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Modeling Brand Correlation Through Iterative Sparsity Search

The quantification of brand correlation is an important task in marketing research to understand the similarity of sales performance between multiple brands of similar products. We propose a statistical model for studying the sales performance of a single brand in multiple geographic locations (markets) as a spatial process, and the similarity between two brands is quantified as the covariance of two spatial processes. For fast computing that scales well to a large number of brands and markets, we propose a new algorithm called iterative sparsity search (ISS), to efficiently estimate a sparse covariance structure in the multivariate spatial model. We demonstrate the effectiveness of the proposed method using real-world data from consumer packaged goods in grocery stores. In this case study, the sales performance of multiple brands in multiple markets is modeled using multivariate spatial processes with a covariance structure incorporating both brand dependence and spatial dependence. The holdout-sample results in the study show that the brand correlation estimates from the proposed method are very useful in predicting the sales performance of grocery brands in unexplored markets.