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Building Response Models in R

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

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4 Hours13 Videos53 Exercises2,403 Learners4600 XPMarketing Analytics Track

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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.

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    Fundamentals of market response models
    50 xp
    Retail sales
    100 xp
    Understanding sales
    100 xp
    Linear response models
    50 xp
    A linear response model for sales
    100 xp
    Making predictions
    100 xp
    Predictive performance
    100 xp
    Nonlinear response models
    50 xp
    Linearizing nonlinear functions
    100 xp
    What 's the value added?
    100 xp
  2. 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.

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  3. 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.

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  4. 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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In the following tracks

Marketing Analytics

Collaborators

dcamposliz
David Campos
chester
Chester Ismay
shoninouye
Shon Inouye

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