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R로 배우는 Machine Learning 기반 마케팅 분석

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업데이트됨 2024. 5.

이 강의에서는 데이터 과학을 활용해 여러 핵심 마케팅 업무를 수행하는 방법을 학습합니다.

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RMachine Learning

4시간

17 동영상

60 연습 문제

4,200 XP

13,517

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R을 사용해 마케팅과 비즈니스 애널리틱스의 세계로 뛰어들어 보세요. 기업은 매일 수많은 의사결정을 내려야 합니다. 통계 모델의 도움을 받으면 감(直感)이 아니라 데이터에 근거해 의사결정을 지원할 수 있어요. 통계 모델링이 비즈니스 성과에 얼마나 큰 영향을 줄 수 있는지 보여 드리겠습니다. 결과를 효과적으로 커뮤니케이션하는 전략을 배우고, 실제로 적용해 의미 있는 변화를 만들어 보세요.

선수 조건

Introduction to Regression in R

1

Modeling Customer Lifetime Value with Linear Regression

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 CRM

Benefits of knowing CLV

Looking at data

Simple linear regression

Understanding residuals

Estimating simple linear regression

Multiple linear regression

Avoiding multicollinearity

Interpretation of coefficients

Model validation, model fit, and prediction

Interpretation of model fit

Future predictions of sales

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.

Churn prevention in online marketing

Application churn prevention

Data discovery

Peculiarities of the dependent variable

Modeling and model selection

Model specification and estimation

Statistical significance

Model specification

In-sample model fit and thresholding

In-sample fit full model

In-sample fit restricted model

Finding the optimal threshold

Danger of overfitting

Out-of-sample validation and cross validation

Assessing out-of-sample model fit

Cross validation

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.

Survival analysis: introduction

Applications of survival analysis

Data for survival analysis

Characteristics of survival analysis

Survival curve analysis by Kaplan Meier

Survival function, hazard function and hazard rate

The survival object

Kaplan-Meier Analysis

Cox PH model with constant covariates

Proportional hazard assumption

Cox Proportional Hazard Model

Interpretation of coefficients

Checking model assumptions and making predictions

Violation of the PH assumption

Model assumptions

Predictions

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.

PCA for CRM data

Purposes of PCA

Getting to know the data

Exploring the correlation structure

PCA computation

Standardization of data

Compute a PCA

The result object of a PCA

Choosing the right number of principal components

How many components are relevant?

Interpretation of components

Visualization with a biplot

Principal components in a regression analysis

Regression analysis with many variables

Linear regression with principal components

R로 배우는 Machine Learning 기반 마케팅 분석

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