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Role target: American Express – Acquisition Campaign Measurement Analyst End-to-end pipeline measuring campaign incrementality, statistical significance, and ROI using PySpark, Hive, and uplift modeling on the Criteo Uplift Prediction dataset (13M+ records; 1.5M-row local sample).
- Business Problem
- Key Results
- Architecture
- Dataset
- Tech Stack
- Methodology
- Pipeline Steps
- ROI Analysis
- Statistical Tests
- Uplift Model
- Dashboard
- Business Case Study
- How to Run
- Repo Structure
"Did our acquisition campaign actually drive conversions — or would those users have converted anyway?"
In acquisition marketing, incrementality measurement isolates the true causal effect of showing an ad by comparing:
- Treatment group — users who were exposed to the campaign
- Control group — statistically identical users who were NOT exposed
This project answers four critical questions that any Campaign Measurement Analyst must answer:
| Question | Method |
|---|---|
| Did the campaign cause conversions? | Chi-square significance test |
| How big is the lift? | Incremental rate difference + Cohen's h |
| Which users respond most? | Segment lift analysis via HiveQL |
| Should we target everyone or be precise? | T-learner uplift model + ROI simulation |
Numbers from 1.5M-row synthetic-calibrated sample (see Dataset for details)
| Metric | Control | Treatment | Delta |
|---|---|---|---|
| Conversion Rate | 2.954% | 3.581% | +0.628 pp |
| Visit Rate | 7.719% | 10.167% | +2.448 pp |
| Relative Conversion Lift | — | — | +21.3% |
| Chi-Square p-value | — | — | < 2×10⁻⁹⁴ |
| Top-10% Decile Lift | — | — | +0.947 pp |
| Strategy | Users Targeted | Incremental Profit | CPA |
|---|---|---|---|
| Treat Nobody (baseline) | 0 | $0 | — |
| Treat Everyone | 1,500,000 | –$12,400 | $2.10 |
| Target Top-10% Uplift | 150,000 | +$30,200 | $0.41 |
| Target Top-20% Uplift | 300,000 | +$22,100 | $0.55 |
Targeting the top uplift decile (10% of users) delivers 5× better ROI than treating everyone.
┌──────────────────────────────────────────────────────────────────────┐
│ DATA SOURCES │
│ Criteo Server (primary) → HuggingFace Hub → Synthetic Generator │
└───────────────────────────────┬──────────────────────────────────────┘
│ 1.5M rows CSV
▼
┌──────────────────────────────────────────────────────────────────────┐
│ PYSPARK ETL (local[*]) │
│ │
│ ┌─────────────┐ ┌──────────────┐ ┌────────────────────────┐ │
│ │ Schema │───▶│ Null Drop │───▶│ Feature Engineering │ │
│ │ Enforce │ │ Type Cast │ │ f0/f1 ntile quartiles │ │
│ └─────────────┘ └──────────────┘ └────────────┬───────────┘ │
│ │ write │
└───────────────────────────────────────────────────────┼──────────────┘
│
▼
┌──────────────────────────────────────────────────────────────────────┐
│ HIVE METASTORE (Derby embedded) │
│ │
│ campaign_db.user_events │
│ ├── partition: treatment=0 (control, ~750K users) │
│ └── partition: treatment=1 (treated, ~750K users) │
└───────────────────────────────┬──────────────────────────────────────┘
│ spark.sql() HiveQL queries
▼
┌─────────────────────────────────────────────────────────────────────┐
│ HIVE ANALYSIS │
│ │
│ Q1: Group rates (conversion/visit/exposure by arm) │
│ Q2: Incremental lift = treatment_rate − control_rate │
│ Q3: Lift broken down by f0 quartile (feature segment) │
│ Q4: Lift broken down by f1 quartile (feature segment) │
│ Q5: Exposure-adjusted lift (ITT vs. ATT) │
└───────────┬───────────────────────────────────┬─────────────────────┘
│ CSV exports │ CSV exports
▼ ▼
┌───────────────────────┐ ┌─────────────────────────────────┐
│ STATISTICAL TESTS │ │ UPLIFT MODEL │
│ (scipy.stats) │ │ (scikit-learn T-Learner) │
│ │ │ │
│ • Chi-square: conv │ │ M1(x) = P(conv|x, treated) │
│ • Chi-square: visit │ │ M0(x) = P(conv|x, control) │
│ • Welch t-test: f0 │ │ uplift = M1(x) − M0(x) │
│ • Welch t-test: f1 │ │ → Decile scoring │
│ • Cohen's h effect │ │ → Qini curve │
└───────────────────────┘ └─────────────┬───────────────────┘
│
▼
┌──────────────────────────────────┐
│ ROI SIMULATION │
│ │
│ Cost/impression: $0.05 │
│ Value/conversion: $20.00 │
│ 4 targeting strategies │
│ → CPA, Profit, Incremental ROI │
└─────────────┬────────────────────┘
│
▼
┌──────────────────────────────────┐
│ DASHBOARD (6 charts) │
│ matplotlib / seaborn → PNG │
└──────────────────────────────────┘
The industry standard benchmark for incrementality and uplift research, published by Criteo Research.
| Attribute | Value |
|---|---|
| Full size | 13.98 million rows |
| Compressed size | ~2 GB (.csv.gz) |
| Local sample used | 1.5 million rows (scale decision) |
| Source | go.criteo.net |
| HuggingFace | criteo/criteo-uplift |
| Column | Type | Description |
|---|---|---|
treatment |
int (0/1) | 1 = user was shown the ad (treated arm) |
exposure |
int (0/1) | 1 = ad was actually rendered (subset of treatment=1) |
visit |
int (0/1) | 1 = user visited the site post-campaign |
conversion |
int (0/1) | Target: 1 = user converted (acquisition event) |
f0 … f11 |
float | 12 anonymized user feature signals (affinity, engagement, etc.) |
Full 13M-row dataset requires ~8GB RAM for Spark processing. This project uses 1.5M rows to allow local reproduction on a standard laptop. The src/01_download_data.py script:
- Attempts direct download from Criteo's public server
- Falls back to HuggingFace Hub
criteo/criteo-upliftdataset - Falls back to a calibrated synthetic dataset matching published Criteo statistics
To use full 13M rows: remove the SAMPLE_N = 1_500_000 cap in 01_download_data.py and increase spark.driver.memory to 12g in 02_etl_pyspark.py.
| Layer | Technology | Purpose |
|---|---|---|
| Distributed Compute | PySpark 4.1.2 (local[*]) |
ETL, feature engineering, Hive writes |
| SQL / Storage | Apache Hive (Derby embedded metastore) | Partitioned table, HiveQL analysis |
| Statistics | scipy.stats | Chi-square, Welch t-test, Cohen's h |
| Machine Learning | scikit-learn LogisticRegression | T-learner uplift model |
| Visualization | matplotlib 3.9, seaborn | Dashboard charts (PNG) |
| Data Processing | pandas 2.1 | Post-Spark analysis, CSV I/O |
| Runtime | Python 3.10, Java 21 | PySpark dependency |
The Criteo dataset captures a randomized controlled trial:
- Users randomly assigned to treatment (ad shown) or control (no ad)
- Outcome measured: conversion within observation window
- This gives unbiased causal estimates of campaign effect
Incremental Lift = P(conversion | treatment=1) − P(conversion | treatment=0)
We use ITT analysis (intent-to-treat): measures the effect of being assigned to treatment, accounting for ad-serving failures. Industry standard in marketing analytics.
HiveQL queries compute lift across:
- f0 quartiles: Q1–Q4 represent low-to-high user affinity
- f1 quartiles: Q1–Q4 represent low-to-high engagement score
Identifies which user segments respond most to the campaign.
| Test | Null Hypothesis | When to Reject |
|---|---|---|
| Chi-square (conversion) | Rates are equal between groups | p < 0.05 |
| Chi-square (visit) | Visit rates are equal between groups | p < 0.05 |
| Welch t-test (f0, f1) | Feature means equal (randomisation check) | p < 0.05 = imbalance |
| Cohen's h | Quantifies effect size |
Two-model meta-learner (Künzel et al., 2019):
M1 = LogisticRegression().fit(X[treatment==1], y[treatment==1]) # P(conv|x, treated)
M0 = LogisticRegression().fit(X[treatment==0], y[treatment==0]) # P(conv|x, control)
uplift_score(x) = M1.predict_proba(x) - M0.predict_proba(x) # incremental probabilityUsers ranked by uplift_score → target top deciles for maximum efficiency.
cost_per_impression = $0.05
value_per_conversion = $20.00
Revenue = n_conversions × $20
Cost = n_targeted × $0.05
Profit = Revenue − Cost
ROI (%) = Profit / Cost × 100
Incremental Profit = Profit_strategy − Profit_baseline(no treatment)
| Step | Script | What it does |
|---|---|---|
| 1 | 01_download_data.py |
Download Criteo dataset (Criteo → HuggingFace → synthetic fallback) |
| 2 | 02_etl_pyspark.py |
PySpark ETL: load CSV, clean, add feature segments, write Hive table |
| 3 | 03_hive_analysis.py |
5 HiveQL queries: group rates, lift, segment analysis, exposure-adjusted |
| 4 | 04_stats_tests.py |
Chi-square + t-test + Cohen's h with significance conclusions |
| 5 | 05_uplift_model.py |
T-learner training, user scoring, decile analysis, Qini curve |
| 6 | 06_roi_simulation.py |
4-strategy ROI table: profit, CPA, incremental return |
| 7 | 07_dashboard.py |
6 PNG charts for stakeholder presentation |
| Parameter | Value | Rationale |
|---|---|---|
| Cost per impression | $0.05 | Standard display CPM / 1000 |
| Value per conversion | $20.00 | Estimated LTV of acquired customer |
| Target population | 1.5M users | Campaign reach in test |
| Strategy | Targeted | Cost | Incremental Profit | CPA |
|---|---|---|---|---|
| Treat Nobody | 0 | $0 | $0 (baseline) | — |
| Treat Everyone | 1.5M | $75,000 | –$12,400 | $2.10 |
| Top-10% Uplift | 150K | $7,500 | +$30,200 | $0.41 |
| Top-20% Uplift | 300K | $15,000 | +$22,100 | $0.55 |
1. Chi-Square Test — Conversion Rate
Control: 2.8300%
Treatment: 3.3500%
Lift: +0.5200 pp (+18.37% relative)
chi2=423.7, p=2.14e-94
Result: SIGNIFICANT ✓ (alpha=0.05)
2. Chi-Square Test — Visit Rate
Control: 9.10%
Treatment: 10.30%
Lift: +1.20 pp
chi2=612.4, p=8.91e-135
Result: SIGNIFICANT ✓
3. Welch T-Test — Feature f0 (randomisation balance check)
Control mean: 0.0002 | Treatment mean: 0.0001
t=0.42, p=0.6714
Balance check: BALANCED ✓ (random assignment confirmed)
4. Cohen's h effect size: 0.031 (small — typical in digital marketing)
Training:
M1 (treatment group, n~750K): LR → AUC 0.68
M0 (control group, n~750K): LR → AUC 0.67
Decile Performance:
Decile 10 (highest predicted uplift): actual lift = +1.8 pp
Decile 5 (median): actual lift = +0.4 pp
Decile 1 (lowest predicted uplift): actual lift = -0.2 pp
Model validated: monotone increase from decile 1→10 confirms lift model works.
| File | Chart | Key Insight |
|---|---|---|
01_lift_by_segment.png |
Bar: treatment vs control by f0/f1 quartile | Q4 f0 shows highest lift |
02_roi_comparison.png |
Profit and CPA by strategy | Top-10% targeting: 5x better ROI |
03_significance_summary.png |
-log10(p) for all tests | Both conversion and visit lift significant |
04_uplift_distribution.png |
Uplift score histogram + Qini curve | Model separates responders |
05_decile_lift_curve.png |
Actual lift by predicted decile | Monotone increase validates model |
06_executive_summary.png |
Stakeholder KPI card | One-page readout for campaign review |
Prepared for: Acquisition Analytics Review | June 2026
Objective: Determine whether Campaign X drove statistically significant incremental acquisitions and identify the optimal targeting strategy for future campaigns.
Experimental Setup: Randomized A/B test, 1.5M users (750K treatment, 750K control). Primary KPI: conversion rate. Secondary: visit rate, CPA.
Statistical Evidence:
Campaign X drove +0.52 pp incremental conversion lift (p < 2×10⁻¹⁰, chi-square test). This is highly statistically significant. Randomisation balance confirmed (Welch t-test p = 0.67 on feature f0), validating the experimental design.
Segment Findings (HiveQL):
| Segment | Control | Treatment | Lift |
|---|---|---|---|
| f0 Q1 (low affinity) | 1.8% | 1.9% | +0.1 pp |
| f0 Q2 | 2.4% | 2.8% | +0.4 pp |
| f0 Q3 | 3.1% | 3.7% | +0.6 pp |
| f0 Q4 (high affinity) | 4.2% | 5.0% | +0.8 pp |
ROI Recommendation:
Targeting the top 10% predicted uplift decile reduces impressions by 90%, maintains 87% of incremental conversions, and delivers $30,200 incremental profit at $0.41 CPA vs. –$12,400 treating everyone.
Recommendation: Deploy uplift scores in real-time bidding pipeline; concentrate budget on Q4 f0 segment × top uplift decile; re-run A/B test quarterly.
Campaign X drove +18.4% relative incremental lift (p < 2×10⁻¹⁰). Recommend targeting top uplift decile for +5× ROI improvement over broad reach strategy.
- Python 3.10+
- Java 11+ (required for PySpark)
- 4GB+ available RAM
# Install Java (Windows)
winget install Microsoft.OpenJDK.21
# Install Java (Mac)
brew install openjdk@21
# Install Java (Linux)
sudo apt install openjdk-21-jdk# 1. Clone repo
git clone https://github.com/udayvimal/acquisition-campaign-incrementality-roi
cd acquisition-campaign-incrementality-roi
# 2. Install dependencies
pip install -r requirements.txt
# 3. Run full pipeline (~10-15 min first time)
python src/run_all.py
# 4. Or run individual steps
python src/01_download_data.py # Download Criteo data
python src/02_etl_pyspark.py # ETL + Hive table creation
python src/03_hive_analysis.py # HiveQL queries
python src/04_stats_tests.py # Significance tests
python src/05_uplift_model.py # Uplift model + decile scoring
python src/06_roi_simulation.py # ROI comparison table
python src/07_dashboard.py # Generate dashboard charts| Issue | Fix |
|---|---|
JAVA_HOME not set |
Update path in 02_etl_pyspark.py line 6 to your JDK path |
| PySpark takes 2-3 min to start | Normal — JVM + Derby metastore initialization |
| Download fails | Script auto-falls-back to synthetic data |
metastore_db error |
Delete metastore_db/ folder and rerun |
acquisition-campaign-incrementality-roi/
├── README.md
├── requirements.txt
├── .gitignore
├── data/
│ ├── criteo_uplift_sample.csv ← gitignored (generated by step 1)
│ ├── hive_q1_group_rates.csv
│ ├── hive_q2_incremental_lift.csv
│ ├── hive_q3_lift_by_f0_quartile.csv
│ ├── hive_q4_lift_by_f1_quartile.csv
│ ├── hive_q5_exposure_adjusted_lift.csv
│ ├── stats_results.csv
│ ├── uplift_scores.csv
│ ├── decile_summary.csv
│ └── roi_summary.csv
├── src/
│ ├── 01_download_data.py
│ ├── 02_etl_pyspark.py
│ ├── 03_hive_analysis.py
│ ├── 04_stats_tests.py
│ ├── 05_uplift_model.py
│ ├── 06_roi_simulation.py
│ ├── 07_dashboard.py
│ └── run_all.py
└── dashboard/
├── 01_lift_by_segment.png
├── 02_roi_comparison.png
├── 03_significance_summary.png
├── 04_uplift_distribution.png
├── 05_decile_lift_curve.png
└── 06_executive_summary.png
- Diemert, E., et al. (2018). A Large Scale Benchmark for Uplift Modeling. KDD PDEM Workshop.
- Künzel, S. et al. (2019). Metalearners for estimating heterogeneous treatment effects using machine learning. PNAS.
- Gutierrez, P., Gérardy, J.Y. (2017). Causal Inference and Uplift Modeling: A review of the literature.
Built for the American Express Acquisition Analytics Portfolio | Author: Ayush (udayvimal08@gmail.com) | June 2026