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Choice Modeling for Marketing in R

Learn to analyze and model customer choice data in R.

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4 Hours17 Videos54 Exercises4,993 Learners4100 XPMarketing Analytics Track

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Course Description

People make choices everyday. They choose products like orange juice or a car, decide who to vote for, and choose how to get to work. Marketers, retailers, product designers, political scientists, transportation planners, sociologists, and many others want to understand what drives these choices. Choice models predict what people will choose as a function of the features of the options available and can be used to make important product design decisions. This course will teach you how to organize choice data, estimate choice models in R and present findings. This course covers both analyses of observed real-world choices and the survey-based approach called conjoint analysis.

  1. 1

    Quickstart Guide


    Our goal for this chapter is to get you through the entire choice modeling process as quickly as possible, so that you get a broad understanding of what we can do with choice models and how the choice modeling process works. The main idea here is that we can use a choice model to understand how customers' product choices depend on the features of those products. Do sportscar buyers prefer manual transmissions to automatic? By how much? In order to give you an overview, we will skip over many of the details. In later chapters, we will go back and cover important issues in preparing data, specifying and interpreting models and reporting your findings, so that you are fully prepared to use these methods with your own choice data.

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    Why choice?
    50 xp
    Choice data
    50 xp
    Inspecting choice data
    50 xp
    Finding the levels of a factor
    50 xp
    Inspecting a choice observation
    50 xp
    What did people choose?
    100 xp
    Fitting and interpreting a choice model
    50 xp
    Fitting a choice model
    100 xp
    Interpreting parameters
    50 xp
    Using choice models to make decisions
    50 xp
    Predicting choice shares
    100 xp
    Plotting choice shares
    100 xp
  2. 2

    Managing and Summarizing Choice Data

    There are many different places to get choice data and different ways it can be formatted. In this chapter, we will take data that is provided in several alternative formats and learn how to get it into shape for choice modeling. We will also discuss how you can build a survey to collect your own choice data.

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  3. 3

    Building Choice Models

    In this chapter, we take deeper dive into estimating choice models. To give you a foundation for thinking about choice models, we will focus on how the multinomial logit model converts the product features into a prediction for what the decision maker will choose. This will give you a framework for making decisions about which features to include in your model.

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  4. 4

    Hierarchical Choice Models

    Different people have different tastes and preferences. This seems intuitively obvious, but there is also extensive research in marketing showing that this is true. This chapter covers choice models where we assume that different decision makers have different preferences that influence their choices. When our models recognize that different consumers have different preferences, they tend to make larger share predictions for niche products that appeal to a subset of consumers. Hierarchical models are used in most commercial choice modeling applications, so it is important to understand how they work.

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In the following tracks

Marketing Analytics


dcamposlizDavid CamposchesterChester IsmayshoninouyeShon Inouye
Elea McDonnell Feit Headshot

Elea McDonnell Feit

Assistant Professor of Marketing at Drexel University

Elea is a marketing professor at Drexel University and a Senior Fellow of Wharton Customer Analytics. She uses analytics to understand people so that companies can make better decisions. She enjoys making analytics accessible to marketers and co-wrote R for Marketing Research and Analytics. Her husband has a much cooler job working on data acquisition systems for race cars and, sadly, she has not convinced him to use R.
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