Optimising Retail Pricing Using Price-Elasticity and Sales Data Analytics

Objective: To determine optimal pricing strategies by analysing price-elasticity and its impact on revenue using historical sales data in the Indian retail sector.

Problem & Context: Dynamic and competitive pricing is critical for Indian retail businesses, especially with the rise of e-commerce and fluctuating consumer demand. Retailers often lack data-driven insights into how changes in price affect sales volume and total revenue, leading to suboptimal pricing decisions.

Approach/Methodology: The project team will collect and analyse historical sales and price data from a mid-sized retail business or use publicly available datasets (e.g., Kaggle Indian Supermarket Sales). They will apply data cleaning, exploratory analysis, and statistical modelling (including regression and elasticity calculations) to measure price sensitivity and simulate revenue outcomes at various pricing points.

Deliverables & Analysis: Key deliverables include data-driven estimates of price-elasticity for major product categories, revenue optimisation models, scenario analyses, and clear visualisations. The team will provide actionable recommendations on pricing adjustments to maximise revenue, validated with sensitivity analysis and model testing.

Business Impact & Decision: The recommendations will enable retail managers to make evidence-based pricing decisions, improve revenue forecasting, and enhance competitiveness in the market, directly impacting profit margins and business growth.