Overcoming data limitations for store choice modelling.: Exploiting retail chain choice data by means of aggregate logit models

This paper reports on research aimed at exploiting certain data sources for store choice modelling purposes. Many databases, such as some consumer panels, only record the firm chosen by consumers and not the specific store at which they shop. Four alternative approaches are proposed in order to use this raw information for studying patronage determinants at store level: (a) an ordinary logit model in which chain utility is averaged across stores within; (b) an ordinary logit model in which the choice set is assumed to be composed of the nearest store for each chain; (c) a straightforward application of an aggregate logit model; and (d) the application of an aggregate logit model with choice sets spatially bounded by a distance threshold representing the maximum distance that consumers are willing to travel for shopping. The models are empirically tested in the context of spatial choice behaviour. Goodness of fit indicators reveal that only models (b), (c) and (d) acceptably represent competitive interaction dynamics. As performance of (b) is slightly better than that of (c), it seems that a priori the ‘nearest store assumption’ is a better approach than the modelling of aggregate choice structures. However, when the latter approach is applied with more reliable choice sets, as suggested in model (d), the best performance is achieved. The results thus lead us to think that the aggregate logit model is a promising methodology for solving the problem at issue, but subject to an appropriate definition of the consumers’ choice sets. In fact, such an approach provides a more suitable modelling solution to the extent that the saturation and the intra-firm store heterogeneity become more intense, because these situations presumably imply that consideration sets include several stores from the same chain.