This algorithm enriches the customer profile with data about their purchasing habits and preferences. For example, whether they spend more during weekends or work days, or which is their favourite store.
Description
You must provide data about the customer’s transactions, including data that can be categorized. For example, the purchase date, payment method, sales assistant and store ID for each transaction.
The output includes information about your customer’s preferences, such as the store they most frequently shop at, and their preferred payment method.
Customer purchase preferences input data and output
| Input data | Output |
| Transactional data
Customer_id. Date. Order_id. Order_line_id. Product_id. Product category. Amount. Discount. Sales assistant. Store_id. Payment method. |
Top categories
Preferred store. Preferred payment method. Preferred sales assistant. Preferred half-year. Preferred month. Preferred weekday. Preferred hour. |
Output examples
The output is provided as new fields that are associated with each customer profile.
Example of the output
| Customer ID | 11223344 |
| Top categories | Shoes, Suits, Accessories |
| Preferred store | Malpensa MXP Corner |
| Preferred payment method | Credit card |
| Preferred sales assistant | Joe Smith |
| Preferred half-year |
1 |
| Preferred month | July |
| Preferred weekday | Weekends |
| Preferred time | Evening |
Tags:
- Clustering.
- Transactional.
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