Easy availability of information on a customer's transactions with the firm and the pressure to establish financial returns from marketing investments has led to a dominance of models that directly connect marketing investments to sales at the customer level. Customer attitudes, on the other hand, have always been assumed to influence customer reactions to a firm's marketing communications, but have rarely been included in models that determine customer value.
We develop hierarchical dynamic models to empirically study whether inclusion of attitudes improves (a) estimates of marketing (sales) effectiveness in CLV models, and/or (b) predictions of CLV. If so, is the effect larger in retention or in spending? We develop models that enable us to fuse survey based customer attitude data with traditional CRM data. We assess whether inclusion of attitudes re-directs focus of marketing efforts, and whether the benefits outweigh the costs of collecting attitudinal information. For this study, we use monthly sales, sales calls, and survey based attitude information collected over three years from the same customers, of a multinational pharmaceutical firm. We develop hierarchical dynamic models that combine the sales and sales calls data that are available at regular time intervals, with customer attitudes that are only irregularly available, and carry out inference in the Bayesian framework.
Keywords: Customer attitudes; Customer profitability; Hierarchical dynamic models; Predictive cross-validation
Biography: Nalini Ravishanker is a Professor and the Undergraduate Program Director in Statistics, University of Connecticut, Storrs, Connecticut, USA. She is a Fellow of the American Statistical Association, Editor for Theory and Methods of Applied Stochastic Models in Business and Industry, and an Associate Editor for the Journal of Forecasting. Her current methodological and applied research interests include Time Series and Spatial data analysis; and Times-to-events Analysis. She also does considerable interdisciplinary research in several areas including Actuarial Science, Environmental Engineering, Finance, Marketing, Psychology, and Transportation Engineering