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.

Fundamentals of market response models

Retail sales

Understanding sales

Linear response models

A linear response model for sales

Making predictions

Predictive performance

Nonlinear response models

Linearizing nonlinear functions

What 's the value added?

Response models for aggregate data

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.

Perluasan model bagian 1: Variabel dummy

Memahami variabel dummy

Dampak display terhadap penjualan

Dampak beberapa dummy terhadap penjualan

Bagaimana dengan harga?

Perluasan model bagian 2: Variabel dinamis

Cara membuat lag?

Menambahkan efek harga terdorong (lag)

Lag tambahan

Apa nilai tambahnya?

Berapa banyak ekstensi yang diperlukan?

Merangkum model

Prediktor yang tidak diperlukan

Menghapus prediktor

Mengeliminasi prediktor

Extended sales-response modeling

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.

Models for individual demand

Customer purchases

Summarizing the data

Competition

A linear probability model for beer demand

Logistic response models

A logistic model for beer demand

Bounded predictions

Average marginal effects

Effect plots

Probit response models

A probit model for beer demand

Logistic vs. probit

Model comparison

Response models for individual-level data

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.

Model selection

Extending the logistic response model

Summarizing the model

The deviance principle

Eliminating predictors

Classifications

Model confusion

ROC curves

Model validation

Subsetting

Model training

Out-of-sample testing

Wrapping it up

Extended choice modeling

Beer sales dataset

Beer choice dataset

Hampir setiap perusahaan mengumpulkan informasi digital sebagai bagian dari kampanye pemasaran dan menggunakannya untuk meningkatkan taktik pemasaran. Ilmuwan data sering ditugaskan menggunakan informasi ini untuk mengembangkan model statistik yang membantu profesional pemasaran menilai apakah tindakan mereka membuahkan hasil. Dalam kursus ini, Anda akan mempelajari cara menemukan pola dari tindakan pemasaran dan reaksi pelanggan dengan membangun model respons pasar yang sederhana. Secara khusus, Anda akan mempelajari cara mengkuantifikasi dampak variabel pemasaran, seperti harga dan berbagai taktik promosi, menggunakan data penjualan agregat dan data pilihan pada tingkat individu.

Introduction to Regression in R

Pelajari cara membuat model respons pasar untuk meningkatkan efektivitas rencana pemasaran Anda.

Membangun Model Respons di R

Pelajari cara membangun model sederhana respons pasar untuk meningkatkan efektivitas rencana pemasaran Anda.

Perluasan model bagian 1: Variabel dummy

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