Optimising Grocery Retail Pricing Through Demand Forecasting, Product Expiry Analytics and Customer Price Sensitivity Modelling

Author

Jumoke Eniola Famoroti


Abstract

 Grocery retail pricing has become increasingly complex due to fluctuating consumer demand, rising operational costs, competitive pressures, and substantial losses associated with perishable inventory. Conventional pricing strategies frequently overlook interactions between purchasing behaviour, product shelf life, and demand variability, resulting in inefficient markdown decisions, inventory waste, and reduced profitability. This study proposes an integrated analytical framework combining demand forecasting, product expiry analytics, and customer price sensitivity modelling to optimise grocery retail pricing decisions. Historical sales transactions, inventory records, promotional activities, product expiration dates, and customer purchasing patterns provide the analytical foundation. Machine learning forecasting models estimate product demand across temporal and seasonal conditions, while expiry risk indicators quantify remaining shelf life and potential spoilage exposure. Price elasticity estimation captures heterogeneous customer responses to price adjustments, enabling differentiated markdown strategies across product categories and consumer segments. A constrained optimisation model subsequently integrates demand predictions, expiry probabilities, and elasticity estimates to recommend prices that balance revenue generation, inventory turnover, and waste reduction. The proposed framework offers a systematic approach to improving pricing responsiveness, strengthening operational efficiency, and supporting economically sustainable grocery retail management across diverse market conditions and retail environments.


Keywords

Grocery Retail Pricing; Demand Forecasting; Product Expiry Analytics; Price Elasticity Modelling; Dynamic Markdown Optimisation; Perishable Inventory Management.



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References


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