Abstract. The correct specification of spatial models, and especially the choice of the spatial weights matrix, represents a crucial decision for researchers using georeferenced data. However, few guidelines exist on which weights matrix is most appropriate in certain cases. This paper therefore (1) studies the sensitivity of testing and estimating spatial models with different weights matrix specifications and (2) formulates recommendations for researchers applying spatial models regarding model selection and weights matrix specification. The research is based on Monte Carlo simulations with synthetic data.JEL classification: C12, C13, C15, C52

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Customer retention has become a focal priority. However, the process of implementing an effective retention campaign is complex and dependent on firms’ ability to accurately identify both at-risk customers and those worth retaining. Drawing on empirical and simulated data from two online retailers, we evaluate the performance of several parametric and nonparametric churn prediction techniques, in order to identify the optimal modeling approach, dependent on context. Results show that under most circumstances (i.e., varying sample sizes, purchase frequencies, and churn ratios), the boosting technique, a nonparametric method, delivers superior predictability. Furthermore, in cases/contexts where churn is more rare, logistic regression prevails. Finally, where the size of the customer base is very small, parametric probability models outperform other techniques.

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Resource exchange theory suggests service recovery compensation is optimal when it is commensurate with what was lost (e.g., refund for overcharging). However, in practice, companies cannot always follow the theory-driven prescriptions, and the complaint recovery literature remains silent on how to best recover in such suboptimal situations. This study takes a resource-based theory stance to propose Mix&Match, a complaint recovery framework for tangible compensation offers (refunds, redeliveries, or credits) to optimize customer retention and lifetime value in both optimal and suboptimal complaint recovery scenarios. We find that matching tangible compensation with the complaint cause (e.g., redelivery for expired products) is the most effective recovery response for improving customer retention and lifetime value. However, in suboptimal nonmatching scenarios, monetary compensation in the form of store credit proves to be the most effective response.