R Tutorial: Why choice?

Key Takeaways

hi I'm Ellie McDonald fight and I'm a marketing professor at Drexel University before I became a professor I worked at General Motors where I used choice models to help design new cars I found my work at GM so interesting that I decided to get a PhD focusing on choice modeling and after I finished that I had a job as a methodologist at a company that's specialized in using choice models to help Intel Warner Brothers dole and other big companies make important product design and pricing decisions choice modeling and it's close cousin conjoint analysis are one of the most popular modeling tools used in marketing they're also used in other fields like political science and transportation employer regression which you should already be familiar with we predict a number for instance we could predict the sales at a store based on the features of that store like its size or the population near the store in that case the thing we are predicting sales is a number but many events that we're interested in as data scientists and marketers are choices from a set of things when a customer goes to an online retailer she selects one dress from many that are available when you decide what to watch on Netflix tonight you'll choose one show from a menu of available content when a customer buys a car she chooses a model from those that are available in her region as a marketer we want to know how features of cars relate to which car a customer will choose and we can use that information to predict what will happen to market share if we change our product date on choices doesn't fit well within the linear regression framework instead we use another type of model called a multinomial logistic regression or the multinomial logit model multinomial logit models are used to predict a choice from a set of two or more options the prediction is based on features of each alternative for instance we can predict the likelihood of choosing a particular car based on the features of available cars you may have heard about logistic regression before logistic regression is a special case of multinomial logistic regression that we use with data on binary yes/no choices like customers deciding whether to redeem an offer we're going to focus on multinomial logistic regression which is more general and can be used to predict choices from among two three or more alternatives choice models have a lot of applications within marketing when I worked at GM we used choice models to understand which features would make our cars more desirable you can also use choice modeling to determine how to price a product based on how customers trade-off price against other product attributes an online retailer can use choice models to understand how features of the website like a customer favorite flag that you put on a product preview affect what customers ultimately buy all of these are great examples of how choice models can be used in marketing in this chapter I'm going to focus on a data set that describes customers choosing sports cars and in later chapters will analyze chocolate choices these are two of my favorite product categories but these are just examples and I hope you will start thinking about all the different types of choices you might want to study