DeepSeek vs Perplexity for Data Analysis

I'll guide you through a systematic approach to analyze repeat purchase drivers and build a churn prediction model using your 50,000-row transaction dataset.

Phase 1: Data Preparation & Exploration

Step 1: Data Assessment

# Initial checks
df.shape  # (50000, ?)
df.info()  # Data types, missing values
df.describe()  # Summary statistics

Step 2: Feature Engineering for RFM Analysis

Create key behavioral features:

• Recency: Days since last purchase
• Frequency: Number of purchases in time window
• Monetary: Total/avg spend
• Category diversity: Number of unique categories purchased
• Purchase patterns: Time between purchases (std/mean)
• Seasonality: Purchase patterns by day/month
• Customer tenure: Days since first purchase

Step 3: Define "Churn"

Common approaches:

• Time-based: No purchase in last X days (e.g., 90 days)
• Statistical: Beyond mean + 2*std of purchase interval
• Business rules: Based on your industry standards

Phase 2: Exploratory Analysis for Repeat Purchase Drivers

Step 4: Cohort Analysis

# Create cohort groups by acquisition month
# Analyze retention rates over time
# Compare repeat purchase rates across cohorts

Step 5: Key Driver Identification

Statistical methods to identify factors:

1. Correlation analysis: RFM metrics vs. repeat purchase rate
2. Segmentation analysis (K-means clustering on RFM)
3. A/B testing mindset: Compare repeat vs. one-time customers
4. Decision trees: Quick feature importance for repeat behavior

Phase 3: Predictive Modeling for Churn

Step 6: Train/Test Split

# Time-based split (e.g., last 30 days as test)
# Or random split with stratification

Step 7: Feature Selection

Potential feature categories:

1. Demographic: Age, location, customer type
2. Behavioral: RFM metrics, category preferences
3. Temporal: Purchase frequency changes, recency trends
4. Product-related: Favorite categories, basket size trends

Step 8: Model Building

Option A: Traditional ML

# Algorithms to try:
1. Logistic Regression (interpretable)
2. Random Forest (feature importance)
3. XGBoost/LightGBM (performance)
4. Survival Analysis (Cox PH for time-to-churn)

Option B: Ensemble Approach

# Stack multiple models
# Use voting classifier for robustness

Step 9: Model Evaluation

Key metrics:

• Precision-Recall curve (imbalanced data)
• AUC-ROC
• F1-score
• Business metrics: Top decile capture rate

Phase 4: Implementation & Insights

Step 10: Interpret Results

# Feature importance analysis
# SHAP values for model interpretability
# Partial dependence plots

Step 11: Actionable Insights

Translate findings to business actions:

1. High-risk segment: Customers with specific patterns
2. Intervention triggers: Early warning signs
3. Personalization opportunities: Based on drivers identified

Practical Considerations

Technical Stack Options:

# Quick start with sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

# Or for larger datasets
import xgboost as xgb
import lightgbm as lgb

Validation Strategy:

• Time-series cross-validation for temporal data
• GroupKFold by customer ID (avoid data leakage)
• Business validation: Test on latest time period

Common Pitfalls to Avoid:

1. Data leakage: Using future information to predict past churn
2. Imbalance handling: 90% non-churn, 10% churn is common
3. Temporal validation: Random split destroys time patterns
4. Interpretation vs. accuracy trade-off

Next Steps

1. Start with simple RFM analysis to segment customers
2. Build a baseline model (logistic regression)
3. Iterate with more complex models
4. Focus on interpretability to identify key drivers

Would you like me to elaborate on any specific step or provide code templates for particular analyses?

Step 1: Data Preparation and Exploration

Load your CSV into Python using pandas (e.g., pd.read_csv('transactions.csv')). Explore the dataset with df.describe(), df.info(), and visualizations like histograms for purchase amounts and bar charts for product categories and demographics to spot distributions and outliers[1][2].

Define repeat purchases and churn explicitly for your transaction data:

• Repeat purchases: Customers with ≥2 transactions within a window (e.g., 30-90 days).
• Churn: Binary target—no purchases in the next 30-90 days (adjust timeframe for business needs; shorter like 30 days yields higher accuracy)[1]. Sort transactions by customer ID and date, then engineer features:
• Aggregate per customer: total/recency/average purchase amount, transaction frequency, days since last purchase, top product categories, demographic summaries (e.g., age group, location).
• Create churn label: For each customer at time t, check if no transaction after t + window[1][3].

Sample if 50k rows create large joins (e.g., via df.sample(frac=0.1))[1].

Step 2: Identify Key Factors Driving Repeat Purchases

Focus on exploratory data analysis (EDA) and feature importance to find drivers:

• Group by demographics/categories: Compute repeat purchase rate (e.g., % customers with ≥2 buys) per age group, gender, product category. High-rate segments (e.g., young customers buying electronics) are key drivers[4].
• Correlation analysis: Use df.corr() or seaborn heatmaps on aggregated features like avg purchase amount vs. repeat frequency.
• RFM segmentation (Recency, Frequency, Monetary): Bin customers; high Frequency/Monetary often drives repeats[1][4].

Method	Purpose	Example Output
Groupby EDA	Spot demographic/category trends	Electronics buyers: 40% repeat rate[4]
Correlation	Link amounts/dates to repeats	Higher avg amount correlates with repeats
Initial model feature importance	Rank drivers (e.g., recency #1)	See Step 4[1][2]

Step 3: Feature Engineering for Modeling

Create 10-20 features from raw data:

• Transaction-based: Count of purchases, avg/median purchase amount, std deviation (volatility), product category diversity (e.g., one-hot encode top categories).
• Time-based: Days since first/last purchase, purchase velocity (count per month).
• Demographic: Age bins, income proxy if available.
• Behavioral: CustServ calls equivalent (if present), intl/high-value flags[1][3]. Handle missing values (impute medians), scale numerics (StandardScaler), encode categoricals (OneHotEncoder)[2].

Split data: 70% train, 15% validation, 15% test (time-based split: train on early dates, test on recent to mimic prediction)[1].

Step 4: Build Predictive Churn Model

Use binary classification (churn=1 if no future purchase). Top models for transaction data: Random Forest or XGBoost (handle non-linearities, interactions, skewed data like rare high-value purchases)[1][2].

Python workflow (using scikit-learn/xgboost):

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score
from xgboost import XGBClassifier

# Assume df_agg has features + 'churn' target
X = df_agg.drop('churn', axis=1)
y = df_agg['churn']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y)

# Random Forest (robust starter)
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)
y_pred_proba = rf.predict_proba(X_test)[:, 1]
auc_rf = roc_auc_score(y_test, y_pred_proba)  # Target >0.8[1]

# XGBoost (often highest performer)
xgb = XGBClassifier(n_estimators=100, random_state=42)
xgb.fit(X_train, y_train)
y_pred_proba_xgb = xgb.predict_proba(X_test)[:, 1]
auc_xgb = roc_auc_score(y_test, y_pred_proba_xgb)

# Feature importance (key churn drivers, also informs repeats)
importances = pd.DataFrame({'feature': X.columns, 'importance': rf.feature_importances_}).sort_values('importance', ascending=False)
print(importances.head(10))  # E.g., low recency, low frequency top drivers[1][2]

Evaluate: Use AUC-ROC (not accuracy, due to imbalance ~5% churn); plot ROC curve. Compare models: XGBoost/RF > Logistic Regression for complex data[1][2].

Interpret for repeats: Invert churn insights—low churn probability (high retention score = 100 - churn_prob*100) highlights repeat drivers (e.g., frequent small purchases in certain categories)[1].

Step 5: Deploy and Act

Score current customers: Aggregate to today’s state, predict churn prob, sort descending[1]. Target top 10% for retention (e.g., A/B test discounts on high-churn segments)[1]. Retrain monthly on new data[5].

For 50k rows, this runs on a laptop; scale with Databricks/AWS if needed[3][4]. If AUC <0.7, iterate features or try AutoML[2].