Twee bestanden samenvoegen

choc_alts en choc_choice zijn nog geladen. In de vorige oefening heb je handmatig de rijen in elk bestand geïdentificeerd die bij dezelfde keuze horen. In deze oefening voeg je de twee bestanden samen om de chocoladedata weer naar long-formaat terug te zetten.

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Keuzemodellering voor marketing in R

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Voeg de bestanden choc_choice en choc_alts samen op Subject en Trial. Ken het resultaat toe aan choc_merge.
Zet de variabele Selection in choc_merge om van een integer die het gekozen alternatief aangeeft naar een logische waarde (TRUE/FALSE), zoals je in eerdere oefeningen hebt gedaan.
Gebruik head() om choc_merge te bekijken.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Merge choc_choice and choc_alts
choc_merge <- merge(___, ___, by=c("___", "___"))

# Convert Selection to a logical variable
choc_merge$Selection <- ___ == ___

# Inspect chocolate_merge using head
head(choc_merge)

Code bewerken en uitvoeren

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Keuzemodellering voor 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: Keuzedata samenstellen Exercise 2: Hoeveel keuzes hebben we?Exercise 3: Attributen en niveaus Exercise 4: Bekijk één enkele keuze Exercise 5: Converteren van breed naar lang Exercise 6: Converteren van breed naar lang Exercise 7: Sorteer de long data Exercise 8: Wijzig de variabele Selection Exercise 9: Keuzedata in twee bestanden Exercise 10: Bekijk een keuze in twee bestanden Exercise 11: Twee bestanden samenvoegen

Huidige oefening

Exercise 12: Keuzedata visualiseren Exercise 13: Welke soorten chocolade kiezen mensen?Exercise 14: Kiezen mensen voor lagere prijzen?Exercise 15: Een conjoint-enquête ontwerpen

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 - under the hood Exercise 2: Create mlogit.data object Exercise 3: Fit a choice model Exercise 4: Remove the intercept Exercise 5: Interpreting choice model parameters Exercise 6: Which chocolate brand is most preferred?Exercise 7: Computing willingness-to-pay Exercise 8: Price as a factor Exercise 9: Likelihood-ratio test Exercise 10: Intercepts and interactions Exercise 11: Interaction between brand and type Exercise 12: Interaction between price and trial Exercise 13: Predicting shares Exercise 14: Predicting shares for the racer segment Exercise 15: Predict shares for chocolate bars Exercise 16: Plot shares for chocolate bars

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!