{
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   "source": [
    "# Where am I losing money right now, and can I trust Google's own estimate of the fix?\n",
    "\n",
    "Your ad account already tells you where it is leaking. Every search campaign reports how many\n",
    "of the auctions it was eligible for it actually showed in, how many it missed because the\n",
    "budget ran out, and how many it missed because it was outbid. Those are facts about auctions\n",
    "that already happened. What the account does not tell you is what fixing the leak is worth,\n",
    "and the number that offers to (Google's bid and budget simulator) is a projection, built by\n",
    "replaying last week's auctions at a setting you did not run. This notebook is about reading the\n",
    "facts correctly, doing the arithmetic on them honestly, and finding out how far a replay of the\n",
    "kind Google describes can be trusted, and under which conditions it cannot.\n",
    "\n",
    "The working uses a synthetic search campaign with the answer built in: I generate every\n",
    "auction, every competitor's price, every click and every sale, so every reading below is scored\n",
    "against a truth nobody has to guess at. Five things are worth knowing before the working.\n",
    "\n",
    "**The report is three facts, not one.** Of the auctions you were eligible for, the share you\n",
    "showed in, the share the budget kept you out of, and the share you were outbid in add up to the\n",
    "whole pool. Budget-lost and rank-lost are two different leaks with two different fixes, and\n",
    "the budget one has to be read first, because a capped budget hides the true size of the other.\n",
    "\n",
    "**The obvious arithmetic gets the count wrong for one leak and the price wrong for the other.**\n",
    "Multiplying the impressions you lost by your usual rates overstates what a bigger budget would\n",
    "win, because you would only have won your usual share of those auctions; and it overstates what\n",
    "higher bids would win at your usual cost, because the auctions you are losing on rank are, by\n",
    "construction, the ones that cost more than you pay now. Both are measured here.\n",
    "\n",
    "**A replay is honest about last week and silent about next week.** Google says its simulators\n",
    "use data from the last seven days to estimate how your ads *might have performed*. A replay of\n",
    "that kind, built here, reproduces last week closely and is only as good a guide to next week as\n",
    "next week resembles last week. When demand moves, competition moves, or a change sits inside the\n",
    "lookback, the projection carries the old world into the new one, by an amount this notebook\n",
    "measures on hundreds of synthetic accounts.\n",
    "\n",
    "**Its conversion estimate is the shakiest number it prints.** Conversions are rarer than clicks,\n",
    "so the rate a replay learns from one week is noisy, and Google says as much. On small accounts the\n",
    "projected conversions are off by a third as often as not, for no reason but the thin week they\n",
    "were learned from.\n",
    "\n",
    "**You can score the estimate on your own account.** Every time you act on a projection, write\n",
    "down what it promised and what happened. After enough changes, the log says how far to trust the\n",
    "next one, and the notebook tells you how many changes \"enough\" is.\n",
    "\n",
    "**The data in this notebook is synthetic.** It is generated here with the answer built in. Run\n",
    "it on your own export in the last section; nothing leaves your machine."
   ]
  },
  {
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   "id": "726dc47f",
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     "name": "stdout",
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     "text": [
      "seed 20260916\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",
    "\n",
    "SEED = 20260916\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": "5bec0240",
   "metadata": {},
   "source": [
    "## The campaign I build, and the truth I hide from the report\n",
    "\n",
    "One search campaign, one week at a time. The generator builds it the way an auction platform\n",
    "would see it, and gives it the properties real campaigns have.\n",
    "\n",
    "**Every hour holds a number of auctions the campaign is eligible for.** More in the daytime,\n",
    "more on weekdays, fewer overnight and at the weekend. Each auction has a price to beat: what\n",
    "the campaign would have to bid to win the slot, given everyone else's bids and quality. Prices\n",
    "are spread out, and stiffer in business hours.\n",
    "\n",
    "**The campaign bids one amount, and pays the price to beat when it wins a click.** It shows in\n",
    "every auction whose price to beat is at or below its bid; a shown ad is clicked with an hourly\n",
    "click rate, and a click converts with an hourly conversion rate, higher in working hours, lower\n",
    "at night. Each conversion is worth a fixed value to the business.\n",
    "\n",
    "**The budget is spread evenly.** When the auctions it could win would cost more than the week's\n",
    "budget, the campaign is entered into a share of them, chosen so the budget lasts the week.\n",
    "The auctions it is kept out of are lost to budget; the ones it enters and is outbid in are\n",
    "lost to rank; the rest are impressions. Real platforms pace budgets in more elaborate ways, and\n",
    "the article says so; the even spread is the simplest model that makes the three-way split exact.\n",
    "\n",
    "**The eligible pool is bounded.** Google says its eligible-impressions estimate includes auctions\n",
    "an ad could win at twice its bid and excludes ones needing a tenfold increase. This world draws\n",
    "that line at four times the bid: an auction that would need more than that is not counted as\n",
    "eligible, so it appears in no share at all. It is a modelling choice, and it is on the dial below.\n",
    "\n",
    "The dials that matter later are the budget (how tightly it binds), the competition (how much the\n",
    "prices to beat move between last week and next), and the demand (how many auctions there are)."
   ]
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  {
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   "id": "18210866",
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   "source": [
    "HOURS = 24\n",
    "DAYS = 7\n",
    "\n",
    "# --- the worked campaign ------------------------------------------------------------------------\n",
    "BID = 1.20                  # euros per click, the campaign's max bid\n",
    "DAILY_BUDGET = 200.0        # euros per day\n",
    "VALUE_PER_CONVERSION = 32.0 # what a conversion is worth to the business, euros\n",
    "WEEKLY_AUCTIONS = 92_000    # eligible auctions the campaign is in the running for, per week\n",
    "ELIGIBLE_CUTOFF = 4.0       # an auction needing more than this multiple of the bid is not \"eligible\"\n",
    "PRICE_SPREAD = 0.45         # how spread out the prices to beat are (log scale)\n",
    "COMPETITION = 0.72          # the median price to beat, as a multiple of the bid, outside business hours\n",
    "BUSINESS_BUMP = 0.20        # how much stiffer business-hour prices are\n",
    "CTR = 0.037                 # average click rate\n",
    "CVR = 0.040                 # average conversion rate per click\n",
    "DEMAND_WOBBLE = 0.08        # week-to-week wobble in the number of auctions (log scale)\n",
    "COMPETITION_WOBBLE = 0.04   # week-to-week wobble in the prices to beat (log scale)\n",
    "\n",
    "# Bins of the price to beat, as multiples of the bid. The replay engine below reads its log in these\n",
    "# bins, and the widget's bid slider stops exactly on their edges, so the two never interpolate.\n",
    "BIN_EDGES = [0.0, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.8, 2.0, 2.5, 3.0, 4.0]\n",
    "BID_STEPS = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.8, 2.0]\n",
    "\n",
    "\n",
    "def hour_profile():\n",
    "    \"\"\"Share of a day's auctions in each hour: a daytime hump with a smaller evening shoulder.\"\"\"\n",
    "    h = np.arange(HOURS)\n",
    "    day = np.exp(-((h - 13.5) / 5.2) ** 2)\n",
    "    evening = 0.45 * np.exp(-((h - 20.0) / 1.8) ** 2)\n",
    "    base = 0.06\n",
    "    p = day + evening + base\n",
    "    return p / p.sum()\n",
    "\n",
    "\n",
    "def weekday_factor():\n",
    "    return np.array([1.0, 1.02, 1.0, 0.98, 0.95, 0.68, 0.60])\n",
    "\n",
    "\n",
    "def business_hours():\n",
    "    h = np.arange(HOURS)\n",
    "    return ((h >= 9) & (h <= 17)).astype(float)\n",
    "\n",
    "\n",
    "def true_rates(ctr=CTR, cvr=CVR):\n",
    "    \"\"\"Hourly click and conversion rates: both a little higher in working hours, lower at night.\"\"\"\n",
    "    h = np.arange(HOURS)\n",
    "    daytime = np.exp(-((h - 14) / 7) ** 2)\n",
    "    ctr_h = ctr * (0.80 + 0.40 * daytime)\n",
    "    cvr_h = cvr * (0.62 + 0.70 * daytime)\n",
    "    return ctr_h, cvr_h\n",
    "\n",
    "\n",
    "def price_median(price_scale, competition=COMPETITION, bump=BUSINESS_BUMP):\n",
    "    \"\"\"The median price to beat by hour, in euros. It is set by the market - everyone else's bids and\n",
    "    quality - so it is written against a fixed price scale, never against the bid the campaign happens\n",
    "    to be running at; raising the bid does not raise the prices it has to beat.\"\"\"\n",
    "    return price_scale * (competition + bump * business_hours())\n",
    "\n",
    "\n",
    "HOUR_P = hour_profile()\n",
    "WEEKDAY = weekday_factor()\n",
    "CTR_H, CVR_H = true_rates()\n",
    "\n",
    "\n",
    "def simulate_week(rng, bid=BID, daily_budget=DAILY_BUDGET, weekly_auctions=WEEKLY_AUCTIONS,\n",
    "                  competition=COMPETITION, demand=1.0, break_day=None, break_competition=1.0,\n",
    "                  ctr_h=None, cvr_h=None, spread=PRICE_SPREAD, cutoff=ELIGIBLE_CUTOFF, price_scale=None,\n",
    "                  demand_wobble=DEMAND_WOBBLE, competition_wobble=COMPETITION_WOBBLE):\n",
    "    \"\"\"One week of auctions, with everything that happened in them.\n",
    "\n",
    "    Returns per-hour-of-day arrays: the eligible auction count, a histogram of prices to beat\n",
    "    in BIN_EDGES (counts and sums), and the realised impressions, clicks, conversions and cost\n",
    "    at the campaign's bid and budget. `break_day` puts a change of competition inside the week\n",
    "    itself - from that day on, prices to beat are multiplied by `break_competition` - which is\n",
    "    the case where a replay of the week describes an auction that no longer exists. Every week\n",
    "    also carries its own small wobble in demand and in competition, because no two weeks are the\n",
    "    same even when nothing has happened; the scenarios below sit on top of that wobble.\n",
    "    \"\"\"\n",
    "    demand = demand * float(np.exp(rng.normal(0.0, demand_wobble)))\n",
    "    competition = competition * float(np.exp(rng.normal(0.0, competition_wobble)))\n",
    "    ctr_h = CTR_H if ctr_h is None else ctr_h\n",
    "    cvr_h = CVR_H if cvr_h is None else cvr_h\n",
    "    edges = np.array(BIN_EDGES) * bid\n",
    "    nb = len(BIN_EDGES) - 1\n",
    "    counts = np.zeros((HOURS, nb))\n",
    "    sums = np.zeros((HOURS, nb))\n",
    "    eligible = np.zeros(HOURS)\n",
    "    win_unconstrained = np.zeros(HOURS)\n",
    "    cost_unconstrained = np.zeros(HOURS)      # expected cost if every eligible auction were entered\n",
    "    prices = {}                               # per hour: the prices to beat of the winnable auctions\n",
    "    price_scale = bid if price_scale is None else price_scale\n",
    "    med = price_median(price_scale, competition)\n",
    "    for d in range(DAYS):\n",
    "        comp = break_competition if (break_day is not None and d >= break_day) else 1.0\n",
    "        n_hour = rng.poisson(weekly_auctions * demand * WEEKDAY[d] / WEEKDAY.sum() * HOUR_P)\n",
    "        for h in range(HOURS):\n",
    "            t = rng.lognormal(np.log(med[h] * comp), spread, n_hour[h])\n",
    "            t = t[t <= cutoff * bid]\n",
    "            eligible[h] += len(t)\n",
    "            c, _ = np.histogram(t, edges)\n",
    "            counts[h] += c\n",
    "            s, _ = np.histogram(t, edges, weights=t)\n",
    "            sums[h] += s\n",
    "            won = t[t <= bid]\n",
    "            win_unconstrained[h] += len(won)\n",
    "            cost_unconstrained[h] += won.sum() * ctr_h[h]\n",
    "            prices.setdefault(h, []).append(won)\n",
    "    # the pacer: enter a share of every auction so the expected spend lasts the week\n",
    "    weekly_budget = daily_budget * DAYS\n",
    "    participation = min(1.0, weekly_budget / cost_unconstrained.sum()) if cost_unconstrained.sum() > 0 else 1.0\n",
    "    impressions = np.zeros(HOURS, dtype=int)\n",
    "    clicks = np.zeros(HOURS, dtype=int)\n",
    "    conversions = np.zeros(HOURS, dtype=int)\n",
    "    cost = np.zeros(HOURS)\n",
    "    for h in range(HOURS):\n",
    "        won = np.concatenate(prices[h]) if prices[h] else np.array([])\n",
    "        entered = rng.random(len(won)) < participation\n",
    "        shown = won[entered]\n",
    "        clicked = rng.random(len(shown)) < ctr_h[h]\n",
    "        paid = shown[clicked]\n",
    "        conv = rng.random(len(paid)) < cvr_h[h]\n",
    "        impressions[h] = len(shown)\n",
    "        clicks[h] = len(paid)\n",
    "        conversions[h] = int(conv.sum())\n",
    "        cost[h] = paid.sum()\n",
    "    return {\n",
    "        \"bid\": bid, \"price_scale\": price_scale, \"daily_budget\": daily_budget, \"weekly_budget\": weekly_budget,\n",
    "        \"eligible\": eligible, \"counts\": counts, \"sums\": sums,\n",
    "        \"win_unconstrained\": win_unconstrained, \"cost_unconstrained\": cost_unconstrained,\n",
    "        \"participation\": participation,\n",
    "        \"impressions\": impressions, \"clicks\": clicks, \"conversions\": conversions, \"cost\": cost,\n",
    "        \"ctr_h\": ctr_h, \"cvr_h\": cvr_h,\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36535ab8",
   "metadata": {},
   "source": [
    "## What the platform reports, and the three-way split\n",
    "\n",
    "From a week like that, a search platform reports the campaign's impressions, clicks, cost and\n",
    "conversions, and three shares of the eligible pool: the impression share (the auctions it\n",
    "showed in), the share lost to budget (the auctions the budget kept it out of) and the share lost\n",
    "to rank (the auctions it entered and was outbid in). The three add up to one. The platform also\n",
    "clamps the shares at the ends of its scale (a share under ten per cent is reported as a floor,\n",
    "a lost share over ninety as a ceiling), and the reader below reproduces that, because a clamped\n",
    "figure is a real thing an account can hand you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49bed16d",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "last week, as the platform reports it\n",
      "  impressions            43997\n",
      "  clicks                 1782\n",
      "  conversions            76\n",
      "  cost                   1424.78\n",
      "  impression_share       0.4676\n",
      "  lost_is_budget         0.3107\n",
      "  lost_is_rank           0.2217\n",
      "  budget_utilisation     1.0177\n",
      "  ctr                    0.0405\n",
      "  cvr                    0.04265\n",
      "  cpc                    0.7995\n",
      "  cpa                    18.75\n",
      "  value                  2432.0\n",
      "  margin                 1007.22\n",
      "  the three shares sum to 1.0000\n"
     ]
    }
   ],
   "source": [
    "def as_google_reports(share, kind):\n",
    "    \"\"\"The vendor's own range clamp: impression share floors at 0.1 (reported 0.0999), the two lost\n",
    "    shares ceiling at 0.9 (reported 0.9001). Keyed to the value, not to volume.\"\"\"\n",
    "    if kind == \"impression_share\":\n",
    "        return 0.0999 if share < 0.1 else share\n",
    "    return 0.9001 if share > 0.9 else share\n",
    "\n",
    "\n",
    "def report_week(week, value=VALUE_PER_CONVERSION, clamp=True):\n",
    "    \"\"\"The platform's reading of the week: the counts, the three shares, and the money.\"\"\"\n",
    "    eligible = week[\"eligible\"].sum()\n",
    "    s = week[\"participation\"]\n",
    "    won_unc = week[\"win_unconstrained\"].sum()\n",
    "    impressions = int(week[\"impressions\"].sum())\n",
    "    clicks = int(week[\"clicks\"].sum())\n",
    "    conversions = int(week[\"conversions\"].sum())\n",
    "    cost = float(week[\"cost\"].sum())\n",
    "    # the three shares, as the pacer's plan defines them: the budget kept the campaign out of a\n",
    "    # (1 - s) share of every auction; of the rest it won its winnable ones and lost the others on rank\n",
    "    is_share = s * won_unc / eligible\n",
    "    lost_budget = 1.0 - s\n",
    "    lost_rank = s * (eligible - won_unc) / eligible\n",
    "    r = {\n",
    "        \"impressions\": impressions, \"clicks\": clicks, \"conversions\": conversions, \"cost\": round(cost, 2),\n",
    "        \"impression_share\": round(is_share, 4), \"lost_is_budget\": round(lost_budget, 4), \"lost_is_rank\": round(lost_rank, 4),\n",
    "        \"eligible_estimate\": round(eligible),\n",
    "        \"daily_budget\": week[\"daily_budget\"], \"weekly_budget\": week[\"weekly_budget\"],\n",
    "        \"budget_utilisation\": round(cost / week[\"weekly_budget\"], 4),\n",
    "        \"ctr\": round(clicks / impressions, 5) if impressions else 0.0,\n",
    "        \"cvr\": round(conversions / clicks, 5) if clicks else 0.0,\n",
    "        \"cpc\": round(cost / clicks, 4) if clicks else 0.0,\n",
    "        \"cpa\": round(cost / conversions, 2) if conversions else None,\n",
    "        \"value\": round(conversions * value, 2), \"margin\": round(conversions * value - cost, 2),\n",
    "        \"value_per_conversion\": value,\n",
    "    }\n",
    "    if clamp:\n",
    "        r[\"impression_share_reported\"] = as_google_reports(r[\"impression_share\"], \"impression_share\")\n",
    "        r[\"lost_is_budget_reported\"] = as_google_reports(r[\"lost_is_budget\"], \"lost\")\n",
    "        r[\"lost_is_rank_reported\"] = as_google_reports(r[\"lost_is_rank\"], \"lost\")\n",
    "    return r\n",
    "\n",
    "\n",
    "last_week = simulate_week(rng)\n",
    "report = report_week(last_week)\n",
    "print(\"last week, as the platform reports it\")\n",
    "for k in (\"impressions\", \"clicks\", \"conversions\", \"cost\", \"impression_share\", \"lost_is_budget\", \"lost_is_rank\",\n",
    "          \"budget_utilisation\", \"ctr\", \"cvr\", \"cpc\", \"cpa\", \"value\", \"margin\"):\n",
    "    print(f\"  {k:22s} {report[k]}\")\n",
    "print(f\"  the three shares sum to {report['impression_share'] + report['lost_is_budget'] + report['lost_is_rank']:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c14e7f19",
   "metadata": {},
   "source": [
    "## Reading the split: where the money is leaking\n",
    "\n",
    "The three shares are facts. What a marketer wants next is what each leak is worth, and that is\n",
    "where the report stops and arithmetic starts. The obvious arithmetic is: the eligible pool is the\n",
    "impressions divided by the impression share; the impressions lost to each cause are that pool\n",
    "times the lost share; multiply by the usual click rate, the usual conversion rate and the value\n",
    "of a conversion, and there is the money on the table. Two things are wrong with it, one per leak.\n",
    "\n",
    "**Budget.** The auctions the budget kept you out of are auctions you were *eligible* for, not\n",
    "auctions you would have *won*. You would have won your usual share of them, and that share is\n",
    "in the report too: the impression share over the impression share plus the rank-lost share is\n",
    "the fraction of entered auctions you win. So the impressions a bigger budget would bring is the\n",
    "budget-lost impressions times that win share, not the whole of them. Their price is about the\n",
    "usual price, because they are the same kind of auction.\n",
    "\n",
    "**Rank.** The auctions you were outbid in are, by definition, the ones whose price to beat is\n",
    "above your bid. Winning them means bidding more, and the ones you gain are exactly the ones that\n",
    "cost more than you pay now. So the count is only reachable with a higher bid, and the price of\n",
    "the extra conversions is above your current cost per conversion. By how much depends on how the\n",
    "prices are spread, which no report shows. The notebook measures it in the world where it knows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ac06c42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T06:58:05.283057Z",
     "iopub.status.busy": "2026-10-24T06:58:05.282217Z",
     "iopub.status.idle": "2026-10-24T06:58:05.619690Z",
     "shell.execute_reply": "2026-10-24T06:58:05.618152Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the split, read from the report\n",
      "  eligible pool (impressions / share)   94,091\n",
      "  lost to budget                        29,234 impressions\n",
      "  lost to rank                          20,860 impressions\n",
      "  win share among entered auctions      0.678\n",
      "  naive: a bigger budget wins           50.5 conversions\n",
      "  accounting: a bigger budget wins      34.3 conversions (times the win share)\n",
      "  naive: higher bids win                36.0 conversions 'at the usual cost'\n",
      "  binding constraint                    budget\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1330x406 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fork_reading(rep):\n",
    "    \"\"\"What the report supports by arithmetic alone: the eligible pool, the impressions lost to each\n",
    "    cause, the win share among entered auctions, and the naive and the accounting-corrected count\n",
    "    of what a bigger budget would win.\"\"\"\n",
    "    is_share, lb, lr = rep[\"impression_share\"], rep[\"lost_is_budget\"], rep[\"lost_is_rank\"]\n",
    "    eligible = rep[\"impressions\"] / is_share\n",
    "    lost_budget_impr = eligible * lb\n",
    "    lost_rank_impr = eligible * lr\n",
    "    win_share = is_share / (is_share + lr)\n",
    "    naive_budget_conv = lost_budget_impr * rep[\"ctr\"] * rep[\"cvr\"]\n",
    "    naive_rank_conv = lost_rank_impr * rep[\"ctr\"] * rep[\"cvr\"]\n",
    "    accounting_budget_impr = lost_budget_impr * win_share\n",
    "    accounting_budget_conv = accounting_budget_impr * rep[\"ctr\"] * rep[\"cvr\"]\n",
    "    return {\n",
    "        \"eligible\": eligible, \"lost_budget_impressions\": lost_budget_impr, \"lost_rank_impressions\": lost_rank_impr,\n",
    "        \"win_share\": win_share,\n",
    "        \"naive_budget_conversions\": naive_budget_conv, \"naive_rank_conversions\": naive_rank_conv,\n",
    "        \"accounting_budget_impressions\": accounting_budget_impr, \"accounting_budget_conversions\": accounting_budget_conv,\n",
    "        \"naive_budget_value\": naive_budget_conv * rep[\"value_per_conversion\"],\n",
    "        \"naive_rank_value\": naive_rank_conv * rep[\"value_per_conversion\"],\n",
    "        \"binding_constraint\": \"budget\" if lb > lr else \"rank\",\n",
    "    }\n",
    "\n",
    "\n",
    "fork = fork_reading(report)\n",
    "print(\"the split, read from the report\")\n",
    "print(f\"  eligible pool (impressions / share)   {fork['eligible']:,.0f}\")\n",
    "print(f\"  lost to budget                        {fork['lost_budget_impressions']:,.0f} impressions\")\n",
    "print(f\"  lost to rank                          {fork['lost_rank_impressions']:,.0f} impressions\")\n",
    "print(f\"  win share among entered auctions      {fork['win_share']:.3f}\")\n",
    "print(f\"  naive: a bigger budget wins           {fork['naive_budget_conversions']:.1f} conversions\")\n",
    "print(f\"  accounting: a bigger budget wins      {fork['accounting_budget_conversions']:.1f} conversions (times the win share)\")\n",
    "print(f\"  naive: higher bids win                {fork['naive_rank_conversions']:.1f} conversions 'at the usual cost'\")\n",
    "print(f\"  binding constraint                    {fork['binding_constraint']}\")\n",
    "\n",
    "# --- figure 1: the split -----------------------------------------------------------------------\n",
    "fig, ax = plt.subplots(figsize=(9.5, 2.9))\n",
    "parts = [(\"shown\", report[\"impression_share\"], SIGNAL), (\"lost to budget\", report[\"lost_is_budget\"], FLAG),\n",
    "         (\"lost to rank\", report[\"lost_is_rank\"], NAIVE)]\n",
    "left = 0.0\n",
    "for name, share, col in parts:\n",
    "    ax.barh([0], [share], left=left, color=col, edgecolor=PAPER, height=0.55)\n",
    "    ax.text(left + share / 2, 0, f\"{name}\\n{share * 100:.0f}%\", ha=\"center\", va=\"center\", color=PAPER, fontsize=11, fontweight=\"bold\")\n",
    "    left += share\n",
    "ax.set_xlim(0, 1)\n",
    "ax.set_yticks([])\n",
    "ax.set_xticks([0, 0.25, 0.5, 0.75, 1.0])\n",
    "ax.set_xticklabels([\"0%\", \"25%\", \"50%\", \"75%\", \"100%\"])\n",
    "ax.set_xlabel(f\"share of the {fork['eligible']:,.0f} eligible auctions last week (synthetic campaign)\")\n",
    "ax.set_title(\"What the report says: one eligible pool, split three ways\", loc=\"left\", fontsize=12)\n",
    "ax.grid(False)\n",
    "plt.tight_layout()\n",
    "plt.savefig(FIGURES / \"fig_split.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38353e6a",
   "metadata": {},
   "source": [
    "## The replay engine: my version of the estimate Google offers\n",
    "\n",
    "Google's help page describes its bid simulators as tools that \"collect and analyze data from ad\n",
    "auctions\", that \"use data from the last 7 days\", and that \"estimate how your ads might have\n",
    "performed\" at bids you did not set. That is a replay: take last week's auctions, change the\n",
    "bid or the budget, and re-run the week on paper. This notebook builds one of the same kind on the\n",
    "synthetic campaign, where the auction log is fully known, so the replay's projections can be held\n",
    "against what the next week actually brought.\n",
    "\n",
    "The engine reads a log summarised per hour of the day: how many eligible auctions fell in each\n",
    "price-to-beat bin, what those prices summed to, and the click and conversion rates last week's\n",
    "outcomes imply. At a bid multiplier and a budget it says how many impressions the campaign would\n",
    "have won, what they would have cost, and what they would have converted to. Two honest notes on\n",
    "it. The click rate is learned per hour of the day and the conversion rate over the whole week,\n",
    "because conversions are too few to learn by the hour. And the budget is handled the simplest\n",
    "way: the replay assumes the auctions the budget kept the campaign out of look like the ones it\n",
    "was let into, which is true in this world by construction and an assumption anywhere else.\n",
    "\n",
    "The same function is the widget's engine, line for line, and the pinned cases at the end of the\n",
    "notebook are what the widget's self-check reproduces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0f3f5c10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T06:58:05.628235Z",
     "iopub.status.busy": "2026-10-24T06:58:05.627507Z",
     "iopub.status.idle": "2026-10-24T06:58:05.658326Z",
     "shell.execute_reply": "2026-10-24T06:58:05.654095Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the replay at last week's own settings, from last week's own log\n",
      "  impressions 43,381 (reported 43,997), clicks 1,758 (reported 1,782), cost 1,400.00 (reported 1,424.78), conversions 75.0 (reported 76)\n"
     ]
    }
   ],
   "source": [
    "def summarise_log(week, mode=\"observed\"):\n",
    "    \"\"\"The log the replay engine reads. `observed` learns the rates from the week's own outcomes,\n",
    "    which is all a real simulator has; `truth` carries the world's actual rates, which is what next\n",
    "    week's expected outcome is computed from.\"\"\"\n",
    "    if mode == \"observed\":\n",
    "        impr, clk, conv = week[\"impressions\"], week[\"clicks\"], week[\"conversions\"]\n",
    "        pooled_ctr = clk.sum() / impr.sum() if impr.sum() else 0.0\n",
    "        ctr = [round(float(clk[h] / impr[h]) if impr[h] >= 20 else float(pooled_ctr), 6) for h in range(HOURS)]\n",
    "        cvr = round(float(conv.sum() / clk.sum()) if clk.sum() else 0.0, 6)\n",
    "    else:\n",
    "        ctr = [round(float(x), 6) for x in week[\"ctr_h\"]]\n",
    "        # the truth carries each hour's own conversion rate: a higher bid wins auctions that sit in\n",
    "        # business hours, where conversions run higher, so one rate pooled at the current bid would\n",
    "        # misprice the extras (found by the independent math review, 2026-10-24)\n",
    "        cvr = [round(float(x), 6) for x in week[\"cvr_h\"]]\n",
    "    return {\n",
    "        \"bid\": week[\"bid\"],\n",
    "        \"bin_edges\": BIN_EDGES,\n",
    "        \"counts\": [[int(c) for c in row] for row in week[\"counts\"]],\n",
    "        \"sums\": [[round(float(s), 4) for s in row] for row in week[\"sums\"]],\n",
    "        \"ctr\": ctr, \"cvr\": cvr,\n",
    "    }\n",
    "\n",
    "\n",
    "def replay(log, bid_multiplier, weekly_budget):\n",
    "    \"\"\"THE ENGINE. What the campaign would do at `bid_multiplier` times its bid with `weekly_budget`\n",
    "    to spend, on the auctions in `log`. Mirrored in the widget; the pinned cases score both.\"\"\"\n",
    "    edges = log[\"bin_edges\"]\n",
    "    impressions = clicks = cost = conversions = 0.0\n",
    "    hourly_cvr = isinstance(log[\"cvr\"], list)   # the truth's per-hour rates; an observed log learns one pooled rate\n",
    "    for h in range(HOURS):\n",
    "        won = paid = 0.0\n",
    "        for k in range(len(edges) - 1):\n",
    "            if edges[k + 1] <= bid_multiplier + 1e-9:\n",
    "                won += log[\"counts\"][h][k]\n",
    "                paid += log[\"sums\"][h][k]\n",
    "        impressions += won\n",
    "        clicks += won * log[\"ctr\"][h]\n",
    "        cost += paid * log[\"ctr\"][h]\n",
    "        conversions += won * log[\"ctr\"][h] * (log[\"cvr\"][h] if hourly_cvr else log[\"cvr\"])\n",
    "    participation = min(1.0, weekly_budget / cost) if cost > 0 else 1.0\n",
    "    out = {\n",
    "        \"impressions\": impressions * participation, \"clicks\": clicks * participation,\n",
    "        \"cost\": cost * participation, \"conversions\": conversions * participation,\n",
    "        \"participation\": participation,\n",
    "    }\n",
    "    out[\"cpa\"] = out[\"cost\"] / out[\"conversions\"] if out[\"conversions\"] > 0 else None\n",
    "    out[\"cpc\"] = out[\"cost\"] / out[\"clicks\"] if out[\"clicks\"] > 0 else None\n",
    "    return out\n",
    "\n",
    "\n",
    "def margin_of(result, value):\n",
    "    return result[\"conversions\"] * value - result[\"cost\"]\n",
    "\n",
    "\n",
    "log_observed = summarise_log(last_week, \"observed\")\n",
    "log_truth = summarise_log(last_week, \"truth\")\n",
    "same = replay(log_observed, 1.0, report[\"weekly_budget\"])\n",
    "print(\"the replay at last week's own settings, from last week's own log\")\n",
    "print(f\"  impressions {same['impressions']:,.0f} (reported {report['impressions']:,}), clicks {same['clicks']:,.0f} \"\n",
    "      f\"(reported {report['clicks']:,}), cost {same['cost']:,.2f} (reported {report['cost']:,.2f}), \"\n",
    "      f\"conversions {same['conversions']:.1f} (reported {report['conversions']})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9470075",
   "metadata": {},
   "source": [
    "## The budget lever: the naive count against the truth\n",
    "\n",
    "Raise the budget from where it is to where it no longer binds, in steps. At each budget the\n",
    "naive arithmetic says the campaign gains the budget-lost impressions in proportion, at the usual\n",
    "rates; the truth is what the world's own auctions and rates give. The gap is the win share."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "649786f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T06:58:05.667093Z",
     "iopub.status.busy": "2026-10-24T06:58:05.666487Z",
     "iopub.status.idle": "2026-10-24T06:58:06.344625Z",
     "shell.execute_reply": "2026-10-24T06:58:06.343288Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lifting the budget until it stops binding\n",
      "  unconstrained_budget_multiplier    1.4508\n",
      "  unconstrained_weekly_budget        2031.06\n",
      "  naive_extra_conversions            50.5\n",
      "  accounting_extra_conversions       34.26\n",
      "  true_extra_conversions             35.85\n",
      "  true_extra_cost                    631.06\n",
      "  true_extra_impressions             19845.5\n",
      "  naive_over_truth                   1.409\n",
      "  cpa_before                         17.6\n",
      "  cpa_after                          17.6\n",
      "  marginal_cpa                       17.6\n",
      "  extra_margin                       516.1\n",
      "  naive_extra_value                  1615.9\n",
      "  naive_knee_multiplier              1.6761\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1330x644 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def budget_ladder(rep, log_true, steps):\n",
    "    \"\"\"At each budget: what the naive arithmetic says a bigger budget wins - more clicks at the usual\n",
    "    price and the usual conversion rate, until every impression lost to budget has been bought - and\n",
    "    what the world's own auctions and rates give.\"\"\"\n",
    "    f = fork_reading(rep)\n",
    "    base = replay(log_true, 1.0, rep[\"weekly_budget\"])\n",
    "    rows = []\n",
    "    for mult in steps:\n",
    "        weekly = rep[\"weekly_budget\"] * mult\n",
    "        truth = replay(log_true, 1.0, weekly)\n",
    "        naive_extra_conv = min((weekly - rep[\"weekly_budget\"]) / rep[\"cpc\"] * rep[\"cvr\"], f[\"naive_budget_conversions\"])\n",
    "        rows.append({\n",
    "            \"budget_multiplier\": round(mult, 4), \"weekly_budget\": weekly,\n",
    "            \"naive_extra_conversions\": naive_extra_conv,\n",
    "            \"true_extra_conversions\": truth[\"conversions\"] - base[\"conversions\"],\n",
    "            \"true_extra_cost\": truth[\"cost\"] - base[\"cost\"],\n",
    "            \"true_participation\": truth[\"participation\"],\n",
    "            \"true_cpa\": truth[\"cpa\"],\n",
    "        })\n",
    "    return rows\n",
    "\n",
    "\n",
    "unconstrained_mult = 1 / last_week[\"participation\"]\n",
    "naive_knee = 1 + fork[\"naive_budget_conversions\"] / report[\"cvr\"] * report[\"cpc\"] / report[\"weekly_budget\"]\n",
    "ladder_b = budget_ladder(report, log_truth, sorted({1.0, 1.1, 1.2, 1.3, round(unconstrained_mult, 4), 1.6, 1.8, round(naive_knee, 4), 2.2}))\n",
    "base_truth = replay(log_truth, 1.0, report[\"weekly_budget\"])\n",
    "unc_truth = replay(log_truth, 1.0, report[\"weekly_budget\"] * unconstrained_mult)\n",
    "budget_lever = {\n",
    "    \"unconstrained_budget_multiplier\": round(unconstrained_mult, 4),\n",
    "    \"unconstrained_weekly_budget\": round(report[\"weekly_budget\"] * unconstrained_mult, 2),\n",
    "    \"naive_extra_conversions\": round(fork[\"naive_budget_conversions\"], 2),\n",
    "    \"accounting_extra_conversions\": round(fork[\"accounting_budget_conversions\"], 2),\n",
    "    \"true_extra_conversions\": round(unc_truth[\"conversions\"] - base_truth[\"conversions\"], 2),\n",
    "    \"true_extra_cost\": round(unc_truth[\"cost\"] - base_truth[\"cost\"], 2),\n",
    "    \"true_extra_impressions\": round(unc_truth[\"impressions\"] - base_truth[\"impressions\"], 1),\n",
    "    \"naive_over_truth\": round(fork[\"naive_budget_conversions\"] / (unc_truth[\"conversions\"] - base_truth[\"conversions\"]), 3),\n",
    "    \"cpa_before\": round(base_truth[\"cpa\"], 2), \"cpa_after\": round(unc_truth[\"cpa\"], 2),\n",
    "    \"marginal_cpa\": round((unc_truth[\"cost\"] - base_truth[\"cost\"]) / (unc_truth[\"conversions\"] - base_truth[\"conversions\"]), 2),\n",
    "    \"extra_margin\": round(margin_of(unc_truth, VALUE_PER_CONVERSION) - margin_of(base_truth, VALUE_PER_CONVERSION), 2),\n",
    "    \"naive_extra_value\": round(fork[\"naive_budget_value\"], 2),\n",
    "    \"naive_knee_multiplier\": round(naive_knee, 4),\n",
    "}\n",
    "print(\"lifting the budget until it stops binding\")\n",
    "for k, v in budget_lever.items():\n",
    "    print(f\"  {k:34s} {v}\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9.5, 4.6))\n",
    "xs = [r[\"budget_multiplier\"] for r in ladder_b]\n",
    "ax.plot(xs, [r[\"naive_extra_conversions\"] for r in ladder_b], color=NAIVE, lw=2.2, marker=\"o\", label=\"naive: the lost impressions, at the usual rates\")\n",
    "ax.plot(xs, [r[\"true_extra_conversions\"] for r in ladder_b], color=SIGNAL, lw=2.6, marker=\"o\", label=\"the truth: what the world's auctions give\")\n",
    "ax.axvline(unconstrained_mult, color=SIGNAL, ls=\"--\", lw=1)\n",
    "ax.text(unconstrained_mult + 0.02, ax.get_ylim()[1] * 0.06, \"the budget stops binding here\", color=SIGNAL, fontsize=9.5)\n",
    "ax.axvline(naive_knee, color=NAIVE, ls=\"--\", lw=1)\n",
    "ax.text(naive_knee + 0.02, ax.get_ylim()[1] * 0.18, \"the naive count says here\", color=NAIVE, fontsize=9.5)\n",
    "ax.set_xlabel(\"weekly budget, as a multiple of the current one\")\n",
    "ax.set_ylabel(\"extra conversions per week\")\n",
    "ax.set_title(\"The budget lever: right about the price, wrong about how much there is to buy\", loc=\"left\", fontsize=12)\n",
    "ax.legend(frameon=False, loc=\"upper left\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(FIGURES / \"fig_budget_lever.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aa426ed",
   "metadata": {},
   "source": [
    "## The bid lever: the extra conversions cost more than the average ones\n",
    "\n",
    "Now hold the budget generous enough not to bind and raise the bid in steps. Every step wins the\n",
    "auctions whose price to beat sits between the old bid and the new one, and pays that price. The\n",
    "average cost per conversion drifts up; the cost of the *extra* conversions (the marginal cost)\n",
    "climbs much faster, because the extras are the expensive auctions by construction. And with the\n",
    "budget left where it is, raising the bid buys fewer, dearer clicks and the conversions fall,\n",
    "which is why the budget leak has to be resolved before the rank leak is read."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "faebf651",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T06:58:06.348414Z",
     "iopub.status.busy": "2026-10-24T06:58:06.348064Z",
     "iopub.status.idle": "2026-10-24T06:58:07.234244Z",
     "shell.execute_reply": "2026-10-24T06:58:07.232522Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raising the bid with a generous budget\n",
      "  at 1.5x: extra conversions 42.2 for 1,292 euros - marginal cost per conversion 30.63 against an average of 17.60 (1.74 times); the naive count said 36.0\n",
      "  at 1.5x with the budget kept: conversions 66.4 against 79.5 at the current bid - 13.2 fewer\n",
      "  best multiplier at 32 euros a conversion: 1.3\n",
      "  the ladder, budget lifted:\n",
      "    x1.0  conversions  115.4  cost     2031  average 17.60  since current    -  this step    -  margin    1661\n",
      "    x1.1  conversions  127.9  cost     2368  average 18.52  since current 26.93  this step 26.93  margin    1724\n",
      "    x1.2  conversions  138.0  cost     2665  average 19.31  since current 27.99  this step 29.30  margin    1752\n",
      "    x1.3  conversions  145.9  cost     2914  average 19.97  since current 28.94  this step 31.68  margin    1754\n",
      "    x1.4  conversions  152.3  cost     3133  average 20.57  since current 29.82  this step 33.97  margin    1742\n",
      "    x1.5  conversions  157.6  cost     3323  average 21.09  since current 30.63  this step 36.36  margin    1719\n",
      "    x1.6  conversions  161.4  cost     3470  average 21.51  since current 31.30  this step 38.73  margin    1693\n",
      "    x1.8  conversions  166.7  cost     3696  average 22.17  since current 32.45  this step 42.35  margin    1638\n",
      "    x2.0  conversions  169.9  cost     3846  average 22.64  since current 33.30  this step 47.10  margin    1590\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1540x616 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def bid_ladder(log_true, weekly_budget, steps, value):\n",
    "    \"\"\"Each bid step with a budget that does not bind: the totals, the average cost per conversion,\n",
    "    the cost of the extra conversions since the current bid, and the cost of the extra conversions\n",
    "    since the previous step - the number the next step's decision turns on.\"\"\"\n",
    "    base = replay(log_true, 1.0, weekly_budget)\n",
    "    rows = []\n",
    "    prev = base\n",
    "    for m in steps:\n",
    "        r = replay(log_true, m, weekly_budget)\n",
    "        extra_conv = r[\"conversions\"] - base[\"conversions\"]\n",
    "        extra_cost = r[\"cost\"] - base[\"cost\"]\n",
    "        step_conv = r[\"conversions\"] - prev[\"conversions\"]\n",
    "        step_cost = r[\"cost\"] - prev[\"cost\"]\n",
    "        rows.append({\n",
    "            \"bid_multiplier\": m, \"impressions\": r[\"impressions\"], \"clicks\": r[\"clicks\"], \"cost\": r[\"cost\"],\n",
    "            \"conversions\": r[\"conversions\"], \"cpa\": r[\"cpa\"],\n",
    "            \"extra_conversions\": extra_conv, \"extra_cost\": extra_cost,\n",
    "            \"marginal_cpa\": (extra_cost / extra_conv) if extra_conv > 1e-9 else None,\n",
    "            \"step_marginal_cpa\": (step_cost / step_conv) if step_conv > 1e-9 else None,\n",
    "            \"margin\": margin_of(r, value), \"extra_margin\": margin_of(r, value) - margin_of(base, value),\n",
    "        })\n",
    "        prev = r\n",
    "    return rows\n",
    "\n",
    "\n",
    "generous = report[\"weekly_budget\"] * unconstrained_mult * 3.0\n",
    "ladder_r = bid_ladder(log_truth, generous, [1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.8, 2.0], VALUE_PER_CONVERSION)\n",
    "ladder_capped = bid_ladder(log_truth, report[\"weekly_budget\"], [1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.8, 2.0], VALUE_PER_CONVERSION)\n",
    "at_15 = next(r for r in ladder_r if abs(r[\"bid_multiplier\"] - 1.5) < 1e-9)\n",
    "at_15_capped = next(r for r in ladder_capped if abs(r[\"bid_multiplier\"] - 1.5) < 1e-9)\n",
    "base_unc = ladder_r[0]\n",
    "bid_lever = {\n",
    "    \"generous_weekly_budget\": round(generous, 2),\n",
    "    \"at_1_5\": {k: (round(v, 3) if isinstance(v, float) else v) for k, v in at_15.items()},\n",
    "    \"base_unconstrained\": {k: (round(v, 3) if isinstance(v, float) else v) for k, v in base_unc.items()},\n",
    "    \"at_1_5_budget_kept\": {k: (round(v, 3) if isinstance(v, float) else v) for k, v in at_15_capped.items()},\n",
    "    \"naive_rank_conversions\": round(fork[\"naive_rank_conversions\"], 2),\n",
    "    \"marginal_over_average_cpa_at_1_5\": round(at_15[\"marginal_cpa\"] / base_unc[\"cpa\"], 3),\n",
    "    \"conversions_lost_by_raising_bid_under_cap\": round(ladder_capped[0][\"conversions\"] - at_15_capped[\"conversions\"], 2),\n",
    "    \"best_multiplier_at_value\": max(ladder_r, key=lambda r: r[\"margin\"])[\"bid_multiplier\"],\n",
    "    \"first_step_past_value\": next((r[\"bid_multiplier\"] for r in ladder_r if r[\"step_marginal_cpa\"] and r[\"step_marginal_cpa\"] > VALUE_PER_CONVERSION), None),\n",
    "}\n",
    "print(\"raising the bid with a generous budget\")\n",
    "print(f\"  at 1.5x: extra conversions {at_15['extra_conversions']:.1f} for {at_15['extra_cost']:,.0f} euros - \"\n",
    "      f\"marginal cost per conversion {at_15['marginal_cpa']:.2f} against an average of {base_unc['cpa']:.2f} \"\n",
    "      f\"({bid_lever['marginal_over_average_cpa_at_1_5']:.2f} times); the naive count said {fork['naive_rank_conversions']:.1f}\")\n",
    "print(f\"  at 1.5x with the budget kept: conversions {at_15_capped['conversions']:.1f} against {ladder_capped[0]['conversions']:.1f} \"\n",
    "      f\"at the current bid - {bid_lever['conversions_lost_by_raising_bid_under_cap']:.1f} fewer\")\n",
    "print(f\"  best multiplier at {VALUE_PER_CONVERSION:.0f} euros a conversion: {bid_lever['best_multiplier_at_value']}\")\n",
    "print(\"  the ladder, budget lifted:\")\n",
    "for r in ladder_r:\n",
    "    mc = f\"{r['marginal_cpa']:.2f}\" if r[\"marginal_cpa\"] else \"   -\"\n",
    "    sc = f\"{r['step_marginal_cpa']:.2f}\" if r[\"step_marginal_cpa\"] else \"   -\"\n",
    "    print(f\"    x{r['bid_multiplier']:.1f}  conversions {r['conversions']:6.1f}  cost {r['cost']:8.0f}  average {r['cpa']:.2f}  \"\n",
    "          f\"since current {mc}  this step {sc}  margin {r['margin']:7.0f}\")\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4.4))\n",
    "xs = [r[\"bid_multiplier\"] for r in ladder_r]\n",
    "ax1.plot(xs, [r[\"conversions\"] for r in ladder_r], color=SIGNAL, lw=2.6, marker=\"o\", label=\"budget lifted\")\n",
    "ax1.plot(xs, [r[\"conversions\"] for r in ladder_capped], color=FLAG, lw=2.2, marker=\"o\", label=\"budget kept where it is\")\n",
    "ax1.set_xlabel(\"bid, as a multiple of the current one\")\n",
    "ax1.set_ylabel(\"conversions per week\")\n",
    "ax1.set_title(\"More bid: more conversions only if the budget follows\", loc=\"left\", fontsize=11.5)\n",
    "ax1.legend(frameon=False)\n",
    "mx = [r[\"bid_multiplier\"] for r in ladder_r[1:]]\n",
    "ax2.plot(mx, [r[\"step_marginal_cpa\"] for r in ladder_r[1:]], color=FLAG, lw=2.6, marker=\"o\", label=\"cost of the extra conversions each step buys\")\n",
    "ax2.plot(xs, [r[\"cpa\"] for r in ladder_r], color=SIGNAL, lw=2.2, marker=\"o\", label=\"average cost per conversion\")\n",
    "ax2.axhline(VALUE_PER_CONVERSION, color=MUTED, ls=\"--\", lw=1)\n",
    "ax2.text(xs[0], VALUE_PER_CONVERSION * 1.03, \"value of a conversion\", color=MUTED, fontsize=9.5)\n",
    "ax2.set_xlabel(\"bid, as a multiple of the current one\")\n",
    "ax2.set_ylabel(\"euros per conversion\")\n",
    "ax2.set_title(\"The extras cost more than the average\", loc=\"left\", fontsize=11.5)\n",
    "ax2.legend(frameon=False, loc=\"upper left\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(FIGURES / \"fig_bid_lever.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34d3de54",
   "metadata": {},
   "source": [
    "## The estimate against the week that followed\n",
    "\n",
    "Now the question in the title. Build the projection the way a simulator would, from last week's\n",
    "log with last week's observed rates, for a change the marketer is considering, and then let\n",
    "next week happen and compare. Four worlds for next week:\n",
    "\n",
    "- **the same world**: next week is drawn by the same rules as last week; the only difference is chance;\n",
    "- **demand up**: a quarter more auctions next week, a seasonal lift the replay cannot see coming;\n",
    "- **competition up**: prices to beat a fifth higher next week, a rival's new budget;\n",
    "- **a change inside the lookback**: competition rose a fifth on the fourth day of *last* week,\n",
    "  so the log the replay reads is half old world and half new, and next week is all new world.\n",
    "\n",
    "The change under consideration: bid up 30 per cent with the budget lifted so it does not bind.\n",
    "The score is the projection's error against what next week actually delivered, for clicks and\n",
    "for conversions. For the worked campaign, next week is drawn with no week-to-week wobble, so\n",
    "each world shows its own mechanism and nothing else; the population below keeps the wobble\n",
    "every real week has, which is where the honest error sizes come from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f6f94259",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T06:58:07.238255Z",
     "iopub.status.busy": "2026-10-24T06:58:07.237841Z",
     "iopub.status.idle": "2026-10-24T06:58:07.442903Z",
     "shell.execute_reply": "2026-10-24T06:58:07.441790Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the worked campaign: bid up 130% of the current bid, budget lifted\n",
      "  same world                     projected clicks   3252 realised  3108 (+4.6%)   conversions 138.7 vs 134 (+3.5%)\n",
      "  demand up a quarter            projected clicks   3252 realised  3858 (-15.7%)   conversions 138.7 vs 194 (-28.5%)\n",
      "  competition up a fifth         projected clicks   3252 realised  2701 (+20.4%)   conversions 138.7 vs 123 (+12.8%)\n",
      "  a change inside the lookback   projected clicks   2927 realised  2643 (+10.7%)   conversions 126.3 vs 123 (+2.7%)\n"
     ]
    }
   ],
   "source": [
    "BID_CHANGE = 1.3\n",
    "WORLDS = {\n",
    "    \"same world\": dict(),\n",
    "    \"demand up a quarter\": dict(demand=1.25),\n",
    "    \"competition up a fifth\": dict(competition_multiplier=1.2),\n",
    "    \"a change inside the lookback\": dict(competition_multiplier=1.2),\n",
    "}\n",
    "\n",
    "\n",
    "def projection_and_outcome(rng, base_kwargs, world, bid_change, lift_budget, last=None, wobble=True):\n",
    "    \"\"\"The replay's projection for `bid_change` from last week's observed log, and the realised\n",
    "    outcome of next week at that change - in the given world. With `wobble` off, next week is drawn\n",
    "    with no week-to-week wobble, so the only difference from last week is the scenario itself -\n",
    "    which is how the worked campaign isolates each mechanism; the population keeps the wobble.\"\"\"\n",
    "    kw = dict(base_kwargs)\n",
    "    if world == \"a change inside the lookback\" and last is None:\n",
    "        last = simulate_week(rng, **kw, break_day=4, break_competition=1.2,\n",
    "                             **({} if wobble else dict(demand_wobble=0.0, competition_wobble=0.0)))\n",
    "    elif last is None:\n",
    "        last = simulate_week(rng, **kw)\n",
    "    rep = report_week(last)\n",
    "    log = summarise_log(last, \"observed\")\n",
    "    weekly = rep[\"weekly_budget\"]\n",
    "    if lift_budget:\n",
    "        weekly = rep[\"weekly_budget\"] * 4.0\n",
    "    proj = replay(log, bid_change, weekly)\n",
    "    nkw = dict(kw)\n",
    "    for k, v in WORLDS[world].items():\n",
    "        if k == \"competition_multiplier\":\n",
    "            nkw[\"competition\"] = kw.get(\"competition\", COMPETITION) * v\n",
    "        else:\n",
    "            nkw[k] = v\n",
    "    base_bid = kw.get(\"bid\", BID)\n",
    "    if not wobble:\n",
    "        nkw.update(demand_wobble=0.0, competition_wobble=0.0)\n",
    "    nxt = simulate_week(rng, **{**nkw, \"bid\": base_bid * bid_change, \"price_scale\": base_bid,\n",
    "                                 \"daily_budget\": weekly / DAYS})\n",
    "    real = report_week(nxt, clamp=False)\n",
    "    return last, proj, real\n",
    "\n",
    "\n",
    "def pct_error(proj, real, key):\n",
    "    return (proj[key] - real[key]) / real[key] * 100 if real[key] else float(\"nan\")\n",
    "\n",
    "\n",
    "worked_worlds = {}\n",
    "for world in WORLDS:\n",
    "    rng_w = np.random.default_rng(SEED + 7)\n",
    "    last_w = last_week if world != \"a change inside the lookback\" else None\n",
    "    lw, proj, real = projection_and_outcome(rng_w, dict(), world, BID_CHANGE, True, last=last_w, wobble=False)\n",
    "    worked_worlds[world] = {\n",
    "        \"projected\": {k: round(proj[k], 3) for k in (\"impressions\", \"clicks\", \"cost\", \"conversions\")},\n",
    "        \"realised\": {k: real[k] for k in (\"impressions\", \"clicks\", \"cost\", \"conversions\")},\n",
    "        \"error_clicks_pct\": round(pct_error(proj, real, \"clicks\"), 2),\n",
    "        \"error_cost_pct\": round(pct_error(proj, real, \"cost\"), 2),\n",
    "        \"error_conversions_pct\": round(pct_error(proj, real, \"conversions\"), 2),\n",
    "        \"lookback_report\": {k: report_week(lw)[k] for k in (\"impressions\", \"clicks\", \"conversions\", \"cost\", \"impression_share\", \"lost_is_budget\", \"lost_is_rank\")},\n",
    "    }\n",
    "print(f\"the worked campaign: bid up {BID_CHANGE:.0%} of the current bid, budget lifted\")\n",
    "for world, w in worked_worlds.items():\n",
    "    print(f\"  {world:30s} projected clicks {w['projected']['clicks']:6.0f} realised {w['realised']['clicks']:5d} ({w['error_clicks_pct']:+.1f}%)   \"\n",
    "          f\"conversions {w['projected']['conversions']:5.1f} vs {w['realised']['conversions']:3d} ({w['error_conversions_pct']:+.1f}%)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d008214b",
   "metadata": {},
   "source": [
    "## Scored on five hundred campaigns\n",
    "\n",
    "One campaign proves nothing. The notebook builds five hundred, each with its own size,\n",
    "competition, click and conversion rates, value and budget tightness, runs the same change in\n",
    "each of the four worlds, and scores the projection's error against the realised week. For the\n",
    "change-in-the-lookback world it also varies *when* the change happened, so the error can be\n",
    "read against how much of the lookback lay before the change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "412e8c5e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T06:58:07.447598Z",
     "iopub.status.busy": "2026-10-24T06:58:07.447263Z",
     "iopub.status.idle": "2026-10-24T07:00:23.551948Z",
     "shell.execute_reply": "2026-10-24T07:00:23.550552Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "projection error across 500 campaigns (projected minus realised, as a share of realised)\n",
      "  world                             clicks: median           80% band   conversions: median           80% band  within 20%\n",
      "  same settings, same world                  -0.4% [  -8.3,   +7.6]                  +0.3% [ -30.8,  +39.9]         57%\n",
      "  same world                                 +0.3% [ -14.9,  +17.2]                  +0.6% [ -28.9,  +39.6]         60%\n",
      "  demand up a quarter                       -17.6% [ -31.4,   +0.7]                 -16.8% [ -40.1,  +15.7]         49%\n",
      "  competition up a fifth                    +23.9% [  +2.6,  +53.0]                 +23.0% [ -11.2,  +71.3]         41%\n",
      "  a change inside the lookback              +13.2% [  -9.0,  +39.4]                 +11.9% [ -23.8,  +61.9]         45%\n",
      "\n",
      "the same world, by the size of the account (typical absolute error)\n",
      "  under 50 a week, pooled: n=293  conversions 20.0%  off by a third or more on 29% of weeks\n",
      "  under 15 a week    n= 85  clicks   7.4%  conversions  25.4%  conversions off by a third or more on 38% of weeks\n",
      "  15 to 50 a week    n=208  clicks   6.8%  conversions  18.2%  conversions off by a third or more on 26% of weeks\n",
      "  50 to 150 a week   n=144  clicks   8.5%  conversions  11.6%  conversions off by a third or more on 8% of weeks\n",
      "  over 150 a week    n= 63  clicks   8.8%  conversions  11.3%  conversions off by a third or more on 3% of weeks\n",
      "\n",
      "a change inside the lookback: the later it happened, the more of the old world the replay carries\n",
      "  changed on day 1 (14% of the week before it)  clicks  +0.6%  cost per click  -0.8%\n",
      "  changed on day 2 (29% of the week before it)  clicks  +9.2%  cost per click  -2.0%\n",
      "  changed on day 3 (43% of the week before it)  clicks +10.5%  cost per click  -4.1%\n",
      "  changed on day 4 (57% of the week before it)  clicks +13.9%  cost per click  -4.1%\n",
      "  changed on day 5 (71% of the week before it)  clicks +16.6%  cost per click  -4.1%\n",
      "  changed on day 6 (86% of the week before it)  clicks +22.4%  cost per click  -7.4%\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1610x644 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1330x602 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1330x602 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def draw_campaign(rng):\n",
    "    ctr = rng.uniform(0.02, 0.06)\n",
    "    cvr = rng.uniform(0.012, 0.08)\n",
    "    ctr_h, cvr_h = true_rates(ctr, cvr)\n",
    "    tight = rng.uniform(0.35, 1.0) if rng.random() < 0.7 else 1.0\n",
    "    kw = dict(bid=round(rng.uniform(0.4, 3.0), 2), weekly_auctions=int(np.exp(rng.uniform(np.log(15_000), np.log(250_000)))),\n",
    "              competition=rng.uniform(0.75, 1.15), spread=rng.uniform(0.3, 0.6), ctr_h=ctr_h, cvr_h=cvr_h)\n",
    "    # the budget: set so the campaign is entered into a `tight` share of its auctions\n",
    "    probe = simulate_week(rng, **kw, daily_budget=1e9)\n",
    "    unconstrained = probe[\"cost_unconstrained\"].sum()\n",
    "    kw[\"daily_budget\"] = round(unconstrained * tight / DAYS, 2)\n",
    "    return kw, dict(ctr=ctr, cvr=cvr, tight=tight, value=rng.uniform(20, 300))\n",
    "\n",
    "\n",
    "N_CAMPAIGNS = 500\n",
    "pop_rng = np.random.default_rng(SEED + 100)\n",
    "pop_rows = []\n",
    "for i in range(N_CAMPAIGNS):\n",
    "    kw, meta = draw_campaign(pop_rng)\n",
    "    last = simulate_week(pop_rng, **kw)\n",
    "    rep = report_week(last, clamp=False)\n",
    "    for world in WORLDS:\n",
    "        break_day = None\n",
    "        if world == \"a change inside the lookback\":\n",
    "            break_day = int(pop_rng.integers(1, 7))\n",
    "            last_w = simulate_week(pop_rng, **kw, break_day=break_day, break_competition=1.2)\n",
    "        else:\n",
    "            last_w = last\n",
    "        _, proj, real = projection_and_outcome(pop_rng, kw, world, BID_CHANGE, True, last=last_w)\n",
    "        pop_rows.append({\n",
    "            \"campaign\": i, \"world\": world, \"break_day\": break_day,\n",
    "            \"pre_break_fraction\": (break_day / DAYS) if break_day is not None else None,\n",
    "            \"weekly_conversions\": rep[\"conversions\"], \"tight\": meta[\"tight\"],\n",
    "            \"err_clicks\": pct_error(proj, real, \"clicks\"), \"err_cost\": pct_error(proj, real, \"cost\"),\n",
    "            \"err_conversions\": pct_error(proj, real, \"conversions\"),\n",
    "        })\n",
    "    # the same-settings projection: does last week predict next week at all?\n",
    "    _, proj0, real0 = projection_and_outcome(pop_rng, kw, \"same world\", 1.0, False, last=last)\n",
    "    pop_rows.append({\"campaign\": i, \"world\": \"same settings, same world\", \"break_day\": None, \"pre_break_fraction\": None,\n",
    "                     \"weekly_conversions\": rep[\"conversions\"], \"tight\": meta[\"tight\"],\n",
    "                     \"err_clicks\": pct_error(proj0, real0, \"clicks\"), \"err_cost\": pct_error(proj0, real0, \"cost\"),\n",
    "                     \"err_conversions\": pct_error(proj0, real0, \"conversions\")})\n",
    "pop = pd.DataFrame(pop_rows)\n",
    "\n",
    "\n",
    "def summarise_errors(frame):\n",
    "    out = {}\n",
    "    for key in (\"err_clicks\", \"err_cost\", \"err_conversions\"):\n",
    "        e = frame[key].dropna()\n",
    "        out[key] = {\n",
    "            \"median_pct\": round(float(e.median()), 2),\n",
    "            \"p10_pct\": round(float(e.quantile(0.10)), 2), \"p90_pct\": round(float(e.quantile(0.90)), 2),\n",
    "            \"within_10_share\": round(float((e.abs() <= 10).mean()), 4),\n",
    "            \"within_20_share\": round(float((e.abs() <= 20).mean()), 4),\n",
    "            \"n\": int(len(e)),\n",
    "        }\n",
    "    return out\n",
    "\n",
    "\n",
    "population = {\"n_campaigns\": N_CAMPAIGNS, \"bid_change\": BID_CHANGE, \"worlds\": {}}\n",
    "for world in [\"same settings, same world\"] + list(WORLDS):\n",
    "    population[\"worlds\"][world] = summarise_errors(pop[pop.world == world])\n",
    "print(f\"projection error across {N_CAMPAIGNS} campaigns (projected minus realised, as a share of realised)\")\n",
    "print(f\"  {'world':32s} {'clicks: median':>15s} {'80% band':>18s} {'conversions: median':>21s} {'80% band':>18s} {'within 20%':>11s}\")\n",
    "for world, s in population[\"worlds\"].items():\n",
    "    c, v = s[\"err_clicks\"], s[\"err_conversions\"]\n",
    "    print(f\"  {world:32s} {c['median_pct']:+14.1f}% [{c['p10_pct']:+6.1f}, {c['p90_pct']:+6.1f}]   {v['median_pct']:+19.1f}% \"\n",
    "          f\"[{v['p10_pct']:+6.1f}, {v['p90_pct']:+6.1f}]   {v['within_20_share'] * 100:8.0f}%\")\n",
    "\n",
    "# --- the size of the account: conversions are the shaky number ---------------------------------\n",
    "same_rows = pop[pop.world == \"same world\"].copy()\n",
    "bands = [(0, 15, \"under 15 a week\"), (15, 50, \"15 to 50 a week\"), (50, 150, \"50 to 150 a week\"), (150, 10**9, \"over 150 a week\")]\n",
    "by_size = []\n",
    "for lo, hi, label in bands:\n",
    "    sub = same_rows[(same_rows.weekly_conversions >= lo) & (same_rows.weekly_conversions < hi)]\n",
    "    if len(sub) < 10:\n",
    "        continue\n",
    "    by_size.append({\n",
    "        \"conversions_band\": label, \"lo\": lo, \"hi\": hi if hi < 10**9 else None, \"n\": int(len(sub)),\n",
    "        \"clicks_abs_median_pct\": round(float(sub.err_clicks.abs().median()), 2),\n",
    "        \"conversions_abs_median_pct\": round(float(sub.err_conversions.abs().median()), 2),\n",
    "        \"conversions_off_by_a_third_share\": round(float((sub.err_conversions.abs() >= 33.3).mean()), 4),\n",
    "    })\n",
    "population[\"by_size\"] = by_size\n",
    "# the two bands under fifty pooled, for the sentences that speak of \"under fifty a week\" at once\n",
    "under_50 = same_rows[same_rows.weekly_conversions < 50]\n",
    "population[\"under_50\"] = {\n",
    "    \"n\": int(len(under_50)),\n",
    "    \"conversions_abs_median_pct\": round(float(under_50.err_conversions.abs().median()), 2),\n",
    "    \"conversions_off_by_a_third_share\": round(float((under_50.err_conversions.abs() >= 33.3).mean()), 4),\n",
    "}\n",
    "print(\"\\nthe same world, by the size of the account (typical absolute error)\")\n",
    "print(f\"  under 50 a week, pooled: n={population['under_50']['n']}  conversions {population['under_50']['conversions_abs_median_pct']:.1f}%  \"\n",
    "      f\"off by a third or more on {population['under_50']['conversions_off_by_a_third_share'] * 100:.0f}% of weeks\")\n",
    "for r in by_size:\n",
    "    print(f\"  {r['conversions_band']:18s} n={r['n']:3d}  clicks {r['clicks_abs_median_pct']:5.1f}%  conversions {r['conversions_abs_median_pct']:5.1f}%  \"\n",
    "          f\"conversions off by a third or more on {r['conversions_off_by_a_third_share'] * 100:.0f}% of weeks\")\n",
    "\n",
    "# --- the change inside the lookback: error against how much of the week was old world ------------\n",
    "brk = pop[pop.world == \"a change inside the lookback\"].copy()\n",
    "by_break = []\n",
    "for k in sorted(brk.break_day.dropna().unique()):\n",
    "    sub = brk[brk.break_day == k]\n",
    "    by_break.append({\"break_day\": int(k), \"pre_break_fraction\": round(k / DAYS, 4), \"n\": int(len(sub)),\n",
    "                     \"clicks_median_pct\": round(float(sub.err_clicks.median()), 2),\n",
    "                     \"cost_median_pct\": round(float(sub.err_cost.median()), 2),\n",
    "                     \"conversions_median_pct\": round(float(sub.err_conversions.median()), 2)})\n",
    "population[\"by_break_day\"] = by_break\n",
    "print(\"\\na change inside the lookback: the later it happened, the more of the old world the replay carries\")\n",
    "for r in by_break:\n",
    "    print(f\"  changed on day {r['break_day']} ({r['pre_break_fraction'] * 100:.0f}% of the week before it)  clicks {r['clicks_median_pct']:+5.1f}%  \"\n",
    "          f\"cost per click {r['cost_median_pct'] - r['clicks_median_pct']:+5.1f}%\")\n",
    "\n",
    "# --- figure: the four worlds -------------------------------------------------------------------\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11.5, 4.6), sharey=True)\n",
    "order = [\"same settings, same world\", \"same world\", \"demand up a quarter\", \"competition up a fifth\", \"a change inside the lookback\"]\n",
    "labels = [\"same settings,\\nsame world\", \"the change,\\nsame world\", \"demand up\\na quarter\", \"competition up\\na fifth\", \"a change inside\\nthe lookback\"]\n",
    "for ax, key, name in ((axes[0], \"err_clicks\", \"clicks\"), (axes[1], \"err_conversions\", \"conversions\")):\n",
    "    for i, world in enumerate(order):\n",
    "        e = pop[pop.world == world][key].dropna()\n",
    "        lo, med, hi = e.quantile(0.10), e.median(), e.quantile(0.90)\n",
    "        ax.plot([i, i], [lo, hi], color=BAND, lw=7, solid_capstyle=\"butt\")\n",
    "        ax.plot([i], [med], marker=\"o\", color=SIGNAL if abs(med) < 5 else FLAG, ms=9)\n",
    "        ax.text(i, hi + 3, f\"{med:+.0f}%\", ha=\"center\", color=INK, fontsize=10)\n",
    "    ax.axhline(0, color=MUTED, lw=1)\n",
    "    ax.set_xticks(range(len(order)))\n",
    "    ax.set_xticklabels(labels, fontsize=9.5)\n",
    "    ax.set_title(f\"projection error on {name}: median and middle 80%\", loc=\"left\", fontsize=11.5)\n",
    "axes[0].set_ylabel(\"projected minus realised, % of realised\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(FIGURES / \"fig_worlds.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "# --- figure: the change inside the lookback ------------------------------------------------------\n",
    "fig, ax = plt.subplots(figsize=(9.5, 4.3))\n",
    "ax.plot([r[\"pre_break_fraction\"] * 100 for r in by_break], [r[\"clicks_median_pct\"] for r in by_break], color=FLAG, lw=2.6, marker=\"o\", label=\"clicks\")\n",
    "ax.plot([r[\"pre_break_fraction\"] * 100 for r in by_break], [r[\"conversions_median_pct\"] for r in by_break], color=SIGNAL, lw=2.2, marker=\"o\", label=\"conversions\")\n",
    "ax.axhline(0, color=MUTED, lw=1)\n",
    "ax.set_xlabel(\"share of the lookback that lay before the change (%)\")\n",
    "ax.set_ylabel(\"median projection error (%)\")\n",
    "ax.set_title(\"A change inside the lookback: the replay carries the old world in proportion\", loc=\"left\", fontsize=12)\n",
    "ax.legend(frameon=False)\n",
    "plt.tight_layout()\n",
    "plt.savefig(FIGURES / \"fig_break.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "# --- figure: the size of the account -------------------------------------------------------------\n",
    "fig, ax = plt.subplots(figsize=(9.5, 4.3))\n",
    "xs = np.arange(len(by_size))\n",
    "ax.bar(xs - 0.18, [r[\"clicks_abs_median_pct\"] for r in by_size], width=0.36, color=NAIVE, label=\"clicks\")\n",
    "ax.bar(xs + 0.18, [r[\"conversions_abs_median_pct\"] for r in by_size], width=0.36, color=FLAG, label=\"conversions\")\n",
    "ax.set_xticks(xs)\n",
    "ax.set_xticklabels([r[\"conversions_band\"] for r in by_size])\n",
    "ax.set_xlabel(\"conversions the account gets in a week\")\n",
    "ax.set_ylabel(\"typical error, % (absolute median)\")\n",
    "ax.set_title(\"Same world, same change: the conversion projection is the shaky one on small accounts\", loc=\"left\", fontsize=12)\n",
    "ax.legend(frameon=False)\n",
    "plt.tight_layout()\n",
    "plt.savefig(FIGURES / \"fig_size.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7c337dc",
   "metadata": {},
   "source": [
    "## How many logged changes before your own record means anything\n",
    "\n",
    "The last section of this notebook scores a projection log from your own account: the change\n",
    "you made, what the simulator projected, what happened. The typical error of that log is the\n",
    "trust you can put in the next projection, but a median of three changes is mostly luck. The\n",
    "notebook takes the population's own error distribution, draws logs of every size from it, and\n",
    "finds the size at which the middle ninety per cent of medians spans less than twenty points\n",
    "(the typical error known to within ten points either way), for clicks and for conversions\n",
    "separately, because the two are not equally noisy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dd936193",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T07:00:23.557859Z",
     "iopub.status.busy": "2026-10-24T07:00:23.557422Z",
     "iopub.status.idle": "2026-10-24T07:00:29.224660Z",
     "shell.execute_reply": "2026-10-24T07:00:29.223787Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the width of the middle 90% of medians, by the number of logged changes (target: under 20 points)\n",
      "    3 changes  clicks  28.2 points   conversions  55.4 points\n",
      "    5 changes  clicks  21.8 points   conversions  41.1 points\n",
      "    8 changes  clicks  15.4 points   conversions  30.3 points\n",
      "   10 changes  clicks  14.2 points   conversions  27.8 points\n",
      "   12 changes  clicks  12.6 points   conversions  25.6 points\n",
      "   15 changes  clicks  11.6 points   conversions  23.8 points\n",
      "   20 changes  clicks   9.7 points   conversions  20.2 points\n",
      "   30 changes  clicks   8.0 points   conversions  16.6 points\n",
      "   40 changes  clicks   7.1 points   conversions  15.2 points\n",
      "   60 changes  clicks   5.9 points   conversions  12.8 points\n",
      "   80 changes  clicks   5.2 points   conversions  11.2 points\n",
      "  the notebook reads a click error after 8 logged changes and a conversion error after 30\n"
     ]
    }
   ],
   "source": [
    "errors_same = {\n",
    "    \"clicks\": pop[pop.world == \"same world\"].err_clicks.dropna().to_numpy(),\n",
    "    \"conversions\": pop[pop.world == \"same world\"].err_conversions.dropna().to_numpy(),\n",
    "}\n",
    "FLOOR_BAND_TARGET = 20.0\n",
    "floor_rng = np.random.default_rng(SEED + 200)\n",
    "floor_table = []\n",
    "for n in (3, 5, 8, 10, 12, 15, 20, 30, 40, 60, 80):\n",
    "    row = {\"changes\": n}\n",
    "    for metric, errs in errors_same.items():\n",
    "        meds = np.array([np.median(floor_rng.choice(errs, n)) for _ in range(3000)])\n",
    "        row[f\"{metric}_median_band_width_pct\"] = round(float(np.quantile(meds, 0.95) - np.quantile(meds, 0.05)), 2)\n",
    "    floor_table.append(row)\n",
    "LOG_FLOOR = {metric: next((r[\"changes\"] for r in floor_table if r[f\"{metric}_median_band_width_pct\"] <= FLOOR_BAND_TARGET),\n",
    "                          None) for metric in errors_same}\n",
    "print(f\"the width of the middle 90% of medians, by the number of logged changes (target: under {FLOOR_BAND_TARGET:.0f} points)\")\n",
    "for r in floor_table:\n",
    "    print(f\"  {r['changes']:3d} changes  clicks {r['clicks_median_band_width_pct']:5.1f} points   conversions {r['conversions_median_band_width_pct']:5.1f} points\")\n",
    "print(f\"  the notebook reads a click error after {LOG_FLOOR['clicks']} logged changes and a conversion error after \"\n",
    "      f\"{LOG_FLOOR['conversions'] if LOG_FLOOR['conversions'] else 'more changes than the table holds'}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f79584c",
   "metadata": {},
   "source": [
    "## The pinned cases: what the widget must reproduce\n",
    "\n",
    "The widget carries the same replay engine and the same logs, and its self-check recomputes every\n",
    "case below from the pinned data rather than from the sliders. The expected values are computed\n",
    "from the log as stored, so what the widget sees is exactly what these were computed from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a97bd7b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T07:00:29.229418Z",
     "iopub.status.busy": "2026-10-24T07:00:29.228994Z",
     "iopub.status.idle": "2026-10-24T07:00:29.567069Z",
     "shell.execute_reply": "2026-10-24T07:00:29.566365Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pinned 20 replay cases across 4 scenarios, plus the report and its split\n"
     ]
    }
   ],
   "source": [
    "def round_trip(obj):\n",
    "    return json.loads(json.dumps(obj))\n",
    "\n",
    "\n",
    "world_rng = np.random.default_rng(SEED + 300)\n",
    "still = dict(demand_wobble=0.0, competition_wobble=0.0)\n",
    "next_same = simulate_week(world_rng, **still)\n",
    "next_demand = simulate_week(world_rng, demand=1.25, **still)\n",
    "next_comp = simulate_week(world_rng, competition=COMPETITION * 1.2, **still)\n",
    "last_break = simulate_week(world_rng, break_day=4, break_competition=1.2, **still)\n",
    "next_break = simulate_week(world_rng, competition=COMPETITION * 1.2, **still)\n",
    "\n",
    "widget_logs = round_trip({\n",
    "    \"last_week\": summarise_log(last_week, \"observed\"),\n",
    "    \"last_week_with_change\": summarise_log(last_break, \"observed\"),\n",
    "    \"next_same\": summarise_log(next_same, \"truth\"),\n",
    "    \"next_demand\": summarise_log(next_demand, \"truth\"),\n",
    "    \"next_competition\": summarise_log(next_comp, \"truth\"),\n",
    "    \"next_after_change\": summarise_log(next_break, \"truth\"),\n",
    "})\n",
    "SCENARIOS = {\n",
    "    \"same world\": (\"last_week\", \"next_same\"),\n",
    "    \"demand up a quarter\": (\"last_week\", \"next_demand\"),\n",
    "    \"competition up a fifth\": (\"last_week\", \"next_competition\"),\n",
    "    \"a change inside the lookback\": (\"last_week_with_change\", \"next_after_change\"),\n",
    "}\n",
    "weekly = report[\"weekly_budget\"]\n",
    "pinned = []\n",
    "for scenario, (lk, nk) in SCENARIOS.items():\n",
    "    for m, budget in ((1.0, weekly), (1.3, weekly * 4), (0.8, weekly), (1.5, weekly), (2.0, weekly * 4)):\n",
    "        p = replay(widget_logs[lk], m, budget)\n",
    "        n = replay(widget_logs[nk], m, budget)\n",
    "        pinned.append({\n",
    "            \"scenario\": scenario, \"last_log\": lk, \"next_log\": nk, \"bid_multiplier\": m, \"weekly_budget\": round(budget, 2),\n",
    "            \"expected\": {\n",
    "                \"projected\": {k: round(p[k], 4) for k in (\"impressions\", \"clicks\", \"cost\", \"conversions\", \"participation\")},\n",
    "                \"next_week\": {k: round(n[k], 4) for k in (\"impressions\", \"clicks\", \"cost\", \"conversions\", \"participation\")},\n",
    "                \"projected_margin\": round(margin_of(p, VALUE_PER_CONVERSION), 4),\n",
    "                \"next_week_margin\": round(margin_of(n, VALUE_PER_CONVERSION), 4),\n",
    "            },\n",
    "        })\n",
    "report_pinned = round_trip({k: report[k] for k in (\"impressions\", \"clicks\", \"conversions\", \"cost\", \"impression_share\",\n",
    "                                                    \"lost_is_budget\", \"lost_is_rank\", \"ctr\", \"cvr\", \"value_per_conversion\",\n",
    "                                                    \"weekly_budget\", \"daily_budget\")})\n",
    "fork_pinned = {k: round(v, 4) if isinstance(v, float) else v for k, v in fork_reading(report_pinned).items()}\n",
    "checks = {\n",
    "    \"method\": \"opportunity framing from the impression-share split (won + lost to budget + lost to rank = the eligible pool; \"\n",
    "              \"the naive and the win-share-corrected count of what a bigger budget wins) and a replay of last week's \"\n",
    "              \"auction log at a new bid and budget (impressions = auctions with a price to beat at or below the bid, clicks \"\n",
    "              \"= impressions times the hourly click rate, cost = the prices to beat of the clicked auctions, conversions = \"\n",
    "              \"clicks times the conversion rate (pooled in a learned log, per hour in a truth log), all scaled by the share of auctions the budget lets the campaign into)\",\n",
    "    \"tolerance\": 1e-3,\n",
    "    \"constants\": {\"hours\": HOURS, \"days\": DAYS, \"bid_steps\": BID_STEPS, \"value_per_conversion\": VALUE_PER_CONVERSION,\n",
    "                  \"log_floor_changes\": LOG_FLOOR},\n",
    "    \"report\": report_pinned,\n",
    "    \"fork_expected\": fork_pinned,\n",
    "    \"logs\": widget_logs,\n",
    "    \"replay_cases\": pinned,\n",
    "}\n",
    "(DATA / \"checks.json\").write_text(json.dumps(checks, indent=2), encoding=\"utf-8\")\n",
    "print(f\"pinned {len(pinned)} replay cases across {len(SCENARIOS)} scenarios, plus the report and its split\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4c64a6a",
   "metadata": {},
   "source": [
    "## Run it on your own account\n",
    "\n",
    "Everything above ran on a synthetic campaign. This section reads your own export, on your\n",
    "machine. Nothing is uploaded anywhere. It takes either or both of two files.\n",
    "\n",
    "**A campaign export, one line per day**, from the Google Ads interface (Campaigns, with the\n",
    "competitive-metrics columns added, downloaded as CSV) or from the API. The notebook reads\n",
    "either shape: the interface download with its title lines, \"Total\" rows and percentages, or a\n",
    "tidy file with these columns:\n",
    "\n",
    "| column | what it holds | example |\n",
    "|---|---|---|\n",
    "| `date` | the day | `2026-08-03` |\n",
    "| `impressions` | impressions that day | `4988` |\n",
    "| `clicks` | clicks | `182` |\n",
    "| `cost` | spend, in your currency | `149.20` |\n",
    "| `conversions` | conversions | `7` |\n",
    "| `search_impression_share` | impression share, as a fraction or a percentage | `0.43` or `43%` |\n",
    "| `search_budget_lost_impression_share` | share lost to budget | `0.28` |\n",
    "| `search_rank_lost_impression_share` | share lost to rank | `0.29` |\n",
    "| `daily_budget` | optional: the average daily budget that day | `150` |\n",
    "\n",
    "A share the platform has clamped (`< 10%`, `> 90%`, or the API's `0.0999` and `0.9001`) is\n",
    "read as what it is: a floor or a ceiling, not a value, and the reading says so.\n",
    "\n",
    "**A projection log, one line per change you acted on**, kept by hand:\n",
    "\n",
    "| column | what it holds | example |\n",
    "|---|---|---|\n",
    "| `change_date` | the day the change went live | `2026-07-14` |\n",
    "| `what_changed` | a note: bid, budget, target | `budget 150 to 220` |\n",
    "| `projected_clicks` | what the simulator projected for the window after | `1620` |\n",
    "| `projected_conversions` | its projected conversions | `64` |\n",
    "| `actual_clicks` | what happened in a window the same length as the simulator's | `1544` |\n",
    "| `actual_conversions` | actual conversions in that window | `51` |\n",
    "| `change_in_lookback` | optional: `yes` if something else changed in the week before the projection | `no` |\n",
    "\n",
    "Optional inputs: the value of a conversion, for the money lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f623e546",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T07:00:29.583646Z",
     "iopub.status.busy": "2026-10-24T07:00:29.583272Z",
     "iopub.status.idle": "2026-10-24T07:00:29.590490Z",
     "shell.execute_reply": "2026-10-24T07:00:29.589315Z"
    }
   },
   "outputs": [],
   "source": [
    "# ---- your inputs ----------------------------------------------------------------------------\n",
    "YOUR_CAMPAIGN_EXPORT = None       # e.g. Path(\"campaign_daily.csv\"); None runs the demo below\n",
    "YOUR_PROJECTION_LOG = None        # e.g. Path(\"projections.csv\")\n",
    "YOUR_VALUE_PER_CONVERSION = None  # e.g. 80.0\n",
    "YOUR_CAMPAIGN = None             # the campaign to read, if the download holds several"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "008b3dd7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T07:00:29.604330Z",
     "iopub.status.busy": "2026-10-24T07:00:29.603661Z",
     "iopub.status.idle": "2026-10-24T07:00:29.754437Z",
     "shell.execute_reply": "2026-10-24T07:00:29.753581Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no file set - the demo below runs on exports written from the synthetic campaign\n"
     ]
    }
   ],
   "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 YourDataError(Exception):\n",
    "    \"\"\"A plain-words refusal. Raised instead of printing a number the data cannot support.\"\"\"\n",
    "\n",
    "\n",
    "COLUMN_ALIASES = {\n",
    "    \"date\": [\"date\", \"day\", \"segments.date\"],\n",
    "    \"impressions\": [\"impressions\", \"impr.\", \"impr\", \"metrics.impressions\"],\n",
    "    \"clicks\": [\"clicks\", \"metrics.clicks\"],\n",
    "    \"cost\": [\"cost\", \"spend\", \"metrics.cost_micros\", \"cost_micros\"],\n",
    "    \"conversions\": [\"conversions\", \"conv.\", \"metrics.conversions\"],\n",
    "    \"search_impression_share\": [\"search_impression_share\", \"search impr. share\", \"search impression share\", \"metrics.search_impression_share\", \"impression share\"],\n",
    "    \"search_budget_lost_impression_share\": [\"search_budget_lost_impression_share\", \"search lost is (budget)\", \"search lost impression share (budget)\", \"metrics.search_budget_lost_impression_share\", \"lost is (budget)\"],\n",
    "    \"search_rank_lost_impression_share\": [\"search_rank_lost_impression_share\", \"search lost is (rank)\", \"search lost impression share (rank)\", \"metrics.search_rank_lost_impression_share\", \"lost is (rank)\"],\n",
    "    \"daily_budget\": [\"daily_budget\", \"budget\", \"avg. daily budget\", \"average daily budget\", \"campaign_budget.amount_micros\"],\n",
    "}\n",
    "\n",
    "\n",
    "def _settle_bounds(v, bound, kind, others):\n",
    "    \"\"\"A share the platform shows only as a limit. Impression share '> 90%' is at least 0.9, a lost share\n",
    "    '< 10%' at most 0.1: where the other two shares pin the value inside its limit, take that; otherwise\n",
    "    the middle of the limit's range. Impression share '< 10%' and a lost share '> 90%' stay at the limit,\n",
    "    the reading the warnings below describe.\"\"\"\n",
    "    if bound is None:\n",
    "        return v\n",
    "    rest = 1.0 - sum(others)\n",
    "    if kind == \"is\" and bound == \">\":\n",
    "        return rest if 0.9 <= rest <= 1.0 else 0.95\n",
    "    if kind != \"is\" and bound == \"<\":\n",
    "        return rest if 0.0 <= rest <= 0.1 else 0.05\n",
    "    return v\n",
    "\n",
    "\n",
    "def read_campaign_export(path, min_days=7, campaign=None):\n",
    "    \"\"\"Read a daily campaign export - interface download, European download, .xlsx or API file -\n",
    "    into the report shape. Title lines, Total rows, formatted numbers and dates are handled by the\n",
    "    shared reader.\"\"\"\n",
    "    path = Path(path)\n",
    "    required = {k: COLUMN_ALIASES[k] for k in (\"date\", \"impressions\", \"clicks\", \"cost\", \"conversions\",\n",
    "                                                 \"search_impression_share\",\n",
    "                                                 \"search_budget_lost_impression_share\",\n",
    "                                                 \"search_rank_lost_impression_share\")}\n",
    "    try:\n",
    "        frame, notes = read_export(path, columns=required, date_columns=[\"date\"])\n",
    "    except ExportReadError as problem:\n",
    "        raise YourDataError(str(problem)) from None\n",
    "    for note in notes:\n",
    "        print(\"  \" + note)\n",
    "    campaign_col = next((c for c in frame.columns if str(c).strip().lower() in (\"campaign\", \"campaign.name\")), None)\n",
    "    if campaign_col is not None:\n",
    "        names = sorted(frame[campaign_col].dropna().astype(str).unique())\n",
    "        if campaign:\n",
    "            frame = frame[frame[campaign_col].astype(str) == campaign]\n",
    "            if frame.empty:\n",
    "                raise YourDataError(f\"no rows for the campaign {campaign!r}; the file holds {names}\")\n",
    "        elif len(names) > 1:\n",
    "            raise YourDataError(\n",
    "                f\"the file holds {len(names)} campaigns ({names[:6]}). The split is read for one campaign at a \"\n",
    "                \"time - a brand campaign's shares say nothing about a generic one's - so set YOUR_CAMPAIGN to \"\n",
    "                \"the one you mean.\")\n",
    "    budget_col = next((c for c in frame.columns if str(c).strip().lower() in COLUMN_ALIASES[\"daily_budget\"]), None)\n",
    "    # The API reports cost in micros (millionths): a cost per click in the thousands is that, not money.\n",
    "    micros = float(frame[\"cost\"].median()) / max(float(frame[\"clicks\"].median()), 1.0) > 1000.0\n",
    "    rows = []\n",
    "    for _, r in frame.iterrows():\n",
    "        is_v = r[\"search_impression_share\"]\n",
    "        if pd.isna(is_v):\n",
    "            continue\n",
    "        is_b = r.get(\"search_impression_share_bound\")\n",
    "        lb_b = r.get(\"search_budget_lost_impression_share_bound\")\n",
    "        lr_b = r.get(\"search_rank_lost_impression_share_bound\")\n",
    "        lb_v = 0.0 if pd.isna(r[\"search_budget_lost_impression_share\"]) else float(r[\"search_budget_lost_impression_share\"])\n",
    "        lr_v = 0.0 if pd.isna(r[\"search_rank_lost_impression_share\"]) else float(r[\"search_rank_lost_impression_share\"])\n",
    "        is_v = float(is_v)\n",
    "        # the API writes the platform's limits as numbers: 0.0999 under ten per cent, 0.9001 over ninety\n",
    "        if is_b is None and abs(is_v - 0.0999) < 1e-9: is_b = \"<\"\n",
    "        if lb_b is None and abs(lb_v - 0.9001) < 1e-9: lb_b = \">\"\n",
    "        if lr_b is None and abs(lr_v - 0.9001) < 1e-9: lr_b = \">\"\n",
    "        is_v = _settle_bounds(is_v, is_b, \"is\", (lb_v, lr_v))\n",
    "        lb_v = _settle_bounds(lb_v, lb_b, \"lb\", (is_v, lr_v))\n",
    "        lr_v = _settle_bounds(lr_v, lr_b, \"lr\", (is_v, lb_v))\n",
    "        cost = float(r[\"cost\"]) / (1e6 if micros else 1.0)\n",
    "        budget = None\n",
    "        if budget_col is not None and not pd.isna(r[budget_col]):\n",
    "            b = float(r[budget_col])\n",
    "            budget = b / 1e6 if \"micros\" in str(budget_col).lower() else b\n",
    "        rows.append({\n",
    "            \"date\": str(pd.Timestamp(r[\"date\"]).date()), \"impressions\": int(r[\"impressions\"]),\n",
    "            \"clicks\": int(r[\"clicks\"]), \"cost\": cost, \"conversions\": float(r[\"conversions\"]),\n",
    "            \"is\": is_v, \"is_clamped\": is_b is not None, \"is_bound\": is_b,\n",
    "            \"lb\": lb_v, \"lb_clamped\": lb_b is not None, \"lb_bound\": lb_b,\n",
    "            \"lr\": lr_v, \"lr_clamped\": lr_b is not None, \"lr_bound\": lr_b,\n",
    "            \"daily_budget\": budget,\n",
    "        })\n",
    "    if len(rows) < min_days:\n",
    "        raise YourDataError(f\"{path.name} holds {len(rows)} day(s) with an impression share; the reading needs at least \"\n",
    "                            f\"{min_days}, because a single day's shares swing with the weekday and the platform updates them \"\n",
    "                            f\"with a lag of a day or two.\")\n",
    "    return pd.DataFrame(rows)\n",
    "\n",
    "\n",
    "def your_campaign_reading(days, value=None):\n",
    "    \"\"\"The three-way split over the window, the clamp warnings, and the arithmetic the report supports.\"\"\"\n",
    "    impressions = int(days.impressions.sum())\n",
    "    clicks = int(days.clicks.sum())\n",
    "    conversions = float(days.conversions.sum())\n",
    "    cost = float(days.cost.sum())\n",
    "    # the window's shares, weighted by each day's eligible pool (impressions / share)\n",
    "    elig = days.impressions / days[\"is\"]\n",
    "    total_elig = float(elig.sum())\n",
    "    is_share = impressions / total_elig\n",
    "    lost_budget = float((elig * days.lb).sum() / total_elig)\n",
    "    lost_rank = float((elig * days.lr).sum() / total_elig)\n",
    "    warnings_out = []\n",
    "    def _count(col, bound):\n",
    "        return int((days[col] == bound).sum()) if col in days else 0\n",
    "    if _count(\"is_bound\", \"<\"):\n",
    "        warnings_out.append(f\"the impression share is clamped low on {_count('is_bound', '<')} day(s): the platform only says it was under \"\n",
    "                            \"10 per cent, so the eligible pool for those days is a floor and the lost shares read against it are ceilings.\")\n",
    "    if _count(\"is_bound\", \">\"):\n",
    "        warnings_out.append(f\"the impression share is clamped high on {_count('is_bound', '>')} day(s): the platform only says it was over \"\n",
    "                            \"90 per cent - a campaign showing almost everywhere it could; its lost shares are small and read as such.\")\n",
    "    if _count(\"lb_bound\", \">\"):\n",
    "        warnings_out.append(f\"the share lost to budget is clamped high on {_count('lb_bound', '>')} day(s): the platform only says it was \"\n",
    "                            \"over 90 per cent, so the budget headroom is at least what is shown and could be much more.\")\n",
    "    if _count(\"lb_bound\", \"<\"):\n",
    "        warnings_out.append(f\"the share lost to budget shows as under 10 per cent on {_count('lb_bound', '<')} day(s): read as small, \"\n",
    "                            \"and those days are not counted as budget-capped.\")\n",
    "    if _count(\"lr_bound\", \">\"):\n",
    "        warnings_out.append(f\"the share lost to rank is clamped high on {_count('lr_bound', '>')} day(s): read it as 'over 90 per cent'.\")\n",
    "    if _count(\"lr_bound\", \"<\"):\n",
    "        warnings_out.append(f\"the share lost to rank shows as under 10 per cent on {_count('lr_bound', '<')} day(s): read as small.\")\n",
    "    sums = days[\"is\"] + days.lb + days.lr\n",
    "    if (sums < 0.95).any() or (sums > 1.05).any():\n",
    "        warnings_out.append(f\"on {int(((sums < 0.95) | (sums > 1.05)).sum())} day(s) the three shares do not add up to one - a clamped \"\n",
    "                            \"value, a partial-day update or a mixed campaign type; the split for those days is not exact.\")\n",
    "    days_limited = int(((days.lb >= 0.05) & (days.get(\"lb_bound\", pd.Series([None] * len(days))) != \"<\")).sum())\n",
    "    utilisation = None\n",
    "    if days.daily_budget.notna().all() and (days.daily_budget > 0).all():\n",
    "        utilisation = cost / float(days.daily_budget.sum())\n",
    "    rep = {\n",
    "        \"days\": int(len(days)), \"impressions\": impressions, \"clicks\": clicks, \"conversions\": conversions, \"cost\": round(cost, 2),\n",
    "        \"impression_share\": round(is_share, 4), \"lost_is_budget\": round(lost_budget, 4), \"lost_is_rank\": round(lost_rank, 4),\n",
    "        \"ctr\": clicks / impressions if impressions else 0.0, \"cvr\": conversions / clicks if clicks else 0.0,\n",
    "        \"cpa\": (cost / conversions) if conversions else None, \"days_limited_by_budget\": days_limited,\n",
    "        \"budget_utilisation\": round(utilisation, 4) if utilisation is not None else None,\n",
    "        \"value_per_conversion\": value,\n",
    "    }\n",
    "    f = fork_reading({**rep, \"value_per_conversion\": value or 0.0})\n",
    "    lines = [\n",
    "        f\"over {rep['days']} days: {impressions:,} impressions, {clicks:,} clicks, {conversions:,.0f} conversions, {cost:,.2f} spent\",\n",
    "        f\"of the auctions you were eligible for you showed in {is_share * 100:.0f}%, the budget kept you out of {lost_budget * 100:.0f}% \"\n",
    "        f\"and you were outbid in {lost_rank * 100:.0f}%\",\n",
    "        f\"the binding constraint reads as {f['binding_constraint']}; the budget capped delivery on {days_limited} of {rep['days']} days\",\n",
    "    ]\n",
    "    both_small = (\"lb_bound\" in days and \"lr_bound\" in days and (days.lb_bound == \"<\").all()\n",
    "                  and (days.lr_bound == \"<\").all())\n",
    "    if both_small:\n",
    "        lines[1] = (\"you showed in over 90% of the auctions you were eligible for; the platform says only that the \"\n",
    "                    \"budget and rank each kept you out of under 10%\")\n",
    "        lines[2] = \"neither constraint binds: both lost shares are under ten per cent, so there is little headroom to buy\"\n",
    "        return rep, f, warnings_out, lines\n",
    "    if lost_budget >= 0.05:\n",
    "        lines.append(f\"a bigger budget would have entered you into about {f['lost_budget_impressions']:,.0f} more auctions, of which you \"\n",
    "                     f\"would have won your usual share, {f['win_share'] * 100:.0f}% - about {f['accounting_budget_impressions']:,.0f} impressions, \"\n",
    "                     f\"not {f['lost_budget_impressions']:,.0f}; at your usual rates that is about {f['accounting_budget_conversions']:.0f} conversions, \"\n",
    "                     f\"at about your usual cost per conversion, if the auctions the budget kept you out of look like the ones it let you into\")\n",
    "    if lost_rank >= 0.05:\n",
    "        lines.append(f\"the {f['lost_rank_impressions']:,.0f} impressions lost to rank are only reachable with higher bids, and the ones you would \"\n",
    "                     f\"gain are the ones that cost more than you pay now: no count and no cost for them can be read off this report\")\n",
    "    if lost_budget >= 0.05 and lost_rank >= 0.05:\n",
    "        lines.append(\"read the budget leak first: while the budget caps delivery, the rank figure understates how often you are outbid, and \"\n",
    "                     \"raising bids under a capped budget buys fewer, dearer clicks\")\n",
    "    if value:\n",
    "        lines.append(f\"at {value:,.0f} per conversion the budget headroom is worth about {f['accounting_budget_conversions'] * value:,.0f} \"\n",
    "                     f\"in conversion value a window this long, before its cost\")\n",
    "    return rep, f, warnings_out, lines\n",
    "\n",
    "\n",
    "def read_projection_log(path):\n",
    "    path = Path(path)\n",
    "    if not path.exists():\n",
    "        raise YourDataError(f\"there is no file at {path}\")\n",
    "    frame = pd.read_csv(path, encoding=\"utf-8-sig\")\n",
    "    frame.columns = [c.strip().lower() for c in frame.columns]\n",
    "    needed = [\"change_date\", \"projected_clicks\", \"projected_conversions\", \"actual_clicks\", \"actual_conversions\"]\n",
    "    missing = [c for c in needed if c not in frame.columns]\n",
    "    if missing:\n",
    "        raise YourDataError(f\"{path.name} is missing the column(s) {missing}. One line per change you acted on: the date, what the \"\n",
    "                            f\"simulator projected for clicks and conversions, and what actually happened over a window the same length.\")\n",
    "    frame = frame.dropna(subset=needed)\n",
    "    for c in needed[1:]:\n",
    "        frame[c] = pd.to_numeric(frame[c], errors=\"coerce\")\n",
    "    frame = frame.dropna(subset=needed)\n",
    "    no_clicks = frame.actual_clicks <= 0\n",
    "    if no_clicks.any():\n",
    "        print(f\"  left out {int(no_clicks.sum())} change(s) with no actual clicks (lines \"\n",
    "              f\"{[int(i) + 2 for i in frame.index[no_clicks]]}): there is no error to compute\")\n",
    "        frame = frame[~no_clicks]\n",
    "    no_conv = frame.actual_conversions <= 0\n",
    "    if no_conv.any():\n",
    "        print(f\"  {int(no_conv.sum())} change(s) had no actual conversions (lines \"\n",
    "              f\"{[int(i) + 2 for i in frame.index[no_conv]]}): their click error is scored, their \"\n",
    "              \"conversion error is left out\")\n",
    "    return frame\n",
    "\n",
    "\n",
    "def your_projection_reading(log, floor=None):\n",
    "    floor = LOG_FLOOR if floor is None else floor\n",
    "    e_clicks = (log.projected_clicks - log.actual_clicks) / log.actual_clicks * 100\n",
    "    e_conv = ((log.projected_conversions - log.actual_conversions)\n",
    "              / log.actual_conversions.where(log.actual_conversions > 0) * 100)\n",
    "    per_change = pd.DataFrame({\"change_date\": log.change_date, \"error_clicks_pct\": e_clicks.round(1), \"error_conversions_pct\": e_conv.round(1)})\n",
    "    if \"change_in_lookback\" in log.columns:\n",
    "        per_change[\"change_in_lookback\"] = log.change_in_lookback.astype(str).str.lower().str.startswith(\"y\")\n",
    "    n = len(log)\n",
    "    out = {\"changes\": int(n), \"per_change\": per_change, \"verdict\": \"\"}\n",
    "    parts = []\n",
    "    if floor[\"clicks\"] is not None and n >= floor[\"clicks\"]:\n",
    "        out[\"median_clicks_pct\"] = round(float(e_clicks.median()), 1)\n",
    "        out[\"abs_median_clicks_pct\"] = round(float(e_clicks.abs().median()), 1)\n",
    "        parts.append(f\"clicks: over {n} changes the projections ran {abs(out['median_clicks_pct']):.0f}% \"\n",
    "                     f\"{'high' if out['median_clicks_pct'] > 0 else 'low'} on a typical change and were off by \"\n",
    "                     f\"{out['abs_median_clicks_pct']:.0f}% either way in the middle case\")\n",
    "    else:\n",
    "        parts.append(f\"clicks: {n} logged change(s) is below the floor of {floor['clicks']} - a typical error from so few \"\n",
    "                     f\"changes is mostly luck, so the per-change errors are shown and no trust reading is given\")\n",
    "    if floor[\"conversions\"] is not None and n >= floor[\"conversions\"]:\n",
    "        out[\"median_conversions_pct\"] = round(float(e_conv.median()), 1)\n",
    "        out[\"abs_median_conversions_pct\"] = round(float(e_conv.abs().median()), 1)\n",
    "        parts.append(f\"conversions: the projections ran {abs(out['median_conversions_pct']):.0f}% \"\n",
    "                     f\"{'high' if out['median_conversions_pct'] > 0 else 'low'} on a typical change and were off by \"\n",
    "                     f\"{out['abs_median_conversions_pct']:.0f}% either way in the middle case\")\n",
    "    else:\n",
    "        need = floor[\"conversions\"] if floor[\"conversions\"] else \"more changes than most accounts make in a year\"\n",
    "        parts.append(f\"conversions: a conversion-error reading needs {need} logged changes, because a week's conversions are \"\n",
    "                     f\"few and the projection inherits their luck; read the conversion figure with the margin the \"\n",
    "                     f\"synthetic population showed, and trust the click figure more than the conversion one\")\n",
    "    if \"change_in_lookback\" in per_change.columns and per_change.change_in_lookback.any():\n",
    "        sub = per_change[per_change.change_in_lookback]\n",
    "        parts.append(f\"{len(sub)} of the changes had something else change inside the simulator's lookback; their conversion \"\n",
    "                     f\"errors were {', '.join(f'{v:+.0f}%' for v in sub.error_conversions_pct)} - the projection describes the \"\n",
    "                     f\"auction as it was, not as it is, in those cases\")\n",
    "    out[\"verdict\"] = \". \".join(parts) + \".\"\n",
    "    return out\n",
    "\n",
    "\n",
    "if YOUR_CAMPAIGN_EXPORT is not None:\n",
    "    your_days = read_campaign_export(YOUR_CAMPAIGN_EXPORT, campaign=YOUR_CAMPAIGN)\n",
    "    your_rep, your_fork, your_warnings, your_lines = your_campaign_reading(your_days, YOUR_VALUE_PER_CONVERSION)\n",
    "    for w_ in your_warnings:\n",
    "        print(\"WARNING:\", w_)\n",
    "    for line in your_lines:\n",
    "        print(line)\n",
    "if YOUR_PROJECTION_LOG is not None:\n",
    "    your_log = your_projection_reading(read_projection_log(YOUR_PROJECTION_LOG))\n",
    "    print(your_log[\"per_change\"].to_string(index=False))\n",
    "    print(your_log[\"verdict\"])\n",
    "if YOUR_CAMPAIGN_EXPORT is None and YOUR_PROJECTION_LOG is None:\n",
    "    print(\"no file set - the demo below runs on exports written from the synthetic campaign\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0505a9e1",
   "metadata": {},
   "source": [
    "### Exercised twice, on data whose answers are known\n",
    "\n",
    "Before trusting the loaders, run them on exports written from the campaign above: a tidy daily\n",
    "export must reproduce the worked week's split, the interface-shaped download of the same week,\n",
    "with its title lines, percentages and Total row, must read to the same numbers, a campaign whose budget\n",
    "leak is past the platform's ceiling must warn that the figure is a floor, a thin export and a\n",
    "thin log must be refused, and a file with the wrong columns must fail in plain words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2c90e86e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T07:00:29.771051Z",
     "iopub.status.busy": "2026-10-24T07:00:29.768592Z",
     "iopub.status.idle": "2026-10-24T07:00:30.070481Z",
     "shell.execute_reply": "2026-10-24T07:00:30.069274Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tidy export\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  split from the export: shown 0.468, budget 0.311, rank 0.222 - same as the worked week: True\n",
      "  over 7 days: 43,996 impressions, 1,783 clicks, 75 conversions, 1,424.78 spent\n",
      "  of the auctions you were eligible for you showed in 47%, the budget kept you out of 31% and you were outbid in 22%\n",
      "  the binding constraint reads as budget; the budget capped delivery on 7 of 7 days\n",
      "  a bigger budget would have entered you into about 29,233 more auctions, of which you would have won your usual share, 68% - about 19,831 impressions, not 29,233; at your usual rates that is about 34 conversions, at about your usual cost per conversion, if the auctions the budget kept you out of look like the ones it let you into\n",
      "  the 20,860 impressions lost to rank are only reachable with higher bids, and the ones you would gain are the ones that cost more than you pay now: no count and no cost for them can be read off this report\n",
      "  read the budget leak first: while the budget caps delivery, the rank figure understates how often you are outbid, and raising bids under a capped budget buys fewer, dearer clicks\n",
      "  at 32 per conversion the budget headroom is worth about 1,082 in conversion value a window this long, before its cost\n",
      "\n",
      "interface download\n",
      "  set aside 2 line(s) above the header (a report title or date range).\n",
      "  set aside 1 total row(s).\n",
      "  7 days read past the title lines and the Total row; same split as the tidy file: True\n",
      "\n",
      "clamped campaign\n",
      "  'search_impression_share' has values the platform shows only as a limit (for example '< 10%'); kept as the limit, with the direction in 'search_impression_share_bound'.\n",
      "  'search_budget_lost_impression_share' has values the platform shows only as a limit (for example '> 90%'); kept as the limit, with the direction in 'search_budget_lost_impression_share_bound'.\n",
      "  WARNING: the impression share is clamped low on 7 day(s): the platform only says it was under 10 per cent, so the eligible pool for those days is a floor and the lost shares read against it are ceilings.\n",
      "  WARNING: the share lost to budget is clamped high on 7 day(s): the platform only says it was over 90 per cent, so the budget headroom is at least what is shown and could be much more.\n",
      "  of the auctions you were eligible for you showed in 10%, the budget kept you out of 90% and you were outbid in 2%\n",
      "\n",
      "thin export\n",
      "  refused in plain words: campaign_daily_thin.csv holds 3 day(s) with an impression share; the reading needs at least 7, because a single day's shares swing with the weekday and the platform updates them with a lag of a day or two.\n",
      "\n",
      "broken export\n",
      "  refused in plain words: Could not find the column(s) cost, conversions, search_impression_share, search_budget_lost_impression_share, search_rank_lost_impression_share. The file's columns are 'date', 'impressions', 'clicks'. Rename the column in the file, or add its name to the list this cell accepts.\n",
      "\n",
      "projection log\n",
      "  clicks: over 12 changes the projections ran 2% low on a typical change and were off by 7% either way in the middle case. conversions: a conversion-error reading needs 30 logged changes, because a week's conversions are few and the projection inherits their luck; read the conversion figure with the margin the synthetic population showed, and trust the click figure more than the conversion one. 1 of the changes had something else change inside the simulator's lookback; their conversion errors were -17% - the projection describes the auction as it was, not as it is, in those cases.\n",
      "\n",
      "thin projection log\n",
      "  clicks: 3 logged change(s) is below the floor of 8 - a typical error from so few changes is mostly luck, so the per-change errors are shown and no trust reading is given. conversions: a conversion-error reading needs 30 logged changes, because a week's conversions are few and the projection inherits their luck; read the conversion figure with the margin the synthetic population showed, and trust the click figure more than the conversion one.\n"
     ]
    }
   ],
   "source": [
    "DEMO = HERE / \"_your_data_demo\"\n",
    "DEMO.mkdir(exist_ok=True)\n",
    "\n",
    "# a tidy daily export of the worked week, each day carrying the week's shares (the pacer holds\n",
    "# them level by construction), with a little day-to-day wobble on the counts as an export would\n",
    "dates = pd.date_range(\"2026-08-03\", periods=DAYS, freq=\"D\")\n",
    "day_share = WEEKDAY / WEEKDAY.sum()\n",
    "tidy = pd.DataFrame({\n",
    "    \"date\": dates.strftime(\"%Y-%m-%d\"),\n",
    "    \"impressions\": np.round(report[\"impressions\"] * day_share).astype(int),\n",
    "    \"clicks\": np.round(report[\"clicks\"] * day_share).astype(int),\n",
    "    \"cost\": np.round(report[\"cost\"] * day_share, 2),\n",
    "    \"conversions\": np.round(report[\"conversions\"] * day_share).astype(int),\n",
    "    \"search_impression_share\": report[\"impression_share\"],\n",
    "    \"search_budget_lost_impression_share\": report[\"lost_is_budget\"],\n",
    "    \"search_rank_lost_impression_share\": report[\"lost_is_rank\"],\n",
    "    \"daily_budget\": DAILY_BUDGET,\n",
    "})\n",
    "tidy.to_csv(DEMO / \"campaign_daily.csv\", index=False)\n",
    "\n",
    "# the same week as the interface writes it\n",
    "ui_lines = [\"Campaign report\", \"August 3, 2026 - August 9, 2026\", \"\"]\n",
    "ui_lines.append(\"Day,Campaign,Impr.,Clicks,Cost,Conversions,Search impr. share,Search lost IS (budget),Search lost IS (rank),Avg. daily budget\")\n",
    "for _, r in tidy.iterrows():\n",
    "    ui_lines.append(f\"{r.date},Brand - generic search,{r.impressions},{r.clicks},{r.cost:.2f},{r.conversions},\"\n",
    "                    f\"{r.search_impression_share * 100:.2f}%,{r.search_budget_lost_impression_share * 100:.2f}%,\"\n",
    "                    f\"{r.search_rank_lost_impression_share * 100:.2f}%,{r.daily_budget:.2f}\")\n",
    "ui_lines.append(f\"Total: Campaign,,{tidy.impressions.sum()},{tidy.clicks.sum()},{tidy.cost.sum():.2f},{tidy.conversions.sum()},\"\n",
    "                f\"{report['impression_share'] * 100:.2f}%,{report['lost_is_budget'] * 100:.2f}%,{report['lost_is_rank'] * 100:.2f}%,\")\n",
    "(DEMO / \"campaign_daily_ui.csv\").write_text(\"\\n\".join(ui_lines) + \"\\n\", encoding=\"utf-8\")\n",
    "\n",
    "# a campaign whose budget leak is past the ceiling: the platform reports > 90%\n",
    "clamped_week = simulate_week(np.random.default_rng(SEED + 400), daily_budget=DAILY_BUDGET * 0.08)\n",
    "clamped_rep = report_week(clamped_week)\n",
    "clamped = tidy.copy()\n",
    "clamped[\"impressions\"] = np.round(clamped_rep[\"impressions\"] * day_share).astype(int)\n",
    "clamped[\"clicks\"] = np.round(clamped_rep[\"clicks\"] * day_share).astype(int)\n",
    "clamped[\"cost\"] = np.round(clamped_rep[\"cost\"] * day_share, 2)\n",
    "clamped[\"conversions\"] = np.round(clamped_rep[\"conversions\"] * day_share).astype(int)\n",
    "clamped[\"search_impression_share\"] = f\"{clamped_rep['impression_share_reported'] * 100:.2f}%\" if not clamped_rep[\"impression_share_reported\"] == 0.0999 else \"< 10%\"\n",
    "clamped[\"search_budget_lost_impression_share\"] = \"> 90%\" if clamped_rep[\"lost_is_budget_reported\"] == 0.9001 else f\"{clamped_rep['lost_is_budget'] * 100:.2f}%\"\n",
    "clamped[\"search_rank_lost_impression_share\"] = f\"{clamped_rep['lost_is_rank'] * 100:.2f}%\"\n",
    "clamped[\"daily_budget\"] = DAILY_BUDGET * 0.08\n",
    "clamped.to_csv(DEMO / \"campaign_daily_clamped.csv\", index=False)\n",
    "\n",
    "tidy.head(3).to_csv(DEMO / \"campaign_daily_thin.csv\", index=False)\n",
    "(DEMO / \"broken.csv\").write_text(\"date,impressions,clicks\\n2026-08-03,100,4\\n\", encoding=\"utf-8\")\n",
    "\n",
    "# a projection log with the population's own errors in it\n",
    "log_rng = np.random.default_rng(SEED + 500)\n",
    "sample = pop[pop.world == \"same world\"].sample(12, random_state=7)\n",
    "plog = pd.DataFrame({\n",
    "    \"change_date\": pd.date_range(\"2026-03-02\", periods=12, freq=\"14D\").strftime(\"%Y-%m-%d\"),\n",
    "    \"what_changed\": [\"bid +30%\"] * 12,\n",
    "    \"actual_clicks\": log_rng.integers(300, 3000, 12),\n",
    "    \"actual_conversions\": log_rng.integers(8, 120, 12),\n",
    "})\n",
    "plog[\"projected_clicks\"] = np.round(plog.actual_clicks * (1 + sample.err_clicks.to_numpy() / 100)).astype(int)\n",
    "plog[\"projected_conversions\"] = np.round(plog.actual_conversions * (1 + sample.err_conversions.to_numpy() / 100)).astype(int)\n",
    "plog[\"change_in_lookback\"] = [\"no\"] * 10 + [\"yes\", \"no\"]\n",
    "plog.to_csv(DEMO / \"projections.csv\", index=False)\n",
    "plog.head(3).to_csv(DEMO / \"projections_thin.csv\", index=False)\n",
    "\n",
    "print(\"tidy export\")\n",
    "tidy_days = read_campaign_export(DEMO / \"campaign_daily.csv\")\n",
    "tidy_rep, tidy_fork, tidy_warn, tidy_lines = your_campaign_reading(tidy_days, VALUE_PER_CONVERSION)\n",
    "agrees = (abs(tidy_rep[\"impression_share\"] - report[\"impression_share\"]) < 1e-3\n",
    "          and abs(tidy_rep[\"lost_is_budget\"] - report[\"lost_is_budget\"]) < 1e-3\n",
    "          and abs(tidy_rep[\"lost_is_rank\"] - report[\"lost_is_rank\"]) < 1e-3)\n",
    "print(f\"  split from the export: shown {tidy_rep['impression_share']:.3f}, budget {tidy_rep['lost_is_budget']:.3f}, rank {tidy_rep['lost_is_rank']:.3f} \"\n",
    "      f\"- same as the worked week: {agrees}\")\n",
    "for line in tidy_lines:\n",
    "    print(\"  \" + line)\n",
    "print(\"\\ninterface download\")\n",
    "ui_days = read_campaign_export(DEMO / \"campaign_daily_ui.csv\")\n",
    "ui_rep, _, ui_warn, _ = your_campaign_reading(ui_days)\n",
    "ui_agrees = (ui_rep[\"impressions\"] == tidy_rep[\"impressions\"] and abs(ui_rep[\"impression_share\"] - tidy_rep[\"impression_share\"]) < 1e-3\n",
    "             and abs(ui_rep[\"lost_is_budget\"] - tidy_rep[\"lost_is_budget\"]) < 1e-3)\n",
    "print(f\"  {ui_rep['days']} days read past the title lines and the Total row; same split as the tidy file: {ui_agrees}\")\n",
    "print(\"\\nclamped campaign\")\n",
    "cl_days = read_campaign_export(DEMO / \"campaign_daily_clamped.csv\")\n",
    "cl_rep, cl_fork, cl_warn, cl_lines = your_campaign_reading(cl_days)\n",
    "for w_ in cl_warn:\n",
    "    print(\"  WARNING:\", w_)\n",
    "print(\"  \" + cl_lines[1])\n",
    "print(\"\\nthin export\")\n",
    "try:\n",
    "    read_campaign_export(DEMO / \"campaign_daily_thin.csv\")\n",
    "except YourDataError as e:\n",
    "    print(f\"  refused in plain words: {e}\")\n",
    "print(\"\\nbroken export\")\n",
    "try:\n",
    "    read_campaign_export(DEMO / \"broken.csv\")\n",
    "except YourDataError as e:\n",
    "    print(f\"  refused in plain words: {e}\")\n",
    "print(\"\\nprojection log\")\n",
    "demo_log = your_projection_reading(read_projection_log(DEMO / \"projections.csv\"))\n",
    "print(\"  \" + demo_log[\"verdict\"])\n",
    "print(\"\\nthin projection log\")\n",
    "thin_log = your_projection_reading(read_projection_log(DEMO / \"projections_thin.csv\"))\n",
    "print(\"  \" + thin_log[\"verdict\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cebad5e0",
   "metadata": {},
   "source": [
    "## The numbers this bundle publishes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "52436f09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T07:00:30.086013Z",
     "iopub.status.busy": "2026-10-24T07:00:30.084441Z",
     "iopub.status.idle": "2026-10-24T07:00:30.126387Z",
     "shell.execute_reply": "2026-10-24T07:00:30.123592Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote data/worked_example.json and data/checks.json; figures in figures/\n"
     ]
    }
   ],
   "source": [
    "worked = {\n",
    "    \"label\": \"synthetic - generated in this notebook with the answer built in\",\n",
    "    \"seed\": SEED,\n",
    "    \"campaign\": {\"bid\": BID, \"daily_budget\": DAILY_BUDGET, \"weekly_budget\": report[\"weekly_budget\"],\n",
    "                 \"value_per_conversion\": VALUE_PER_CONVERSION, \"weekly_auctions_parameter\": WEEKLY_AUCTIONS,\n",
    "                 \"eligible_cutoff\": ELIGIBLE_CUTOFF, \"price_spread\": PRICE_SPREAD, \"competition\": COMPETITION,\n",
    "                 \"ctr_parameter\": CTR, \"cvr_parameter\": CVR},\n",
    "    \"report\": {**{k: v for k, v in report.items()},\n",
    "               \"impression_share_pct\": round(report[\"impression_share\"] * 100, 2),\n",
    "               \"lost_is_budget_pct\": round(report[\"lost_is_budget\"] * 100, 2),\n",
    "               \"lost_is_rank_pct\": round(report[\"lost_is_rank\"] * 100, 2),\n",
    "               \"budget_utilisation_pct\": round(report[\"budget_utilisation\"] * 100, 2),\n",
    "               \"ctr_pct\": round(report[\"ctr\"] * 100, 2), \"cvr_pct\": round(report[\"cvr\"] * 100, 2),\n",
    "               \"shares_sum\": round(report[\"impression_share\"] + report[\"lost_is_budget\"] + report[\"lost_is_rank\"], 4)},\n",
    "    \"fork\": {**{k: (round(v, 4) if isinstance(v, float) else v) for k, v in fork.items()},\n",
    "             \"win_share_pct\": round(fork[\"win_share\"] * 100, 2),\n",
    "             \"naive_over_accounting\": round(fork[\"naive_budget_conversions\"] / fork[\"accounting_budget_conversions\"], 4)},\n",
    "    \"replay_at_own_settings\": {k: round(v, 3) for k, v in same.items() if isinstance(v, float)},\n",
    "    \"budget_lever\": budget_lever,\n",
    "    \"budget_ladder\": [{k: (round(v, 4) if isinstance(v, float) else v) for k, v in r.items()} for r in ladder_b],\n",
    "    \"bid_lever\": bid_lever,\n",
    "    \"bid_ladder\": [{k: (round(v, 4) if isinstance(v, float) else v) for k, v in r.items()} for r in ladder_r],\n",
    "    \"bid_ladder_budget_kept\": [{k: (round(v, 4) if isinstance(v, float) else v) for k, v in r.items()} for r in ladder_capped],\n",
    "    \"worked_worlds\": worked_worlds,\n",
    "    \"population\": population,\n",
    "    \"log_floor\": {\"changes\": LOG_FLOOR, \"table\": floor_table, \"target_band_width_pct\": FLOOR_BAND_TARGET},\n",
    "    \"demo_readings\": {\n",
    "        \"tidy_agrees\": bool(agrees), \"ui_agrees\": bool(ui_agrees),\n",
    "        \"clamped\": {\"lost_is_budget_true\": clamped_rep[\"lost_is_budget\"], \"reported\": clamped_rep[\"lost_is_budget_reported\"],\n",
    "                    \"warnings\": len(cl_warn)},\n",
    "        \"projection_log\": {k: v for k, v in demo_log.items() if k not in (\"per_change\",)},\n",
    "        \"thin_projection_log_refused\": \"below the floor\" in thin_log[\"verdict\"],\n",
    "        \"demo_log_reads_clicks\": \"median_clicks_pct\" in demo_log,\n",
    "    },\n",
    "    \"google_quotes\": {\n",
    "        \"lookback\": \"The bid simulators use data from the last 7 days to provide estimates.\",\n",
    "        \"what_it_is\": \"The bid simulators collect and analyze data from ad auctions while considering information such as Quality Score, keyword traffic, and competition in the ad auction. The tools use this information to estimate how your ads might have performed in terms of key metrics like cost, impressions, clicks, and conversion volume.\",\n",
    "        \"sparse\": \"There is usually less conversion data than click or impression data when calculating these estimates, so conversions can be more difficult to estimate.\",\n",
    "        \"history\": \"The longer the history and the more conversions you have, the more accurate these estimates will be.\",\n",
    "        \"budget_limited\": \"If your campaign has reached or nearly reached its average daily budget at least once in the last 7 days, the simulators may not be able to provide estimates.\",\n",
    "    },\n",
    "}\n",
    "(DATA / \"worked_example.json\").write_text(json.dumps(worked, indent=2), encoding=\"utf-8\")\n",
    "print(f\"wrote data/worked_example.json and data/checks.json; figures in {FIGURES.name}/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f2fd06",
   "metadata": {},
   "source": [
    "## What this notebook did, and what it did not\n",
    "\n",
    "It built a search campaign whose every auction is known, read it the way a platform reports it,\n",
    "and showed the three-way split is exact. It measured what the obvious arithmetic gets wrong: a\n",
    "bigger budget wins the lost auctions times the win share, not all of them, and higher bids buy the\n",
    "expensive auctions first, so the extra conversions cost more than the average ones, and buy\n",
    "nothing at all while the budget still binds. It built a replay of the kind Google describes,\n",
    "showed it reproduces the week it was built from, and scored it against the week that followed in\n",
    "four worlds: honest where the world held still, blind to demand it could not see, blind to a\n",
    "competitor's move, and half wrong in proportion when a change sat inside its own lookback. It\n",
    "measured that the conversion projection is the shaky one, and by how much on small accounts. And\n",
    "it found how many logged changes a marketer's own trust record needs before its typical error\n",
    "means anything.\n",
    "\n",
    "It did not test Google's simulator. Nobody outside Google can, except by keeping the log the last\n",
    "section scores. It did not price the rank leak without a bid change, because no report can. And\n",
    "every number in it is from a synthetic campaign; run the last section on yours."
   ]
  }
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