Menafsirkan parameter

Parameter model memberi tahu kita tentang tingkat keinginan terhadap fitur-fitur dari alternatif. Misalnya, parameter untuk convertyes menunjukkan seberapa besar orang lebih menyukai atap convertible dibandingkan tingkat dasar, yaitu convertno. Jika parameter untuk convertyes bernilai positif, maka rata-rata orang lebih menyukai mobil dengan atap convertible. Jika bernilai negatif, mereka lebih menyukai mobil tanpa atap convertible.

Gunakan summary() untuk melihat parameter model untuk objek model m1. Lalu tafsirkan parameter untuk convertyes guna menentukan apakah mobil convertible lebih atau kurang diminati dibandingkan mobil non-convertible.

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Pemodelan Pilihan untuk Pemasaran di R

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Pemodelan Pilihan untuk Pemasaran di 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: Mengapa pilihan?Exercise 2: Data pilihan Exercise 3: Memeriksa data pilihan Exercise 4: Menemukan level dari suatu faktor Exercise 5: Memeriksa sebuah observasi pilihan Exercise 6: Apa yang dipilih orang?Exercise 7: Membangun dan menafsirkan model pilihan Exercise 8: Memasang model pilihan Exercise 9: Menafsirkan parameter

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Exercise 10: Menggunakan model pilihan untuk mengambil keputusan Exercise 11: Memprediksi pangsa pilihan Exercise 12: Memplot pangsa pilihan

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 - 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!