Spatial Models in Marketing

Marketing Letters (Impact Factor: 0.63). 02/2005; 16(3):267-278. DOI: 10.1007/s11002-005-5891-3
Source: RePEc

ABSTRACT Marketing science models typically assume that responses of one entity (firm or consumer) are unrelated to responses of other entities. In contrast, models constructed using tools from spatial statistics allow for cross-sectional and longitudinal correlations among responses to be explicitly modeled by locating entities on some type of map. By generalizing the notion of a map to include demographic and psychometric representations, spatial models can capture a variety of effects (spatial lags, spatial autocorrelation, and spatial drift) that impact firm or consumer decision behavior. Marketing science applications of spatial models and important research opportunities are discussed. Copyright Springer Science + Business Media, Inc. 2005

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Abstract 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
  • [Show abstract] [Hide abstract]
    ABSTRACT: Traditional CRM models often ignore the correlation that could exist among the purchasing behavior of surrounding prospects. Hence, a generalized linear autologistic regression model can be used to capture this interdependence and improve the predictive performance of the model. In particular, customer acquisition models can benefit from this. These models often suffer from a lack of data quality due to the limited amount of information available about potential new customers. Based on a customer acquisition model of a Japanese automobile brand, this study shows that the extra value resulting from incorporating neighborhood effects can vary significantly depending on the granularity level on which the neighborhoods are composed. A model based on a granularity level that is too coarse or too fine will incorporate too much or too little interdependence resulting in a less than optimal predictive improvement. Since neighborhood effects can have several sources (i.e. social influence, homophily and exogeneous shocks), this study suggests that the autocorrelation can be divided into several parts, each optimally measured at a different level of granularity. Therefore, a model is introduced that simultaneously incorporates multiple levels of granularity resulting in even more accurate predictions. Further, the effect of the sample size is examined. This shows that including spatial interdependence using finer levels of granularity is only useful when enough data is available to construct stable spatial lag effects. As a result, extending a spatial model with multiple granularity levels becomes increasingly valuable when the data sample becomes larger.
    Journal of Intelligent Information Systems 41(1). · 0.83 Impact Factor
  • Source

Full-text (2 Sources)

Available from
May 30, 2014