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Customer & Purchase Analytics using Segmentation, Targeting, Positioning, Marketing Mix, Price Elasticity
Historical Sales Using Price Elasticity to determine customer responsiveness to future price changes
Data Science Portfolio
Price Elasticities and Purchase Incidence Model
Customer segmentation, price elasticity modelling and conversion modelling.
Key: clustering, using logistic regression to build elasticity modeling for purchase probability, brand choice, and purchase quantity & deep neural network to build a black-box model to predict future customer behaviors.
Study of customer preference, price elasticity and customer segmentation using RFM
Constrained portfolio rate optimisation for insurance pricing — SLSQP, FCA ENBP, efficient frontier, shadow prices, JSON audit trail
Insurance demand modelling. Conversion, retention, DML price elasticity, demand curves, FCA GIPP-compliant optimisation. CatBoost + Polars.
This project was a POC to determine the pricing strategy for a product using Conjoint Analysis. This is a survey-based statistical technique used in quantitative market research to determine how people value different features of a product. It helps capture the relative preference of a user over different product features.
Determine the effectiveness of advertising activities on sales
Cost & Commercial Analytics — should-cost modeling, OCOGS tracking, make-vs-buy analysis, price elasticity, DoWhy causal inference, CUPED A/B testing (-55%) | 500 SKUs · 12 Plants · 5 Suppliers · 7 Countries | Enterprise: K8s + Helm + Terraform + MLflow | 209 tests
Analyzing Marketing Resilience During the Pandemic Era
📊 Dynamic pricing dashboard for RFM customer segmentation and price elasticity simulation. React + TypeScript + Vite. Case study on e-commerce pricing strategies.
SQL-first price elasticity and scenario modeling using simulated retail sales data
Price elasticity estimation per customer segment via log-log OLS regression. Revenue-maximising discount found using SciPy Brent's optimisation. Python · SciPy · Pandas
Data-driven SaaS pricing optimization using ML to maximize LTV/CAC ratios. Employs Random Forest, clustering, and elasticity analysis on 4,222 customers to recommend tiered pricing strategies that balance revenue growth with sustainable churn rates.
Deprecated — merged into insurance-causal
Causal inference for insurance pricing — DML, CatBoost nuisance models, confounding bias reports
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