1. Apprendre
    2. /
  2. Cours
    3. /
  3. Choice Modeling for Marketing in R
  • 1

    Quickstart Guide

    Gratuit

    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.

  • 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.

  • 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.

  • 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.

Initializing

Exercice

Predicting shares for chocolates

A store wants to carry a line of chocolates that is in a data frame I've already loaded called choc_line. I've already effects coded it and stored the code model matrix in choc_line_coded. In this stepped exercise, you will fill in the key elements of the code for computing shares for choc_line from the hierarchical model choc_m8.

Instructions 1/4

undefined XP
    1
    2
    3
    4

First, we are going to obtain the mean coefficients for all users from the model object. These are stored in the first 9 elements of coef(choc_m8). Assign them to coef_means so that we can use them later.