Advances in spatial dependence modeling of consumer attitudes with Bayesian factor models

The primary goal of this thesis was to understand how the spatial dependence of consumer attitudes can be modeled, what additional benefits the recovering of spatial dependence can bring and how these benefits can be utilized by researchers and practitioners. So far, most of the approaches to study attitudes assumed spatial independence, i.e. that location does not matter. We argue, that in many cases space matters and attitudes observed in one location are correlated with attitudes observed in other, usually neighboring, locations. Three studies contributed to achieve the aforementioned goal. The first study addressed the specification issues of spatial models and the consequences of their misspecification. Recommendations for researchers regarding model selection and specification of weights matrices were formulated. In the second study a new Bayesian Spatial Factor Analytic (BSFA) model was developed. It was applied on Schwartz value priority data obtained from five European countries and this application revealed interesting spatial patterns of some value domains which were not possible to detect otherwise. Finally, the third study was devoted to extending the proposed BSFA model by introducing exogenous covariates which can influence the latent constructs and adapting the model with cutoff point formulation to accommodate the discrete measurement scale. The applicability of the model was demonstrated in empirical study on consumer attitudes in the financial domain. Further, a new procedure for spatial scheduling, based on the model results, was introduced which has substantial managerial relevance.

Managing B2B customer churn, retention and profitability

It is now widely accepted that firms should direct more effort into retaining existing customers than to attracting new ones. To achieve this, customers likely to defect need to be identified so that they can be approached with tailored incentives or other bespoke retention offers. Such strategies call for predictive models capable of identifying customers with higher probabilities of defecting in the relatively near future. A review of the extant literature on customer churn models reveals that although several predictive models have been developed to model churn in B2C contexts, the B2B context in general, and non-contractual settings in particular, have received less attention in this regard. Therefore, to address these gaps, this study proposes a data-mining approach to model non-contractual customer churn in B2B contexts. Several modeling techniques are compared in terms of their ability to predict true churners. The best performing data-mining technique (boosting) is then applied to develop a profit maximizing retention campaign. Results confirm that the model driven approach to churn prediction and developing retention strategies outperforms commonly used managerial heuristics. © 2014 Elsevier Inc

Comparing churn prediction techniques and assessing their performance: a contingent perspective

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

Spatial Dependence and Heterogeneity in Bayesian Factor Analysis: A Cross-National Investigation of Schwartz Values

In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the model parameters and demonstrates the consequences of ignoring spatial dependence. Specifically, we find inefficiency in the estimates of the factor score means and bias and inefficiency in the estimates of the corresponding covariance matrix. We apply the model to Schwartz value priority data obtained from 5 European countries. We show that the Schwartz motivational types of values, such as Conformity, Tradition, Benevolence, and Hedonism, possess high spatial autocorrelation. We identify several spatial patternsspecifically, Conformity and Hedonism have a country-specific structure, Tradition has a NorthSouth gradient that cuts across national borders, and Benevolence has SouthNorth cross-national gradient. Finally, we show that conventional factor analysis may lead to a loss of valuable insights compared with the proposed approach