Loved by learners at thousands of companies
This is your chance to dive into the worlds of marketing and business analytics using R. Day by day, there are a multitude of decisions that companies have to face. With the help of statistical models, you're going to be able to support the business decision-making process based on data, not your gut feeling. Let us show you what a great impact statistical modeling can have on the performance of businesses. You're going to learn about and apply strategies to communicate your results and help them make a difference.
Modeling Customer Lifetime Value with Linear RegressionFree
How can you decide which customers are most valuable for your business? Learn how to model the customer lifetime value using linear regression.Customer lifetime value in CRM50 xpBenefits of knowing CLV50 xpLooking at data100 xpSimple linear regression50 xpUnderstanding residuals50 xpEstimating simple linear regression100 xpMultiple linear regression50 xpAvoiding multicollinearity100 xpInterpretation of coefficients50 xpModel validation, model fit, and prediction50 xpInterpretation of model fit50 xpFuture predictions of sales100 xp
Logistic Regression for Churn Prevention
Predicting if a customer will leave your business, or churn, is important for targeting valuable customers and retaining those who are at risk. Learn how to model customer churn using logistic regression.Churn prevention in online marketing50 xpApplication churn prevention50 xpData discovery100 xpPeculiarities of the dependent variable50 xpModeling and model selection50 xpModel specification and estimation100 xpStatistical significance50 xpModel specification100 xpIn-sample model fit and thresholding50 xpIn-sample fit full model100 xpIn-sample fit restricted model100 xpFinding the optimal threshold100 xpDanger of overfitting50 xpOut-of-sample validation and cross validation50 xpAssessing out-of-sample model fit100 xpCross validation100 xp
Modeling Time to Reorder with Survival Analysis
Learn how to model the time to an event using survival analysis. This could be the time until next order or until a person churns.Survival analysis: introduction50 xpApplications of survival analysis50 xpData for survival analysis100 xpCharacteristics of survival analysis50 xpSurvival curve analysis by Kaplan Meier50 xpSurvival function, hazard function and hazard rate50 xpThe survival object100 xpKaplan-Meier Analysis100 xpCox PH model with constant covariates50 xpProportional hazard assumption50 xpCox Proportional Hazard Model100 xpInterpretation of coefficients50 xpChecking model assumptions and making predictions50 xpViolation of the PH assumption50 xpModel assumptions100 xpPredictions100 xp
Reducing Dimensionality with Principal Component Analysis
CRM data can get very extensive. Each metric you collect could carry some interesting information about your customers. But handling a dataset with too many variables is difficult. Learn how to reduce the number of variables in your data using principal component analysis. Not only does this help to get a better understanding of your data. PCA also enables you to condense information to single indices and to solve multicollinearity problems in a regression analysis with many intercorrelated variables.PCA for CRM data50 xpPurposes of PCA50 xpGetting to know the data100 xpExploring the correlation structure50 xpPCA computation50 xpStandardization of data50 xpCompute a PCA100 xpThe result object of a PCA50 xpChoosing the right number of principal components50 xpHow many components are relevant?100 xpInterpretation of components100 xpVisualization with a biplot100 xpPrincipal components in a regression analysis50 xpRegression analysis with many variables50 xpLinear regression with principal components100 xpClosing50 xp
In the following tracksMarketing Analytics
DatasetsChurn dataSales dataSales data, months 2-4Survival dataDefault dataNews dataFirst CLV datasetSecond CLV datasetCustomer data
PrerequisitesIntroduction to Regression in R
Data Scientist at INWT Statistics
Data analytics was already part of Verena’s skill set during her studies of political and administrative science and statistics. Since earning her degree in 2014 Verena has been applying her experience and expertise in training and data science to her work at INWT.
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