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As competition is getting fiercer between retail formats, the strategy to prevent shoppers from churning is very important in terms of shopper relationship management. In particular, as the traditional market is threatened and the national chain enters the market, the impact from entry of a national chain such as failure of shopper retention becomes an important concern not only for local retailers but also for local communities. Also, it is important for national chains to predict potential new shoppers in newly launched market because it is directly related to market entry feasibility. In this case, accurate predictions can become more important than the relationship analysis between the influential factors. In general, shopper churn for retail stores is known as partial churn, not complete churn. Therefore, it is necessary to develop a predictive model to consider not only retail format choice (i.e., national chains or traditional market) but also the possibility of cross-shopping derived from the partial churn. The existing (linear or logistic) regression model is a very useful tool for analyzing the relationship between influential factors, but it requires a preliminary assumption of the population and has limitations in establishing a predictive model of complex nonlinear relationships.

Abstract

1. Introduction

2. Results & Conclusion

References

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