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.

Model extension part 1: Dummy variables

Understanding dummy variables

The effect of display on sales

The effect of multiple dummies on sales

What about price?

Model extensions part 2: Dynamic variables

How to lag?

Adding lagged price effects

More lags

What's the value added?

How many extensions are needed?

Summarizing the model

Unnecessary predictors

Dropping predictors

Eliminating predictors

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

The deviance principle

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

Almost every company collects digital information as part of their marketing campaigns and uses it to improve their marketing tactics. Data scientists are often tasked with using this information to develop statistical models that enable marketing professionals to see if their actions are paying off. In this course, you will learn how to uncover patterns of marketing actions and customer reactions by building simple models of market response. In particular, you will learn how to quantify the impact of marketing variables, such as price and different promotional tactics, using aggregate sales and individual-level choice data.

Introduction to Regression in R

Learn to build simple models of market response to increase the effectiveness of your marketing plans.

Building Response Models in R

Análise de marketing in R

Assistant Professor of Econometrics Erasmus University Rotterdam

Model extensions part 2: Lagged effects

About lags

Let's practice!

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Extended sales-response modeling - Model extensions part 2: Dynamic variables

Model extensions part 2: Dynamic variables

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