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Identifying Drivers of Outcomes: Linear Models

In: Python for Marketing Research and Analytics

Author

Listed:
  • Jason S. Schwarz

    (Google)

  • Chris Chapman

    (Google)

  • Elea McDonnell Feit

    (Drexel University)

Abstract

In this chapter we investigate linear models, which are often used in marketing to explore the relationship between an outcome of interest and other variables. A common application in survey analysis is to model satisfaction with a product in relation to specific elements of the product and its delivery; this is called “satisfaction drivers analysis.” Linear models are also used to understand how price and advertising are related to sales, and this is called “marketing mix modeling.” There are many other situations in which it is helpful to model an outcome, known formally as a response or dependent variable, as a function of predictor variables, known as explanatory or independent variables. Once a relationship is estimated, one can use the model to make predictions of the outcome for other values of the predictors. For example, in a course, we might find that final exam scores can be predicted based on the midterm exam score.

Suggested Citation

  • Jason S. Schwarz & Chris Chapman & Elea McDonnell Feit, 2020. "Identifying Drivers of Outcomes: Linear Models," Springer Books, in: Python for Marketing Research and Analytics, chapter 0, pages 137-165, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-49720-0_7
    DOI: 10.1007/978-3-030-49720-0_7
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