  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.
