Customer Analytics in R

Customer Analytics in R

taught by Karolis Urbonas

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Aim of Course: 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.
This course may be taken individually (one-off) or as part of a certificate program.
Course Program:

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


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.


Customer Analytics in R

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.


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

Organization of the course

Options for credit and recognition

Course Text:
All required study materials will be provided in the course.
You must have a copy of R for the course. Click here for information on obtaining a free copy.


May 17, 2019 to June 14, 2019 November 15, 2019 to December 13, 2019 May 15, 2020 to June 12, 2020 November 13, 2020 to December 11, 2020 May 14, 2021 to June 11, 2021 November 12, 2021 to December 10, 2021

Customer Analytics in R


May 17, 2019 to June 14, 2019 November 15, 2019 to December 13, 2019 May 15, 2020 to June 12, 2020 November 13, 2020 to December 11, 2020 May 14, 2021 to June 11, 2021 November 12, 2021 to December 10, 2021

Course Fee: $549.00

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Click here to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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