Data-Driven Analytics for Benchmarking and Optimizing Retail Store Performance

TL;DR

This paper introduces a data-driven framework for benchmarking and optimizing retail store performance by segmenting stores with model-based clustering and providing tailored recommendations to enhance profitability and sales.

Contribution

It presents a novel Finite Mixture of Regressions clustering method with constraints and an optimization framework for personalized store recommendations, validated on real-world data.

Findings

Effective store segmentation using model-based clustering.

Tailored recommendations improve profitability and sales.

Validated approach on automotive dealership network.

Abstract

Growing competitiveness and increasing availability of data is generating tremendous interest in data-driven analytics across industries. In the retail sector, stores need targeted guidance to improve both the efficiency and effectiveness of individual stores based on their specific locations, demographics, and environment. We propose an effective data-driven framework for internal benchmarking that can lead to targeted guidance for individual stores. In particular, we propose an objective method for segmenting stores using a model-based clustering technique that accounts for similarity in store performance dynamics. The proposed method relies on an effective Finite Mixture of Regressions technique based on competitive learning for carrying out the model-based clustering with `must-link' constraints and modeling store performance. We also propose an optimization framework to derive tailored recommendations for individual stores within store clusters that jointly improves profitability for the store while also improving sales to satisfy franchiser requirements. We validate the methods using synthetic experiments as well as a real-world automotive dealership network study for a leading global automotive manufacturer.