Introduction to Customer Analytics

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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



To be scheduled.

Introduction to Customer Analytics


To be scheduled.

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