Skip to content
Customer Analytics in R

Customer Analytics in R

In this course you will work through a customer analytics project from beginning to end, using R.

Overview

In this course you will work through a customer analytics project from beginning to end, using R. You will start by gaining an understanding of the problem and the context, and continue to clean, prepare and explore the relevant data. You’ll work on feature engineering, handling dates, summarization, and how to work with the customer lifecycle concept in data analysis. The course culminates with a report that you will write, and a recommendation that you will prepare for a hypothetical company.

  • Introductory
  • 4 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

After completing this course you will be able to:

  • Explore and prepare a transactional database for analysis
  • Explore distribution of variables and build behavioral customer segments
  • Make business recommendations on basis of segmentation
  • Incorporate customer lifecycle analysis into planning
  • Apply best industry practices in plotting transactional data trends of customers with ggplot2

Who Should Take This Course

Marketing and IT managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters. This course is especially useful if you want to understand customer analytics, undertake pilots with minimum setup costs, manage analytics, or work with consultants or technical experts.

Our Instructors

Course Syllabus

Week 0

  • Practical exploration of transactional retail industry dataset – understanding distributions and meaning of variables
  • Cleaning data
  • Summarizing data with dplyr
  • Preparing a customer summary table for initial analysis
  • Homework – finishing R code in the R Markdown
  • Analyzing customers using the customer summary view built in week 1
  • Looking for outliers and dealing with them
  • Plotting data with ggplot2
  • Exploring distribution of variables and building behavioral customer segments
  • Writing your own R functions for dplyr & ggplot2 for faster analysis
  • Analyzing created segments and making business recommendations
  • Homework – create new segments on your own, build new features, make your own business recommendations
  • Introduction to customer lifecycle and how to think about it from data perspective
  • Advanced dplyr – introduction to window functions e.g. LAG, to build monthly customer summary data snapshots
  • Introduction to cross-joins in R to build monthly summary table
  • Extensive dealing with dates – learning about lubridate package
  • Creating new segments based on learnings from weeks 1 and 2
  • Homework – Detect outliers and make a decision how to define new monthly behavioral customer segments
  • Best industry practices in plotting transactional data trends of customers with ggplot2
  • Analyzing monthly summary data and making conclusions
  • Capstone project: Practical customer analytics case project where you will write a business recommendation for a hypothetical company

Week 1

Exploring and preparing transactional dataset for analysis with R

  • Practical exploration of transactional retail industry dataset – understanding distributions and meaning of variables
  • Cleaning data
  • Summarizing data with dplyr
  • Preparing a customer summary table for initial analysis
  • Homework – finishing R code in the R Markdown

Week 2

Analyzing customer summary table with R

  • Analyzing customers using the customer summary view built in week 1
  • Looking for outliers and dealing with them
  • Plotting data with ggplot2
  • Exploring distribution of variables and building behavioral customer segments
  • Writing your own R functions for dplyr & ggplot2 for faster analysis
  • Analyzing created segments and making business recommendations
  • Homework – create new segments on your own, build new features, make your own business recommendations

Week 3

More advanced techniques for feature engineering and transactional data analysis with R

  • Introduction to customer lifecycle and how to think about it from data perspective
  • Advanced dplyr – introduction to window functions e.g. LAG, to build monthly customer summary data snapshots
  • Introduction to cross-joins in R to build monthly summary table
  • Extensive dealing with dates – learning about lubridate package
  • Creating new segments based on learnings from weeks 1 and 2
  • Homework – Detect outliers and make a decision how to define new monthly behavioral customer segments

Week 4

Exploring trends in customer behavior with R and the Capstone project

  • Best industry practices in plotting transactional data trends of customers with ggplot2
  • Analyzing monthly summary data and making conclusions
  • Capstone project: Practical customer analytics case project where you will write a business recommendation for a hypothetical company

Class Dates

2022

11/11/2022 to 12/09/2022
Instructors:

2023

05/12/2023 to 06/09/2023
Instructors:
11/10/2023 to 12/08/2023
Instructors:

2024

05/10/2024 to 06/07/2024
Instructors:
11/08/2024 to 12/06/2024
Instructors:

Prerequisites

Familiarity with R (including the package ggplot2 and dplyer) is needed.

Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

Frequently Asked Questions

  • What is your satisfaction guarantee and how does it work?

  • Can I transfer or withdraw from a course?

  • Who are the instructors at Statistics.com?

Visit our knowledge base and learn more.

Register For This Course

Analysis of Survey Data from Complex Sample Designs

Additional Information

Homework

Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and end of course capstone project.

Note: There will be a mid-week discussion exercise in the first week of the course.

Course Text

All required study materials will be provided in the course.

Software

You must have a copy of R for the course.

Options for Credit and Recognition

ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for the upper-division baccalaureate degree, 3 semester hours in statistics, data mining, or programming. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:

 

Chrome

 

Firefox

 

Safari

  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

Register For This Course

Analysis of Survey Data from Complex Sample Designs