Python for Marketing Research and Analytics by Jason S. Schwarz & Chris Chapman & Elea McDonnell Feit

Author:Jason S. Schwarz & Chris Chapman & Elea McDonnell Feit
Language: eng
Format: epub
ISBN: 9783030497200
Publisher: Springer International Publishing

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In this case, if someone received a 70% on the midterm exam, we would expect them to receive a score of 87% on the final exam.

In this chapter, we illustrate linear modeling with a satisfaction drivers analysis using survey data for customers who have visited an amusement park. In the survey, respondents report their levels of satisfaction with different aspects of their experience, and their overall satisfaction. Marketers frequently use this type of data to figure out what aspects of the experience drive overall satisfaction, asking questions such as, “Are people who are more satisfied with the rides also more satisfied with their experience overall?” If the answer to this question is “no,” then the company may decide to invest in improving other aspects of the experience.

An important thing to understand is that, despite its name, driver does not imply causation. A model only represents an association among variables. Consider a survey of automobile purchasers that finds a positive association between satisfaction and price paid. If a brand manager wants customers to be more satisfied, does this imply that she should raise prices? Probably not. It is more likely that price is associated with higher quality, which then leads to higher satisfaction. Results should be interpreted cautiously and considered in the context of domain knowledge.

Linear models are a core tool in statistics, and the statsmodels package offers an excellent set of functions for estimating them. As in other chapters, we review the basics and demonstrate how to conduct linear modeling in Python. The chapter does not review everything that one would wish to know in practice. We encourage readers who are unfamiliar with linear modeling to supplement this chapter with a review of linear modeling in a statistics or marketing research textbook, where it might appear under a name such as regression analysis, linear regression, or least-squares fitting.

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