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

Determine the effectiveness of advertising activities on sales

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.

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.