n",
    "- $\\mathrm{CVR}_{\\max}$ is the **ceiling** - the share who would buy if you could show the ad\n",
    "  without limit.\n",
    "- $K$ is the **half-way point** - the frequency at which the share reaches half the ceiling.\n",
    "- $n$ is the **shape**. Below one, the first impression does the most and every one after\n",
    "  does less; above one, the first impression or two do little and the curve accelerates\n",
    "  before it flattens - the S-shape the effective-frequency school describes. The fit\n",
    "  chooses; nobody decides it in advance.\n",
    "\n",
    "The fit weights each bucket by how many people are in it, so a thin high-frequency bucket\n",
    "that happened to land high does not drag the curve. A curve with a fixed shape and a\n",
    "reader's-eye peak are the two ends of the same trade: the peak believes every bucket\n",
    "equally, the curve believes the shape.\n",
    "\n",
    "**The cap is where the next impression stops paying.** The slope of the curve is how much\n",
    "one more impression adds to the chance of a sale:\n",
    "\n",
    "$$\\frac{d\\,\\mathrm{CVR}}{d f} = \\mathrm{CVR}_{\\max}\\, n\\, K^{\\,n}\\, \\frac{f^{\\,n-1}}{(K^{\\,n}+f^{\\,n})^{2}}$$\n",
    "\n",
    "An impression pays while that slope, times what a sale is worth, exceeds what the impression\n",
    "costs. Divide through by the value and the cap is the last frequency where the slope still\n",
    "clears\n",
    "\n",
    "$$\\tau = \\frac{\\text{cost of one impression}}{\\text{value of one sale}}$$\n",
    "\n",
    "which is a number you own, not one the curve owns. Cheaper impressions or dearer sales push\n",
    "the cap out; the reverse pulls it in. Written as a decision rule: the cap is the frequency\n",
    "that maximises what a reached person is worth, sale value times the curve minus impression\n",
    "cost times the frequency, and the notebook reads it off that maximum directly.\n",
    "\n",
    "**Two gates stand in front of any cap.** The fit must beat a flat line by more than chance\n",
    "would - a curve that barely bends is not a curve, and a fit always returns some numbers. And\n",
    "the cap must sit inside the frequencies the audience actually reached; a cap read beyond\n",
    "the top bucket is the shape of the curve guessing, not the data speaking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "19ddb79a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:17:49.150678Z",
     "iopub.status.busy": "2026-10-20T07:17:49.150402Z",
     "iopub.status.idle": "2026-10-20T07:17:49.182109Z",
     "shell.execute_reply": "2026-10-20T07:17:49.181211Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitted to the report:\n",
      "  ceiling 0.0185   half-way 5.16   shape 1.18\n",
      "  bend gate: p = 1.17e-41 -> passes\n",
      "  -> cap at 10\n",
      "\n",
      "the within-person truth we built in:\n",
      "  ceiling 0.0130   half-way 3.00   shape 1.50\n",
      "  -> the honest cap, at these economics, is 7\n",
      "  the report's cap sits +3 impressions from it\n"
     ]
    }
   ],
   "source": [
    "N_GRID = np.round(np.arange(0.5, 4.0001, 0.05), 6)\n",
    "K_GRID = np.round(np.arange(0.5, 40.0001, 0.25), 6)\n",
    "REFINE_STEPS = 2          # each refinement rescans a +/-2-cell neighbourhood at a tenth of the step\n",
    "CAP_SEARCH_MAX = 60       # caps are searched from zero to here\n",
    "GATE_LEVEL = 0.05         # the bend must be unlikelier than one in twenty under a flat line\n",
    "\n",
    "\n",
    "def grid_search(f, y, w, k_grid, n_grid):\n",
    "    \"\"\"Every (K, n) pair on the grid at once. For a fixed pair the ceiling has a closed form:\n",
    "    the weighted least-squares scale of the unit curve g = f^n / (K^n + f^n) against y.\n",
    "\n",
    "    The scan runs n outer, K inner, and the first minimum wins, so a second implementation\n",
    "    that loops in the same order lands on the same pair.\n",
    "    \"\"\"\n",
    "    fn = f[None, None, :] ** n_grid[None, :, None]                 # (1, N, F)\n",
    "    kn = k_grid[:, None, None] ** n_grid[None, :, None]            # (K, N, 1)\n",
    "    g = fn / (kn + fn)                                             # (K, N, F)\n",
    "    denom = np.sum(w * g * g, axis=2)\n",
    "    a = np.where(denom > 0, np.sum(w * g * y, axis=2) / np.where(denom > 0, denom, 1.0), 0.0)\n",
    "    a = np.maximum(a, 0.0)\n",
    "    sse = np.sum(w * (y - a[:, :, None] * g) ** 2, axis=2)         # (K, N)\n",
    "    flat = np.argmin(sse.T)                                        # n outer, K inner\n",
    "    ni, ki = divmod(int(flat), len(k_grid))\n",
    "    return float(a[ki, ni]), float(k_grid[ki]), float(n_grid[ni]), float(sse[ki, ni])\n",
    "\n",
    "\n",
    "def fit_hill(f, y, w, weighted=True):\n",
    "    \"\"\"Volume-weighted least squares on a (K, n) grid, refined twice. Deterministic.\n",
    "\n",
    "    Returns the ceiling, half-way point, shape and weighted sum of squares. `weighted=False`\n",
    "    is for one experiment only - measuring what the weighting is worth.\n",
    "    \"\"\"\n",
    "    f = np.asarray(f, dtype=float)\n",
    "    y = np.asarray(y, dtype=float)\n",
    "    w = np.asarray(w, dtype=float) if weighted else np.ones_like(f)\n",
    "    w = w / w.sum()\n",
    "    k_grid, n_grid = K_GRID, N_GRID\n",
    "    k_step, n_step = 0.25, 0.05\n",
    "    best = None\n",
    "    for level in range(REFINE_STEPS + 1):\n",
    "        best = grid_search(f, y, w, k_grid, n_grid)\n",
    "        if level < REFINE_STEPS:\n",
    "            k_step, n_step = k_step / 10, n_step / 10\n",
    "            k_grid = np.round(best[1] + k_step * np.arange(-20, 21), 6)\n",
    "            k_grid = k_grid[k_grid > 0]\n",
    "            n_grid = np.round(best[2] + n_step * np.arange(-20, 21), 6)\n",
    "            n_grid = n_grid[n_grid > 0]\n",
    "    return {\"cvr_max\": best[0], \"k\": best[1], \"n\": best[2], \"sse\": best[3]}\n",
    "\n",
    "\n",
    "def profit_per_person(cap, cvr_max, k, n, cpi=COST_PER_IMPRESSION, value=VALUE_EUR):\n",
    "    \"\"\"What one person who would see the ad `cap` times is worth, under the curve.\"\"\"\n",
    "    return value * hill(cap, cvr_max, k, n) - cpi * np.asarray(cap, dtype=float)\n",
    "\n",
    "\n",
    "def best_cap(cvr_max, k, n, cpi=COST_PER_IMPRESSION, value=VALUE_EUR, search_max=CAP_SEARCH_MAX):\n",
    "    \"\"\"The frequency that maximises what a reached person is worth - the cap. Zero means 'do not run'.\"\"\"\n",
    "    caps = np.arange(0, search_max + 1)\n",
    "    return int(caps[np.argmax(profit_per_person(caps, cvr_max, k, n, cpi, value))])\n",
    "\n",
    "\n",
    "def binomial_loglik(reach, conversions, p):\n",
    "    p = np.clip(np.asarray(p, dtype=float), 1e-12, 1 - 1e-12)\n",
    "    return float(np.sum(conversions * np.log(p) + (reach - conversions) * np.log(1 - p)))\n",
    "\n",
    "\n",
    "def bend_gate(rep, fit, level=GATE_LEVEL):\n",
    "    \"\"\"Does the curve beat a flat line by more than chance? A likelihood-ratio test on the buckets.\"\"\"\n",
    "    reach = rep.reach.to_numpy(dtype=float)\n",
    "    conv = rep.conversions.to_numpy(dtype=float)\n",
    "    flat = conv.sum() / reach.sum()\n",
    "    ll_flat = binomial_loglik(reach, conv, flat)\n",
    "    ll_hill = binomial_loglik(reach, conv, hill(rep.f_fit.to_numpy(), fit[\"cvr_max\"], fit[\"k\"], fit[\"n\"]))\n",
    "    stat = max(0.0, 2 * (ll_hill - ll_flat))\n",
    "    p = float(chi2.sf(stat, df=2))\n",
    "    return {\"statistic\": stat, \"p_value\": p, \"passes\": p < level, \"flat_rate\": flat}\n",
    "\n",
    "\n",
    "def read_cap(rep, cpi=COST_PER_IMPRESSION, value=VALUE_EUR, weighted=True):\n",
    "    \"\"\"The whole method on one report: fit, gate, cap, and whether the cap sits inside the data.\"\"\"\n",
    "    usable = rep[rep.reach > 0]\n",
    "    fit = fit_hill(usable.f_fit, usable.cvr, usable.reach, weighted=weighted)\n",
    "    gate = bend_gate(usable, fit)\n",
    "    cap = best_cap(fit[\"cvr_max\"], fit[\"k\"], fit[\"n\"], cpi, value)\n",
    "    max_seen = float(usable.f_fit.max())\n",
    "    return {\n",
    "        **fit, \"gate\": gate, \"cap\": cap, \"max_frequency_seen\": max_seen,\n",
    "        \"cap_beyond_data\": cap > max_seen,\n",
    "        \"verdict\": (\"no detectable saturation - the report does not bend more than chance would\"\n",
    "                    if not gate[\"passes\"] else\n",
    "                    (f\"cap at {cap}\" if cap <= max_seen else\n",
    "                     f\"cap at {cap}, which is past the {max_seen:.0f} the audience reached - treat as 'at least {int(max_seen)}'\")),\n",
    "    }\n",
    "\n",
    "\n",
    "result = read_cap(report)\n",
    "honest_cap = best_cap(TRUE_CVR_MAX, TRUE_K, TRUE_N)\n",
    "\n",
    "print(\"fitted to the report:\")\n",
    "print(f\"  ceiling {result['cvr_max']:.4f}   half-way {result['k']:.2f}   shape {result['n']:.2f}\")\n",
    "print(f\"  bend gate: p = {result['gate']['p_value']:.2e} -> {'passes' if result['gate']['passes'] else 'FAILS'}\")\n",
    "print(f\"  -> {result['verdict']}\")\n",
    "print()\n",
    "print(\"the within-person truth we built in:\")\n",
    "print(f\"  ceiling {TRUE_CVR_MAX:.4f}   half-way {TRUE_K:.2f}   shape {TRUE_N:.2f}\")\n",
    "print(f\"  -> the honest cap, at these economics, is {honest_cap}\")\n",
    "print(f\"  the report's cap sits {result['cap'] - honest_cap:+d} impressions from it\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "387b8868",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:17:49.184812Z",
     "iopub.status.busy": "2026-10-20T07:17:49.184532Z",
     "iopub.status.idle": "2026-10-20T07:17:49.714236Z",
     "shell.execute_reply": "2026-10-20T07:17:49.712716Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x672 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_dense = np.linspace(0.3, 18, 300)\n",
    "fig, ax = plt.subplots(figsize=(10, 4.8))\n",
    "sizes = 12 + 220 * report.reach / report.reach.max()\n",
    "ax.scatter(report.f_fit, report.cvr * 100, s=sizes, color=INK, alpha=0.55, zorder=3,\n",
    "           label=\"the report (dot size = people in the bucket)\")\n",
    "ax.plot(f_dense, hill(f_dense, result[\"cvr_max\"], result[\"k\"], result[\"n\"]) * 100, color=FLAG, lw=2.2,\n",
    "        label=f\"fitted curve - cap at {result['cap']}\")\n",
    "ax.plot(f_dense, hill(f_dense, TRUE_CVR_MAX, TRUE_K, TRUE_N) * 100, color=SIGNAL, lw=2.2, ls=\"--\",\n",
    "        label=f\"within-person truth (hidden from the report) - cap at {honest_cap}\")\n",
    "ax.axvline(result[\"cap\"], color=FLAG, lw=1.0, ls=\":\")\n",
    "ax.axvline(honest_cap, color=SIGNAL, lw=1.0, ls=\":\")\n",
    "ax.set_xlabel(\"times one person has seen the ad\")\n",
    "ax.set_ylabel(\"share who bought, %\")\n",
    "ax.set_ylim(0, None)\n",
    "ax.set_title(\"The curve the report shows sits above the one a person actually follows\")\n",
    "ax.legend(frameon=False, loc=\"lower right\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_curve.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04fbb0d5",
   "metadata": {},
   "source": [
    "### What a cap is worth, in money\n",
    "\n",
    "A cap is a decision about every person who would otherwise have seen the ad more than the\n",
    "cap allows. Under the truth I built in, the notebook can price any cap exactly: for each\n",
    "person, the sales they would bring at their capped frequency times what a sale is worth,\n",
    "minus the impressions they would be served times what one costs. The best cap for this\n",
    "audience is whichever integer maximises that sum, and every reading is scored against it.\n",
    "\n",
    "One subtlety the report cannot see is priced here too. Capping a heavy viewer costs more\n",
    "than the average person's curve says, because the platform sent the heavy impressions to\n",
    "the keener people; and the honest cap read off the within-person curve is priced as if\n",
    "every person were average. Where the best cap for the audience as delivered actually lands\n",
    "- between the two curves' caps, or on one of them - is a finding, not an assumption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aa37dc6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:17:49.719735Z",
     "iopub.status.busy": "2026-10-20T07:17:49.719295Z",
     "iopub.status.idle": "2026-10-20T07:17:50.222739Z",
     "shell.execute_reply": "2026-10-20T07:17:50.221583Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this audience, at 12 euros per thousand and 25 euros a sale, costs 7,432 euros a month to serve with no cap:\n",
      "  no cap                   cap none   worth    15,471 euros   leaves   579 on the table (7.8% of the spend)\n",
      "  three a week             cap   12   worth    15,808 euros   leaves   243 on the table (3.3% of the spend)\n",
      "  the report's curve       cap   10   worth    15,938 euros   leaves   113 on the table (1.5% of the spend)\n",
      "  the within-person truth  cap    7   worth    16,049 euros   leaves     1 on the table (0.0% of the spend)\n",
      "  best for this audience   cap    8   worth    16,050 euros   leaves     0 on the table (0.0% of the spend)\n"
     ]
    }
   ],
   "source": [
    "def audience_value(people, cap, cvr_max=TRUE_CVR_MAX, k=TRUE_K, n=TRUE_N, cpi=COST_PER_IMPRESSION, value=VALUE_EUR):\n",
    "    \"\"\"Total value of the audience if nobody saw the ad more than `cap` times, under the truth.\"\"\"\n",
    "    f = np.minimum(people.impressions.to_numpy(dtype=float), cap)\n",
    "    sales = np.minimum(1.0, people.keen.to_numpy() * hill(f, cvr_max, k, n))\n",
    "    return float(np.sum(value * sales - cpi * f))\n",
    "\n",
    "\n",
    "caps_all = np.arange(0, CAP_SEARCH_MAX + 1)\n",
    "value_by_cap = np.array([audience_value(people, c) for c in caps_all])\n",
    "best_delivered_cap = int(caps_all[np.argmax(value_by_cap)])\n",
    "no_cap_value = audience_value(people, CAP_SEARCH_MAX)\n",
    "\n",
    "readings = {\n",
    "    \"no cap\": CAP_SEARCH_MAX,\n",
    "    \"three a week\": rule_of_thumb_cap,\n",
    "    \"the report's curve\": result[\"cap\"],\n",
    "    \"the within-person truth\": honest_cap,\n",
    "    \"best for this audience\": best_delivered_cap,\n",
    "}\n",
    "spend_uncapped = float(people.impressions.sum() * COST_PER_IMPRESSION)\n",
    "money = {}\n",
    "for name, cap in readings.items():\n",
    "    v = audience_value(people, cap)\n",
    "    money[name] = {\"cap\": cap, \"value_eur\": v, \"shortfall_eur\": value_by_cap.max() - v,\n",
    "                   \"shortfall_pct_of_spend\": (value_by_cap.max() - v) / spend_uncapped * 100}\n",
    "print(f\"this audience, at {CPM_EUR:.0f} euros per thousand and {VALUE_EUR:.0f} euros a sale, costs \"\n",
    "      f\"{spend_uncapped:,.0f} euros a month to serve with no cap:\")\n",
    "for name, row in money.items():\n",
    "    cap_txt = \"none\" if row[\"cap\"] >= CAP_SEARCH_MAX else str(row[\"cap\"])\n",
    "    print(f\"  {name:<24} cap {cap_txt:>4}   worth {row['value_eur']:9,.0f} euros   \"\n",
    "          f\"leaves {row['shortfall_eur']:5,.0f} on the table ({row['shortfall_pct_of_spend']:.1f}% of the spend)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4ae60942",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:17:50.225669Z",
     "iopub.status.busy": "2026-10-20T07:17:50.225331Z",
     "iopub.status.idle": "2026-10-20T07:17:50.711478Z",
     "shell.execute_reply": "2026-10-20T07:17:50.709722Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x616 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 4.4))\n",
    "show = caps_all[(caps_all >= 1) & (caps_all <= 30)]\n",
    "ax.plot(show, value_by_cap[show] / 1000, color=INK, lw=2.0, label=\"what the audience is worth at each cap (thousand euros)\")\n",
    "for name, color in [(\"the report's curve\", FLAG), (\"the within-person truth\", SIGNAL), (\"three a week\", NAIVE)]:\n",
    "    c = readings[name]\n",
    "    ax.axvline(c, color=color, lw=1.2, ls=\":\")\n",
    "    ax.text(c + 0.2, ax.get_ylim()[0] + 0.05 * (ax.get_ylim()[1] - ax.get_ylim()[0]), name, color=color,\n",
    "            fontsize=9, rotation=90, va=\"bottom\")\n",
    "ax.axvline(best_delivered_cap, color=INK, lw=1.0, ls=\"--\", label=f\"best for this audience as delivered: {best_delivered_cap}\")\n",
    "ax.set_xlabel(\"frequency cap (impressions per person per month)\")\n",
    "ax.set_ylabel(\"thousand euros\")\n",
    "ax.set_title(\"The money curve is flat near the top: being a few impressions off costs little, no cap costs more\")\n",
    "ax.legend(frameon=False, loc=\"lower right\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_money.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a15db15",
   "metadata": {},
   "source": [
    "### Scored on five hundred audiences, not one\n",
    "\n",
    "One audience proves nothing - its buckets could have landed kindly. Below, five hundred\n",
    "audiences are built by the same rules with their own ceilings, half-way points, shapes,\n",
    "sizes and exposure patterns, their own cost per thousand and value per sale, and their own\n",
    "strength of the platform's lean toward keen people - including audiences where the lean\n",
    "runs the other way, toward people who are simply online more and no keener. Every reading\n",
    "is scored on every audience against the cap that is best for that audience as delivered,\n",
    "and the money it leaves on the table is counted as a share of what the audience costs to\n",
    "serve without a cap.\n",
    "\n",
    "The rules are fixed before the run. Ceilings run from about half a percent to five;\n",
    "half-way points from two to nine impressions; shapes from gently concave to a clear\n",
    "S-curve; audiences from five thousand to a hundred thousand people; the lean from mildly\n",
    "reversed to twice the worked example's; the cost per thousand from four to twenty-five\n",
    "euros and the value of a sale from eight to eighty. The fitted curve and the honest cap use\n",
    "each audience's own economics, because that is how they would be used; the rule of thumb\n",
    "is twelve regardless, because that is what a rule of thumb is.\n",
    "\n",
    "Two kinds of audience come out, and they are scored separately. On most, the best cap sits\n",
    "inside the frequencies the audience reached, so a cap matters. On the rest, the audience\n",
    "never reaches the frequency where an impression stops paying, and the right answer is no\n",
    "cap at all - which is also an answer a reading can get wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "151c6085",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:17:50.716769Z",
     "iopub.status.busy": "2026-10-20T07:17:50.716352Z",
     "iopub.status.idle": "2026-10-20T07:19:03.428402Z",
     "shell.execute_reply": "2026-10-20T07:19:03.426463Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "499 audiences scored; on 71% of them the best cap sits inside the frequencies reached, so a cap matters\n",
      "\n",
      "ALL 499 AUDIENCES - money lost as a share of the uncapped spend\n",
      "reading        typical miss  within 2  says no cap  spend lost (typ.)  (worst tenth)\n",
      "fitted                  2.0       52%          35%               0.4%           7.5%\n",
      "unweighted              3.0       44%          35%               0.7%           9.8%\n",
      "peak                    5.0       29%          23%               2.0%          19.0%\n",
      "rule                    5.0       25%           0%               2.9%          20.7%\n",
      "no cap                    -         -         100%               1.4%          16.2%\n",
      "\n",
      "THE 354 WHERE A CAP MATTERS\n",
      "reading        typical miss  within 2  says no cap  spend lost (typ.)  (worst tenth)\n",
      "fitted                  2.0       59%          20%               0.8%           7.8%\n",
      "unweighted              2.0       51%          21%               1.3%           9.7%\n",
      "peak                    4.0       39%          19%               1.7%          18.8%\n",
      "rule                    4.0       36%           0%               1.2%          14.0%\n",
      "no cap                    -         -         100%               2.8%          20.6%\n",
      "\n",
      "THE 145 WHERE NO CAP IS RIGHT - how often each reading imposes one anyway, and what that costs\n",
      "  fitted       imposes a cap on  29%   spend lost typ. 0.1%, worst tenth 3.7%\n",
      "  unweighted   imposes a cap on  32%   spend lost typ. 0.2%, worst tenth 12.5%\n",
      "  peak         imposes a cap on  68%   spend lost typ. 2.6%, worst tenth 18.9%\n",
      "  rule         imposes a cap on 100%   spend lost typ. 8.9%, worst tenth 29.6%\n",
      "\n",
      "where the best cap sits decides whether twelve was a good guess (spend lost, typical):\n",
      "  best cap  0-4  ( 44 audiences)  fitted   0.0%   twelve  23.4%   peak  24.2%   no cap  30.3%\n",
      "  best cap  5-8  ( 63 audiences)  fitted   1.5%   twelve   3.9%   peak   4.6%   no cap  10.0%\n",
      "  best cap  9-14 (147 audiences)  fitted   0.8%   twelve   0.2%   peak   0.5%   no cap   2.6%\n",
      "  best cap 15-24 (100 audiences)  fitted   0.5%   twelve   1.7%   peak   1.4%   no cap   1.1%\n",
      "\n",
      "the bend gate refused 3.8% of these audiences, every one of which genuinely bends\n",
      "the fitted cap sat beyond the top bucket on 34.7% of all audiences, and on 71% of those where no cap is right - which is the method saying so\n"
     ]
    }
   ],
   "source": [
    "N_POP = 500\n",
    "\n",
    "\n",
    "def population_truths(n, generator):\n",
    "    return pd.DataFrame({\n",
    "        \"cvr_max\": generator.uniform(0.005, 0.05, n),\n",
    "        \"k\": generator.uniform(2.0, 9.0, n),\n",
    "        \"n\": generator.uniform(0.7, 2.8, n),\n",
    "        \"mean_freq\": generator.uniform(2.5, 8.0, n),\n",
    "        \"n_users\": np.round(10 ** generator.uniform(3.7, 5.0, n)).astype(int),\n",
    "        \"confound\": generator.uniform(-0.3, 0.6, n),\n",
    "        \"cpm\": generator.uniform(4.0, 25.0, n),\n",
    "        \"value\": generator.uniform(8.0, 80.0, n),\n",
    "    })\n",
    "\n",
    "\n",
    "def simulate_totals(truth, generator):\n",
    "    \"\"\"A month's frequency and conversions for one population audience, from monthly totals directly.\"\"\"\n",
    "    rate, keen = draw_people(int(truth.n_users), generator,\n",
    "                             rate_log_mean=np.log(truth.mean_freq) - RATE_LOG_SD ** 2 / 2,\n",
    "                             confound=float(truth.confound))\n",
    "    impressions = generator.poisson(rate)\n",
    "    reached = impressions >= 1\n",
    "    impressions, keen = impressions[reached], keen[reached]\n",
    "    p = np.minimum(1.0, keen * hill(impressions, truth.cvr_max, truth.k, truth.n))\n",
    "    converted = generator.uniform(size=len(p)) < p\n",
    "    return pd.DataFrame({\"impressions\": impressions, \"converted\": converted, \"keen\": keen})\n",
    "\n",
    "\n",
    "def score_audience(truth, generator):\n",
    "    ppl = simulate_totals(truth, generator)\n",
    "    rep = frequency_report(ppl)\n",
    "    if rep.reach.gt(0).sum() < 4 or ppl.converted.sum() < 5:\n",
    "        return None\n",
    "    cpi, value = truth.cpm / 1000.0, truth.value\n",
    "    fitted = read_cap(rep, cpi=cpi, value=value)\n",
    "    unweighted = read_cap(rep, cpi=cpi, value=value, weighted=False)\n",
    "    honest = best_cap(truth.cvr_max, truth.k, truth.n, cpi, value)\n",
    "    values = np.array([audience_value(ppl, c, truth.cvr_max, truth.k, truth.n, cpi, value) for c in caps_all])\n",
    "    best = int(caps_all[np.argmax(values)])\n",
    "    spend = float(ppl.impressions.sum() * cpi)          # what the audience costs to serve with no cap\n",
    "    peak = read_peak(rep)\n",
    "    peak_cap = CAP_SEARCH_MAX if peak >= TOP_BUCKET else peak\n",
    "    # a fitted cap past the top bucket is reported as \"at least the top bucket\", which in money\n",
    "    # terms is no cap - the honest answer when the audience never reaches its knee\n",
    "    fitted_applied = CAP_SEARCH_MAX if fitted[\"cap_beyond_data\"] else fitted[\"cap\"]\n",
    "    unweighted_applied = CAP_SEARCH_MAX if unweighted[\"cap_beyond_data\"] else unweighted[\"cap\"]\n",
    "    row = {\n",
    "        \"conversions\": int(ppl.converted.sum()), \"reach\": int(len(ppl)), \"confound\": float(truth.confound),\n",
    "        \"cpm\": float(truth.cpm), \"value\": float(truth.value), \"spend\": spend,\n",
    "        \"true_n\": float(truth.n), \"honest_cap\": honest, \"best_cap\": best,\n",
    "        \"fitted_cap\": fitted_applied, \"unweighted_cap\": unweighted_applied, \"peak_cap\": peak_cap,\n",
    "        \"rule_cap\": rule_of_thumb_cap, \"gate_passes\": bool(fitted[\"gate\"][\"passes\"]),\n",
    "        \"cap_beyond_data\": bool(fitted[\"cap_beyond_data\"]), \"max_seen\": fitted[\"max_frequency_seen\"],\n",
    "        \"cap_matters\": bool(best < fitted[\"max_frequency_seen\"]), \"fitted_n\": fitted[\"n\"],\n",
    "    }\n",
    "    top = values.max()\n",
    "    for name in [\"honest\", \"best\", \"fitted\", \"unweighted\", \"peak\", \"rule\"]:\n",
    "        c = row[f\"{name}_cap\"]\n",
    "        row[f\"{name}_loss_pct\"] = float((top - values[min(c, CAP_SEARCH_MAX)]) / spend * 100)\n",
    "    row[\"no_cap_loss_pct\"] = float((top - values[-1]) / spend * 100)\n",
    "    return row\n",
    "\n",
    "\n",
    "g_pop = np.random.default_rng(SEED + 101)\n",
    "truths = population_truths(N_POP, g_pop)\n",
    "rows = [score_audience(t, g_pop) for t in truths.itertuples(index=False)]\n",
    "pop = pd.DataFrame([r for r in rows if r is not None])\n",
    "READINGS = [\"fitted\", \"unweighted\", \"peak\", \"rule\"]\n",
    "print(f\"{len(pop)} audiences scored; on {pop.cap_matters.mean():.0%} of them the best cap sits inside \"\n",
    "      f\"the frequencies reached, so a cap matters\")\n",
    "\n",
    "\n",
    "def summarise(frame):\n",
    "    out = {}\n",
    "    for name in READINGS:\n",
    "        err = frame[f\"{name}_cap\"] - frame.best_cap\n",
    "        landed = err[frame[f\"{name}_cap\"] < CAP_SEARCH_MAX]\n",
    "        out[name] = {\n",
    "            \"median_abs_error\": float(np.median(np.abs(landed))) if len(landed) else float(\"nan\"),\n",
    "            \"within_2_pct\": float(np.mean(np.abs(landed) <= 2)) * 100 if len(landed) else 0.0,\n",
    "            \"says_no_cap_pct\": float(np.mean(frame[f\"{name}_cap\"] >= CAP_SEARCH_MAX)) * 100,\n",
    "            \"median_loss_pct_of_spend\": float(np.median(frame[f\"{name}_loss_pct\"])),\n",
    "            \"p90_loss_pct_of_spend\": float(np.percentile(frame[f\"{name}_loss_pct\"], 90)),\n",
    "        }\n",
    "    out[\"no cap\"] = {\"median_loss_pct_of_spend\": float(np.median(frame.no_cap_loss_pct)),\n",
    "                     \"p90_loss_pct_of_spend\": float(np.percentile(frame.no_cap_loss_pct, 90))}\n",
    "    return out\n",
    "\n",
    "\n",
    "def print_summary(title, summ):\n",
    "    print(f\"\\n{title}\")\n",
    "    print(f\"{'reading':<14}{'typical miss':>13}{'within 2':>10}{'says no cap':>13}{'spend lost (typ.)':>19}{'(worst tenth)':>15}\")\n",
    "    for name, s in summ.items():\n",
    "        if name == \"no cap\":\n",
    "            print(f\"{name:<14}{'-':>13}{'-':>10}{'100%':>13}{s['median_loss_pct_of_spend']:>18.1f}%{s['p90_loss_pct_of_spend']:>14.1f}%\")\n",
    "        else:\n",
    "            print(f\"{name:<14}{s['median_abs_error']:>13.1f}{s['within_2_pct']:>9.0f}%{s['says_no_cap_pct']:>12.0f}%\"\n",
    "                  f\"{s['median_loss_pct_of_spend']:>18.1f}%{s['p90_loss_pct_of_spend']:>14.1f}%\")\n",
    "\n",
    "\n",
    "matters = pop[pop.cap_matters]\n",
    "no_cap_right = pop[~pop.cap_matters]\n",
    "summary_all = summarise(pop)\n",
    "summary_matters = summarise(matters)\n",
    "summary_no_cap = summarise(no_cap_right)\n",
    "print_summary(f\"ALL {len(pop)} AUDIENCES - money lost as a share of the uncapped spend\", summary_all)\n",
    "print_summary(f\"THE {len(matters)} WHERE A CAP MATTERS\", summary_matters)\n",
    "print(f\"\\nTHE {len(no_cap_right)} WHERE NO CAP IS RIGHT - how often each reading imposes one anyway, and what that costs\")\n",
    "for name in READINGS:\n",
    "    s = summary_no_cap[name]\n",
    "    print(f\"  {name:<12} imposes a cap on {100 - s['says_no_cap_pct']:3.0f}%   spend lost typ. {s['median_loss_pct_of_spend']:.1f}%, worst tenth {s['p90_loss_pct_of_spend']:.1f}%\")\n",
    "\n",
    "# the rule of thumb against the fitted curve, by where the best cap actually sits\n",
    "rule_bands = []\n",
    "for lo, hi in [(0, 5), (5, 9), (9, 15), (15, 25), (25, CAP_SEARCH_MAX + 1)]:\n",
    "    sub = matters[(matters.best_cap >= lo) & (matters.best_cap < hi)]\n",
    "    if len(sub) == 0:\n",
    "        continue\n",
    "    rule_bands.append({\"best_cap_from\": lo, \"best_cap_to\": hi - 1, \"audiences\": int(len(sub)),\n",
    "                       \"fitted_median_loss_pct\": float(np.median(sub.fitted_loss_pct)),\n",
    "                       \"rule_median_loss_pct\": float(np.median(sub.rule_loss_pct)),\n",
    "                       \"peak_median_loss_pct\": float(np.median(sub.peak_loss_pct)),\n",
    "                       \"no_cap_median_loss_pct\": float(np.median(sub.no_cap_loss_pct))})\n",
    "print(\"\\nwhere the best cap sits decides whether twelve was a good guess (spend lost, typical):\")\n",
    "for b in rule_bands:\n",
    "    print(f\"  best cap {b['best_cap_from']:>2}-{b['best_cap_to']:<2} ({b['audiences']:3d} audiences)  fitted {b['fitted_median_loss_pct']:5.1f}%\"\n",
    "          f\"   twelve {b['rule_median_loss_pct']:5.1f}%   peak {b['peak_median_loss_pct']:5.1f}%   no cap {b['no_cap_median_loss_pct']:5.1f}%\")\n",
    "gate_fail_share = float(np.mean(~pop.gate_passes)) * 100\n",
    "beyond_share = float(np.mean(pop.cap_beyond_data)) * 100\n",
    "beyond_share_no_cap_right = float(np.mean(no_cap_right.cap_beyond_data)) * 100\n",
    "print(f\"\\nthe bend gate refused {gate_fail_share:.1f}% of these audiences, every one of which genuinely bends\")\n",
    "print(f\"the fitted cap sat beyond the top bucket on {beyond_share:.1f}% of all audiences, and on \"\n",
    "      f\"{beyond_share_no_cap_right:.0f}% of those where no cap is right - which is the method saying so\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "777f381e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:19:03.434754Z",
     "iopub.status.busy": "2026-10-20T07:19:03.434388Z",
     "iopub.status.idle": "2026-10-20T07:19:04.693752Z",
     "shell.execute_reply": "2026-10-20T07:19:04.693011Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x588 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x616 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "by the strength of the lean (audiences where a cap matters):\n",
      "  lean -0.30 to -0.15: report's cap -1.1 from the best when it lands, within-person cap +1.0, cap past the data on 14%\n",
      "  lean -0.15 to +0.00: report's cap -1.8 from the best when it lands, within-person cap +0.3, cap past the data on 10%\n",
      "  lean +0.00 to +0.15: report's cap -0.2 from the best when it lands, within-person cap -0.3, cap past the data on 14%\n",
      "  lean +0.15 to +0.30: report's cap -0.2 from the best when it lands, within-person cap -0.9, cap past the data on 31%\n",
      "  lean +0.30 to +0.45: report's cap +0.3 from the best when it lands, within-person cap -1.4, cap past the data on 44%\n",
      "  lean +0.45 to +0.60: report's cap +0.5 from the best when it lands, within-person cap -1.6, cap past the data on 21%\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 4.2))\n",
    "for name, color, label in [(\"fitted\", FLAG, \"fitted curve\"), (\"rule\", NAIVE, \"twelve a month\"), (\"peak\", MUTED, \"where the rate peaks\")]:\n",
    "    loss = matters[f\"{name}_loss_pct\"].clip(upper=30)\n",
    "    ax.hist(loss, bins=np.linspace(0, 30, 31), color=color, alpha=0.55, label=label)\n",
    "ax.set_xlabel(\"spend lost to the reading, % of the uncapped spend (the last bar is 30% or more)\")\n",
    "ax.set_ylabel(\"audiences where a cap matters\")\n",
    "ax.set_title(\"What each reading costs, audience by audience\")\n",
    "ax.legend(frameon=False)\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_population.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 4.4))\n",
    "bins = np.linspace(-0.3, 0.6, 7)\n",
    "centres = 0.5 * (bins[1:] + bins[:-1])\n",
    "landed = matters[matters.fitted_cap < CAP_SEARCH_MAX]\n",
    "mean_fit = [float(np.mean((landed.fitted_cap - landed.best_cap)[(landed.confound >= lo) & (landed.confound < hi)]))\n",
    "            for lo, hi in zip(bins[:-1], bins[1:])]\n",
    "mean_honest = [float(np.mean((matters.honest_cap - matters.best_cap)[(matters.confound >= lo) & (matters.confound < hi)]))\n",
    "               for lo, hi in zip(bins[:-1], bins[1:])]\n",
    "beyond_by_bin = [float(np.mean(matters.cap_beyond_data[(matters.confound >= lo) & (matters.confound < hi)])) * 100\n",
    "                 for lo, hi in zip(bins[:-1], bins[1:])]\n",
    "ax.plot(centres, mean_fit, color=FLAG, lw=2.0, marker=\"o\", label=\"cap read off the report, when it lands in the data\")\n",
    "ax.plot(centres, mean_honest, color=SIGNAL, lw=2.0, marker=\"s\", ls=\"--\", label=\"cap read off the within-person truth\")\n",
    "ax.axhline(0, color=MUTED, lw=0.9)\n",
    "ax.set_xlabel(\"the lean: how strongly exposure follows keenness (negative = toward the merely online)\")\n",
    "ax.set_ylabel(\"cap minus the best cap (mean)\")\n",
    "ax.set_ylim(-3, 3)\n",
    "ax2 = ax.twinx()\n",
    "ax2.bar(centres, beyond_by_bin, width=0.12, color=LINE, alpha=0.9, label=\"share of fits whose cap ran past the data\")\n",
    "ax2.set_ylabel(\"fits with the cap past the data, %\")\n",
    "ax2.set_ylim(0, 100)\n",
    "ax2.grid(False)\n",
    "h1, l1 = ax.get_legend_handles_labels()\n",
    "h2, l2 = ax2.get_legend_handles_labels()\n",
    "ax.legend(h1 + h2, l1 + l2, frameon=False, loc=\"upper left\", fontsize=9)\n",
    "ax.set_zorder(ax2.get_zorder() + 1)\n",
    "ax.patch.set_visible(False)\n",
    "ax.set_title(\"A lean toward keen people pushes the report's cap late, and off the end of the data\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_lean.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)\n",
    "\n",
    "confound_bins = [{\"lean_from\": float(lo), \"lean_to\": float(hi), \"mean_fitted_minus_best\": round(mf, 2),\n",
    "                  \"mean_honest_minus_best\": round(mh, 2), \"cap_past_data_pct\": round(bd, 1)}\n",
    "                 for lo, hi, mf, mh, bd in zip(bins[:-1], bins[1:], mean_fit, mean_honest, beyond_by_bin)]\n",
    "print(\"by the strength of the lean (audiences where a cap matters):\")\n",
    "for b in confound_bins:\n",
    "    print(f\"  lean {b['lean_from']:+.2f} to {b['lean_to']:+.2f}: report's cap {b['mean_fitted_minus_best']:+.1f} from the best when it lands, \"\n",
    "          f\"within-person cap {b['mean_honest_minus_best']:+.1f}, cap past the data on {b['cap_past_data_pct']:.0f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba391c9b",
   "metadata": {},
   "source": [
    "### The gate, measured on audiences that do not bend\n",
    "\n",
    "A least-squares fit always returns a ceiling, a half-way point and a shape, and a cap can\n",
    "be read off any of them. On an audience whose buying rate does not move with frequency at\n",
    "all, that cap is invented. Five hundred flat audiences below, with the same sizes and\n",
    "exposure patterns as the real ones: how often does the fit hand back a cap with the gate\n",
    "off, and with it on?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f455a530",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:19:04.699999Z",
     "iopub.status.busy": "2026-10-20T07:19:04.699595Z",
     "iopub.status.idle": "2026-10-20T07:19:15.838471Z",
     "shell.execute_reply": "2026-10-20T07:19:15.837200Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500 flat audiences:\n",
      "  with the gate off, the fit hands back a cap on 100% of them\n",
      "  with the gate on, 1.8% - within the one-in-twenty the gate's own level allows\n"
     ]
    }
   ],
   "source": [
    "def flat_audience(truth, generator):\n",
    "    rate, keen = draw_people(int(truth.n_users), generator,\n",
    "                             rate_log_mean=np.log(truth.mean_freq) - RATE_LOG_SD ** 2 / 2, confound=0.0)\n",
    "    impressions = generator.poisson(rate)\n",
    "    reached = impressions >= 1\n",
    "    impressions = impressions[reached]\n",
    "    flat_rate = truth.cvr_max * 0.6\n",
    "    converted = generator.uniform(size=len(impressions)) < flat_rate\n",
    "    return pd.DataFrame({\"impressions\": impressions, \"converted\": converted, \"keen\": np.ones(len(impressions))})\n",
    "\n",
    "\n",
    "g_flat = np.random.default_rng(SEED + 202)\n",
    "flat_rows = []\n",
    "for t in population_truths(N_POP, g_flat).itertuples(index=False):\n",
    "    rep = frequency_report(flat_audience(t, g_flat))\n",
    "    if rep.reach.gt(0).sum() < 4:\n",
    "        continue\n",
    "    r = read_cap(rep)\n",
    "    flat_rows.append({\"gate_passes\": r[\"gate\"][\"passes\"], \"cap\": r[\"cap\"], \"beyond\": r[\"cap_beyond_data\"]})\n",
    "flat = pd.DataFrame(flat_rows)\n",
    "false_cap_no_gate = float(np.mean(flat.cap < CAP_SEARCH_MAX)) * 100\n",
    "false_cap_with_gate = float(np.mean(flat.gate_passes & (flat.cap < CAP_SEARCH_MAX))) * 100\n",
    "print(f\"{len(flat)} flat audiences:\")\n",
    "print(f\"  with the gate off, the fit hands back a cap on {false_cap_no_gate:.0f}% of them\")\n",
    "print(f\"  with the gate on, {false_cap_with_gate:.1f}% - within the one-in-twenty the gate's own level allows\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bfda2fc",
   "metadata": {},
   "source": [
    "### The report that turns down: when buyers leave the audience\n",
    "\n",
    "Most retargeting audiences exclude people who have already bought - there is no point\n",
    "showing a shoe ad to someone who bought the shoes. Run the same audience that way and the\n",
    "frequency report changes shape entirely, because a person who buys at their fourth\n",
    "impression stops at four while a person who never buys keeps going to twenty. The\n",
    "high-frequency buckets fill with the people who did not buy, the buying rate falls with\n",
    "frequency, and the peak reading says: cap low, the ad is wearing people out. Nothing wore\n",
    "out. The same within-person curve built both reports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f84c2384",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:19:15.842461Z",
     "iopub.status.busy": "2026-10-20T07:19:15.842020Z",
     "iopub.status.idle": "2026-10-20T07:19:16.626400Z",
     "shell.execute_reply": "2026-10-20T07:19:16.625070Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the same audience, buyers dropped the day after they buy:\n",
      "label  reach  conversions      cvr\n",
      "    1  12856          244 0.018979\n",
      "    2  16173          265 0.016385\n",
      "    3  15877          173 0.010896\n",
      "    4  14199          104 0.007324\n",
      "    5  12031           62 0.005153\n",
      "    6   9564           37 0.003869\n",
      "    7   7449           24 0.003222\n",
      "    8   5856           12 0.002049\n",
      "    9   4380            5 0.001142\n",
      "   10   3321            4 0.001204\n",
      "   11   2581            3 0.001162\n",
      "   12   1967            1 0.000508\n",
      "   13   1574            3 0.001906\n",
      "   14   1218            1 0.000821\n",
      "  15+   4545            5 0.001100\n",
      "\n",
      "  where the rate peaks -> cap at 1\n",
      "  the fitted curve     -> no detectable saturation - the report does not bend more than chance would (fitted shape 4.11, gate p = 1.00e+00)\n",
      "  neither is the answer: this report cannot say what an extra impression does, because\n",
      "  frequency here is partly a consequence of buying rather than a cause of it\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x616 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "people_drop = simulate_month(N_USERS, np.random.default_rng(SEED), drop_buyers=True)\n",
    "report_drop = frequency_report(people_drop)\n",
    "peak_drop = read_peak(report_drop)\n",
    "result_drop = read_cap(report_drop)\n",
    "print(\"the same audience, buyers dropped the day after they buy:\")\n",
    "print(report_drop[[\"label\", \"reach\", \"conversions\", \"cvr\"]].to_string(index=False))\n",
    "print(f\"\\n  where the rate peaks -> cap at {peak_drop}\")\n",
    "print(f\"  the fitted curve     -> {result_drop['verdict']} (fitted shape {result_drop['n']:.2f}, \"\n",
    "      f\"gate p = {result_drop['gate']['p_value']:.2e})\")\n",
    "print(\"  neither is the answer: this report cannot say what an extra impression does, because\")\n",
    "print(\"  frequency here is partly a consequence of buying rather than a cause of it\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 4.4))\n",
    "ax.plot(report.f_fit, report.cvr * 100, color=INK, lw=1.8, marker=\"o\", ms=4, label=\"buyers stay in the audience\")\n",
    "ax.plot(report_drop.f_fit, report_drop.cvr * 100, color=FLAG, lw=1.8, marker=\"s\", ms=4, label=\"buyers leave the day after buying\")\n",
    "ax.set_xlabel(\"times one person saw the ad in the month\")\n",
    "ax.set_ylabel(\"share who bought, %\")\n",
    "ax.set_ylim(0, None)\n",
    "ax.set_title(\"One within-person curve, two reports: whether buyers stay decides which way the line goes\")\n",
    "ax.legend(frameon=False)\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_exclusion.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faa5a601",
   "metadata": {},
   "source": [
    "### How many conversions the fit needs\n",
    "\n",
    "The five hundred audiences differ in size, and what the fitted cap costs differs with them.\n",
    "Below, the audiences where a cap matters are grouped by how many conversions the month\n",
    "brought, and the spend the fitted cap loses is read per group. The floor the your-data\n",
    "section warns at is read off this table, not typed: the first group whose worst tenth\n",
    "loses less than a tenth of its spend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c52c4c90",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:19:16.629933Z",
     "iopub.status.busy": "2026-10-20T07:19:16.629624Z",
     "iopub.status.idle": "2026-10-20T07:19:16.653452Z",
     "shell.execute_reply": "2026-10-20T07:19:16.652608Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conversions   audiences  fitted: spend lost typ.  worst tenth  no cap, typ.\n",
      "under 50             54                     1.2%        18.0%          3.3%\n",
      "50-99                46                     0.7%         8.3%          4.1%\n",
      "100-199              83                     0.6%         6.6%          2.6%\n",
      "200-399              70                     0.5%         6.6%          2.7%\n",
      "400-799              63                     0.8%         4.0%          2.3%\n",
      "800+                 38                     0.6%         3.4%          3.5%\n",
      "\n",
      "from the '50-99' band up, the worst tenth of audiences lose under 10% of their spend to the fitted cap,\n",
      "so the your-data section treats fewer than 50 conversions as too thin for a cap\n"
     ]
    }
   ],
   "source": [
    "edges = [0, 50, 100, 200, 400, 800, 100000]\n",
    "labels = [\"under 50\", \"50-99\", \"100-199\", \"200-399\", \"400-799\", \"800+\"]\n",
    "band_floor = {\"under 50\": 0, \"50-99\": 50, \"100-199\": 100, \"200-399\": 200, \"400-799\": 400, \"800+\": 800}\n",
    "matters = matters.assign(conv_band=pd.cut(matters.conversions, bins=edges, labels=labels, right=False))\n",
    "sufficiency = []\n",
    "for band in labels:\n",
    "    sub = matters[matters.conv_band == band]\n",
    "    if len(sub) == 0:\n",
    "        continue\n",
    "    sufficiency.append({\"band\": band, \"min_conversions\": band_floor[band], \"audiences\": int(len(sub)),\n",
    "                        \"fitted_median_loss_pct\": float(np.median(sub.fitted_loss_pct)),\n",
    "                        \"fitted_p90_loss_pct\": float(np.percentile(sub.fitted_loss_pct, 90)),\n",
    "                        \"no_cap_median_loss_pct\": float(np.median(sub.no_cap_loss_pct))})\n",
    "print(f\"{'conversions':<13}{'audiences':>10}{'fitted: spend lost typ.':>25}{'worst tenth':>13}{'no cap, typ.':>14}\")\n",
    "for r in sufficiency:\n",
    "    print(f\"{r['band']:<13}{r['audiences']:>10}{r['fitted_median_loss_pct']:>24.1f}%{r['fitted_p90_loss_pct']:>12.1f}%{r['no_cap_median_loss_pct']:>13.1f}%\")\n",
    "FLOOR_TARGET_P90_PCT = 10.0     # the worst tenth of audiences may lose at most this share of spend to the reading\n",
    "floor_band = next((r[\"band\"] for r in sufficiency if r[\"fitted_p90_loss_pct\"] <= FLOOR_TARGET_P90_PCT), None)\n",
    "MIN_CONVERSIONS = band_floor[floor_band] if floor_band else 800\n",
    "print(f\"\\nfrom the '{floor_band}' band up, the worst tenth of audiences lose under {FLOOR_TARGET_P90_PCT:.0f}% of their spend to the fitted cap,\")\n",
    "print(f\"so the your-data section treats fewer than {MIN_CONVERSIONS} conversions as too thin for a cap\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f928e04",
   "metadata": {},
   "source": [
    "## Part two - how long does an audience stay worth targeting\n",
    "\n",
    "### The audience's own history, and the truth I hide from it\n",
    "\n",
    "Same store, same kind of audience, a different report: not how often each person saw the\n",
    "ad, but when each person joined the audience and whether they have bought since. People\n",
    "join every day. Some of them buy - mostly soon after joining - and most never do.\n",
    "\n",
    "The generator plants three numbers. A **share who will ever buy**: twelve per cent here;\n",
    "the rest never will, however long they sit in the audience. Among the buyers, a **timing\n",
    "curve**: how long after joining they buy, drawn from a Weibull distribution with a scale and\n",
    "a shape. And a **watching window**: the audience is observed for sixty days, so a person who\n",
    "joined last week has had a week to buy and no more.\n",
    "\n",
    "The question is when a person is old enough that the audience has got most of what it will\n",
    "ever get from them. The number the method reads is the **half-life**: the age by which half\n",
    "of the people who were ever going to buy have bought."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eff5b5cd",
   "metadata": {
    "execution": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20,000 people joined over 60 days; 1,971 have bought so far (9.86% of the audience)\n",
      "the truth built in: 12% will ever buy; among them, half have bought by day 9.2\n"
     ]
    }
   ],
   "source": [
    "WATCH_DAYS = 60\n",
    "N_MEMBERS = 20_000\n",
    "TRUE_SHARE = 0.12          # share who will ever buy\n",
    "TRUE_SHAPE = 1.4           # Weibull shape; above one, a buyer who has waited is more likely to buy soon\n",
    "TRUE_SCALE = 12.0          # Weibull scale in days\n",
    "\n",
    "\n",
    "def weibull_survival(t, shape, scale):\n",
    "    t = np.asarray(t, dtype=float)\n",
    "    return np.exp(-(np.maximum(t, 0) / scale) ** shape)\n",
    "\n",
    "\n",
    "def half_life(shape, scale):\n",
    "    \"\"\"The age by which half the eventual buyers have bought: the Weibull median.\"\"\"\n",
    "    return float(scale * np.log(2) ** (1.0 / shape))\n",
    "\n",
    "\n",
    "def simulate_audience(n_members, watch_days, generator, share=TRUE_SHARE, shape=TRUE_SHAPE, scale=TRUE_SCALE):\n",
    "    \"\"\"Join days uniform over the window; buyers get a Weibull delay; ages are whole days plus a half.\n",
    "\n",
    "    Returns one row per member: join_day, age (at purchase for buyers, at the window's end\n",
    "    otherwise), and whether the purchase was seen inside the window.\n",
    "    \"\"\"\n",
    "    join = generator.integers(0, watch_days, n_members)\n",
    "    will_buy = generator.uniform(size=n_members) < share\n",
    "    delay = scale * (-np.log(1 - generator.uniform(size=n_members))) ** (1.0 / shape)\n",
    "    buy_day = join + np.floor(delay).astype(int)\n",
    "    seen = will_buy & (buy_day < watch_days)\n",
    "    age = np.where(seen, buy_day - join, watch_days - 1 - join) + 0.5\n",
    "    return pd.DataFrame({\"join_day\": join, \"age\": age, \"converted\": seen})\n",
    "\n",
    "\n",
    "members = simulate_audience(N_MEMBERS, WATCH_DAYS, np.random.default_rng(SEED + 7))\n",
    "true_half_life = half_life(TRUE_SHAPE, TRUE_SCALE)\n",
    "print(f\"{len(members):,} people joined over {WATCH_DAYS} days; {int(members.converted.sum()):,} have bought so far \"\n",
    "      f\"({members.converted.mean():.2%} of the audience)\")\n",
    "print(f\"the truth built in: {TRUE_SHARE:.0%} will ever buy; among them, half have bought by day {true_half_life:.1f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97d76cb7",
   "metadata": {},
   "source": [
    "### What a reader does today: the rate-by-age table\n",
    "\n",
    "Group the audience by how old each person is, divide buyers by people in each group, and\n",
    "read off where the rate has halved. Two things are wrong with it before any arithmetic.\n",
    "\n",
    "**A person who joined last week and has not bought is not a person who will not buy.** They\n",
    "are an unfinished observation. The young age groups are full of them, and counting them as\n",
    "non-buyers drags the young rates down and the whole curve with it.\n",
    "\n",
    "**Most people will never buy, and the table has no place to put that.** A curve fitted to\n",
    "everyone's timing has to explain the never-buyers as buyers who take a very long time, and\n",
    "it stretches the audience's life to do it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0ad91a35",
   "metadata": {
    "execution": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the rate-by-age table (five-day groups):\n",
      " age_from  people  buyers     rate\n",
      "        0    2195     567 0.258314\n",
      "        5    2127     599 0.281617\n",
      "       10    1952     419 0.214652\n",
      "       15    1686     220 0.130486\n",
      "       20    1576      90 0.057107\n",
      "       25    1567      51 0.032546\n",
      "       30    1467      16 0.010907\n",
      "       35    1488       5 0.003360\n",
      "  ... the rate first halves at about day 20\n",
      "\n",
      "reading                                  half-life   truth\n",
      "where the table halves                        20.0     9.2\n",
      "Weibull on buyers only                         8.3     9.2\n",
      "Weibull with the unfinished kept             448.9     9.2\n",
      "the method (share who ever buy + Weibull)       9.4     9.2\n",
      "\n",
      "the method also reads the share who will ever buy: 0.120 (truth 0.12)\n"
     ]
    }
   ],
   "source": [
    "def kaplan_meier(age, converted):\n",
    "    \"\"\"Share not yet bought by each age, counting a person only while they are still being watched.\"\"\"\n",
    "    order = np.argsort(age)\n",
    "    t, e = np.asarray(age)[order], np.asarray(converted)[order]\n",
    "    times, surv = [0.0], [1.0]\n",
    "    at_risk, s, i = len(t), 1.0, 0\n",
    "    while i < len(t):\n",
    "        j = i\n",
    "        while j < len(t) and t[j] == t[i]:\n",
    "            j += 1\n",
    "        d = int(e[i:j].sum())\n",
    "        if d > 0 and at_risk > 0:\n",
    "            s *= (at_risk - d) / at_risk\n",
    "            times.append(float(t[i]))\n",
    "            surv.append(s)\n",
    "        at_risk -= (j - i)\n",
    "        i = j\n",
    "    return np.array(times), np.array(surv)\n",
    "\n",
    "\n",
    "def km_at(times, surv, t):\n",
    "    idx = np.searchsorted(times, t, side=\"right\") - 1\n",
    "    return surv[np.clip(idx, 0, len(surv) - 1)]\n",
    "\n",
    "\n",
    "def naive_rate_by_age(frame, width=5):\n",
    "    \"\"\"Buyers over people, by age group - the table a reader builds by hand.\"\"\"\n",
    "    bands = (frame.age // width).astype(int)\n",
    "    tab = frame.groupby(bands).agg(people=(\"converted\", \"size\"), buyers=(\"converted\", \"sum\"))\n",
    "    tab[\"age_from\"] = tab.index * width\n",
    "    tab[\"rate\"] = tab.buyers / tab.people\n",
    "    return tab.reset_index(drop=True)\n",
    "\n",
    "\n",
    "def naive_half_life_from_table(tab):\n",
    "    \"\"\"Where the observed rate first falls to half its opening value, in days.\"\"\"\n",
    "    opening = tab.rate.iloc[0]\n",
    "    below = tab[tab.rate <= opening / 2]\n",
    "    return float(below.age_from.iloc[0]) if len(below) else float(\"nan\")\n",
    "\n",
    "\n",
    "def weibull_negloglik_censored(params, age, converted, weights):\n",
    "    log_shape, log_scale = params\n",
    "    shape, scale = np.exp(log_shape), np.exp(log_scale)\n",
    "    z = (age / scale) ** shape\n",
    "    log_f = np.log(shape / scale) + (shape - 1) * np.log(age / scale) - z\n",
    "    return -float(np.sum(weights * np.where(converted, log_f, -z)))\n",
    "\n",
    "\n",
    "def cure_negloglik(params, age, converted, weights):\n",
    "    \"\"\"Buyers contribute share * density; the rest contribute (1 - share) + share * survival.\"\"\"\n",
    "    logit_share, log_shape, log_scale = params\n",
    "    share = 1.0 / (1.0 + np.exp(-logit_share))\n",
    "    shape, scale = np.exp(log_shape), np.exp(log_scale)\n",
    "    z = (age / scale) ** shape\n",
    "    log_f = np.log(shape / scale) + (shape - 1) * np.log(age / scale) - z\n",
    "    ll_buy = np.log(share) + log_f\n",
    "    ll_wait = np.log(np.clip((1 - share) + share * np.exp(-z), 1e-300, None))\n",
    "    return -float(np.sum(weights * np.where(converted, ll_buy, ll_wait)))\n",
    "\n",
    "\n",
    "def aggregate(frame):\n",
    "    \"\"\"The likelihood only needs (age, converted) with counts - much faster on a big audience.\"\"\"\n",
    "    g = frame.groupby([\"age\", \"converted\"]).size().reset_index(name=\"w\")\n",
    "    return g.age.to_numpy(dtype=float), g.converted.to_numpy(bool), g.w.to_numpy(dtype=float)\n",
    "\n",
    "\n",
    "def fit_naive_weibull(frame):\n",
    "    \"\"\"Weibull on the buyers alone - the not-yet-bought thrown away.\"\"\"\n",
    "    buyers = frame[frame.converted]\n",
    "    age, conv, w = aggregate(buyers)\n",
    "    res = minimize(weibull_negloglik_censored, [np.log(1.0), np.log(10.0)], args=(age, conv, w),\n",
    "                   method=\"Nelder-Mead\", options={\"maxiter\": 4000, \"xatol\": 1e-7, \"fatol\": 1e-9})\n",
    "    shape, scale = np.exp(res.x)\n",
    "    return {\"shape\": float(shape), \"scale\": float(scale), \"half_life\": half_life(shape, scale)}\n",
    "\n",
    "\n",
    "def fit_censored_weibull(frame):\n",
    "    \"\"\"Weibull on everyone, the not-yet-bought kept as unfinished - but no room for never-buyers.\"\"\"\n",
    "    age, conv, w = aggregate(frame)\n",
    "    res = minimize(weibull_negloglik_censored, [np.log(1.0), np.log(10.0)], args=(age, conv, w),\n",
    "                   method=\"Nelder-Mead\", options={\"maxiter\": 4000, \"xatol\": 1e-7, \"fatol\": 1e-9})\n",
    "    shape, scale = np.exp(res.x)\n",
    "    return {\"shape\": float(shape), \"scale\": float(scale), \"half_life\": half_life(shape, scale)}\n",
    "\n",
    "\n",
    "def fit_cure(frame):\n",
    "    \"\"\"The method: a share who will ever buy, a Weibull for when, and the unfinished kept as unfinished.\"\"\"\n",
    "    age, conv, w = aggregate(frame)\n",
    "    start = [np.log(0.1 / 0.9), np.log(1.0), np.log(10.0)]\n",
    "    res = minimize(cure_negloglik, start, args=(age, conv, w), method=\"Nelder-Mead\",\n",
    "                   options={\"maxiter\": 6000, \"xatol\": 1e-7, \"fatol\": 1e-9})\n",
    "    share = 1.0 / (1.0 + np.exp(-res.x[0]))\n",
    "    shape, scale = np.exp(res.x[1]), np.exp(res.x[2])\n",
    "    return {\"share\": float(share), \"shape\": float(shape), \"scale\": float(scale),\n",
    "            \"half_life\": half_life(shape, scale), \"converged\": bool(res.success)}\n",
    "\n",
    "\n",
    "table = naive_rate_by_age(members)\n",
    "naive_table_hl = naive_half_life_from_table(table)\n",
    "fit_naive = fit_naive_weibull(members)\n",
    "fit_cens = fit_censored_weibull(members)\n",
    "fit_method = fit_cure(members)\n",
    "\n",
    "print(\"the rate-by-age table (five-day groups):\")\n",
    "print(table[[\"age_from\", \"people\", \"buyers\", \"rate\"]].head(8).to_string(index=False))\n",
    "print(f\"  ... the rate first halves at about day {naive_table_hl:.0f}\")\n",
    "print()\n",
    "print(f\"{'reading':<40}{'half-life':>10}{'truth':>8}\")\n",
    "print(f\"{'where the table halves':<40}{naive_table_hl:>10.1f}{true_half_life:>8.1f}\")\n",
    "print(f\"{'Weibull on buyers only':<40}{fit_naive['half_life']:>10.1f}{true_half_life:>8.1f}\")\n",
    "print(f\"{'Weibull with the unfinished kept':<40}{fit_cens['half_life']:>10.1f}{true_half_life:>8.1f}\")\n",
    "print(f\"{'the method (share who ever buy + Weibull)':<40}{fit_method['half_life']:>10.1f}{true_half_life:>8.1f}\")\n",
    "print(f\"\\nthe method also reads the share who will ever buy: {fit_method['share']:.3f} (truth {TRUE_SHARE})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83275315",
   "metadata": {},
   "source": [
    "### The method, in one formula\n",
    "\n",
    "The audience is watched as a survival problem: the \"event\" is buying, the clock starts when a\n",
    "person joins, and a person who has not bought yet is **still being watched**, not written\n",
    "off. The share of the audience that has not bought by age $t$ is\n",
    "\n",
    "$$S(t) = (1 - c) + c\\,\\exp\\!\\left[-\\left(\\frac{t}{\\lambda}\\right)^{k}\\right]$$\n",
    "\n",
    "- $c$ is the **share who will ever buy**. The curve flattens at $1 - c$ - the never-buyers -\n",
    "  instead of falling to zero, and that flat part is where the never-buyers are counted.\n",
    "- $\\lambda$ (scale) and $k$ (shape) describe **when the buyers buy**. The exponential term\n",
    "  is the share of eventual buyers who have not bought yet at age $t$.\n",
    "\n",
    "The three numbers are chosen to make the data most likely: a person who bought at age $t$\n",
    "contributes the chance of buying exactly then; a person still unbought at age $t$\n",
    "contributes the chance of not having bought yet - either a never-buyer, or a buyer whose\n",
    "time has not come. That second term is what the rate-by-age table throws away.\n",
    "\n",
    "**The half-life** is the age by which half the eventual buyers have bought - the median of\n",
    "the buyers' own curve, not of the whole audience's, because the whole audience's curve never\n",
    "reaches one half when most people never buy:\n",
    "\n",
    "$$H = \\lambda\\,(\\ln 2)^{1/k}$$\n",
    "\n",
    "Two half-lives in, about five in six of what the audience will ever give has been given;\n",
    "three in, over nineteen in twenty. The exact shares depend on the shape and are computed\n",
    "below rather than quoted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "20dd5955",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  by 1 half-life (9 days), 50% of the eventual buyers have bought\n",
      "  by 2 half-lives (19 days), 84% of the eventual buyers have bought\n",
      "  by 3 half-lives (28 days), 96% of the eventual buyers have bought\n",
      "  by 4 half-lives (37 days), 99% of the eventual buyers have bought\n"
     ]
    }
   ],
   "source": [
    "def share_of_eventual_buyers_by(age, shape, scale):\n",
    "    return float(1.0 - weibull_survival(age, shape, scale))\n",
    "\n",
    "\n",
    "multiples = {}\n",
    "for m in [1, 2, 3, 4]:\n",
    "    multiples[m] = share_of_eventual_buyers_by(m * fit_method[\"half_life\"], fit_method[\"shape\"], fit_method[\"scale\"])\n",
    "    print(f\"  by {m} half-li{'fe' if m == 1 else 'ves'} ({m * fit_method['half_life']:.0f} days), \"\n",
    "          f\"{multiples[m]:.0%} of the eventual buyers have bought\")"
   ]
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    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1540x616 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "km_t, km_s = kaplan_meier(members.age.to_numpy(), members.converted.to_numpy())\n",
    "ages = np.linspace(0, WATCH_DAYS, 400)\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11, 4.4))\n",
    "ax = axes[0]\n",
    "ax.step(km_t, km_s * 100, where=\"post\", color=INK, lw=1.6, label=\"share not yet bought (the data)\")\n",
    "whole = ((1 - fit_method[\"share\"]) + fit_method[\"share\"] * weibull_survival(ages, fit_method[\"shape\"], fit_method[\"scale\"])) * 100\n",
    "ax.plot(ages, whole, color=FLAG, lw=2.0, ls=\"--\", label=\"the method's fit\")\n",
    "ax.axhline((1 - fit_method[\"share\"]) * 100, color=MUTED, lw=0.9, ls=\":\")\n",
    "ax.text(WATCH_DAYS * 0.98, (1 - fit_method[\"share\"]) * 100 + 0.5, \"the never-buyers\", ha=\"right\", fontsize=9, color=MUTED)\n",
    "ax.set_xlabel(\"days since joining the audience\")\n",
    "ax.set_ylabel(\"share not yet bought, %\")\n",
    "ax.set_ylim((1 - fit_method[\"share\"]) * 100 - 2, 100.5)\n",
    "ax.set_title(\"Whole audience: the curve flattens where the never-buyers are\")\n",
    "ax.legend(frameon=False, loc=\"upper right\")\n",
    "ax = axes[1]\n",
    "buyers_curve = weibull_survival(ages, fit_method[\"shape\"], fit_method[\"scale\"]) * 100\n",
    "ax.plot(ages, 100 - buyers_curve, color=SIGNAL, lw=2.2, label=\"share of eventual buyers who have bought\")\n",
    "ax.axhline(50, color=MUTED, lw=0.9, ls=\":\")\n",
    "ax.axvline(fit_method[\"half_life\"], color=SIGNAL, lw=1.0, ls=\":\")\n",
    "ax.text(fit_method[\"half_life\"] + 0.8, 90, f\"half-life {fit_method['half_life']:.1f} days\", color=SIGNAL, fontsize=9.5)\n",
    "ax.set_xlabel(\"days since joining the audience\")\n",
    "ax.set_ylabel(\"%\")\n",
    "ax.set_ylim(0, 100)\n",
    "ax.set_title(\"Buyers only: half have bought by the half-life\")\n",
    "ax.legend(frameon=False, loc=\"lower right\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_decay.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4f031b4",
   "metadata": {},
   "source": [
    "### Scored on four hundred audiences\n",
    "\n",
    "Four hundred audiences with their own share of eventual buyers, timing curves, sizes and\n",
    "watching windows, each read three ways. The rules are fixed before the run: the share who\n",
    "ever buy from three to thirty per cent, the timing scale from five to thirty days, the shape\n",
    "from front-loaded to peaked, two to thirty thousand people, watched for thirty to ninety\n",
    "days. Each reading's half-life is scored as a ratio to the truth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "06363c93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:19:17.586847Z",
     "iopub.status.busy": "2026-10-20T07:19:17.585672Z",
     "iopub.status.idle": "2026-10-20T07:19:40.460509Z",
     "shell.execute_reply": "2026-10-20T07:19:40.458261Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400 audiences scored\n",
      "reading              typical ratio  within 20%  no answer\n",
      "table                         1.83         12%         0%\n",
      "buyers_only                   0.84         58%         0%\n",
      "unfinished_kept              16.73          0%         0%\n",
      "method                        1.00         86%         0%\n",
      "\n",
      "the method's answer by how many half-lives the audience had been watched:\n",
      "  under 2     27 audiences   within 20%:   7%   typical ratio 3.83\n",
      "  2 to 3      62 audiences   within 20%:  56%   typical ratio 1.01\n",
      "  3 or more  311 audiences   within 20%:  98%   typical ratio 1.00\n"
     ]
    }
   ],
   "source": [
    "N_POP_B = 400\n",
    "\n",
    "\n",
    "def population_truths_b(n, generator):\n",
    "    return pd.DataFrame({\n",
    "        \"share\": generator.uniform(0.03, 0.30, n),\n",
    "        \"shape\": generator.uniform(0.8, 2.2, n),\n",
    "        \"scale\": generator.uniform(5.0, 30.0, n),\n",
    "        \"n_members\": np.round(10 ** generator.uniform(3.3, 4.5, n)).astype(int),\n",
    "        \"watch_days\": generator.integers(30, 91, n),\n",
    "    })\n",
    "\n",
    "\n",
    "g_pop_b = np.random.default_rng(SEED + 303)\n",
    "truths_b = population_truths_b(N_POP_B, g_pop_b)\n",
    "rows_b = []\n",
    "for t in truths_b.itertuples(index=False):\n",
    "    frame = simulate_audience(int(t.n_members), int(t.watch_days), g_pop_b, t.share, t.shape, t.scale)\n",
    "    if frame.converted.sum() < 20:\n",
    "        continue\n",
    "    truth_hl = half_life(t.shape, t.scale)\n",
    "    rows_b.append({\n",
    "        \"truth\": truth_hl, \"watch_days\": int(t.watch_days), \"buyers\": int(frame.converted.sum()),\n",
    "        \"windows_of_half_life\": float(t.watch_days / truth_hl),\n",
    "        \"table\": naive_half_life_from_table(naive_rate_by_age(frame)),\n",
    "        \"buyers_only\": fit_naive_weibull(frame)[\"half_life\"],\n",
    "        \"unfinished_kept\": fit_censored_weibull(frame)[\"half_life\"],\n",
    "        \"method\": fit_cure(frame)[\"half_life\"],\n",
    "    })\n",
    "pop_b = pd.DataFrame(rows_b)\n",
    "print(f\"{len(pop_b)} audiences scored\")\n",
    "summary_b = {}\n",
    "print(f\"{'reading':<20}{'typical ratio':>14}{'within 20%':>12}{'no answer':>11}\")\n",
    "for name in [\"table\", \"buyers_only\", \"unfinished_kept\", \"method\"]:\n",
    "    ratio = pop_b[name] / pop_b.truth\n",
    "    ok = ratio.notna()\n",
    "    summary_b[name] = {\n",
    "        \"median_ratio\": float(np.median(ratio[ok])),\n",
    "        \"p10_ratio\": float(np.percentile(ratio[ok], 10)),\n",
    "        \"p90_ratio\": float(np.percentile(ratio[ok], 90)),\n",
    "        \"within_20pct\": float(np.mean(np.abs(ratio[ok] - 1) <= 0.2)) * 100,\n",
    "        \"no_answer_pct\": float(np.mean(~ok)) * 100,\n",
    "    }\n",
    "    s = summary_b[name]\n",
    "    print(f\"{name:<20}{s['median_ratio']:>14.2f}{s['within_20pct']:>11.0f}%{s['no_answer_pct']:>10.0f}%\")\n",
    "\n",
    "# the method's misses, by how long the audience was watched in units of its own half-life\n",
    "watched_bands = []\n",
    "for lo, hi, label in [(0, 2, \"under 2\"), (2, 3, \"2 to 3\"), (3, 1000, \"3 or more\")]:\n",
    "    sub = pop_b[(pop_b.windows_of_half_life >= lo) & (pop_b.windows_of_half_life < hi)]\n",
    "    if len(sub) == 0:\n",
    "        continue\n",
    "    ratio = sub.method / sub.truth\n",
    "    watched_bands.append({\"half_lives_watched\": label, \"audiences\": int(len(sub)),\n",
    "                          \"within_20pct\": float(np.mean(np.abs(ratio - 1) <= 0.2)) * 100,\n",
    "                          \"median_ratio\": float(np.median(ratio))})\n",
    "print(\"\\nthe method's answer by how many half-lives the audience had been watched:\")\n",
    "for b in watched_bands:\n",
    "    print(f\"  {b['half_lives_watched']:<10} {b['audiences']:3d} audiences   within 20%: {b['within_20pct']:3.0f}%   typical ratio {b['median_ratio']:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ff8f70e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:19:40.465761Z",
     "iopub.status.busy": "2026-10-20T07:19:40.465372Z",
     "iopub.status.idle": "2026-10-20T07:19:41.635739Z",
     "shell.execute_reply": "2026-10-20T07:19:41.634688Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x616 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 4.4))\n",
    "for name, color, label in [(\"buyers_only\", NAIVE, \"Weibull on buyers only\"),\n",
    "                           (\"unfinished_kept\", FLAG, \"Weibull with the unfinished kept, no never-buyers\"),\n",
    "                           (\"method\", SIGNAL, \"the method\")]:\n",
    "    ratio = (pop_b[name] / pop_b.truth).dropna()\n",
    "    ax.hist(np.clip(np.log2(ratio), -2.5, 5.4), bins=np.linspace(-2.5, 5.5, 81), color=color, alpha=0.55, label=label)\n",
    "ax.axvline(0, color=INK, lw=1.0)\n",
    "ax.set_xticks([-2, -1, 0, 1, 2, 3, 4, 5])\n",
    "ax.set_xticklabels([\"a quarter\", \"half\", \"right\", \"double\", \"4x\", \"8x\", \"16x\", \"32x or more\"])\n",
    "ax.set_xlabel(\"the half-life each reading returns, as a multiple of the truth\")\n",
    "ax.set_ylabel(\"audiences\")\n",
    "ax.set_title(\"Throw the unfinished away and the half-life comes out short; forget the never-buyers and it comes out long\")\n",
    "ax.legend(frameon=False, loc=\"upper left\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_decay_population.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce43afe2",
   "metadata": {},
   "source": [
    "### How long you have to watch\n",
    "\n",
    "The share who will ever buy can only be told apart from slow buyers if the curve has had\n",
    "time to flatten. Watch an audience for a week and every unbought person could still be a\n",
    "buyer whose time has not come; the fit has nothing to pin the never-buyers on and the\n",
    "half-life drifts. Below, one audience is watched for windows from one week to twelve, a\n",
    "hundred times each, and the method's half-life is scored against the truth in each window.\n",
    "The answer is read in half-lives, so it transfers to an audience with a different clock."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "47e80fbe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:19:41.638983Z",
     "iopub.status.busy": "2026-10-20T07:19:41.638624Z",
     "iopub.status.idle": "2026-10-20T07:20:05.967097Z",
     "shell.execute_reply": "2026-10-20T07:20:05.965435Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " watch_days  half_lives_watched  median_ratio  p10_ratio  p90_ratio  within_20pct\n",
      "          7            0.757902      4.410469   3.894966   5.001598           0.0\n",
      "         10            1.082717      5.511031   4.724977   6.032885           2.0\n",
      "         14            1.515803      6.907455   1.100597   7.655690          16.0\n",
      "         21            2.273705      1.043159   0.966094   1.125759          97.0\n",
      "         28            3.031607      1.010351   0.974075   1.060842         100.0\n",
      "         42            4.547410      1.004527   0.981644   1.032608         100.0\n",
      "         56            6.063214      0.999664   0.973813   1.025145         100.0\n",
      "         84            9.094821      1.002730   0.979460   1.022933         100.0\n",
      "\n",
      "from 3 half-lives of watching, nine runs in ten land within 20% of the truth\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x588 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "WINDOWS = [7, 10, 14, 21, 28, 42, 56, 84]\n",
    "N_REPS_WINDOW = 100\n",
    "g_win = np.random.default_rng(SEED + 404)\n",
    "window_rows = []\n",
    "for w in WINDOWS:\n",
    "    ratios = []\n",
    "    for _ in range(N_REPS_WINDOW):\n",
    "        frame = simulate_audience(N_MEMBERS, w, g_win)\n",
    "        if frame.converted.sum() < 20:\n",
    "            continue\n",
    "        ratios.append(fit_cure(frame)[\"half_life\"] / true_half_life)\n",
    "    ratios = np.array(ratios)\n",
    "    window_rows.append({\"watch_days\": w, \"half_lives_watched\": w / true_half_life,\n",
    "                        \"median_ratio\": float(np.median(ratios)),\n",
    "                        \"p10_ratio\": float(np.percentile(ratios, 10)), \"p90_ratio\": float(np.percentile(ratios, 90)),\n",
    "                        \"within_20pct\": float(np.mean(np.abs(ratios - 1) <= 0.2)) * 100})\n",
    "windows = pd.DataFrame(window_rows)\n",
    "print(windows.to_string(index=False))\n",
    "WINDOW_TARGET_PCT = 90\n",
    "enough = windows[windows.within_20pct >= WINDOW_TARGET_PCT]\n",
    "HALF_LIVES_NEEDED = float(np.ceil(enough.half_lives_watched.iloc[0])) if len(enough) else float(\"nan\")\n",
    "print(f\"\\nfrom {HALF_LIVES_NEEDED:.0f} half-lives of watching, nine runs in ten land within 20% of the truth\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 4.2))\n",
    "ax.fill_between(windows.half_lives_watched, windows.p10_ratio, windows.p90_ratio, color=BAND, alpha=0.7,\n",
    "                label=\"middle 80% of a hundred runs\")\n",
    "ax.plot(windows.half_lives_watched, windows.median_ratio, color=SIGNAL, lw=2.0, marker=\"o\", label=\"typical run\")\n",
    "ax.axhline(1.0, color=INK, lw=1.0)\n",
    "ax.axhspan(0.8, 1.2, color=LINE, alpha=0.5)\n",
    "ax.set_xlabel(\"how long the audience was watched, in half-lives\")\n",
    "ax.set_ylabel(\"half-life returned / truth\")\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_yticks([0.5, 1, 2, 4, 8])\n",
    "ax.set_yticklabels([\"half\", \"right\", \"double\", \"4x\", \"8x\"])\n",
    "ax.set_ylim(0.4, 9)\n",
    "ax.set_title(\"Watched for less than a couple of half-lives, the half-life is a guess with the truth's units\")\n",
    "ax.legend(frameon=False, loc=\"upper right\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_window.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e914633e",
   "metadata": {},
   "source": [
    "### When two audiences hide in one: the promotion spike\n",
    "\n",
    "The method assumes the people who joined on Tuesday are the same kind of people as those\n",
    "who joined the Tuesday before. A promotion breaks that: a burst of people join in three\n",
    "days, and they behave as a group - deal-seekers who buy fast or not at all - rather than\n",
    "like the steady trickle around them. The half-life read across the mixed pool describes\n",
    "neither. Below, forty per cent of the audience joins in a three-day burst with a faster\n",
    "clock, and the pooled half-life is compared with each group's own."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0651668f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:20:05.971396Z",
     "iopub.status.busy": "2026-10-20T07:20:05.970968Z",
     "iopub.status.idle": "2026-10-20T07:20:06.079712Z",
     "shell.execute_reply": "2026-10-20T07:20:06.078136Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "joins per day: about 198 normally, 2872 during the three-day burst\n",
      "half-life of the steady trickle on its own: 9.6 days\n",
      "half-life of the burst on its own:          4.8 days\n",
      "half-life read across the whole pool:       6.4 days - neither group's\n"
     ]
    }
   ],
   "source": [
    "def simulate_spiked_audience(generator, burst_share=0.4, burst_days=(20, 23), burst_scale=6.0, burst_buy_share=0.20):\n",
    "    n_burst = int(N_MEMBERS * burst_share)\n",
    "    steady = simulate_audience(N_MEMBERS - n_burst, WATCH_DAYS, generator)\n",
    "    join = generator.integers(burst_days[0], burst_days[1], n_burst)\n",
    "    will_buy = generator.uniform(size=n_burst) < burst_buy_share\n",
    "    delay = burst_scale * (-np.log(1 - generator.uniform(size=n_burst))) ** (1.0 / TRUE_SHAPE)\n",
    "    buy_day = join + np.floor(delay).astype(int)\n",
    "    seen = will_buy & (buy_day < WATCH_DAYS)\n",
    "    age = np.where(seen, buy_day - join, WATCH_DAYS - 1 - join) + 0.5\n",
    "    burst = pd.DataFrame({\"join_day\": join, \"age\": age, \"converted\": seen})\n",
    "    steady[\"cohort\"], burst[\"cohort\"] = \"steady\", \"burst\"\n",
    "    return pd.concat([steady, burst], ignore_index=True)\n",
    "\n",
    "\n",
    "spiked = simulate_spiked_audience(np.random.default_rng(SEED + 505))\n",
    "hl_pooled = fit_cure(spiked)[\"half_life\"]\n",
    "hl_steady = fit_cure(spiked[spiked.cohort == \"steady\"])[\"half_life\"]\n",
    "hl_burst = fit_cure(spiked[spiked.cohort == \"burst\"])[\"half_life\"]\n",
    "joins_per_day = spiked.groupby(\"join_day\").size()\n",
    "baseline_joins = float(joins_per_day[(joins_per_day.index < 20) | (joins_per_day.index >= 23)].median())\n",
    "burst_joins = float(joins_per_day[(joins_per_day.index >= 20) & (joins_per_day.index < 23)].mean())\n",
    "print(f\"joins per day: about {baseline_joins:.0f} normally, {burst_joins:.0f} during the three-day burst\")\n",
    "print(f\"half-life of the steady trickle on its own: {hl_steady:.1f} days\")\n",
    "print(f\"half-life of the burst on its own:          {hl_burst:.1f} days\")\n",
    "print(f\"half-life read across the whole pool:       {hl_pooled:.1f} days - neither group's\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96a92c7c",
   "metadata": {},
   "source": [
    "### What the half-life is worth, in money\n",
    "\n",
    "The half-life says how fast an audience spends itself. Whether to keep serving it is, again,\n",
    "an economic question: each person still in the audience costs something to keep showing the\n",
    "ad to, and brings in an expected sale value that falls as they age. The notebook prices it\n",
    "on the worked audience - a cost per person per day for keeping them in the audience (their\n",
    "daily impressions times the cost of an impression) against what a sale is worth times the\n",
    "chance an unbought person of that age buys today. The age where the second falls under the\n",
    "first is the economic membership window. The usual defaults - thirty, sixty, ninety days -\n",
    "are scored against it.\n",
    "\n",
    "One assumption sits underneath, and it is the one the report cannot check: that the sales\n",
    "audience members make are sales the ads caused. If half of them would have bought anyway,\n",
    "a person is worth half as much to keep, and the window shortens. The notebook prices the\n",
    "window at three values of that share rather than picking one, because only an experiment\n",
    "- refresh the audience for a random slice, leave the rest - measures it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e55c5b92",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:20:06.084691Z",
     "iopub.status.busy": "2026-10-20T07:20:06.084337Z",
     "iopub.status.idle": "2026-10-20T07:20:06.635999Z",
     "shell.execute_reply": "2026-10-20T07:20:06.634184Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keeping one person in the audience costs 0.42 cents a day; a sale is worth 25 euros\n",
      "the economic membership window on this audience, if every sale is the ads' doing: 38 days (about 4.1 half-lives)\n",
      "  keep for  14 days: worth    41,368 euros, leaves  15,449 on the table\n",
      "  keep for  30 days: worth    56,190 euros, leaves     627 on the table\n",
      "  keep for  38 days: worth    56,817 euros, leaves       0 on the table <- the economic window\n",
      "  keep for  60 days: worth    55,560 euros, leaves   1,257 on the table\n",
      "  keep for  90 days: worth    53,347 euros, leaves   3,470 on the table\n",
      "  keep for 180 days: worth    46,696 euros, leaves  10,121 on the table\n",
      "if only some of those sales are the ads' doing, the window shortens:\n",
      "  ads cause 100% of the sales -> keep for 38 days (4.1 half-lives)\n",
      "  ads cause 50% of the sales -> keep for 34 days (3.6 half-lives)\n",
      "  ads cause 25% of the sales -> keep for 30 days (3.2 half-lives)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x588 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "IMPRESSIONS_PER_MEMBER_DAY = 0.35\n",
    "COST_PER_MEMBER_DAY = IMPRESSIONS_PER_MEMBER_DAY * COST_PER_IMPRESSION\n",
    "\n",
    "\n",
    "def daily_buy_chance(age, share, shape, scale):\n",
    "    \"\"\"Chance an as-yet-unbought person of this age buys in the next day (the audience-wide hazard).\"\"\"\n",
    "    age = np.asarray(age, dtype=float)\n",
    "    s_w = weibull_survival(age, shape, scale)\n",
    "    s_w_next = weibull_survival(age + 1, shape, scale)\n",
    "    unbought = (1 - share) + share * s_w\n",
    "    return share * (s_w - s_w_next) / unbought\n",
    "\n",
    "\n",
    "def membership_window(share, shape, scale, value=VALUE_EUR, cost=COST_PER_MEMBER_DAY, ad_effect=1.0, horizon=365):\n",
    "    \"\"\"The last age at which an unbought person's expected sale value, times the share of it the\n",
    "    ads cause, still covers a day of serving them - plus one, so it reads as a window length.\"\"\"\n",
    "    ages = np.arange(0, horizon)\n",
    "    worth = value * ad_effect * daily_buy_chance(ages, share, shape, scale)\n",
    "    paying = ages[worth >= cost]\n",
    "    return int(paying.max()) + 1 if len(paying) else 0\n",
    "\n",
    "\n",
    "def window_value(frame_size, share, shape, scale, window, value=VALUE_EUR, cost=COST_PER_MEMBER_DAY, ad_effect=1.0):\n",
    "    \"\"\"Value of keeping each joining person for `window` days: the sales the ads bring minus the days paid for.\"\"\"\n",
    "    ages = np.arange(0, window)\n",
    "    s_w = weibull_survival(ages, shape, scale)\n",
    "    unbought = (1 - share) + share * s_w\n",
    "    sales = share * (1 - weibull_survival(window, shape, scale))\n",
    "    return float(frame_size * (value * ad_effect * sales - cost * unbought.sum()))\n",
    "\n",
    "\n",
    "AD_EFFECT_SHARES = [1.0, 0.5, 0.25]\n",
    "econ_window = membership_window(fit_method[\"share\"], fit_method[\"shape\"], fit_method[\"scale\"])\n",
    "econ_window_by_effect = {str(e): membership_window(fit_method[\"share\"], fit_method[\"shape\"], fit_method[\"scale\"], ad_effect=e)\n",
    "                         for e in AD_EFFECT_SHARES}\n",
    "window_money = {}\n",
    "for w in [14, 30, econ_window, 60, 90, 180]:\n",
    "    window_money[w] = window_value(N_MEMBERS, fit_method[\"share\"], fit_method[\"shape\"], fit_method[\"scale\"], w)\n",
    "best_window_value = max(window_money.values())\n",
    "print(f\"keeping one person in the audience costs {COST_PER_MEMBER_DAY * 100:.2f} cents a day; a sale is worth {VALUE_EUR:.0f} euros\")\n",
    "print(f\"the economic membership window on this audience, if every sale is the ads' doing: {econ_window} days \"\n",
    "      f\"(about {econ_window / fit_method['half_life']:.1f} half-lives)\")\n",
    "for w, v in window_money.items():\n",
    "    tag = \" <- the economic window\" if w == econ_window else \"\"\n",
    "    print(f\"  keep for {w:>3} days: worth {v:9,.0f} euros, leaves {best_window_value - v:7,.0f} on the table{tag}\")\n",
    "print(\"if only some of those sales are the ads' doing, the window shortens:\")\n",
    "for e, w in econ_window_by_effect.items():\n",
    "    print(f\"  ads cause {float(e):.0%} of the sales -> keep for {w} days ({w / fit_method['half_life']:.1f} half-lives)\")\n",
    "\n",
    "ages_plot = np.arange(0, 121)\n",
    "fig, ax = plt.subplots(figsize=(10, 4.2))\n",
    "ax.plot(ages_plot, VALUE_EUR * daily_buy_chance(ages_plot, fit_method[\"share\"], fit_method[\"shape\"], fit_method[\"scale\"]) * 100,\n",
    "        color=SIGNAL, lw=2.0, label=\"expected sale value per unbought person per day (cents)\")\n",
    "ax.axhline(COST_PER_MEMBER_DAY * 100, color=FLAG, lw=1.6, ls=\"--\", label=\"cost of keeping them one more day (cents)\")\n",
    "ax.axvline(econ_window, color=INK, lw=1.0, ls=\":\")\n",
    "ax.text(econ_window + 1.5, COST_PER_MEMBER_DAY * 100 * 4, f\"stop at day {econ_window}\", fontsize=9.5)\n",
    "ax.set_xlabel(\"days since joining the audience\")\n",
    "ax.set_ylabel(\"cents per person per day\")\n",
    "ax.set_title(\"Keep serving while a person's expected sale value covers their cost; the half-life sets how fast it falls\")\n",
    "ax.legend(frameon=False, loc=\"upper right\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_membership.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfa26d0e",
   "metadata": {},
   "source": [
    "## Where these methods lie to you\n",
    "\n",
    "Every number above came from an audience built with its answer known. Real audiences are\n",
    "not built that way, and the ways this goes wrong are measurable, so here they are with\n",
    "their sizes.\n",
    "\n",
    "**The report's curve is not the person's curve, and the report cannot tell you which way it\n",
    "is off.** When the platform leans exposure toward people who are merely online more, the\n",
    "fitted cap lands about an impression early; when it leans toward keen people, late, and\n",
    "more and more often past the end of the data. The population run measures both at each\n",
    "strength of the lean. Only a holdout - cap a random slice of the audience, leave the rest -\n",
    "measures the within-person curve directly.\n",
    "\n",
    "**A cap is a property of the audience and the window.** The curve here was fitted over\n",
    "twenty-eight days, and the number it returns is impressions in twenty-eight days. A cap read\n",
    "off a week is a different quantity, and comparing the two is comparing a monthly figure with\n",
    "a weekly one. This notebook's people never forget an impression; real people do, which is\n",
    "the reason the window belongs on the number.\n",
    "\n",
    "**How sharply the curve bends depends on how big the brand already is.** The\n",
    "effective-frequency literature found that dominant brands respond to frequency differently\n",
    "from small ones; the fitted shape carries the brand's market position as well as the\n",
    "audience's response, and the curve cannot separate them.\n",
    "\n",
    "**A cap read past the top bucket is the shape guessing.** The population run counts how\n",
    "often the fitted cap sat beyond the frequencies the audience reached; when it does, the\n",
    "honest statement is \"at least the top bucket\", not the number.\n",
    "\n",
    "**A join-time burst mixes two audiences, and the half-life describes neither.** Measured\n",
    "above with a three-day promotion.\n",
    "\n",
    "**Watched too briefly, the half-life is a guess.** Measured above: the number of half-lives\n",
    "of history before nine runs in ten land within twenty per cent.\n",
    "\n",
    "**Both halves cap at \"modelled\".** Neither curve saw a counterfactual: nobody randomised how\n",
    "often a person saw the ad, or when their audience was rebuilt. A holdout does, and the\n",
    "platform proposes one where a cap or a refresh matters enough to pay for the test."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ca4fa1e",
   "metadata": {},
   "source": [
    "## Run it on your own figures\n",
    "\n",
    "Everything above is synthetic so the answers could be scored. This section runs both methods\n",
    "on files you export yourself. It runs on your machine; nothing is uploaded.\n",
    "\n",
    "### The frequency file\n",
    "\n",
    "One line per frequency bucket, three columns, any order:\n",
    "\n",
    "| column | what it holds | example |\n",
    "|---|---|---|\n",
    "| `frequency` | times a person saw the ad in the window; the top bucket may be written `15+` | `3` |\n",
    "| `reach` | people in that bucket | `16240` |\n",
    "| `conversions` | of those people, how many converted | `271` |\n",
    "\n",
    "The window the report covers is yours to state - the cap this returns is in impressions\n",
    "over that window and must be compared only with caps over the same window. A top bucket\n",
    "written `15+` is placed at fifteen; if your export gives the average frequency inside it,\n",
    "put that number instead.\n",
    "\n",
    "**Where it comes from - and where it does not.** No ad platform's dashboard gives this:\n",
    "Meta's frequency breakdown carries people per frequency level and no conversions, and Google\n",
    "Ads' frequency distribution gives people in cumulative buckets ('1+', '2+' ...) and no\n",
    "conversions. The buckets with conversions come from a per-person log: one line per\n",
    "impression with a person identifier plus one line per conversion with the same identifier -\n",
    "Campaign Manager 360's Data Transfer files, an ad server's log, or a clean room - and the\n",
    "consolidator below builds them. A report in cumulative buckets is refused, because every\n",
    "person is counted in every bucket up to their own frequency.\n",
    "\n",
    "**What it refuses, and why.** Fewer than four buckets with people in them - a curve with\n",
    "three numbers cannot be told from noise on three points. Fewer conversions than the floor\n",
    "measured above - the cap would be a guess. And it warns, rather than refuses, when the cap\n",
    "lands past the top bucket, and when the share who bought falls with frequency - which is\n",
    "the shape a buyers-leave audience produces and which no cap should be read from.\n",
    "\n",
    "### The audience file\n",
    "\n",
    "One line per person, three columns, any order:\n",
    "\n",
    "| column | what it holds | example |\n",
    "|---|---|---|\n",
    "| `joined` | the date the person entered the audience | `2026-05-03` |\n",
    "| `converted_on` | the date they bought, or blank if they have not | `2026-05-11` |\n",
    "| `as_of` | the date the export was taken (the same on every line) | `2026-07-01` |\n",
    "\n",
    "A person who bought before joining, or after the export date, is skipped with a note. From\n",
    "a raw events log (one line per event with a person identifier, an event name and a\n",
    "timestamp), the consolidator below takes the first visit as the join and the first purchase\n",
    "after it as the conversion.\n",
    "\n",
    "**What it refuses, and why.** Fewer than twenty buyers - the timing curve needs them. A\n",
    "window shorter than the number of half-lives measured above, once the half-life is read -\n",
    "the fit will return a number, and the run above says not to trust it yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "abce836a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:20:06.645780Z",
     "iopub.status.busy": "2026-10-20T07:20:06.645302Z",
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     "shell.execute_reply": "2026-10-20T07:20:06.760722Z"
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   },
   "outputs": [],
   "source": [
    "# ---- export reader (shared across every Stochastic Strata notebook; do not edit here) ----------\n",
    "# One reader for the file you bring. Ad platforms and analytics tools do not hand over clean tables:\n",
    "# a Google Ads download opens with a title line and a date-range line and closes with \"Total:\" rows,\n",
    "# a GA4 download opens with \"#\" lines and writes dates as 20261019, a European download separates\n",
    "# with semicolons and writes 1.234,56, and Excel turns numbers into \"1,234.56\" or \"€12.30\". This\n",
    "# reader finds the real header row, sets the title, comment and total rows aside, turns formatted\n",
    "# numbers back into numbers, and reads dates in the platforms' own forms. Where a file is genuinely\n",
    "# ambiguous - 06/07/2026 could be the 6th of July or the 7th of June - it stops and asks rather than\n",
    "# guessing, because a guessed date silently moves a day to the wrong weekday.\n",
    "#\n",
    "#     table, notes = read_export(\"my_export.csv\", columns={\"date\": [\"day\", \"date\"], ...})\n",
    "#     for note in notes: print(note)\n",
    "#\n",
    "# `columns` maps the names this notebook uses to the names your file might use (case and spacing do\n",
    "# not matter, and a currency suffix such as \"(EUR)\" is ignored). `date_columns` are read as dates,\n",
    "# `id_columns` are kept as text. Everything else that reads as numbers becomes numbers; a share\n",
    "# written \"49.41%\" becomes 0.4941. A share the platform shows only as \"< 10%\" or \"> 90%\" is read as\n",
    "# 0.10 or 0.90 and the direction is kept in a column ending \"_bound\", because it is a limit, not a\n",
    "# measurement. Every row set aside, and why, comes back in `notes`.\n",
    "import csv as _csv\n",
    "import io as _io\n",
    "import re as _re\n",
    "from pathlib import Path as _Path\n",
    "\n",
    "import numpy as _np\n",
    "import pandas as _pd\n",
    "\n",
    "\n",
    "class ExportReadError(ValueError):\n",
    "    \"\"\"The file cannot be read without guessing; the message says what to change.\"\"\"\n",
    "\n",
    "\n",
    "_TOTAL_ROW = _re.compile(r\"^\\s*(grand\\s+)?totals?\\b\", _re.I)\n",
    "_CURRENCY = _re.compile(r\"[€$£¥₹]|\\b(EUR|USD|GBP|CHF|SEK|NOK|DKK|PLN|AUD|CAD)\\b\", _re.I)\n",
    "_EMPTY = {\"\", \"--\", \"-\", \"—\", \"–\", \"n/a\", \"na\", \"nan\", \"none\", \"null\", \"(not set)\"}\n",
    "_MONTHS = {m: i for i, m in enumerate(\n",
    "    [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"], 1)}\n",
    "\n",
    "\n",
    "def _norm(name):\n",
    "    name = _re.sub(r\"\\((?:[A-Z]{3}|[€$£])\\)\", \"\", str(name))\n",
    "    return _re.sub(r\"[\\s_\\-./]+\", \" \", name).strip().lower()\n",
    "\n",
    "\n",
    "def _decode(raw):\n",
    "    if raw[:2] in (b\"\\xff\\xfe\", b\"\\xfe\\xff\"):\n",
    "        return raw.decode(\"utf-16\")\n",
    "    for encoding in (\"utf-8-sig\", \"cp1252\", \"latin-1\"):\n",
    "        try:\n",
    "            return raw.decode(encoding)\n",
    "        except UnicodeDecodeError:\n",
    "            continue\n",
    "    return raw.decode(\"utf-8\", errors=\"replace\")\n",
    "\n",
    "\n",
    "def _rows_from(source):\n",
    "    \"\"\"Every row of the file as a list of strings, whatever the container.\"\"\"\n",
    "    if isinstance(source, _pd.DataFrame):\n",
    "        return [list(map(str, source.columns))] + source.astype(str).values.tolist(), \",\"\n",
    "    path = _Path(source)\n",
    "    if not path.exists():\n",
    "        raise ExportReadError(f\"There is no file at {path}. Check the name and the folder.\")\n",
    "    if path.suffix.lower() in (\".xlsx\", \".xlsm\", \".xls\"):\n",
    "        sheet = _pd.read_excel(path, header=None, dtype=str).fillna(\"\")\n",
    "        return sheet.values.tolist(), \",\"\n",
    "    text = _decode(path.read_bytes())\n",
    "    lines = [ln for ln in text.splitlines() if not ln.lstrip().startswith(\"#\")]\n",
    "    counts = {d: sum(ln.count(d) for ln in lines[:60]) for d in (\",\", \";\", \"\\t\")}\n",
    "    delimiter = max(counts, key=counts.get) if any(counts.values()) else \",\"\n",
    "    return [row for row in _csv.reader(_io.StringIO(\"\\n\".join(lines)), delimiter=delimiter)], delimiter\n",
    "\n",
    "\n",
    "def _find_header(rows, wanted):\n",
    "    \"\"\"The first row that names the columns: the one matching most expected names, else the first\n",
    "    row as wide as the data below it that is mostly words rather than numbers.\"\"\"\n",
    "    widths = [len([c for c in r if str(c).strip()]) for r in rows]\n",
    "    if wanted:\n",
    "        best, best_hits = None, 0\n",
    "        for i, row in enumerate(rows[:40]):\n",
    "            names = {_norm(c) for c in row if str(c).strip()}\n",
    "            hits = sum(1 for options in wanted.values() if names & set(options))\n",
    "            if hits > best_hits:\n",
    "                best, best_hits = i, hits\n",
    "        if best is not None:\n",
    "            return best\n",
    "    body = [w for w in widths if w > 1]\n",
    "    width = max(set(body), key=body.count) if body else 1\n",
    "    for i, row in enumerate(rows[:40]):\n",
    "        cells = [str(c).strip() for c in row if str(c).strip()]\n",
    "        if len(cells) >= max(2, width - 1) and sum(_looks_number(c) for c in cells) <= len(cells) / 2:\n",
    "            return i\n",
    "    return 0\n",
    "\n",
    "\n",
    "def _looks_number(text):\n",
    "    if _re.fullmatch(r\"\\d{1,4}[./\\-]\\d{1,2}[./\\-]\\d{1,4}\", str(text).strip()):\n",
    "        return False  # a date such as 22.09.2026, not a number with separators\n",
    "    return bool(_re.fullmatch(r\"[<>]?\\s*[-+(]?[€$£]?\\s*[\\d.,\\s']+\\s*%?\\)?\", str(text).strip()))\n",
    "\n",
    "\n",
    "def _decimal_comma(values):\n",
    "    \"\"\"True when these values write 1.234,56 (a comma followed by one or two final digits, or dots\n",
    "    grouping thousands with a comma after them), False when they write 1,234.56, None when they do\n",
    "    not say (whole numbers only, or a lone '1.340').\"\"\"\n",
    "    comma_votes = dot_votes = 0\n",
    "    for v in values:\n",
    "        s = _re.sub(r\"[^\\d.,]\", \"\", v)\n",
    "        if \",\" in s and \".\" in s:\n",
    "            comma_votes += s.rfind(\",\") > s.rfind(\".\")\n",
    "            dot_votes += s.rfind(\".\") > s.rfind(\",\")\n",
    "        elif _re.search(r\",\\d{1,2}$\", s):\n",
    "            comma_votes += 1\n",
    "        elif _re.search(r\"\\.\\d{1,2}$\", s) or _re.search(r\"\\.\\d{4,}$\", s):\n",
    "            dot_votes += 1\n",
    "    if comma_votes == dot_votes:\n",
    "        return None\n",
    "    return comma_votes > dot_votes\n",
    "\n",
    "\n",
    "def _to_number(text, comma_decimal):\n",
    "    s = str(text).strip().replace(\" \", \" \")\n",
    "    if s.lower() in _EMPTY:\n",
    "        return _np.nan, None, True\n",
    "    bound = None\n",
    "    if s[:1] in \"<>\":\n",
    "        bound, s = s[0], s[1:].strip()\n",
    "    negative = s.startswith(\"(\") and s.endswith(\")\")\n",
    "    s = _CURRENCY.sub(\"\", s).strip(\"() \").replace(\" \", \"\").replace(\"'\", \"\")\n",
    "    percent = s.endswith(\"%\")\n",
    "    s = s.rstrip(\"%\")\n",
    "    if comma_decimal:\n",
    "        s = s.replace(\".\", \"\").replace(\",\", \".\")\n",
    "    else:\n",
    "        s = s.replace(\",\", \"\")\n",
    "    try:\n",
    "        value = float(s)\n",
    "    except ValueError:\n",
    "        return _np.nan, None, False\n",
    "    if negative:\n",
    "        value = -value\n",
    "    return (value / 100.0 if percent else value), bound, True\n",
    "\n",
    "\n",
    "def _parse_dates(values, column, dayfirst):\n",
    "    text = [str(v).strip() for v in values]\n",
    "    present = [t for t in text if t.lower() not in _EMPTY]\n",
    "    if not present:\n",
    "        return _pd.Series([_pd.NaT] * len(text)), []\n",
    "    notes = []\n",
    "    if all(_re.fullmatch(r\"\\d{8}\", t) for t in present):\n",
    "        return _pd.to_datetime(_pd.Series(text), format=\"%Y%m%d\", errors=\"coerce\"), notes\n",
    "    if all(_re.fullmatch(r\"\\d{10}(\\.\\d+)?|\\d{13}|\\d{16}\", t) for t in present):\n",
    "        digits = len(present[0].split(\".\")[0])\n",
    "        unit = {10: \"s\", 13: \"ms\", 16: \"us\"}[digits]\n",
    "        notes.append(f\"'{column}' holds epoch times in {dict(s='seconds', ms='milliseconds', us='microseconds')[unit]}; read as such.\")\n",
    "        return _pd.to_datetime(_pd.to_numeric(_pd.Series(text), errors=\"coerce\"), unit=unit), notes\n",
    "    cleaned = [_re.sub(r\"\\s+(UTC|GMT|Z)$\", \"\", t) for t in text]\n",
    "    slash = [t for t in cleaned if _re.fullmatch(r\"\\d{1,2}[/.\\-]\\d{1,2}[/.\\-]\\d{2,4}(\\s.*)?\", t)]\n",
    "    if slash and len(slash) >= len(present) * 0.9:\n",
    "        firsts = [int(_re.split(r\"[/.\\-]\", t)[0]) for t in slash]\n",
    "        seconds = [int(_re.split(r\"[/.\\-]\", t)[1]) for t in slash]\n",
    "        dotted = all(\".\" in t.split(\" \")[0] for t in slash)\n",
    "        if dayfirst is None:\n",
    "            if max(firsts) > 12 and max(seconds) <= 12:\n",
    "                dayfirst = True\n",
    "            elif max(seconds) > 12 and max(firsts) <= 12:\n",
    "                dayfirst = False\n",
    "            elif dotted:\n",
    "                dayfirst = True\n",
    "            else:\n",
    "                raise ExportReadError(\n",
    "                    f\"The dates in '{column}' (for example {slash[0]}) could be day-first or month-first, \"\n",
    "                    \"and nothing in the file says which. Re-run with dayfirst=True for 6 July written \"\n",
    "                    \"06/07, or dayfirst=False for June 7 written 06/07 - or export the dates as \"\n",
    "                    \"2026-07-06.\")\n",
    "        return _pd.to_datetime(_pd.Series(cleaned), dayfirst=dayfirst, errors=\"coerce\"), notes\n",
    "    parsed = _pd.to_datetime(_pd.Series(cleaned), errors=\"coerce\", format=\"mixed\")\n",
    "    return parsed, notes\n",
    "\n",
    "\n",
    "def read_export(source, columns=None, date_columns=(), id_columns=(), dayfirst=None,\n",
    "                max_unreadable_share=0.02):\n",
    "    \"\"\"Read a downloaded report into a table with this notebook's column names. Returns\n",
    "    (table, notes); raises ExportReadError with a plain-words fix when it cannot read without\n",
    "    guessing.\"\"\"\n",
    "    columns = columns or {}\n",
    "    # Each name's candidates in the caller's order of preference, the name itself first.\n",
    "    wanted = {name: list(dict.fromkeys([_norm(name)] + [_norm(a) for a in aliases]))\n",
    "              for name, aliases in columns.items()}\n",
    "    rows, delimiter = _rows_from(source)\n",
    "    rows = [r for r in rows if any(str(c).strip() for c in r)]\n",
    "    if not rows:\n",
    "        raise ExportReadError(\"The file is empty.\")\n",
    "    notes = []\n",
    "    h = _find_header(rows, wanted)\n",
    "    if h:\n",
    "        notes.append(f\"set aside {h} line(s) above the header (a report title or date range).\")\n",
    "    header = [str(c).strip() for c in rows[h]]\n",
    "    body, totals = [], 0\n",
    "    for r in rows[h + 1:]:\n",
    "        first = next((str(c) for c in r if str(c).strip()), \"\")\n",
    "        if _TOTAL_ROW.match(first):\n",
    "            totals += 1\n",
    "            continue\n",
    "        if [str(c).strip() for c in r] == header:\n",
    "            continue\n",
    "        body.append([str(c) for c in r] + [\"\"] * (len(header) - len(r)))\n",
    "    if totals:\n",
    "        notes.append(f\"set aside {totals} total row(s).\")\n",
    "    frame = _pd.DataFrame([r[:len(header)] for r in body], columns=header)\n",
    "    rename = {}\n",
    "    for name, options in wanted.items():\n",
    "        by_norm = {}\n",
    "        for c in frame.columns:\n",
    "            by_norm.setdefault(_norm(c), c)\n",
    "        match = next((by_norm[o] for o in options if o in by_norm and by_norm[o] not in rename), None)\n",
    "        if match is not None:\n",
    "            rename[match] = name\n",
    "    frame = frame.rename(columns=rename)\n",
    "    missing = [name for name in columns if name not in frame.columns]\n",
    "    if missing:\n",
    "        seen = \", \".join(f\"'{c}'\" for c in header[:12])\n",
    "        raise ExportReadError(\n",
    "            f\"Could not find the column(s) {', '.join(missing)}. The file's columns are {seen}. \"\n",
    "            \"Rename the column in the file, or add its name to the list this cell accepts.\")\n",
    "    dates = set(date_columns)\n",
    "    # The file's own convention, for a column that cannot tell by itself: a clicks column of whole\n",
    "    # numbers says nothing, but its \"1.340\" means one thousand three hundred and forty when the\n",
    "    # file's other columns write decimals with a comma.\n",
    "    every = [str(v) for c in frame.columns if c not in date_columns for v in frame[c].tolist()\n",
    "             if _looks_number(v)]\n",
    "    file_comma = _decimal_comma(every)\n",
    "    if file_comma is None:\n",
    "        file_comma = delimiter == \";\"\n",
    "    ids = set(id_columns) | {c for c in frame.columns if _re.search(r\"(^|[ _])(id|pseudo id)$\", _norm(c))}\n",
    "    for col in frame.columns:\n",
    "        values = frame[col].tolist()\n",
    "        if col in dates:\n",
    "            parsed, extra = _parse_dates(values, col, dayfirst)\n",
    "            notes += extra\n",
    "            bad = int(parsed.isna().sum() - sum(str(v).strip().lower() in _EMPTY for v in values))\n",
    "            if bad > max(1, max_unreadable_share * len(values)):\n",
    "                example = next(v for v, p in zip(values, parsed) if _pd.isna(p) and str(v).strip())\n",
    "                raise ExportReadError(f\"{bad} value(s) in '{col}' are not dates I can read, for example '{example}'.\")\n",
    "            frame[col] = parsed.values\n",
    "            continue\n",
    "        if col in ids:\n",
    "            continue\n",
    "        present = [v for v in values if str(v).strip().lower() not in _EMPTY]\n",
    "        if not present or sum(_looks_number(v) for v in present) < 0.5 * len(present):\n",
    "            continue\n",
    "        comma = _decimal_comma(present)\n",
    "        if comma is None:\n",
    "            comma = file_comma\n",
    "        parsed = [_to_number(v, comma) for v in values]\n",
    "        unreadable = [v for v, (_, _, ok) in zip(values, parsed) if not ok]\n",
    "        if unreadable and col not in columns:\n",
    "            notes.append(f\"'{col}' mixes numbers with labels such as '{unreadable[0]}'; kept as text.\")\n",
    "            continue\n",
    "        if unreadable:\n",
    "            notes.append(f\"{len(unreadable)} value(s) in '{col}' could not be read as numbers, for example \"\n",
    "                         f\"'{unreadable[0]}'; they are left empty.\")\n",
    "            if len(unreadable) > max(1, max_unreadable_share * len(values)):\n",
    "                raise ExportReadError(\n",
    "                    f\"{len(unreadable)} of {len(values)} values in '{col}' are not numbers I can read, for \"\n",
    "                    f\"example '{unreadable[0]}'. Fix them in the file, or export the column unformatted.\")\n",
    "        frame[col] = [p[0] for p in parsed]\n",
    "        bounds = [p[1] for p in parsed]\n",
    "        if any(bounds):\n",
    "            # An object column, so a day with no limit stays None: a newer pandas would infer a string\n",
    "            # column here and turn None into NaN, which is truthy and reads as a limit.\n",
    "            frame[f\"{col}_bound\"] = _pd.Series(bounds, index=frame.index, dtype=object)\n",
    "            notes.append(f\"'{col}' has values the platform shows only as a limit (for example \"\n",
    "                         f\"'{next(v for v, b in zip(values, bounds) if b)}'); kept as the limit, with the \"\n",
    "                         f\"direction in '{col}_bound'.\")\n",
    "    if dates:\n",
    "        undated = frame[list(dates)].isna().all(axis=1)\n",
    "        if undated.any():\n",
    "            frame = frame[~undated].reset_index(drop=True)\n",
    "            notes.append(f\"set aside {int(undated.sum())} row(s) with no date (GA4 writes its grand total \"\n",
    "                         \"this way, under the header).\")\n",
    "    return frame, notes\n",
    "# ---- end of export reader -------------------------------------------------------------------------\n",
    "\n",
    "\n",
    "class DataProblem(Exception):\n",
    "    \"\"\"Raised with a plain-words explanation the reader can act on.\"\"\"\n",
    "\n",
    "\n",
    "def _find_columns(frame, wanted):\n",
    "    lowered = {c.lower().strip(): c for c in frame.columns}\n",
    "    found = {w: lowered.get(w) for w in wanted}\n",
    "    missing = [w for w, c in found.items() if c is None]\n",
    "    if missing:\n",
    "        raise DataProblem(\n",
    "            f\"This file has the columns {list(frame.columns)}. It needs {wanted}; \"\n",
    "            f\"{missing} {'is' if len(missing) == 1 else 'are'} missing. Rename and try again - the order \"\n",
    "            \"does not matter and any other column is ignored.\"\n",
    "        )\n",
    "    return found\n",
    "\n",
    "\n",
    "def read_frequency_csv(path, top=TOP_BUCKET):\n",
    "    \"\"\"The reader's frequency report -> the report frame the method reads, or a refusal in plain words.\"\"\"\n",
    "    try:\n",
    "        frame, notes = read_export(path, columns={\"frequency\": [\"frequency\", \"frequency bucket\", \"impressions per person\"],\n",
    "                                                  \"reach\": [\"reach\", \"people\", \"users\"],\n",
    "                                                  \"conversions\": [\"conversions\", \"buyers\", \"results\", \"purchases\"]},\n",
    "                                   id_columns=[\"frequency\"])\n",
    "    except ExportReadError as problem:\n",
    "        raise DataProblem(str(problem)) from None\n",
    "    for note in notes:\n",
    "        print(\"  \" + note)\n",
    "    raw = frame[\"frequency\"].astype(str).str.strip()\n",
    "    plus = raw.str.endswith(\"+\")\n",
    "    if plus.sum() > 1:\n",
    "        raise DataProblem(f\"{int(plus.sum())} buckets end in '+' ({list(raw[plus])[:4]}). That is a cumulative report, \"\n",
    "                          \"where every person is counted in every bucket up to their own frequency; this method needs \"\n",
    "                          \"each person in exactly one bucket. Export the exact frequencies, or build them from the \"\n",
    "                          \"impression log with the consolidator below.\")\n",
    "    ranged = raw.str.fullmatch(r\"\\d+\\s*-\\s*\\d+\")\n",
    "    freq = pd.to_numeric(raw.str.rstrip(\"+\"), errors=\"coerce\")\n",
    "    if ranged.any():\n",
    "        mids = raw[ranged].str.split(\"-\").apply(lambda ab: (float(ab[0]) + float(ab[1])) / 2)\n",
    "        freq[ranged] = mids\n",
    "        print(f\"  {int(ranged.sum())} range bucket(s) such as '{raw[ranged].iloc[0]}' placed at their middle \"\n",
    "              \"frequency; the cap reads less sharply past them\")\n",
    "    reach = pd.to_numeric(frame[\"reach\"], errors=\"coerce\")\n",
    "    conv = pd.to_numeric(frame[\"conversions\"], errors=\"coerce\")\n",
    "    keep = freq.notna() & reach.notna() & conv.notna()\n",
    "    if (~keep).sum():\n",
    "        print(f\"  skipped {int((~keep).sum())} line(s) that could not be read as numbers: \"\n",
    "              f\"{list(raw[~keep])[:4]}\")\n",
    "    rep = pd.DataFrame({\"frequency\": freq[keep], \"reach\": reach[keep].astype(int),\n",
    "                        \"conversions\": conv[keep], \"plus\": plus[keep]})\n",
    "    if plus.any():\n",
    "        print(f\"  the top bucket '{raw[plus].iloc[0]}' is placed at {freq[plus].iloc[0]:g}; \"\n",
    "              \"if you know its average frequency, use that number instead - the worked example places \"\n",
    "              \"it at its average, which is why its cap differs from one read with the bucket at its label\")\n",
    "    if (rep.conversions > rep.reach).any():\n",
    "        raise DataProblem(\"Some buckets have more conversions than people. Check the export.\")\n",
    "    rep = rep[rep.reach > 0].sort_values(\"frequency\").reset_index(drop=True)\n",
    "    if len(rep) < 4:\n",
    "        raise DataProblem(f\"Only {len(rep)} buckets have people in them. A curve with three numbers \"\n",
    "                          \"cannot be told from noise on fewer than four points.\")\n",
    "    rep[\"cvr\"] = rep.conversions / rep.reach\n",
    "    rep[\"f_fit\"] = rep.frequency.astype(float)\n",
    "    rep[\"label\"] = [f\"{f:g}+\" if p else f\"{f:g}\" for f, p in zip(rep.frequency, rep.plus)]\n",
    "    rep[\"frequency\"] = rep.frequency.round().astype(int)\n",
    "    return rep\n",
    "\n",
    "\n",
    "def consolidate_impression_log(impressions_path, conversions_path, person_column=\"person_id\", top=TOP_BUCKET):\n",
    "    \"\"\"One line per impression and one line per conversion -> one line per frequency bucket.\"\"\"\n",
    "    imps = pd.read_csv(impressions_path)\n",
    "    convs = pd.read_csv(conversions_path)\n",
    "    pcol = _find_columns(imps, [person_column])[person_column]\n",
    "    ccol = _find_columns(convs, [person_column])[person_column]\n",
    "    counts = imps.groupby(pcol).size().rename(\"impressions\")\n",
    "    converted = pd.Series(True, index=pd.Index(convs[ccol].unique(), name=pcol), name=\"converted\")\n",
    "    ppl = pd.concat([counts, converted], axis=1).fillna({\"converted\": False})\n",
    "    ppl = ppl[ppl.impressions.notna()]\n",
    "    ppl[\"converted\"] = ppl.converted.astype(bool)\n",
    "    ppl[\"impressions\"] = ppl.impressions.astype(int)\n",
    "    print(f\"  {len(imps):,} impressions over {len(ppl):,} people; {int(ppl.converted.sum()):,} converted\")\n",
    "    out = frequency_report(ppl.assign(keen=1.0, tip=0, conv_day=-1))\n",
    "    out = out[out.reach > 0]\n",
    "    return out[[\"label\", \"reach\", \"conversions\"]].rename(columns={\"label\": \"frequency\"})\n",
    "\n",
    "\n",
    "def report_cap(rep, cpm, value, window_days):\n",
    "    \"\"\"The whole reader-facing routine: refuse, warn, or read the cap.\"\"\"\n",
    "    cpi = cpm / 1000.0\n",
    "    total_conv = int(rep.conversions.sum())\n",
    "    notes = []\n",
    "    if total_conv < MIN_CONVERSIONS:\n",
    "        notes.append(f\"TOO THIN: {total_conv} conversions in the report. Only from {MIN_CONVERSIONS} conversions up \"\n",
    "                     f\"do the worst tenth of audiences lose under {FLOOR_TARGET_P90_PCT:.0f}% of their spend to the \"\n",
    "                     \"fitted cap; below that the cap is a guess. Widen the date range or the audience first.\")\n",
    "    if len(rep) >= 4:\n",
    "        heavy = rep[rep.reach >= 0.02 * rep.reach.sum()]\n",
    "        if len(heavy) >= 3 and np.polyfit(heavy.f_fit, heavy.cvr, 1)[0] < 0:\n",
    "            notes.append(\"WARNING: the share who bought FALLS with frequency. That is what a report looks like when \"\n",
    "                         \"buyers leave the audience after buying; no cap should be read from it, because frequency \"\n",
    "                         \"here is partly a consequence of buying rather than a cause.\")\n",
    "    for note in notes:\n",
    "        print(\"  \" + note)\n",
    "    if total_conv < MIN_CONVERSIONS:\n",
    "        return None\n",
    "    r = read_cap(rep, cpi=cpi, value=value)\n",
    "    shape_words = (\"S-shaped: the first impressions do little\" if r[\"n\"] > 1.2 else\n",
    "                   \"mildly S-shaped: above one, so the first impressions do a little less\" if r[\"n\"] > 1.0 else\n",
    "                   \"plain diminishing returns\" if r[\"n\"] >= 0.8 else\n",
    "                   \"concave: the first impression does the most\")\n",
    "    print(f\"  fitted: ceiling {r['cvr_max']:.4f}, half-way {r['k']:.2f}, shape {r['n']:.2f} ({shape_words})\")\n",
    "    print(f\"  bend gate: {'passes' if r['gate']['passes'] else 'FAILS'} (p = {r['gate']['p_value']:.3g})\")\n",
    "    print(f\"  at {cpm:.2f} per thousand and {value:.2f} a conversion: {r['verdict']}, in impressions per {window_days} days\")\n",
    "    print(\"  compare this only with caps measured over the same window; it says nothing about a different one.\")\n",
    "    return r\n",
    "\n",
    "\n",
    "def read_audience_csv(path):\n",
    "    \"\"\"The reader's membership export -> (age, converted) rows, or a refusal in plain words.\"\"\"\n",
    "    try:\n",
    "        frame, notes = read_export(path, columns={\"joined\": [\"joined\"], \"converted_on\": [\"converted_on\"],\n",
    "                                                  \"as_of\": [\"as_of\"]},\n",
    "                                   date_columns=[\"joined\", \"converted_on\", \"as_of\"])\n",
    "    except ExportReadError as problem:\n",
    "        raise DataProblem(str(problem)) from None\n",
    "    cols = {\"joined\": \"joined\", \"converted_on\": \"converted_on\", \"as_of\": \"as_of\"}\n",
    "    joined = pd.to_datetime(frame[cols[\"joined\"]], errors=\"coerce\")\n",
    "    bought = pd.to_datetime(frame[cols[\"converted_on\"]], errors=\"coerce\")\n",
    "    as_of = pd.to_datetime(frame[cols[\"as_of\"]], errors=\"coerce\")\n",
    "    if joined.isna().any():\n",
    "        print(f\"  skipped {int(joined.isna().sum())} line(s) with an unreadable join date\")\n",
    "    as_of_day = as_of.dropna().max()\n",
    "    if pd.isna(as_of_day):\n",
    "        raise DataProblem(\"The 'as_of' column has no readable date. It should carry the date the export was taken.\")\n",
    "    # Compare by day: a join or a purchase later on the export day itself is still inside the export.\n",
    "    as_of_day = as_of_day.normalize()\n",
    "    keep = joined.notna() & (joined.dt.normalize() <= as_of_day)\n",
    "    late = bought.notna() & (bought.dt.normalize() > as_of_day)\n",
    "    early = bought.notna() & (bought < joined)\n",
    "    if late.any() or early.any():\n",
    "        print(f\"  {int(early.sum())} purchase(s) before the join date and {int(late.sum())} after the export date \"\n",
    "              \"were treated as not yet bought\")\n",
    "    converted = bought.notna() & ~late & ~early\n",
    "    age = np.where(converted, (bought - joined).dt.days, (as_of_day - joined).dt.days).astype(float) + 0.5\n",
    "    out = pd.DataFrame({\"age\": age[keep], \"converted\": converted[keep].to_numpy()})\n",
    "    if out.converted.sum() < 20:\n",
    "        raise DataProblem(f\"Only {int(out.converted.sum())} buyers. The timing curve needs at least twenty; \"\n",
    "                          \"widen the export.\")\n",
    "    return out, as_of_day\n",
    "\n",
    "\n",
    "def consolidate_events_log(path, person_column=\"person_id\", event_column=\"event\", time_column=\"timestamp\",\n",
    "                           join_event=\"first_visit\", buy_event=\"purchase\"):\n",
    "    \"\"\"A raw events log -> one line per person with join, conversion and export dates.\"\"\"\n",
    "    # GA4's BigQuery export names these user_pseudo_id, event_name and event_timestamp (microseconds);\n",
    "    # the shared reader reads the timestamp's unit and keeps the person id as text.\n",
    "    try:\n",
    "        frame, notes = read_export(path, columns={person_column: [person_column, \"user_pseudo_id\", \"user_id\"],\n",
    "                                                  event_column: [event_column, \"event_name\"],\n",
    "                                                  time_column: [time_column, \"event_timestamp\", \"event_time\"]},\n",
    "                                   date_columns=[time_column], id_columns=[person_column])\n",
    "    except ExportReadError as problem:\n",
    "        raise DataProblem(str(problem)) from None\n",
    "    for note in notes:\n",
    "        print(\"  \" + note)\n",
    "    cols = {person_column: person_column, event_column: event_column, time_column: time_column}\n",
    "    frame = frame.assign(_t=frame[time_column]).dropna(subset=[\"_t\"])\n",
    "    joins = frame[frame[cols[event_column]] == join_event].groupby(cols[person_column])._t.min()\n",
    "    buys = frame[frame[cols[event_column]] == buy_event].groupby(cols[person_column])._t.min()\n",
    "    out = pd.DataFrame({\"joined\": joins}).join(buys.rename(\"converted_on\"), how=\"left\")\n",
    "    out[\"as_of\"] = frame._t.max().normalize()\n",
    "    out = out.reset_index().rename(columns={cols[person_column]: \"person_id\"})\n",
    "    print(f\"  {len(out):,} people joined; {int(out.converted_on.notna().sum()):,} bought\")\n",
    "    return out\n",
    "\n",
    "\n",
    "def report_half_life(rows, as_of_day):\n",
    "    \"\"\"Refuse, warn, or read the half-life and the share who ever buy.\"\"\"\n",
    "    r = fit_cure(rows)\n",
    "    watched = float(rows.age.max())\n",
    "    print(f\"  {len(rows):,} people, {int(rows.converted.sum()):,} buyers, watched for up to {watched:.0f} days\")\n",
    "    print(f\"  share who will ever buy: {r['share']:.1%}; half-life {r['half_life']:.1f} days\")\n",
    "    for m in [2, 3]:\n",
    "        print(f\"    by {m} half-lives ({m * r['half_life']:.0f} days), {share_of_eventual_buyers_by(m * r['half_life'], r['shape'], r['scale']):.0%} of eventual buyers have bought\")\n",
    "    if watched < HALF_LIVES_NEEDED * r[\"half_life\"]:\n",
    "        print(f\"  TREAT AS A GUESS: this audience has been watched for {watched / r['half_life']:.1f} half-lives; \"\n",
    "              f\"the run above needs {HALF_LIVES_NEEDED:.0f} before nine fits in ten land within 20%.\")\n",
    "    else:\n",
    "        print(f\"  watched for {watched / r['half_life']:.1f} half-lives - enough for the fit to have settled\")\n",
    "    return r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a547b0",
   "metadata": {},
   "source": [
    "### Your files\n",
    "\n",
    "Put your file names and your own economics below and run every cell. Leave the files as\n",
    "`None` to see the demonstrations that follow instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "35592ddb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:20:06.770279Z",
     "iopub.status.busy": "2026-10-20T07:20:06.769825Z",
     "iopub.status.idle": "2026-10-20T07:20:06.782682Z",
     "shell.execute_reply": "2026-10-20T07:20:06.781118Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No files set - the demonstrations below run instead.\n"
     ]
    }
   ],
   "source": [
    "MY_FREQUENCY_FILE = None       # <- buckets with reach and conversions, e.g. \"frequency_report.csv\"\n",
    "MY_AUDIENCE_FILE = None        # <- one line per member (joined, converted_on, as_of), e.g. \"audience.csv\"\n",
    "MY_CPM = CPM_EUR               # what a thousand impressions cost you\n",
    "MY_VALUE = VALUE_EUR           # what one sale is worth to you (margin, not revenue)\n",
    "MY_WINDOW_DAYS = WINDOW_DAYS   # the days the frequency report covers\n",
    "MY_IMPRESSIONS_PER_MEMBER_DAY = IMPRESSIONS_PER_MEMBER_DAY   # how often a member is served a day\n",
    "\n",
    "if MY_FREQUENCY_FILE:\n",
    "    print(f\"YOUR FREQUENCY FILE: {MY_FREQUENCY_FILE}\")\n",
    "    my_cap = report_cap(read_frequency_csv(MY_FREQUENCY_FILE), cpm=MY_CPM, value=MY_VALUE,\n",
    "                        window_days=MY_WINDOW_DAYS)\n",
    "if MY_AUDIENCE_FILE:\n",
    "    print(f\"YOUR AUDIENCE FILE: {MY_AUDIENCE_FILE}\")\n",
    "    my_rows, my_as_of = read_audience_csv(MY_AUDIENCE_FILE)\n",
    "    my_decay = report_half_life(my_rows, my_as_of)\n",
    "    my_cost_per_day = MY_IMPRESSIONS_PER_MEMBER_DAY * MY_CPM / 1000.0\n",
    "    for effect in (1.0, 0.5, 0.25):\n",
    "        days = membership_window(my_decay[\"share\"], my_decay[\"shape\"], my_decay[\"scale\"],\n",
    "                                 value=MY_VALUE, cost=my_cost_per_day, ad_effect=effect)\n",
    "        print(f\"  membership window if {effect:.0%} of members' sales are the ads' doing: {days} days \"\n",
    "              f\"(a day of serving one member costs {my_cost_per_day * 100:.2f} cents)\")\n",
    "if not (MY_FREQUENCY_FILE or MY_AUDIENCE_FILE):\n",
    "    print(\"No files set - the demonstrations below run instead.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883a9f9e",
   "metadata": {},
   "source": [
    "### Exercised twice, on data whose answers are known\n",
    "\n",
    "Both loaders are run on clean exports they should read, on raw logs they should consolidate,\n",
    "and on broken files they should refuse - the wrong columns, too few buckets, a report that\n",
    "falls with frequency, and too few buyers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1d49e6cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:20:06.789596Z",
     "iopub.status.busy": "2026-10-20T07:20:06.789219Z",
     "iopub.status.idle": "2026-10-20T07:20:07.795287Z",
     "shell.execute_reply": "2026-10-20T07:20:07.793548Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A CLEAN FREQUENCY REPORT\n",
      "  the top bucket '15+' is placed at 15; if you know its average frequency, use that number instead - the worked example places it at its average, which is why its cap differs from one read with the bucket at its label\n",
      "  fitted: ceiling 0.0204, half-way 6.17, shape 1.10 (mildly S-shaped: above one, so the first impressions do a little less)\n",
      "  bend gate: passes (p = 1.12e-41)\n",
      "  at 12.00 per thousand and 25.00 a conversion: cap at 11, in impressions per 28 days\n",
      "  compare this only with caps measured over the same window; it says nothing about a different one.\n",
      "\n",
      "A RAW IMPRESSION LOG, CONSOLIDATED\n",
      "  10,442 impressions over 1,903 people; 22 converted\n",
      "  the top bucket '15+' is placed at 15; if you know its average frequency, use that number instead - the worked example places it at its average, which is why its cap differs from one read with the bucket at its label\n",
      "  TOO THIN: 22 conversions in the report. Only from 50 conversions up do the worst tenth of audiences lose under 10% of their spend to the fitted cap; below that the cap is a guess. Widen the date range or the audience first.\n",
      "\n",
      "WRONG COLUMN NAMES\n",
      "  refused: Could not find the column(s) frequency, conversions. The file's columns are 'Freq', 'People', 'Sales'. Rename the column in the file, or add its name to the list this cell accepts.\n",
      "\n",
      "THREE BUCKETS ONLY\n",
      "  the top bucket '3+' is placed at 3; if you know its average frequency, use that number instead - the worked example places it at its average, which is why its cap differs from one read with the bucket at its label\n",
      "  refused: Only 3 buckets have people in them. A curve with three numbers cannot be told from noise on fewer than four points.\n",
      "\n",
      "A REPORT WHERE BUYERS LEFT THE AUDIENCE\n",
      "  the top bucket '15+' is placed at 15; if you know its average frequency, use that number instead - the worked example places it at its average, which is why its cap differs from one read with the bucket at its label\n",
      "  WARNING: the share who bought FALLS with frequency. That is what a report looks like when buyers leave the audience after buying; no cap should be read from it, because frequency here is partly a consequence of buying rather than a cause.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  fitted: ceiling 0.0083, half-way 0.00, shape 4.11 (S-shaped: the first impressions do little)\n",
      "  bend gate: FAILS (p = 1)\n",
      "  at 12.00 per thousand and 25.00 a conversion: no detectable saturation - the report does not bend more than chance would, in impressions per 28 days\n",
      "  compare this only with caps measured over the same window; it says nothing about a different one.\n",
      "\n",
      "TOO FEW CONVERSIONS\n",
      "  the top bucket '15+' is placed at 15; if you know its average frequency, use that number instead - the worked example places it at its average, which is why its cap differs from one read with the bucket at its label\n",
      "  TOO THIN: 14 conversions in the report. Only from 50 conversions up do the worst tenth of audiences lose under 10% of their spend to the fitted cap; below that the cap is a guess. Widen the date range or the audience first.\n",
      "  returned None - no cap invented from a thin report\n",
      "\n",
      "A CLEAN AUDIENCE EXPORT\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  5,000 people, 514 buyers, watched for up to 60 days\n",
      "  share who will ever buy: 12.5%; half-life 9.3 days\n",
      "    by 2 half-lives (19 days), 84% of eventual buyers have bought\n",
      "    by 3 half-lives (28 days), 96% of eventual buyers have bought\n",
      "  watched for 6.4 half-lives - enough for the fit to have settled\n",
      "\n",
      "A RAW EVENTS LOG, CONSOLIDATED\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  2,000 people joined; 197 bought\n",
      "  2,000 people, 197 buyers, watched for up to 46 days\n",
      "  share who will ever buy: 12.3%; half-life 7.7 days\n",
      "    by 2 half-lives (15 days), 83% of eventual buyers have bought\n",
      "    by 3 half-lives (23 days), 95% of eventual buyers have bought\n",
      "  watched for 5.9 half-lives - enough for the fit to have settled\n",
      "\n",
      "TOO FEW BUYERS\n",
      "  refused: Only 6 buyers. The timing curve needs at least twenty; widen the export.\n",
      "\n",
      "WATCHED FOR A WEEK ONLY\n",
      "  20,000 people, 478 buyers, watched for up to 6 days\n",
      "  share who will ever buy: 100.0%; half-life 43.6 days\n",
      "    by 2 half-lives (87 days), 83% of eventual buyers have bought\n",
      "    by 3 half-lives (131 days), 95% of eventual buyers have bought\n",
      "  TREAT AS A GUESS: this audience has been watched for 0.1 half-lives; the run above needs 3 before nine fits in ten land within 20%.\n"
     ]
    }
   ],
   "source": [
    "SCRATCH = HERE / \"_your_data_demo\"\n",
    "SCRATCH.mkdir(exist_ok=True)\n",
    "\n",
    "print(\"A CLEAN FREQUENCY REPORT\")\n",
    "clean_freq = SCRATCH / \"frequency_report.csv\"\n",
    "demo = report[report.reach > 0][[\"label\", \"reach\", \"conversions\"]].rename(columns={\"label\": \"frequency\"})\n",
    "demo.to_csv(clean_freq, index=False)\n",
    "rep_ok = read_frequency_csv(clean_freq)\n",
    "cap_ok = report_cap(rep_ok, cpm=CPM_EUR, value=VALUE_EUR, window_days=WINDOW_DAYS)\n",
    "\n",
    "print(\"\\nA RAW IMPRESSION LOG, CONSOLIDATED\")\n",
    "g_log = np.random.default_rng(SEED + 606)\n",
    "small = simulate_month(2_000, g_log)\n",
    "imp_rows = np.repeat(np.arange(len(small)), small.impressions.to_numpy())\n",
    "pd.DataFrame({\"person_id\": [f\"p{i}\" for i in imp_rows],\n",
    "              \"timestamp\": pd.Timestamp(\"2026-06-01\") + pd.to_timedelta(g_log.integers(0, WINDOW_DAYS * 86400, len(imp_rows)), unit=\"s\")\n",
    "              }).to_csv(SCRATCH / \"impressions.csv\", index=False)\n",
    "pd.DataFrame({\"person_id\": [f\"p{i}\" for i in np.flatnonzero(small.converted.to_numpy())]}).to_csv(SCRATCH / \"conversions.csv\", index=False)\n",
    "consolidated = consolidate_impression_log(SCRATCH / \"impressions.csv\", SCRATCH / \"conversions.csv\")\n",
    "consolidated.to_csv(SCRATCH / \"consolidated_frequency.csv\", index=False)\n",
    "cap_log = report_cap(read_frequency_csv(SCRATCH / \"consolidated_frequency.csv\"), cpm=CPM_EUR, value=VALUE_EUR, window_days=WINDOW_DAYS)\n",
    "\n",
    "print(\"\\nWRONG COLUMN NAMES\")\n",
    "pd.DataFrame({\"Freq\": [1, 2], \"People\": [10, 5], \"Sales\": [1, 0]}).to_csv(SCRATCH / \"wrong_columns.csv\", index=False)\n",
    "try:\n",
    "    read_frequency_csv(SCRATCH / \"wrong_columns.csv\")\n",
    "except DataProblem as exc:\n",
    "    print(f\"  refused: {exc}\")\n",
    "\n",
    "print(\"\\nTHREE BUCKETS ONLY\")\n",
    "pd.DataFrame({\"frequency\": [1, 2, \"3+\"], \"reach\": [5000, 3000, 2000], \"conversions\": [40, 30, 25]}).to_csv(SCRATCH / \"three_buckets.csv\", index=False)\n",
    "try:\n",
    "    read_frequency_csv(SCRATCH / \"three_buckets.csv\")\n",
    "except DataProblem as exc:\n",
    "    print(f\"  refused: {exc}\")\n",
    "\n",
    "print(\"\\nA REPORT WHERE BUYERS LEFT THE AUDIENCE\")\n",
    "drop_path = SCRATCH / \"buyers_left.csv\"\n",
    "report_drop[report_drop.reach > 0][[\"label\", \"reach\", \"conversions\"]].rename(columns={\"label\": \"frequency\"}).to_csv(drop_path, index=False)\n",
    "cap_drop = report_cap(read_frequency_csv(drop_path), cpm=CPM_EUR, value=VALUE_EUR, window_days=WINDOW_DAYS)\n",
    "\n",
    "print(\"\\nTOO FEW CONVERSIONS\")\n",
    "thin = report[report.reach > 0][[\"label\", \"reach\", \"conversions\"]].rename(columns={\"label\": \"frequency\"}).copy()\n",
    "thin[\"reach\"] = (thin.reach // 40).clip(lower=1)\n",
    "thin[\"conversions\"] = (thin.conversions // 40)\n",
    "thin.to_csv(SCRATCH / \"thin_report.csv\", index=False)\n",
    "cap_thin = report_cap(read_frequency_csv(SCRATCH / \"thin_report.csv\"), cpm=CPM_EUR, value=VALUE_EUR, window_days=WINDOW_DAYS)\n",
    "print(f\"  returned {cap_thin} - no cap invented from a thin report\")\n",
    "\n",
    "print(\"\\nA CLEAN AUDIENCE EXPORT\")\n",
    "start = pd.Timestamp(\"2026-05-01\")\n",
    "sample = members.sample(5_000, random_state=SEED)       # a slice of the worked audience, as an export would be\n",
    "aud = pd.DataFrame({\n",
    "    \"joined\": start + pd.to_timedelta(sample.join_day, unit=\"D\"),\n",
    "    \"converted_on\": np.where(sample.converted, start + pd.to_timedelta(sample.join_day + np.floor(sample.age).astype(int), unit=\"D\"), pd.NaT),\n",
    "    \"as_of\": start + pd.Timedelta(days=WATCH_DAYS - 1),\n",
    "})\n",
    "aud[\"converted_on\"] = pd.to_datetime(aud.converted_on)\n",
    "aud.to_csv(SCRATCH / \"audience_export.csv\", index=False)\n",
    "rows_ok, as_of_ok = read_audience_csv(SCRATCH / \"audience_export.csv\")\n",
    "hl_ok = report_half_life(rows_ok, as_of_ok)\n",
    "\n",
    "print(\"\\nA RAW EVENTS LOG, CONSOLIDATED\")\n",
    "g_ev = np.random.default_rng(SEED + 707)\n",
    "ev_members = simulate_audience(2000, 45, g_ev)\n",
    "events = []\n",
    "for i, row in enumerate(ev_members.itertuples(index=False)):\n",
    "    pid = f\"u{i}\"\n",
    "    t0 = start + pd.Timedelta(days=int(row.join_day), hours=int(g_ev.integers(0, 24)))\n",
    "    events.append((pid, \"first_visit\", t0))\n",
    "    events.append((pid, \"page_view\", t0 + pd.Timedelta(minutes=3)))\n",
    "    if row.converted:\n",
    "        events.append((pid, \"purchase\", t0 + pd.Timedelta(days=int(np.floor(row.age)), hours=int(g_ev.integers(0, 24)))))\n",
    "pd.DataFrame(events, columns=[\"person_id\", \"event\", \"timestamp\"]).to_csv(SCRATCH / \"events_log.csv\", index=False)\n",
    "consolidate_events_log(SCRATCH / \"events_log.csv\").to_csv(SCRATCH / \"consolidated_audience.csv\", index=False)\n",
    "rows_ev, as_of_ev = read_audience_csv(SCRATCH / \"consolidated_audience.csv\")\n",
    "hl_ev = report_half_life(rows_ev, as_of_ev)\n",
    "\n",
    "print(\"\\nTOO FEW BUYERS\")\n",
    "few = aud.head(60).copy()\n",
    "few.to_csv(SCRATCH / \"few_buyers.csv\", index=False)\n",
    "try:\n",
    "    read_audience_csv(SCRATCH / \"few_buyers.csv\")\n",
    "except DataProblem as exc:\n",
    "    print(f\"  refused: {exc}\")\n",
    "\n",
    "print(\"\\nWATCHED FOR A WEEK ONLY\")\n",
    "week = simulate_audience(20_000, 7, np.random.default_rng(SEED + 808))\n",
    "week_rows = pd.DataFrame({\"age\": week.age, \"converted\": week.converted})\n",
    "hl_week = report_half_life(week_rows, None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe996aad",
   "metadata": {},
   "source": [
    "## The numbers this bundle publishes\n",
    "\n",
    "Two files. `worked_example.json` carries every figure the article quotes, so the prose can\n",
    "never drift from the arithmetic. `checks.json` carries pinned cases - the worked report's own\n",
    "buckets and three others - with the fit, the gate and the cap each must reproduce, so the\n",
    "interactive version on the page recomputes the method from the same numbers this notebook\n",
    "used rather than from a regenerated copy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4409f15b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-20T07:20:07.805503Z",
     "iopub.status.busy": "2026-10-20T07:20:07.805089Z",
     "iopub.status.idle": "2026-10-20T07:20:07.982699Z",
     "shell.execute_reply": "2026-10-20T07:20:07.980739Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote data/checks.json and data/worked_example.json\n",
      "  4 pinned frequency cases, 3 pinned decay cases\n"
     ]
    }
   ],
   "source": [
    "TOLERANCE = 1e-4\n",
    "\n",
    "\n",
    "def pinned_frequency_case(name, note, rep, cpm=CPM_EUR, value=VALUE_EUR):\n",
    "    usable = rep[rep.reach > 0]\n",
    "    buckets = [{\"frequency\": round(float(f), 6), \"reach\": int(r), \"conversions\": int(c)}\n",
    "               for f, r, c in zip(usable.f_fit, usable.reach, usable.conversions)]\n",
    "    pinned = pd.DataFrame({\"f_fit\": [b[\"frequency\"] for b in buckets], \"reach\": [b[\"reach\"] for b in buckets],\n",
    "                           \"conversions\": [b[\"conversions\"] for b in buckets]})\n",
    "    pinned[\"cvr\"] = pinned.conversions / pinned.reach\n",
    "    r = read_cap(pinned, cpi=cpm / 1000.0, value=value)\n",
    "    return {\n",
    "        \"name\": name, \"note\": note, \"cpm\": cpm, \"value\": value, \"buckets\": buckets,\n",
    "        \"expected\": {\n",
    "            \"cvr_max\": round(r[\"cvr_max\"], 6), \"k\": round(r[\"k\"], 6), \"n\": round(r[\"n\"], 6),\n",
    "            \"gate_statistic\": round(r[\"gate\"][\"statistic\"], 4), \"gate_passes\": bool(r[\"gate\"][\"passes\"]),\n",
    "            \"cap\": int(r[\"cap\"]), \"cap_beyond_data\": bool(r[\"cap_beyond_data\"]),\n",
    "            \"curve\": [round(float(v), 6) for v in hill(np.arange(1, 21), r[\"cvr_max\"], r[\"k\"], r[\"n\"])],\n",
    "        },\n",
    "    }\n",
    "\n",
    "\n",
    "g_pin = np.random.default_rng(SEED + 909)\n",
    "concave_truth = pd.Series({\"cvr_max\": 0.02, \"k\": 2.5, \"n\": 0.8, \"mean_freq\": 5.0, \"n_users\": 60_000, \"confound\": 0.0})\n",
    "sshape_truth = pd.Series({\"cvr_max\": 0.045, \"k\": 6.0, \"n\": 2.6, \"mean_freq\": 7.0, \"n_users\": 60_000, \"confound\": 0.0})\n",
    "flat_truth = pd.Series({\"cvr_max\": 0.02, \"k\": 4.0, \"n\": 1.0, \"mean_freq\": 5.0, \"n_users\": 60_000, \"confound\": 0.0})\n",
    "report_concave = frequency_report(simulate_totals(concave_truth, g_pin))\n",
    "report_sshape = frequency_report(simulate_totals(sshape_truth, g_pin))\n",
    "report_flat = frequency_report(flat_audience(flat_truth, g_pin))\n",
    "\n",
    "\n",
    "def pinned_decay_case(name, share, shape, scale, age):\n",
    "    hl = half_life(shape, scale)\n",
    "    return {\n",
    "        \"name\": name, \"share\": share, \"shape\": shape, \"scale\": scale, \"age\": age,\n",
    "        \"expected\": {\n",
    "            \"half_life\": round(hl, 6),\n",
    "            \"share_bought_by_age\": round(share_of_eventual_buyers_by(age, shape, scale), 6),\n",
    "            \"unbought_share_at_age\": round(float((1 - share) + share * weibull_survival(age, shape, scale)), 6),\n",
    "            \"daily_buy_chance_at_age\": round(float(daily_buy_chance(age, share, shape, scale)), 8),\n",
    "            \"membership_window\": membership_window(share, shape, scale),\n",
    "        },\n",
    "    }\n",
    "\n",
    "\n",
    "checks = {\n",
    "    \"method\": \"volume-weighted Hill fit on a (K, n) grid with a closed-form ceiling, a likelihood-ratio bend \"\n",
    "              \"gate against a flat line, and the cap as the integer frequency maximising sale value times the \"\n",
    "              \"curve minus impression cost times frequency; and the mixture-cure Weibull half-life\",\n",
    "    \"tolerance\": TOLERANCE,\n",
    "    \"constants\": {\n",
    "        \"n_grid\": {\"from\": 0.5, \"to\": 4.0, \"step\": 0.05}, \"k_grid\": {\"from\": 0.5, \"to\": 40.0, \"step\": 0.25},\n",
    "        \"refine_steps\": REFINE_STEPS, \"cap_search_max\": CAP_SEARCH_MAX, \"gate_level\": GATE_LEVEL,\n",
    "        \"impressions_per_member_day\": IMPRESSIONS_PER_MEMBER_DAY,\n",
    "        # the half-way point and the shape are grid-searched, and a second implementation can land\n",
    "        # one refined cell over on a near-flat minimum without moving the curve or the cap; they\n",
    "        # are checked at a cell's width, the curve values and the cap at the tolerance above\n",
    "        \"parameter_tolerance\": 0.05,\n",
    "    },\n",
    "    \"frequency_cases\": [\n",
    "        pinned_frequency_case(\"the worked example\", \"the retargeting audience's own report\", report),\n",
    "        pinned_frequency_case(\"a concave audience\", \"the first impression does the most\", report_concave),\n",
    "        pinned_frequency_case(\"an S-shaped audience\", \"the first impressions do little\", report_sshape),\n",
    "        pinned_frequency_case(\"a flat audience\", \"no saturation to find - the gate should refuse\", report_flat),\n",
    "    ],\n",
    "    \"decay_cases\": [\n",
    "        pinned_decay_case(\"the worked example\", round(fit_method[\"share\"], 6), round(fit_method[\"shape\"], 6),\n",
    "                          round(fit_method[\"scale\"], 6), 30),\n",
    "        pinned_decay_case(\"a fast audience\", 0.20, 1.6, 6.0, 14),\n",
    "        pinned_decay_case(\"a slow, front-loaded audience\", 0.05, 0.9, 25.0, 60),\n",
    "    ],\n",
    "}\n",
    "(DATA / \"checks.json\").write_text(json.dumps(checks, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "worked = {\n",
    "    \"label\": \"synthetic\",\n",
    "    \"seed\": SEED,\n",
    "    # The demonstration reads the worked report back from its own CSV, where the top bucket is\n",
    "    # written \"15+\" and placed at fifteen; the analysis above places it at its average. A reader\n",
    "    # rebuilding from the published report gets this cap, not the analysis's.\n",
    "    \"loader_reading\": {\"cap_top_bucket_at_label\": (cap_ok or {}).get(\"cap\")},\n",
    "    \"economics\": {\n",
    "        \"cpm_eur\": CPM_EUR, \"value_eur\": VALUE_EUR, \"cost_per_impression_eur\": COST_PER_IMPRESSION,\n",
    "        \"cost_per_impression_cents\": round(COST_PER_IMPRESSION * 100, 2),\n",
    "        \"tau\": round(TAU, 8), \"tau_points\": round(TAU * 100, 4),\n",
    "        \"impressions_per_member_day\": IMPRESSIONS_PER_MEMBER_DAY,\n",
    "        \"cost_per_member_day_cents\": round(COST_PER_MEMBER_DAY * 100, 3),\n",
    "    },\n",
    "    \"audience\": {\n",
    "        \"window_days\": WINDOW_DAYS, \"people_reached\": int(len(people)),\n",
    "        \"conversions\": int(people.converted.sum()), \"conversion_rate_pct\": round(float(people.converted.mean()) * 100, 2),\n",
    "        \"median_frequency\": int(people.impressions.median()), \"mean_frequency\": round(float(people.impressions.mean()), 1),\n",
    "        \"p90_frequency\": int(people.impressions.quantile(0.9)),\n",
    "        \"truth\": {\"cvr_max\": TRUE_CVR_MAX, \"k\": TRUE_K, \"n\": TRUE_N, \"confound\": CONFOUND, \"keen_sd\": KEEN_SD,\n",
    "                  \"keen_ratio_12_vs_2\": round(keen_ratio_12_vs_2, 3)},\n",
    "        \"report\": [{\"label\": l, \"frequency\": round(float(f), 4), \"reach\": int(r), \"conversions\": int(c),\n",
    "                    \"cvr_pct\": round(float(v) * 100, 3) if pd.notna(v) else None}\n",
    "                   for l, f, r, c, v in zip(report.label, report.f_fit, report.reach, report.conversions, report.cvr)],\n",
    "        \"top_bucket\": TOP_BUCKET,\n",
    "    },\n",
    "    \"readings\": {\n",
    "        \"peak_cap\": peak_cap, \"peak_says_no_cap\": bool(peak_cap >= TOP_BUCKET),\n",
    "        \"rule_of_thumb_per_week\": RULE_OF_THUMB_PER_WEEK, \"rule_of_thumb_cap\": rule_of_thumb_cap,\n",
    "        \"fitted\": {\"cvr_max\": round(result[\"cvr_max\"], 4), \"k\": round(result[\"k\"], 3), \"n\": round(result[\"n\"], 3),\n",
    "                   \"gate_p\": result[\"gate\"][\"p_value\"], \"gate_passes\": bool(result[\"gate\"][\"passes\"]),\n",
    "                   \"cap\": result[\"cap\"], \"cap_beyond_data\": bool(result[\"cap_beyond_data\"]),\n",
    "                   \"max_frequency_seen\": round(result[\"max_frequency_seen\"], 2)},\n",
    "        \"honest_cap\": honest_cap, \"best_delivered_cap\": best_delivered_cap,\n",
    "        \"fitted_minus_honest\": result[\"cap\"] - honest_cap,\n",
    "        \"curve_at\": {str(f): {\"fitted_pct\": round(float(hill(f, result[\"cvr_max\"], result[\"k\"], result[\"n\"])) * 100, 3),\n",
    "                              \"truth_pct\": round(float(hill(f, TRUE_CVR_MAX, TRUE_K, TRUE_N)) * 100, 3),\n",
    "                              \"fitted_slope_points\": round(float(hill_slope(f, result[\"cvr_max\"], result[\"k\"], result[\"n\"])) * 100, 4)}\n",
    "                     for f in [1, 3, 5, 8, 10, 12, 15]},\n",
    "    },\n",
    "    \"money\": {\"spend_uncapped_eur\": round(spend_uncapped, 1),\n",
    "              \"readings\": {name: {\"cap\": (None if row[\"cap\"] >= CAP_SEARCH_MAX else row[\"cap\"]),\n",
    "                                  \"value_eur\": round(row[\"value_eur\"], 1), \"shortfall_eur\": round(row[\"shortfall_eur\"], 1),\n",
    "                                  \"shortfall_pct_of_spend\": round(row[\"shortfall_pct_of_spend\"], 2)}\n",
    "                           for name, row in money.items()}},\n",
    "    \"population\": {\n",
    "        \"audiences\": int(len(pop)), \"cap_matters\": int(len(matters)), \"no_cap_right\": int(len(no_cap_right)),\n",
    "        \"cap_matters_pct\": round(float(pop.cap_matters.mean()) * 100, 1),\n",
    "        \"summary_all\": summary_all, \"summary_matters\": summary_matters, \"summary_no_cap_right\": summary_no_cap,\n",
    "        \"rule_bands\": rule_bands,\n",
    "        \"gate_refused_pct\": round(gate_fail_share, 2), \"cap_beyond_data_pct\": round(beyond_share, 2),\n",
    "        \"cap_beyond_data_pct_where_no_cap_right\": round(beyond_share_no_cap_right, 1),\n",
    "        \"confound_bins\": confound_bins,\n",
    "        \"flat_audiences\": int(len(flat)), \"false_cap_no_gate_pct\": round(false_cap_no_gate, 2),\n",
    "        \"false_cap_with_gate_pct\": round(false_cap_with_gate, 2),\n",
    "        \"sufficiency\": sufficiency, \"floor_target_p90_pct_of_spend\": FLOOR_TARGET_P90_PCT,\n",
    "        \"min_conversions\": MIN_CONVERSIONS, \"floor_band\": floor_band,\n",
    "    },\n",
    "    \"buyers_leave\": {\n",
    "        \"peak_cap\": peak_drop, \"fitted_n\": round(result_drop[\"n\"], 3), \"verdict\": result_drop[\"verdict\"],\n",
    "        \"gate_p\": result_drop[\"gate\"][\"p_value\"],\n",
    "        \"report\": [{\"label\": l, \"reach\": int(r), \"conversions\": int(c), \"cvr_pct\": round(float(v) * 100, 3) if pd.notna(v) else None}\n",
    "                   for l, r, c, v in zip(report_drop.label, report_drop.reach, report_drop.conversions, report_drop.cvr)],\n",
    "    },\n",
    "    \"decay\": {\n",
    "        \"watch_days\": WATCH_DAYS, \"members\": int(len(members)), \"buyers\": int(members.converted.sum()),\n",
    "        \"bought_pct\": round(float(members.converted.mean()) * 100, 2),\n",
    "        \"truth\": {\"share\": TRUE_SHARE, \"shape\": TRUE_SHAPE, \"scale\": TRUE_SCALE, \"half_life\": round(true_half_life, 3)},\n",
    "        \"readings\": {\"table\": round(naive_table_hl, 2), \"buyers_only\": round(fit_naive[\"half_life\"], 2),\n",
    "                     \"unfinished_kept\": round(fit_cens[\"half_life\"], 2), \"method\": round(fit_method[\"half_life\"], 2)},\n",
    "        \"fitted\": {\"share\": round(fit_method[\"share\"], 4), \"shape\": round(fit_method[\"shape\"], 4), \"scale\": round(fit_method[\"scale\"], 4)},\n",
    "        \"share_bought_by_half_lives\": {str(m): round(v, 4) for m, v in multiples.items()},\n",
    "        \"table_head\": [{\"age_from\": int(a), \"people\": int(p), \"buyers\": int(b), \"rate_pct\": round(float(r) * 100, 3)}\n",
    "                       for a, p, b, r in zip(table.age_from.head(8), table.people.head(8), table.buyers.head(8), table.rate.head(8))],\n",
    "        # the audience's own rows, so the workbook recomputes the article's numbers from live formulas\n",
    "        \"km_curve\": [{\"age\": round(float(t), 2), \"unbought\": round(float(s), 6)} for t, s in zip(km_t, km_s)],\n",
    "        \"population\": {\"audiences\": int(len(pop_b)), \"summary\": summary_b, \"watched_bands\": watched_bands},\n",
    "        \"windows\": window_rows, \"half_lives_needed\": HALF_LIVES_NEEDED, \"window_target_pct\": WINDOW_TARGET_PCT,\n",
    "        \"spike\": {\"burst_share\": 0.4, \"baseline_joins_per_day\": round(baseline_joins, 1), \"burst_joins_per_day\": round(burst_joins, 1),\n",
    "                  \"half_life_steady\": round(hl_steady, 4), \"half_life_burst\": round(hl_burst, 4), \"half_life_pooled\": round(hl_pooled, 4)},\n",
    "        \"membership\": {\"economic_window_days\": econ_window, \"half_lives\": round(econ_window / fit_method[\"half_life\"], 2),\n",
    "                       \"window_by_ad_effect\": {e: {\"days\": w, \"half_lives\": round(w / fit_method[\"half_life\"], 2)}\n",
    "                                               for e, w in econ_window_by_effect.items()},\n",
    "                       \"windows\": {str(w): {\"value_eur\": round(v, 1), \"shortfall_eur\": round(best_window_value - v, 1)}\n",
    "                                   for w, v in window_money.items()}},\n",
    "    },\n",
    "    \"your_data\": {\n",
    "        \"clean_report_cap\": cap_ok[\"cap\"] if cap_ok else None,\n",
    "        \"consolidated_log_cap\": cap_log[\"cap\"] if cap_log else None,\n",
    "        \"buyers_left_verdict\": cap_drop[\"verdict\"] if cap_drop else None,\n",
    "        \"thin_returned_none\": cap_thin is None,\n",
    "        \"clean_audience_half_life\": round(hl_ok[\"half_life\"], 2), \"clean_audience_share\": round(hl_ok[\"share\"], 4),\n",
    "        \"events_log_half_life\": round(hl_ev[\"half_life\"], 2),\n",
    "        \"week_only_half_life\": round(hl_week[\"half_life\"], 2),\n",
    "    },\n",
    "}\n",
    "(DATA / \"worked_example.json\").write_text(json.dumps(worked, indent=2), encoding=\"utf-8\")\n",
    "print(\"wrote data/checks.json and data/worked_example.json\")\n",
    "print(f\"  {len(checks['frequency_cases'])} pinned frequency cases, {len(checks['decay_cases'])} pinned decay cases\")"
   ]
  },
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   "id": "9fd3b13a",
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   "source": [
    "## What this notebook did, and what it did not\n",
    "\n",
    "It built a retargeting audience whose response to repetition was decided in advance, read a\n",
    "frequency cap off its report the way the platform's method does, and priced every reading\n",
    "against the cap that was actually best. It built the same audience's membership history,\n",
    "read a half-life off it, and scored that against the truth on hundreds of audiences and at\n",
    "every watching window. The data is synthetic and labelled as such; the numbers are the\n",
    "numbers this arithmetic produces, and every one the article quotes is in `data/`.\n",
    "\n",
    "It did not measure a real account. The strength and direction of the platform's lean toward\n",
    "keen people is the one number this notebook assumes rather than measures, and it is the\n",
    "number a holdout would settle. Both methods stop at \"modelled\" for exactly that reason."
   ]
  }
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