Building Response Models in R
Learn to build simple models of market response to increase the effectiveness of your marketing plans.Start Course for Free
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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.
Response models for aggregate dataFree
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 models50 xpRetail sales100 xpUnderstanding sales100 xpLinear response models50 xpA linear response model for sales100 xpMaking predictions100 xpPredictive performance100 xpNonlinear response models50 xpLinearizing nonlinear functions100 xpWhat 's the value added?100 xp
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.Model extension part 1: Dummy variables50 xpUnderstanding dummy variables100 xpThe effect of display on sales100 xpThe effect of multiple dummies on sales100 xpWhat about price?100 xpModel extensions part 2: Dynamic variables50 xpHow to lag?100 xpAdding lagged price effects100 xpMore lags100 xpWhat's the value added?100 xpHow many extensions are needed?50 xpSummarizing the model100 xpUnnecessary predictors50 xpDropping predictors100 xpEliminating predictors100 xp
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.Models for individual demand50 xpCustomer purchases100 xpSummarizing the data100 xpCompetition100 xpA linear probability model for beer demand100 xpLogistic response models50 xpA logistic model for beer demand100 xpBounded predictions100 xpAverage marginal effects100 xpEffect plots100 xpProbit response models50 xpA probit model for beer demand100 xpLogistic vs. probit100 xpModel comparison100 xp
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.Model selection50 xpExtending the logistic response model100 xpSummarizing the model100 xpThe deviance principle100 xpEliminating predictors100 xpPredictive performance50 xpClassifications100 xpModel confusion100 xpROC curves100 xpModel validation50 xpSubsetting100 xpModel training100 xpOut-of-sample testing100 xpWrapping it up50 xp
In the following tracksMarketing Analytics with R
PrerequisitesIntroduction to Regression in R
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