Cohort Analysis allows to track user behavior over time and is the stepping stone in calculating retention rates.
- Python 3.7.3
- Data: Online Retail II Data Set, UCI Machine Learning Repository
- Libraries: pandas, NumPy, Matplotlib, Seaborn
Because the dataset is large and publicly available, I did not upload it here.
The analysis can be found as Jupyter Notebook here:
In this project, I analyzed customer behavior for online retail store that sells unique all-occasion gift-ware in the UK.
The dataset consists of 1,067,371 transactions and has the following variables:
| Variable | Description |
|---|---|
| InvoiceNo | Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation. |
| StockCode | Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product. |
| Description | Product (item) name. Nominal. |
| Quantity | The quantities of each product (item) per transaction. Numeric. |
| InvoiceDate | Invice date and time. Numeric. The day and time when a transaction was generated. |
| UnitPrice | Unit price. Numeric. Product price per unit in sterling. |
| CustomerID | Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer. |
| Country | Country name. Nominal. The name of the country where a customer resides. |
I created cohorts based on monthly data between years 2009 and 2011, calculated retention rates and visualized them via a heatmap.
This project is part of "Customer Segmentation in Python" course on Data Camp taught by Karolis Urbonas, Global Head of Machine Learning and Science at Amazon Web Services (AWS).