Led the data science and analytics function, developing machine learning models for customer retention
and revenue growth. Implemented comprehensive web scraping solutions and semantic search capabilities to
monitor competitor pricing and product positioning in real-time. Used RFM (Recency, Frequency, Monetary)
analysis and churn prediction models to optimize marketing campaigns and improve customer lifetime
value.
System Architecture
flowchart TD
A[Data Sources
Transactions, Web, CRM] --> B[Data Ingestion
ETL Pipelines]
B --> C[Data Lake
Clean & Structured Data]
C --> D[Analytics Engine]
C --> E[Competitive Intelligence
Web Scraping]
C --> F[Customer Segmentation
RFM Analysis]
D --> G[Churn Prediction Models
Random Forest, XGBoost]
E --> H[Price Comparison
Market Positioning]
F --> I[Customer Segments
High/Medium/Low Value]
G --> J[Marketing Optimization]
H --> J
I --> J
J --> K[Campaign Targeting
Personalized Offers]
K --> L[Billing Productivity
44% → 49%]
Business Impact
- Billing productivity increased from 44% to 49%
- Real-time competitive intelligence on pricing
- Data-driven sales strategy adjustments
- Improved customer retention through targeted campaigns
- Better allocation of marketing resources
Approach & Methodology
The project followed a data-driven approach with iterative model development:
- Data Collection: Integrated data from POS systems, CRM, e-commerce
platforms, and external sources
- Exploratory Analysis: Performed comprehensive EDA to understand customer
behavior patterns
- Feature Engineering: Created 200+ features including transaction patterns,
product affinities, and seasonality
- Model Development: Built ensemble models combining multiple algorithms for
robust predictions
- Validation & Testing: Implemented cross-validation and A/B testing for
model evaluation
- Deployment: Deployed models with monitoring dashboards and automated
retraining pipelines
Key Learnings
Building this end-to-end predictive analytics platform provided valuable insights into retail data
science and competitive intelligence:
- RFM Segmentation Power: Simple but effective—RFM analysis outperformed
complex clustering for identifying high-value customers and targeting retention campaigns
- Model Drift in Retail: Customer behavior changes rapidly with seasons,
promotions, and market conditions. Automated retraining every 2-4 weeks was essential for
maintaining model accuracy
- Feature Engineering Impact: Domain knowledge (product categories, purchase
timing, cross-sell patterns) drove more value than algorithm selection. Well-engineered
features with simple models outperformed complex models with basic features
- Web Scraping Challenges: Retailer websites change frequently. Need robust
error handling, fallback strategies, and monitoring systems to maintain competitive
intelligence data quality
- Stakeholder Communication: Business impact (billing productivity increase)
resonated more than technical metrics (AUC, F1-score). Translating model outputs into
actionable business insights was critical for adoption
- Experimentation Culture: A/B testing of marketing campaigns based on model
predictions created a feedback loop that improved both models and business outcomes
simultaneously