Time-Series Models in Marketing

  • Marnik G. Dekimpe
  • Philip Hans Franses
  • Dominique M. Hanssens
  • Prasad A. Naik
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 121)


1Marketing data appear in a variety of forms. A frequently occurring form is time-series data, for example, sales per week, market shares per month, the price evolution over the last few years, or historically-observed advertising-spending patterns. The main feature of time-series data is that the observations are ordered over time, and hence that it is likely that earlier observations have predictive content for future observations. Indeed, if relative prices are, say, 1.50 today, they most likely will be around 1.50 tomorrow too, or in any case, not a value of 120.

Time series can refer to a single variable, such as sales or advertising, but can also cover a vector of variables, for example sales, prices and advertising, jointly. In some instances, marketing modelers may want to build a univariate model for a time series, and analyze the series strictly as a function of its own past. This is, for example, the case when one has to forecast (or extrapolate) exogenous...


Kalman Filter Brand Equity Price Promotion Persistence Modeling Lucas Critique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Marnik G. Dekimpe
    • 1
  • Philip Hans Franses
  • Dominique M. Hanssens
  • Prasad A. Naik
  1. 1.Tilburg University, Tilburg, The Netherlands and Catholic University LeuvenLeuvenBelgium

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