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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.
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.Why choice?50 xpChoice data50 xpInspecting choice data50 xpFinding the levels of a factor50 xpInspecting a choice observation50 xpWhat did people choose?100 xpFitting and interpreting a choice model50 xpFitting a choice model100 xpInterpreting parameters50 xpUsing choice models to make decisions50 xpPredicting choice shares100 xpPlotting choice shares100 xp
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.Assembling choice data50 xpHow many choices do we have?50 xpAttributes and levels100 xpInspect a single choice50 xpConverting from wide to long50 xpConvert from wide to long100 xpSort the long data100 xpChange the Selection variable100 xpChoice data in two files50 xpInspect a choice in two files50 xpMerging two files100 xpVisualizing choice data50 xpWhat types of chocolate do people choose?100 xpDo people choose lower prices?100 xpDesigning a conjoint survey50 xp
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.Choice models - under the hood50 xpCreate mlogit.data object100 xpFit a choice model100 xpRemove the intercept100 xpInterpreting choice model parameters50 xpWhich chocolate brand is most preferred?50 xpComputing willingness-to-pay100 xpPrice as a factor100 xpLikelihood-ratio test100 xpIntercepts and interactions50 xpInteraction between brand and type100 xpInteraction between price and trial100 xpPredicting shares50 xpPredicting shares for the racer segment100 xpPredict shares for chocolate bars100 xpPlot shares for chocolate bars100 xp
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.What is a hierarchical choice model?50 xpHow many people answered the chocolate suvey?100 xpChocolate model with random price coefficient100 xpHeterogeneity in preferences for other features50 xpSetting effects codes100 xpChocolate model with all coefficients random100 xpInterpreting hierarchical model parameters50 xpHow do people value white chocolate?100 xpPredicting shares with hierarchical models50 xpPredicting shares for chocolates100 xpGoodbye and good luck!50 xp
In the following tracksMarketing Analytics
PrerequisitesIntermediate Regression in R
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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