Modeling Geo-dependent Attitudes Using Bayesian Spatial Factor Analysis

Spatial variation in attitudes plays an important role in decisions on geographical marketing efforts, such as targeting of direct mail campaigns and scheduling of sales representatives. Similarly, for financial service companies, it is important to schedule their financial planners across servable geographical regions based on the spatial heterogeneity in consumer preferences and attitudes towards financial products. However, studying these attitudes is difficult because they are latent in nature, often spatially correlated, and data might be sparse for some regions. To address these challenges, we propose a heterogeneous spatial factor analytical model which allows extracting spatially correlated latent factors. The model is implemented in a Bayesian framework dealing with the sparse data problem by regions borrowing information from neighboring regions. Next, we propose a procedure for spatial scheduling based on the model results. Model performance is evaluated on artificial data. In an empirical study on consumer attitudes in the financial domain, we demonstrate model applicability. In particular, we show that our approach yields important insights on spatially-varying attitudes, which can be used for improved assigning of financial planners to regions. Finally, we increase managerial relevance by discussing additional marketing decisions that can be supported by this approach and discuss areas for future research.