{
  "schema_version": "1.0",
  "study_id": "S03",
  "run_id": "S03-2f7d21cd-572e3627",
  "code_version": "2f7d21cdbdd1255931a9297ca19303f3551e421e",
  "source_sha256": {
    "research_program/__init__.py": "5abb4829b477fd3068ee4c11dad2b59cd33e90aad06a17a5e9fb2f840a75abf1",
    "research_program/evaluation.py": "09a5d060c34b8cd1e5a3330d90f5ff3e12adbca8cfe1ebe3297da2b91d227e45",
    "research_program/io.py": "16de123c16d80bce5eb8d6766224dec1729d7c00449c2528f2348d86ad68d35f",
    "research_program/validate.py": "3e6a591e2b77799bdf73683db438b2998e2e457340626c98b7ceed510ab3329e",
    "studies/S03/config.json": "10fde8c88f0e5ac9e5547d8864ffa3ab4cc59ceb10a5e4f945c08cf8a5e33eba",
    "studies/S03/ingest.py": "a685723972aa3ae1eb33c568f5a09c6184aaf04b9a3afd0ae19932b78084600f",
    "studies/S03/models.py": "3cb199b6dbf11676de63980bdc69fe5c06652ec915af2a61ab15a813bc438908",
    "studies/S03/study.py": "1f907bd37a0362a7457cb309504cc3732cee1d3de3bcc1f0f2bc2da419956c84",
    "uv.lock": "5985583acc764c8b04a4a783eace913b25f13c19b338039e96abcb1ccf7524bf"
  },
  "evaluated_on": "2026-09-29",
  "data": {
    "source_url": "https://archive.ics.uci.edu/dataset/502/online+retail+ii",
    "release": "Online Retail II, official workbook retrieved September 29, 2026",
    "sha256": "572e36277c2390fbfde10664750731e0a86f55e33470d91919085f0408e67bfb",
    "license": "CC BY 4.0",
    "citation": "Chen, D. (2012). Online Retail II [Dataset]. UCI Machine Learning Repository. doi:10.24432/C5CG6D.",
    "workbook_sha256": "bcbe73b35f5b7babf197fb0cb983a11f5d9ff929078d4aa53d171b1f2df2e980"
  },
  "target": {
    "label": "Positive purchase-day counts, probability of any future purchase and gross positive spend",
    "estimand": "Future observed purchasing among identified customers with at least 30 days of prior history",
    "prediction_cutoff": "History strictly before each March/June/September 2011 cutoff; supervised labels complete before each fit",
    "horizon": "30, 60 and 90 days; primary 90 days",
    "unit": "Customer-cutoff; repeated windows clustered by customer"
  },
  "cohort": "Three nonoverlapping final 90-day windows, beginning March 1, June 1 and September 1, 2011; first observed purchase need not be acquisition",
  "samples": {
    "total": 5878,
    "source_rows": 1067371,
    "source_purchase_days": 33107,
    "test": 14388,
    "test_unique_customers": 5155,
    "test_purchase_days": 10569,
    "test_customer_windows_with_purchase": 5310,
    "final_windows": 3,
    "all_horizon_observations": 43164
  },
  "models": [
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      "id": "recent_rule",
      "label": "Recent 90-day purchasing",
      "specification": "Frozen recent_rule; purchase-day grain, completed-label rolling fits, no current-window outcome features. Hurdle leaves 7. See protocol."
    },
    {
      "id": "rfm_rule",
      "label": "Historical RFM cohorts",
      "specification": "Frozen rfm_rule; purchase-day grain, completed-label rolling fits, no current-window outcome features. Hurdle leaves 7. See protocol."
    },
    {
      "id": "bgnbd",
      "label": "BG/NBD + Gamma-Gamma",
      "specification": "Frozen bgnbd; purchase-day grain, completed-label rolling fits, no current-window outcome features. Hurdle leaves 7. See protocol."
    },
    {
      "id": "boosting",
      "label": "Hurdle count–spend boosting",
      "specification": "Frozen boosting; purchase-day grain, completed-label rolling fits, no current-window outcome features. Hurdle leaves 7. See protocol."
    }
  ],
  "metrics": [
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    {
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    {
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    {
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    {
      "model": "bgnbd",
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      "lower": 2.36491216,
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      "split": "final_90_days"
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    {
      "model": "bgnbd",
      "name": "spend_coverage_90",
      "estimate": 0.96246872,
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      "split": "final_90_days"
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    {
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      "split": "final_90_days"
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    {
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    {
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      "split": "final_90_days"
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    {
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    {
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      "split": "final_90_days"
    },
    {
      "model": "boosting",
      "name": "spend_mae",
      "estimate": 369.54456564,
      "lower": 342.80044116,
      "upper": 406.78180068,
      "confidence": 0.95,
      "unit": "GBP per customer-cutoff",
      "split": "final_90_days"
    },
    {
      "model": "boosting",
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      "estimate": 1.22662184,
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      "confidence": 0.95,
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      "split": "final_90_days"
    },
    {
      "model": "boosting",
      "name": "count_coverage_90",
      "estimate": 0.98422296,
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      "confidence": 0.95,
      "unit": "proportion",
      "split": "final_90_days"
    },
    {
      "model": "boosting",
      "name": "count_interval_width",
      "estimate": 2.81971087,
      "lower": 2.7910031,
      "upper": 2.84922563,
      "confidence": 0.95,
      "unit": "purchase days per customer-cutoff",
      "split": "final_90_days"
    },
    {
      "model": "boosting",
      "name": "spend_coverage_90",
      "estimate": 0.9175702,
      "lower": 0.91310944,
      "upper": 0.92205104,
      "confidence": 0.95,
      "unit": "proportion",
      "split": "final_90_days"
    },
    {
      "model": "boosting",
      "name": "spend_interval_width",
      "estimate": 984.33722413,
      "lower": 910.39817634,
      "upper": 1080.53934604,
      "confidence": 0.95,
      "unit": "GBP per customer-cutoff",
      "split": "final_90_days"
    }
  ],
  "uncertainty": "1,000 paired customer-cluster bootstrap draws with all final windows retained per sampled customer; 95% percentile intervals condition on fitted models. BG/NBD predictive distributions use 2,000 draws/customer; 90% challenger residual bands use separate December calibration customers.",
  "assumptions": [
    "First observed purchase approximates the start of the customer relationship; actual acquisition is unavailable.",
    "BG/NBD latent activity is not observed churn; zero-repeat histories have model alive probability one.",
    "Gamma-Gamma assumes stable customer spend and independence from purchasing frequency.",
    "Contact cost and contribution margin are hypothetical; break-even assumes one incremental purchase occasion and does not measure campaign lift."
  ],
  "limitations": [
    "Older single-retailer giftware and wholesale behavior may not transfer to a current consumer business.",
    "Missing IDs, overlapping sheets, repeated line ambiguity and cancellations materially affect accounting.",
    "Positive-purchase spend excludes returns and is not profit or net realized revenue.",
    "Seasonality, refitting and customer dependence undermine guaranteed predictive interval coverage.",
    "Only three primary final windows are observed; unseen future shocks and parameter uncertainty remain unresolved.",
    "No randomized win-back intervention or observed permanent churn label is available.",
    "Independent technical review is pending."
  ],
  "tables": {
    "audit": {
      "raw_rows": 1067371,
      "primary_rows": 1033036,
      "multiset_rows": 1044848,
      "within_sheet_duplicates": [
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      "overlapping_distinct_rows": 22202,
      "missing_customer_rows": 235151,
      "missing_descriptions": 4275,
      "positive_purchase_days": 33107,
      "positive_customers": 5878,
      "positive_spend_gbp": 17374804.268,
      "signed_net_spend_gbp": 16289991.288,
      "source_first_date": "2009-12-01 07:45:00",
      "source_last_date": "2011-12-09 12:50:00"
    },
    "selected_leaves": 7,
    "tuning": [
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        "leaves": 7,
        "validation_90_day_deviance": 1.50118399
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      {
        "leaves": 15,
        "validation_90_day_deviance": 1.53242577
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    ],
    "interval_calibration_quantiles": {
      "30": {
        "count": 0.61775392,
        "spend": 1.0
      },
      "60": {
        "count": 0.8340784,
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      "90": {
        "count": 1.08338748,
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    },
    "calibration_observations": 5958,
    "fit_information": [
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        "cutoff": "2011-03-01",
        "history_customers": 4410,
        "supervised_training_rows": 26709,
        "latest_label_end": "2011-03-01 00:00:00",
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        "gamma_gamma_parameters": {
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        "gamma_gamma_fit": {
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        },
        "repeat_spend_customers": 2968,
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