After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
The most successful companies today are the ones that know their customers so well that they can anticipate their needs. Data analysts play a key role in unlocking these in-depth insights, and segmenting the customers to better serve them. In this course, you will learn real-world techniques on customer segmentation and behavioral analytics, using a real dataset containing anonymized customer transactions from an online retailer. You will first run cohort analysis to understand customer trends. You will then learn how to build easy to interpret customer segments. On top of that, you will prepare the segments you created, making them ready for machine learning. Finally, you will make your segments more powerful with k-means clustering, in just few lines of code! By the end of this course, you will be able to apply practical customer behavioral analytics and segmentation techniques.
Prerequisites
Curriculum
Course outline
1
Cohort Analysis
In this first chapter, you will learn about cohorts and how to analyze them. You will create your own customer cohorts, get some metrics and visualize your results.
- Introduction to cohort analysis50 XP
- How many customers acquired?50 XP
- Cohort analysis50 XP
- Assign daily acquisition cohort100 XP
- Calculate time offset in days - part 1100 XP
- Calculate time offset in days - part 2100 XP
- Cohort metrics50 XP
- Customer retention50 XP
- Calculate retention rate from scratch100 XP
- Calculate average price100 XP
- Visualizing cohort analysis50 XP
- Visualize average quantity metric100 XP
2
Recency, Frequency, and Monetary Value Analysis
In this second chapter, you will learn about customer segments. Specifically, you will get exposure to recency, frequency and monetary value, create customer segments based on these concepts, and analyze your results.
3
Data Preprocessing for Clustering
Once you created some segments, you want to make predictions. However, you first need to master practical data preparation methods to ensure your k-means clustering algorithm will uncover well-separated, sensible segments.
4
Customer Segmentation with K-means
In this final chapter, you will use the data you pre-processed in Chapter 3 to identify customer clusters based on their recency, frequency, and monetary value.
Customer Segmentation in Python
Course
Complete
