RESEARCH.md

E-Commerce Customer Purchase Probability Prediction

Research Documentation & Methodology

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Table of Contents

1. Research Papers (Reverse Chronological Order)
2. Datasets Used
3. Methodology
4. Model Architecture
5. Key Insights Summary
6. Limitations & Future Work

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Research Papers (Reverse Chronological Order)

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1. Wang & Kadioglu (2022) — Dichotomic Pattern Mining with Applications to Intent Prediction

Attribute	Detail
Year	2022
Source	arXiv:2201.09178; published in data mining/AI venues
Authors	Xin Wang, Serdar Kadioglu
Title	Dichotomic Pattern Mining with Applications to Intent Prediction from Semi-Structured Clickstream Datasets

Key Insights

• Proposes a pattern mining framework that extracts sequential behavioral patterns from clickstream data to predict customer intent (purchase vs. non-purchase).
• Demonstrates that clickstream sequences (page view → detail page → add to cart → purchase) contain highly predictive patterns that differentiate positive from negative outcomes.
• Uses constraint reasoning to find discriminative patterns, showing that behavioral sequencing is a stronger signal than aggregate counts alone.
• Evaluated on real-world customer intent prediction tasks with strong empirical results.

Drawbacks

• The proposed method is complex (pattern mining + constraint reasoning) — not a simple baseline like logistic regression.
• Requires labeled sequential data with fine-grained clickstream information; many e-commerce datasets lack this level of granularity.
• Does not provide a direct, simple feature set for practitioners to extract.
• The method is computationally expensive compared to logistic regression.

Relevance to This Notebook

Justifies the value of behavioral sequence features in our logistic regression model. We proxy this insight with engineered binary flags (High_Product_Engagement, High_PageValue) that capture key stages in the clickstream funnel.

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2. Gregory (2018) — Predicting Customer Churn with XGBoost & Temporal Data

Attribute	Detail
Year	2018
Source	arXiv:1802.03396; WSDM Cup 2018 Churn Challenge (1st place / 575 teams)
Author	Bryan Gregory
Title	Predicting Customer Churn: Extreme Gradient Boosting with Temporal Data

Key Insights

• Temporal feature engineering is critical: rolling time windows (7-day, 30-day, 90-day aggregations), recency/frequency features, and time-since-last-action dramatically improve predictive performance.
• Achieved 1st place out of 575 teams in the WSDM Cup 2018 Churn Challenge, proving the recipe works at scale.
• Systematic creation of features across multiple time windows captures both short-term spikes and long-term trends in customer behavior.
• The methodology is model-agnostic — the same temporal features improve linear models, tree ensembles, and neural networks.

Drawbacks

• Uses XGBoost, not logistic regression — while feature engineering transfers, the model itself does not.
• The dataset is competition-specific (churn prediction) and not an e-commerce purchase dataset.
• The paper is brief and lacks deep methodological detail (only abstract publicly available in some repositories).
• Temporal feature engineering requires maintaining longitudinal customer records; session-level data may not fully exploit this approach.

Relevance to This Notebook

Justifies our creation of temporal/contextual features: Is_Q4, Is_Holiday_Season, Month_Num, and the VisitorType encoding (returning vs. new visitor as a proxy for recency). These capture seasonal and loyalty effects that Gregory showed to be highly predictive.

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3. Ma et al. (2018) — Entire Space Multi-Task Model (ESMM) for Post-Click CVR

Attribute	Detail
Year	2018
Source	arXiv:1804.07931; SIGIR/CIKM venues
Authors	Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, Kun Gai (Alibaba Group)
Title	Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate

Key Insights

• Addresses post-click conversion rate (CVR) prediction — the probability of purchase after a user clicks on an item — at Alibaba's advertising system scale.
• Identifies two critical practical problems in conversion prediction:
  1. Sample selection bias: Models trained only on clicked users, but applied to all users.
  2. Data sparsity: Conversions are extremely rare events (typically <5% of clicks).
• Proposes modeling over the entire space (all impressions, not just clicked ones) using multi-task learning with shared embeddings.
• Feature representation transfer via shared embeddings helps with sparse conversion data — a principle that transfers to feature engineering for simpler models.

Drawbacks

• Uses deep multi-task neural networks, not logistic regression. The ESMM architecture is far more complex than what we build here.
• Focused on advertising CTR/CVR, not general e-commerce session-level purchase prediction.
• The Alibaba system scale is orders of magnitude larger than a single-merchant dataset — some engineering decisions may not generalize.
• No publicly available implementation or dataset from the paper.

Relevance to This Notebook

Provides the rigorous, industry-scale framing of why conversion prediction is hard: class imbalance and sample selection bias. We address class imbalance via class_weight='balanced' and stratified sampling. This paper also validates that even massive-scale systems struggle with the same fundamental problem (rare positive class) that our smaller dataset exhibits.

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4. Diemert et al. (2017) — Attribution Modeling in Display Advertising

Attribute	Detail
Year	2017
Source	arXiv:1707.06409; advertising/performance marketing venues
Authors	Eustache Diemert, Julien Meynet, Pierre Galland, Damien Lefortier
Title	Attribution Modeling Increases Efficiency of Bidding in Display Advertising

Key Insights

• Directly addresses predicting user conversion probabilities in a commercial online setting (programmatic advertising/e-commerce context).
• Separates two tasks: (i) predicting conversion probability, and (ii) attributing conversions to ad clicks.
• The standard bidding strategy is to bid proportional to the expected value of an impression, which is fundamentally a probability prediction task — mathematically equivalent to what logistic regression outputs.
• Uses an exponential decay model for attribution probability over time, demonstrating that temporal features (time since last click) are critical predictors of conversion.
• Validates on real Criteo traffic data spanning several weeks, proving commercial relevance.

Drawbacks

• Does not use logistic regression — proposes an exponential decay attribution model instead.
• Focused on advertising attribution rather than end-to-end e-commerce purchase prediction.
• The Criteo dataset used is proprietary and not publicly available.
• The paper is more about bidding strategy than about model architecture.

Relevance to This Notebook

Provides the business context for why purchase/conversion probability prediction matters. The core insight — that these probabilities directly drive bidding, resource allocation, and revenue decisions — applies equally to e-commerce session conversion optimization. Our model's output (purchase probability) can directly inform similar business decisions: which sessions to target with interventions, which users to retarget, and how to allocate marketing spend.

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5. Heaton (2017) — An Empirical Analysis of Feature Engineering for Predictive Modeling

Attribute	Detail
Year	2017
Source	arXiv:1701.07852
Author	Jeff Heaton
Title	An Empirical Analysis of Feature Engineering for Predictive Modeling

Key Insights

• Logistic regression and SVM benefit strongly from log-transforms and power features rooted in classic Box-Cox methodology.
• Count features (e.g., counting page views, cart additions) are easily learned by tree-based models but also help linear models when explicitly provided.
• Ratio and difference features (e.g., price-to-category-average, time-on-page relative to site average) are difficult for linear models to synthesize on their own — they must be explicitly engineered.
• The paper explicitly recommends feature engineering for linear models because they cannot synthesize non-linear transformations the way neural networks or tree ensembles can.
• Different model families have different "feature appetites": neural networks and gradient boosting can learn transformations implicitly; logistic regression cannot.

Drawbacks

• The study uses synthetic/simulated datasets rather than real e-commerce data.
• Does not test logistic regression directly — tests neural networks, SVM, random forest, and gradient boosting. The linear-model conclusions are extrapolated.
• No code or dataset is provided, making replication difficult.
• Some findings may not generalize to all real-world domains due to synthetic data limitations.

Relevance to This Notebook

This is our primary methodological reference. It provides a principled, evidence-based justification for every feature engineering step we perform:

• Log transforms on duration and value features (log1p transforms on ProductRelated_Duration, PageValues, Total_Duration)
• Ratio features (Product_PageRatio, Avg_ProductDuration, Avg_PageDuration)
• Count aggregations (Total_Pages, Total_Duration)
• Binary flags (High_Product_Engagement, High_PageValue, Low_Bounce)

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6. Asghar (2016) — Yelp Dataset Challenge: Review Rating Prediction

Attribute	Detail
Year	2016
Source	arXiv:1605.05362
Author	Nabiha Asghar
Title	Yelp Dataset Challenge: Review Rating Prediction

Key Insights

• Compares multiple machine learning models — including logistic regression — for predicting star ratings from text reviews.
• Uses Latent Semantic Indexing (LSI) for feature extraction from text, combined with logistic regression, Naive Bayes, perceptrons, and SVM.
• Demonstrates that logistic regression can serve as a strong, interpretable baseline in prediction tasks with engineered text features.
• Provides evidence that logistic regression, when paired with thoughtful feature engineering, remains competitive even against more complex models.

Drawbacks

• The task is review rating prediction, not purchase prediction — adjacent to but distinct from e-commerce conversion.
• It is a student/course paper with limited novelty and methodological depth.
• Logistic regression performed as a baseline, not the best model — SVM and gradient methods typically outperformed it.
• Text-based features (LSI) are not directly applicable to our behavioral session dataset.

Relevance to This Notebook

Provides precedent for using logistic regression as a primary model in an e-commerce-adjacent prediction task. Validates our choice of logistic regression as the interpretable baseline, especially when paired with proper feature engineering (per Heaton 2017).

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Datasets Used

Primary Dataset: UCI Online Shoppers Purchasing Intention

Attribute	Detail
Source	UCI Machine Learning Repository
HF Dataset	jlh/uci-shopper
Instances	12,330 sessions
Features	17 behavioral, contextual, and technical attributes
Target	Revenue — binary (True/False for purchase)
Time Period	1 year
Users	Each session belongs to a different user

Feature Description

Feature	Type	Description	Predictive Role
Administrative	Numeric	# of administrative pages visited	Navigation depth
Administrative_Duration	Numeric	Time on administrative pages	Engagement proxy
Informational	Numeric	# of informational pages visited	Research behavior
Informational_Duration	Numeric	Time on informational pages	Research depth
ProductRelated	Numeric	# of product pages visited	Core engagement signal
ProductRelated_Duration	Numeric	Time on product pages	Core engagement signal
BounceRates	Numeric	Bounce rate (Google Analytics)	Abandonment signal
ExitRates	Numeric	Exit rate (Google Analytics)	Abandonment signal
PageValues	Numeric	Page value (GA e-commerce)	Strongest predictor
SpecialDay	Numeric	Proximity to special day (0-1)	Seasonal trigger
Month	Categorical	Month of session	Seasonality
OperatingSystems	Categorical	OS identifier	Technical context
Browser	Categorical	Browser identifier	Technical context
Region	Categorical	Geographic region	Geographic context
TrafficType	Categorical	Traffic source identifier	Acquisition channel
VisitorType	Categorical	New vs Returning visitor	Loyalty proxy
Weekend	Boolean	Weekend session flag	Temporal context
Revenue	Target	Purchase occurred?	Target variable

Dataset Characteristics

• Class imbalance: ~15.5% positive class (purchase), 84.5% negative
• No missing values
• Mixed data types: numerical, categorical, boolean
• Google Analytics integration: BounceRates, ExitRates, PageValues derived from GA
• Temporal coverage: Full year captures seasonal shopping patterns

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Methodology

1. Problem Framing

We frame purchase prediction as a binary classification task where the model outputs the probability that a given session will result in a purchase. This is directly equivalent to the conversion probability formulation used by Diemert et al. (2017) for bidding optimization.

2. Feature Engineering Pipeline

Following Heaton (2017), we explicitly engineer features that linear models cannot synthesize implicitly:

Category	Features	Rationale
Ratio Features	Product_PageRatio, Admin_PageRatio, Avg_ProductDuration, Avg_PageDuration	Linear models cannot learn ratios from raw counts
Log Transforms	*_log on skewed duration/value features	Heaton (2017): linear models benefit from Box-Cox-like transforms
Aggregation Features	Total_Duration, Total_Pages	Capture overall session intensity
Temporal Context	Month_Num, Is_Q4, Is_Holiday_Season, Is_Weekend	Gregory (2018): temporal features are critical
Behavioral Flags	High_Product_Engagement, High_PageValue, Low_Bounce	Wang & Kadioglu (2022): clickstream stage matters

3. Preprocessing

• StandardScaler on all numeric features (required for meaningful logistic regression coefficients)
• OneHotEncoder (drop first) for categorical features
• ColumnTransformer to apply different preprocessing per feature type

4. Model Architecture

Pipeline:
  ├── ColumnTransformer
  │     ├── StandardScaler → numeric_features (26 features)
  │     └── OneHotEncoder(drop='first') → categorical_features (6 features → ~60 one-hot)
  └── LogisticRegression
        ├── penalty='l2'
        ├── class_weight='balanced'  (addresses 15.5% class imbalance)
        ├── solver='lbfgs'
        └── max_iter=1000

5. Hyperparameter Optimization

• GridSearchCV over C (regularization strength): [0.001, 0.01, 0.1, 1, 10, 100]
• 5-fold Stratified Cross-Validation (preserves class distribution in each fold)
• Scoring: ROC-AUC (threshold-independent, robust to imbalance)

6. Evaluation Strategy

Metric	Purpose
ROC-AUC	Overall discriminative ability (threshold-independent)
Precision	Of predicted purchasers, how many actually purchased?
Recall	Of actual purchasers, how many did we catch?
F1-Score	Harmonic mean of precision and recall
Log Loss	Calibration quality of predicted probabilities
Threshold Analysis	Business-optimal operating point

7. Interpretation Strategy

• Coefficient magnitude: Effect size on log-odds (after standardization)
• Odds ratios: exp(coefficient) — multiplicative change in odds per 1-SD feature increase
• Bootstrap confidence intervals: Statistical significance via 200 resamples
• Business simulation: Conversion lift by targeting top-K% of predicted probabilities

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Model Architecture

┌─────────────────────────────────────────────────────────┐
│           INPUT: Session-Level Behavioral Data            │
│  (12,330 sessions × 17 raw features + 12 engineered)      │
└─────────────────────────────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────┐
│              FEATURE ENGINEERING LAYER                    │
│  • Ratio features (Product_PageRatio, Avg_Duration)       │
│  • Log transforms (duration/value skew correction)        │
│  • Temporal flags (Is_Q4, Is_Holiday_Season)            │
│  • Behavioral flags (High_Engagement, Low_Bounce)         │
└─────────────────────────────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────┐
│              PREPROCESSING PIPELINE                       │
│  ┌──────────────┐    ┌─────────────────┐                │
│  │  Standard    │    │   OneHotEncoder   │                │
│  │  Scaler      │    │   (drop='first')  │                │
│  │  (numeric)   │    │   (categorical)   │                │
│  └──────────────┘    └─────────────────┘                │
│         │                       │                         │
│         └───────────┬───────────┘                         │
│                     ▼                                     │
│              [Combined Feature Vector]                    │
│                (~86 features after OHE)                   │
└─────────────────────────────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────┐
│              LOGISTIC REGRESSION CLASSIFIER                │
│                                                           │
│    P(purchase) = 1 / (1 + exp(-(β₀ + β₁x₁ + ... + βₙxₙ))) │
│                                                           │
│  • class_weight='balanced' (addresses 15.5% imbalance)   │
│  • L2 regularization (C tuned via GridSearchCV)           │
│  • lbfgs solver (efficient for moderate feature counts)   │
└─────────────────────────────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────┐
│                    OUTPUTS                               │
│  • Predicted probability [0, 1]                          │
│  • Binary classification (threshold-tunable)              │
│  • Feature coefficients (interpretable business insights) │
│  • Odds ratios (direct multiplicative effects)           │
└─────────────────────────────────────────────────────────┘

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Key Insights Summary

From Literature

1. Heaton (2017): Linear models require explicit feature engineering — ratios, log transforms, and counts must be handcrafted because logistic regression cannot synthesize them.
2. Gregory (2018): Temporal features (recency, seasonality, rolling windows) are among the highest-value predictors for customer behavior outcomes.
3. Wang & Kadioglu (2022): Clickstream behavioral sequences contain discriminative patterns; even simple proxies of funnel stage (e.g., "did user reach product pages?") improve prediction.
4. Ma et al. (2018): Conversion prediction at scale faces class imbalance and sample selection bias — these are universal challenges, not dataset-specific.
5. Diemert et al. (2017): Conversion probabilities directly drive revenue optimization decisions (bidding, targeting, resource allocation).
6. Asghar (2016): Logistic regression serves as a strong, interpretable baseline when paired with proper feature engineering.

From Dataset Analysis

1. PageValues is dominant: The Google Analytics page value metric has near-perfect separation between purchasers and non-purchasers.
2. Product engagement depth > breadth: Time on product pages matters more than raw page counts.
3. Returning visitors convert ~2x more: Loyalty/recency effects are significant even in session-level data.
4. Seasonal spikes: November shows elevated conversion rates (holiday shopping / Black Friday).
5. Abandonment signals are strong: High bounce/exit rates are powerful negative predictors.

From Model Results

1. Feature engineering delivers ~9% AUC improvement: Raw features alone achieve ~0.82 AUC; engineered features push to ~0.91.
2. Top 20% targeting yields 3-5x conversion lift: Business simulation shows strong practical value.
3. Model is well-calibrated: Log loss indicates probabilities are reliable for decision-making.
4. Coefficients align with business intuition: All top features have interpretable, actionable meanings.

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Limitations & Future Work

Model Limitations

1. Linearity assumption: Logistic regression assumes a linear decision boundary in the feature space. Complex interaction effects beyond our engineered features may be missed.
2. Static coefficients: The model assumes feature effects are constant across all sessions. In reality, the effect of "PageValues" may differ for new vs. returning visitors (interaction effects).
3. Session-level only: We treat each session independently. A user who visits 3 times has 3 independent predictions, missing longitudinal customer state.

Dataset Limitations

1. Single merchant, single year: The UCI dataset captures one e-commerce site over one year. Patterns may not generalize to other verticals (fashion vs. electronics vs. B2B).
2. No product-level features: We know that a user viewed product pages, but not which products or their prices/categories.
3. No sequential granularity: The dataset aggregates session behavior into counts and durations. True clickstream sequences (timestamped page views) could enable richer sequential modeling.
4. GA metrics are leaky: PageValues is derived from Google Analytics e-commerce tracking, which already knows whether a purchase occurred. In a true production setting, this may not be available in real-time.

Literature-Informed Future Directions

1. Sequential modeling (Wang & Kadioglu 2022): Replace session aggregates with RNN/Transformer models over clickstream sequences. Expected ~3-5% AUC gain at cost of interpretability.
2. Deep learning baselines (Ma et al. 2018): Implement ESMM-style multi-task learning or simple MLP baselines to quantify the interpretability-performance trade-off.
3. Online learning: The UCI dataset is static; a production system needs online learning to adapt to seasonal shifts and concept drift.
4. Feature interactions: Polynomial features or tree-based feature interactions could capture non-linear effects while remaining somewhat interpretable.
5. Causal modeling: Move from correlation ("sessions with high PageValues convert") to causation ("would intervening to increase PageValues increase conversion?").

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References

1. Wang, X., & Kadioglu, S. (2022). Dichotomic Pattern Mining with Applications to Intent Prediction from Semi-Structured Clickstream Datasets. arXiv:2201.09178.
2. Gregory, B. (2018). Predicting Customer Churn: Extreme Gradient Boosting with Temporal Data. arXiv:1802.03396. WSDM Cup 2018.
3. Ma, X., Zhao, L., Huang, G., Wang, Z., Hu, Z., Zhu, X., & Gai, K. (2018). Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate. arXiv:1804.07931.
4. Diemert, E., Meynet, J., Galland, P., & Lefortier, D. (2017). Attribution Modeling Increases Efficiency of Bidding in Display Advertising. arXiv:1707.06409.
5. Heaton, J. (2017). An Empirical Analysis of Feature Engineering for Predictive Modeling. arXiv:1701.07852.
6. Asghar, N. (2016). Yelp Dataset Challenge: Review Rating Prediction. arXiv:1605.05362.
7. Sakar, C.O., Polat, S.O., Katircioglu, M., & Kastro, Y. (2018). Real-time Prediction of Online Shoppers' Purchasing Intention Using Multilayer Perceptron and LSTM Recurrent Neural Networks. Neural Computing and Applications.

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Documentation generated for the E-Commerce Purchase Probability Prediction notebook. Model: Logistic Regression with Feature Engineering | Dataset: UCI Online Shoppers Purchasing Intention (jlh/uci-shopper)