Interazione tra marca e tipo

I dati chocolate sono caricati come oggetto mlogit.data e sono pronti all'uso. Se vuoi, esplorali per rinfrescarti la memoria. Poi adatta un modello con un'interazione tra Brand e Type.

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Choice Modeling for Marketing in R

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Istruzioni dell'esercizio

Aggiungi una formula al comando mlogit() per un modello con Brand, Type, Price e l'interazione tra Brand e Type. Ricorda che la colonna Selection contiene la scelta osservata e non dimenticare di escludere l'intercetta.
Riassumi choc_m4 per vedere i coefficienti del modello.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# add the formula for mlogit
choc_m4 <- mlogit(___, data=chocolate)

# use summary to see the coefficients

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Questo esercizio fa parte del corso

Choice Modeling for Marketing in R

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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.

Exercise 1: Why choice?Exercise 2: Choice data Exercise 3: Inspecting choice data Exercise 4: Finding the levels of a factor Exercise 5: Inspecting a choice observation Exercise 6: What did people choose?Exercise 7: Fitting and interpreting a choice model Exercise 8: Fitting a choice model Exercise 9: Interpreting parameters Exercise 10: Using choice models to make decisions Exercise 11: Predicting choice shares Exercise 12: Plotting choice shares

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.

Exercise 1: Assembling choice data Exercise 2: How many choices do we have?Exercise 3: Attributes and levels Exercise 4: Inspect a single choice Exercise 5: Converting from wide to long Exercise 6: Convert from wide to long Exercise 7: Sort the long data Exercise 8: Change the Selection variable Exercise 9: Choice data in two files Exercise 10: Inspect a choice in two files Exercise 11: Merging two files Exercise 12: Visualizing choice data Exercise 13: What types of chocolate do people choose?Exercise 14: Do people choose lower prices?Exercise 15: Designing a conjoint survey

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.

Exercise 1: Choice models - dietro le quinte Exercise 2: Crea un oggetto mlogit.data Exercise 3: Stima un modello di scelta Exercise 4: Rimuovi l'intercetta Exercise 5: Interpretare i parametri del modello di scelta Exercise 6: Qual è la marca di cioccolato più preferita?Exercise 7: Calcolare la willingness-to-pay Exercise 8: Prezzo come fattore Exercise 9: Test del rapporto di verosimiglianza Exercise 10: Intercette e interazioni Exercise 11: Interazione tra marca e tipo

Esercizio in corso

Exercise 12: Interazione tra prezzo e ordine della domanda Exercise 13: Prevedere le quote Exercise 14: Prevedere le quote per il segmento racer Exercise 15: Prevedi le quote per le tavolette di cioccolato Exercise 16: Traccia le quote per le barrette di cioccolato

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.

Exercise 1: What is a hierarchical choice model?Exercise 2: How many people answered the chocolate suvey?Exercise 3: Chocolate model with random price coefficient Exercise 4: Heterogeneity in preferences for other features Exercise 5: Setting effects codes Exercise 6: Chocolate model with all coefficients random Exercise 7: Interpreting hierarchical model parameters Exercise 8: How do people value white chocolate?Exercise 9: Predicting shares with hierarchical models Exercise 10: Predicting shares for chocolates Exercise 11: Goodbye and good luck!