'Hello Jumbo!' The Spatio-Temporal Rollout and Traffic to a New Grocery Chain After Acquisition
Forthcoming in Management Science
77 Pages Posted: 13 Feb 2018
Date Written: 2017
Grocery retailers increasingly use acquisitions to expand their presence. Such acquisitions are risky, especially when retailers decide to subsume the acquired stores under their own banner, which can take years and demands careful planning. We show how the dynamics of consumer valuations of the old and new banner affect the traffic implications of various (spatio-temporal) rollouts, and how this can be incorporated in the conversion planning. In a store-choice model with Bayesian learning, consumers update their perceptions about the old banner as its value deteriorates, and learn about the new banner through visits. Our model thus captures consumers’ responses to store conversion over time, in function of the local presence of other (own and competitive) outlets and new-banner advertising. An application to the national rollout of a Dutch EDLP player after its acquisition of a HILO chain, analyzes the shopping patterns of about 1,500 households over four years, involving over 100 store conversions. Our findings show that (i) uncertainty about the new banner dampens traffic to converted stores initially, while (ii) value deterioration of the old banner jeopardizes traffic to stores converted late, and (iii) the magnitude of these effects strongly depends on local-market characteristics, that is, competition and access to other old-banner and new-banner stores. With simulations, we show that to attract and keep customers, (i) outlets in competitive markets should not always get precedence and (ii) in locations with multiple outlets, conversions are better separated. Retailers can use these demand-side effects to their benefit in planning the rollout.
Keywords: store rollout, acquisitions, choice models, Bayesian learning, store choice
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