Tailoring Discount Strategies to Retailer Segments: A Data Driven Approach to Optimizing Snack Industry Revenue

This study addresses the challenge of optimizing discount strategies for a consumer goods manufacturer in the snack industry, aiming to enhance direct sales to retailers and gain a competitive edge. Unlike traditional one-size-fits-all approaches, we propose a nuanced method that considers the diverse landscape of retail outlets, from small local stores to medium-sized supermarkets. Our research utilizes historical sales data from over 180, 000 retailers to develop tailored promotion plans. To overcome data sparsity and operational constraints, we employ a two-step clustering approach: first grouping retailers based on product-specific purchase histories, then by average monthly purchase amounts. We incorporate Monte Carlo simulations to account for uncertainties in retailer inventory levels and purchasing behaviors. This method enables us to estimate purchase probabilities and promotional offer acceptance rates for each retailer cluster. The study's findings contribute to the field of retail analytics by demonstrating the effectiveness of personalized discount strategies in a large-scale, data-driven context. Our approach offers a practical solution for manufacturers seeking to optimize their pricing mechanisms and strengthen their position in the competitive consumer goods market. Experimental results from our field study indicate a significant 36% increase in total revenue and a notable 21% rise in the number of retailers compared to the average sales over the past year.