Dampak beberapa dummy terhadap penjualan

Pabrik bir juga mendistribusikan kupon pabrikan yang menawarkan potongan harga untuk Hoppiness. Strategi pemasaran ketiga adalah menggabungkan aktivitas display dan kupon untuk meningkatkan volume penjualan.

Kali ini, Anda akan menghitung mean dari log(SALES) untuk setiap kombinasi tingkat DISPLAY, COUPON, dan DISPLAYCOUPON dengan menggunakan fungsi aggregate(). Anda dapat melakukannya dengan menambahkan variabel-variabel tersebut secara berurutan ke argumen formula.

Untuk menggambarkan efek dari aktivitas kupon dan display, Anda kembali mengestimasi model respons linear dengan menggunakan fungsi lm(). Seperti biasa, Anda menelaah koefisiennya.

Latihan ini adalah bagian dari kursus

Membangun Model Respons di R

Petunjuk latihan

Hitung mean dari log(SALES) untuk setiap kombinasi tingkat DISPLAY, COUPON, dan DISPLAYCOUPON.
Jelaskan log(SALES) dengan DISPLAY, COUPON, dan DISPLAYCOUPON. Gunakan fungsi lm() dan simpan hasilnya kembali ke objek bernama dummy.model.
Dapatkan koefisien dari dummy.model dengan menggunakan fungsi coef().

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Mean log(SALES)
___(___ ~ ___ + ___ + ___, FUN = ___, data = sales.data)

# Explain log(SALES) by DISPLAY, COUPON and DISPLAYCOUPON
dummy.model <- ___(___ ~ ___ + ___ + ___, data = sales.data)

# Obtain the coefficients

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Membangun Model Respons di R

SkillTag.level.intermediateSkillTag.label

4.8+

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

Latihan Saat Ini

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 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