Introduction to Customer Analytics

COMING SOON!  Please check back

Course Program:
Week 1 - Exploring and preparing transactional dataset for analysis with R
  • Practical exploration of transactional Retail industry dataset - understanding distributions and what each variable means
    • Using dplyr and ggplot to explore variables
    • Raising hypotheses about meaning of data distributions and confirming / rejecting them
  • Cleaning data
    • Understanding outliers and which ones should be removed, which ones - not
    • Visualizing data to raise new questions
  • Summarizing data with dplyr to understand what it represents
    • Getting insights to raise new hypotheses and confirm/refute previous ones
  • Preparing latest customer summary table for initial analysis
  • Homework - finishing R code in the R Markdown document for almost every step
Week 2 - Analyzing customer summary table with R
  • Analyzing customers by pivoting customer summary table
  • Looking for outliers and dealing with them
  • Plotting data with ggplot 
  • Analyze each customer attribute distribution and taking first shot at building segments based on these variables
  • Writing your own R functions for dplyr for faster analysis
  • Exploring newly created segments and trying to make business sense from the analysis
  • 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
  • Advanced dplyr - introduction to window functions e.g. LAG to build monthly customer summary data snapshots
  • Extensive dealing with dates - learning about lubridate package
  • Plotting data and understanding customer trends
  • Creating new segments based on learnings from weeks 1 and 2
  • Introduction to customer lifecycle
    • What is customer lifecycle and why it is important for customers analytics?
    • Encode new features based on major customer lifecycles - "new" vs. "existing", "retained" vs. churned" etc.
  • Encode new behavioral features:
    • inter-purchase frequency
    • changes in product portfolio 
    • boolean variables for changes in behavior e.g. 'added new product to portfolio', 'increased spending' etc.
Week 4 - Exploring trends in customer behavior with R and preparing for the capstone project
  • Best industry practices in plotting transactional data trends of customers with ggplot
  • Analyzing monthly summary data and making conclusions
  • ... more will be added...
  • Capstone project:
    • PART 1 - Business case analysis - 2-3 business questions will be provided and students will need to use the new monthly customer summary dataset to answer those and provide actionable recommendations
    • PART 2 - Exploring unseen dataset - students will be provided with a transactional dataset and will be asked to prepare a behavioral customer summary dataset by themselves.

Introduction to Customer Analytics

Level:
Intro
Prerequisite:
Instructor(s):

Dates:

To be scheduled.

Introduction to Customer Analytics

Instructor(s):

Dates:
To be scheduled.

Course Fee: $

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

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.

Register Now

Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

The Institute for Statistics Education is certified to operate by the State Council of Higher Education in Virginia (SCHEV).

Want to be notified of future courses?

Yes
Student comments