Interactive Course

Building Response Models in R

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

  • 4 hours
  • 13 Videos
  • 53 Exercises
  • 935 Participants
  • 4,600 XP

Loved by learners at thousands of top companies:

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Course Description

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.

  1. 1

    Response models for aggregate data

    Free

    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. 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.

  3. 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.

  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.

  1. 1

    Response models for aggregate data

    Free

    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.

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Devon

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Devon Edwards Joseph

Lloyd's Banking Group

Louis

“DataCamp is the top resource I recommend for learning data science.”

Louis Maiden

Harvard Business School

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Ronald Bowers

Decision Science Analytics @ USAA

Kathrin Gruber
Kathrin Gruber

Assistant Professor of Econometrics, Erasmus University Rotterdam

Kathrin is Assistant Professor of Marketing Analytics and Data Science at the Department of Econometrics, Erasmus University Rotterdam. Her research interests are on the interface between Bayesian statistics and machine learning with focus on methods that are scalable to large problems. She likes to data puzzle and loves to code.

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Collaborators
  • Chester Ismay

    Chester Ismay

  • David Campos

    David Campos

  • Shon Inouye

    Shon Inouye

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