Recency, Frequency and Monetary Value segmentation of customers.
- 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 in two parts as Jupyter Notebooks 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 RFM segments for 2011 year, calculated RFM Score for each customer and segmented into 3 custom segments 'Top', 'Middle' and 'Low' based on the total RFM Score. In the next step, I segmented the RFM data with k-means clustering technique and visualized the results as a snake plot and a heatmap.
It seems like there are 159 customers (probably wholesalers) in Cluster 1 who buy a lot from us pretty frequent. These are our core business clients.
The snake plot shows the distinction between 3 Clusters - there is Cluster 1 with customers that spend the most, buy most frequently and most recently. The next good segment is Cluster 2 (1586 customers) with average spending of 6624.6 sterling in 2011, 7.5 transactions and about a month since the last purchase. The least attractive is Cluster 0 (2499) - with about 4 months since last transaction on average, almost 2 transactions per year and no more than 5000 sterling in yearly purchases.