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Machine Learning for Marketing Analytics in R

In this course you'll learn how to use data science for several common marketing tasks.

Comience El Curso Gratis

4 Horas17 Videos60 Ejercicios

12.412 AprendicesDeclaración de cumplimiento

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Preferido por estudiantes en miles de empresas

Descripción del curso

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.

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Obtenga acceso de su equipo a la biblioteca completa de DataCamp, con informes centralizados, tareas, proyectos y más

En las siguientes pistas

Análisis de marketing con R

1
Modeling Customer Lifetime Value with Linear Regression
Gratuito
How can you decide which customers are most valuable for your business? Learn how to model the customer lifetime value using linear regression.
Reproducir Capítulo Ahora
Customer lifetime value in CRM
50 xp
Benefits of knowing CLV
50 xp
Looking at data
100 xp
Simple linear regression
50 xp
Understanding residuals
50 xp
Estimating simple linear regression
100 xp
Multiple linear regression
50 xp
Avoiding multicollinearity
100 xp
Interpretation of coefficients
50 xp
Model validation, model fit, and prediction
50 xp
Interpretation of model fit
50 xp
Future predictions of sales
100 xp
2
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.
Reproducir Capítulo Ahora
Churn prevention in online marketing
50 xp
Application churn prevention
50 xp
Data discovery
100 xp
Peculiarities of the dependent variable
50 xp
Modeling and model selection
50 xp
Model specification and estimation
100 xp
Statistical significance
50 xp
Model specification
100 xp
In-sample model fit and thresholding
50 xp
In-sample fit full model
100 xp
In-sample fit restricted model
100 xp
Finding the optimal threshold
100 xp
Danger of overfitting
50 xp
Out-of-sample validation and cross validation
50 xp
Assessing out-of-sample model fit
100 xp
Cross validation
100 xp
3
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.
Reproducir Capítulo Ahora
Survival analysis: introduction
50 xp
Applications of survival analysis
50 xp
Data for survival analysis
100 xp
Characteristics of survival analysis
50 xp
Survival curve analysis by Kaplan Meier
50 xp
Survival function, hazard function and hazard rate
50 xp
The survival object
100 xp
Kaplan-Meier Analysis
100 xp
Cox PH model with constant covariates
50 xp
Proportional hazard assumption
50 xp
Cox Proportional Hazard Model
100 xp
Interpretation of coefficients
50 xp
Checking model assumptions and making predictions
50 xp
Violation of the PH assumption
50 xp
Model assumptions
100 xp
Predictions
100 xp
4
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.
Reproducir Capítulo Ahora
PCA for CRM data
50 xp
Purposes of PCA
50 xp
Getting to know the data
100 xp
Exploring the correlation structure
50 xp
PCA computation
50 xp
Standardization of data
50 xp
Compute a PCA
100 xp
The result object of a PCA
50 xp
Choosing the right number of principal components
50 xp
How many components are relevant?
100 xp
Interpretation of components
100 xp
Visualization with a biplot
100 xp
Principal components in a regression analysis
50 xp
Regression analysis with many variables
50 xp
Linear regression with principal components
100 xp
Closing
50 xp

Empresas

¿Entrenar a 2 o más personas?

Obtenga acceso de su equipo a la biblioteca completa de DataCamp, con informes centralizados, tareas, proyectos y más

En las siguientes pistas

Análisis de marketing con R

Sets De Datos

Churn data Sales data Sales data, months 2-4 Survival data Default data News data First CLV dataset Second CLV dataset Customer data

Colaboradores

Chester Ismay

Nick Solomon

Requisitos Previos

Introduction to Regression in R

Verena Pflieger

Data Scientist at INWT Statistics

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