No abstract

Purpose: The purpose of the paper is to identify store format attributes that impact on store format choice when consumers conduct fill-in or major trips to buy groceries. By doing so, we take into consideration that consumers patronise multiple (store-based) formats depending on the shopping situation operationalised by the type of shopping trip.Design/methodology/approach: The paper adopts the conceptual framework of random utility theory via application of a multinomial logit modelling framework. The analysis is based on a survey of 408 consumers representing households in a clearly defined central European retail area. Findings:The results reveal a considerable moderating effect of the shopping situation on the relationship between perceived store format attributes and store format choice.Consumers' utilities are significantly higher for discount stores and hypermarkets when conducting major trips. To the contrary, supermarkets are preferred for fill-in trips in the focused retail market. Merchandise-related attributes of store formats have a higher impact on the utility formation regarding major-trips, whereas service-and convenience-related attributes do so regarding fill-in trips. Research limitations:The findings can only be generalised to retail markets having similar characteristics like the one under study. It is highly concentrated, contains considerable share of small size retail stores, it is urban and has clear cut boundaries due to its geographical location.Originality/value: This paper considers the fact that consumers patronise multiple store formats and investigates the moderating effect of the shopping situation -operationalised by different types of shopping trips -on store format choice.

No abstract

Using a probabilistic approach for exploring latent patterns in high-dimensional co-occurrence data, topic models offer researchers a flexible and open framework for soft-clustering large data sets. In recent years, there has been a growing interest among marketing scholars and practitioners to adopt topic models in various marketing application domains. However, to this date, there is no comprehensive overview of this rapidly evolving field. By analyzing a set of 61 published papers along with conceptual contributions, we systematically review this highly heterogeneous area of research. In doing so, we characterize extant contributions employing topic models in marketing along the dimensions data structures and retrieval of input data, implementation and extensions of basic topic models, and model performance evaluation. Our findings confirm that there is considerable progress done in various marketing sub-areas. However, there is still scope for promising future research, in particular with respect to integrating multiple, dynamic data sources, including time-varying covariates and the combination of exploratory topic models with powerful predictive marketing models.

Accurate predictions of a customer’s activity status and future purchase propensities are crucial for managing customer relationships. This article extends the recency–frequency paradigm of customer-base analysis by integrating regularity in interpurchase timing in a modeling framework. By definition, regularity implies less variation in timing patterns and thus better predictability. Whereas most stochastic customer behavior models assume a Poisson process of “random” purchase occurrence, allowing for regularity in the purchase timings is beneficial in noncontractual settings because it improves inferences about customers’ latent activity status. This especially applies to those valuable customers who were previously very frequently active but have recently exhibited a longer purchase hiatus. A newly developed generalization of the well-known Pareto/NBD model accounts for varying degrees of regularity across customers by replacing the NBD component with a mixture of gamma distributions (labeled Pareto/GGG). The authors demonstrate the impact of incorporating regularity on forecasting accuracy using an extensive simulation study and a range of empirical applications. Even for mildly regular timing patterns, it is possible to improve customer-level predictions; the stronger the regularity, the greater the gain. Furthermore, the cost in terms of data requirements is marginal because only one additional summary statistic, in addition to recency and frequency, is needed that captures historical transaction timing. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0963 .