PersonaPulse – Predictive Persona Strategy for E-Commerce
Category
Data Analytics · ML
Date
June 2025
Client
Self-Initiated Project (Portfolio)
Objective
Identify personas that convert; reduce drop-offs.
PersonaPulse is a full-stack data analytics and ML project built to understand how customer personas impact e-commerce behavior. I created a PostgreSQL database, ran advanced SQL analyses like RFM and LTV, and built five interactive Power BI dashboards. The project includes a machine learning model to predict buyer intent with 89% accuracy. It helps businesses improve targeting, reduce drop-offs, and make smarter marketing decisions.
PROBLEM STATEMENT
Why Are We Targeting the Wrong Customers?
Most e-commerce companies rely on generic targeting — blasting promotions at large user bases without understanding who actually converts and who churns or returns products. That’s where PersonaPulse comes in — an end-to-end analytics solution that helps businesses find their best customers, reduce returns, and boost revenue by merging SQL-based analysis, ML prediction, and visual storytelling.
MY APPROACH
To answer the business question — "Which customers should we focus on?" — I designed a modular analytics system covering the full customer journey:
✅ PostgreSQL Database Design Structured raw CSVs into normalized tables and used SQL to run RFM segmentation, drop-off detection, and LTV analysis.
✅ Power BI Dashboards (5 Pages) Created interactive dashboards for KPIs, funnel drop-offs, persona segmentation, lifetime value, and ML-driven buyer intent.
✅ Buyer Intent Prediction Model Trained a Random Forest classifier using user behavior + persona attributes to predict who is likely to place an order — and when.
✅ End-to-End Flow From ingestion to insights to prediction, the system integrates SQL, ML, and BI into one seamless workflow.
WHY IT MATTERS?
PersonaPulse goes beyond descriptive analytics. It gives businesses:
A data-backed persona strategy
A way to predict buyer actions before they happen
Visual tools to understand where revenue is leaking and how to fix it
For e-commerce companies, this means better targeting, lower returns, and higher ROI from every campaign.
LESSONS LEARNED
Built a complete SQL-to-dashboard pipeline using PostgreSQL and Power BI, strengthening my skills in data modeling, segmentation, and KPI design.
Applied RFM segmentation and LTV analysis in a business context, learning how data defines user value and marketing strategy.
Developed a classification ML model using Random Forest, gaining hands-on experience with feature engineering and model deployment.
Learned how to connect machine learning predictions to business use cases, such as forecasting buyer intent for strategic targeting.
Improved my ability to tell a data story by translating insights into clear visual dashboards and actionable recommendations.
Key Insights & Recommendations
Insights:
🟢 Loyal Minimalists had the highest LTV but were under-targeted in marketing campaigns.
🔴 Impulsive Buyers had high return rates and lower profitability, suggesting caution in retargeting.
📉 23% of users dropped off after viewing a product but never reached the cart.
This drop was most common among budget-conscious personas.
✅ The Buyer Intent Model achieved 89% accuracy, making it a valuable lead scoring tool.
Recommendations:
Shift marketing spend toward high-LTV personas
Introduce personalized re-engagement campaigns
Rework product views → cart experience for certain personas
FEATURES HIGHLIGHTS
Key Features At a Glance
✔️ Relational PostgreSQL database with RFM, drop-off & LTV analysis
✔️ Power BI dashboards with 5 detailed views
✔️ ML prediction on buyer intent (Random Forest, 89% accuracy)
✔️ Persona tagging + behavior-driven segmentation
✔️ Funnel stage tracking & re-engagement strategy triggers
✔️ Joblib model export + SQL scripts + .pbix dashboards
TECH STACK
GIT LINK
GitHub Repository
LINK
