Course
Building Response Models in R
IntermediateSkill Level
Updated 11/2022RProbability & Statistics4 hr13 videos53 Exercises4,600 XP3,424Statement of Accomplishment
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Prerequisites
Introduction to Regression in R1
Response models for aggregate data
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.
2
Extended sales-response modeling
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.
3
Response models for individual-level data
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.
4
Extended choice modeling
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.
Building Response Models in R
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FAQs
What types of marketing models will I build in this course?
You will build sales-response models using both aggregate sales data and individual-level choice data, including linear, non-linear, logistic, and probit response models.
Do I need prior experience with regression in R?
Yes, Introduction to Regression in R is a prerequisite. You should also be comfortable with dplyr, ggplot2, and basic statistics in R before enrolling.
How does this course help marketing professionals?
It teaches you to quantify the impact of price changes, advertising, and promotions on sales and customer brand choice, helping marketers evaluate which strategies are most effective.
Does the course cover how to predict future customer behavior?
Yes, the final chapter focuses on evaluating the predictive performance of logistic response models, enabling you to forecast future payoff from marketing actions.
How many exercises are included in the course?
The course includes 91 exercises across 4 chapters, with an estimated median completion time of about 2 hours and an average of roughly 2.4 hours.
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