Lag tambahan

Akan menarik untuk mengetahui apakah penggunaan kupon hanya menimbulkan dampak langsung pada penjualan ataukah dampaknya berlanjut ke periode berikutnya.

Sekali lagi, Anda menggeser prediktor COUPON ke belakang dengan menggunakan fungsi lag(). Hasilnya ditetapkan ke variabel baru Coupon.lag. Baik variabel COUPON asli maupun Coupon.lag ditambahkan ke hubungan log(SALES) ~ PRICE + Price.lag dengan menggunakan fungsi update().

Latihan ini adalah bagian dari kursus

Membangun Model Respons di R

Lihat Kursus

Petunjuk latihan

Buat variabel lag untuk COUPON dengan nama Coupon.lag.
Perbarui objek lag.model untuk COUPON dan Coupon.lag dengan menggunakan fungsi update().

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Take the lag of COUPON
Coupon.lag <- ___

# Update the model for COUPON and Coupon.lag
___(lag.model, . ~ . + ___ + ___)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Membangun Model Respons di R

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Mulai Kursus Gratis

The first chapter introduces you to the basic principles and concepts of market response models. Here, you will learn how to build simple response models for product sales. In addition, you will learn about the theoretical and practical differences between linear and non-linear models for sales responses.

Exercise 1: Fundamentals of market response models Exercise 2: Retail sales Exercise 3: Understanding sales Exercise 4: Linear response models Exercise 5: A linear response model for sales Exercise 6: Making predictions Exercise 7: Predictive performance Exercise 8: Nonlinear response models Exercise 9: Linearizing nonlinear functions Exercise 10: What 's the value added?

An effective marketing strategy combines all the tools available to communicate the benefits of a product. The key is crafting the right mix of these tools to achieve sales increases and market share goals. In the second chapter, you will learn how to incorporate the effects of advertising and promotion in your sales-response model and how to identify the marketing strategy that is most likely to succeed.

Exercise 1: Perluasan model bagian 1: Variabel dummy Exercise 2: Memahami variabel dummy Exercise 3: Dampak display terhadap penjualan Exercise 4: Dampak beberapa dummy terhadap penjualan Exercise 5: Bagaimana dengan harga?Exercise 6: Perluasan model bagian 2: Variabel dinamis Exercise 7: Cara membuat lag?Exercise 8: Menambahkan efek harga terdorong (lag)Exercise 9: Lag tambahan

Latihan Saat Ini

Exercise 10: Apa nilai tambahnya?Exercise 11: Berapa banyak ekstensi yang diperlukan?Exercise 12: Merangkum model Exercise 13: Prediktor yang tidak diperlukan Exercise 14: Menghapus prediktor Exercise 15: Mengeliminasi prediktor

A company can only be successful in the market if its products have a competitive advantage over those of its rivals. To develop an effective marketing strategy in a competitive environment, it is essential to understand the interrelationship between marketing activity and customer behavior. In this chapter, you will learn how to explain the effects of temporary price changes on customer brand choice by employing logistic and probit response models.

Exercise 1: Models for individual demand Exercise 2: Customer purchases Exercise 3: Summarizing the data Exercise 4: Competition Exercise 5: A linear probability model for beer demand Exercise 6: Logistic response models Exercise 7: A logistic model for beer demand Exercise 8: Bounded predictions Exercise 9: Average marginal effects Exercise 10: Effect plots Exercise 11: Probit response models Exercise 12: A probit model for beer demand Exercise 13: Logistic vs. probit Exercise 14: Model comparison

The main goal of response modeling is to enable marketers to not only see a payoff for their actions today, but also tomorrow. In order to view this future payoff, a simple but reliable statistical model is required. In this last chapter, you will learn how to evaluate the predictive performance of logistic response models.

Exercise 1: Model selection Exercise 2: Extending the logistic response model Exercise 3: Summarizing the model Exercise 4: The deviance principle Exercise 5: Eliminating predictors Exercise 6: Predictive performance Exercise 7: Classifications Exercise 8: Model confusion Exercise 9: ROC curves Exercise 10: Model validation Exercise 11: Subsetting Exercise 12: Model training Exercise 13: Out-of-sample testing Exercise 14: Wrapping it up