Data-driven analytics for benchmarking and optimizing the performance of automotive dealerships

Growing competition 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 automotive dealerships. In particular, we propose an objective method for segmenting automotive dealerships 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 dealerships within store clusters that jointly improves profitability for the store while also improving sales to satisfy manufacturer requirements. We validate the methods using synthetic experiments as well as a real-world automotive dealership network study for a leading global automotive manufacturer.