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.

Constrained portfolio rate optimisation for insurance pricing — SLSQP, FCA ENBP, efficient frontier, shadow prices, JSON audit trail

TagSignal — local-first pricing evidence: price experiments, sales history, or willingness to pay, kept separate and fed into margin economics with the uncertainty intact

End-to-end retail analytics consulting project built to BCG X standards churn prediction, price elasticity, marketing mix modelling, SHAP explainability, hypothesis testing, and an AI Copilot (agentic tool-use loop) all production Python, no notebooks.

Study of customer preference, price elasticity and customer segmentation using RFM

Price elasticity estimation per customer segment via log-log OLS regression. Revenue-maximising discount found using SciPy Brent's optimisation. Python · SciPy · Pandas

Price elasticity estimation by category and revenue simulation using Brazilian Olist e-commerce data

Exploratory analysis and modelling of avocado sales data (organic and conventional) in multiple US markets using R. The project includes outlier detection, correlation analysis, calculation of price-sales elasticities and price forecasting using time series models.

SAS statistical programming project — 11 hypothesis tests and price elasticity modeling on a 4-year $50B consumer market dataset

Determine the effectiveness of advertising activities on sales

Econometric analysis of price & income elasticity for Toyota, Honda, Mazda, Nissan & Subaru in the US market (2008–2025) using CPI, finance rates, and GDP growth. Python · Tableau

Data-driven pricing strategy analysis — price elasticity by category, competitor benchmarking, discount ROI, and bundling recommendations using Python & SQL.

Your pricing data will lose you money if you trust it. Proves observational price data is confounded, refuses to guess where it can't know, then closes the loop with deliberate price experiments. Trusting your data loses money in 75% of runs; the loop beats it in 100/100.