Supermarket Features. These include frequency of supermarket events, spending at specific supermarkets, the supermarkets available are carrefour, quickmart and naivas. They are returned in the kenya/features/supermarkets endpoint.Definitions of these features generated by Pngme are:
| Feature | Features Available | Feature Definition | Use Case | Value Proposition | Return Value |
|---|---|---|---|---|---|
| count_supermarket_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending at supermarkets. The counts are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | • Improve customer satisfaction and loyalty by offering customers access to exclusive shopping platform events, such as flash sales, early access events, and member-only discounts, rewarding their loyalty and encouraging repeat purchases. • Enhance customer engagement and retention by offering tailored product recommendations, enhancing their shopping experience and increasing the likelihood of purchase • Improve data quality and reliability by using SMS data as a source of truth for verifying ecommerce activities of customers. | • This feature may be indicative of an individual’s spending capacity across a broad spectrum of goods and services, offering insights into their overall financial behaviour and preferences. • Understanding a user’s activity related to shopping events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_[supermarket categories]_events _{t0_days}_{t1_days} | count_carrefour_events_{t0_days}_{t1_days} count_naivas_events_{t0_days}_{t1_days} count_quickmart_events_{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending at various supermarkets, these are carrefour, naivas and quickmart. The counts are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | • Improve customer satisfaction and loyalty by offering customers access to exclusive shopping platform events, such as flash sales, early access events, and member-only discounts, rewarding their loyalty and encouraging repeat purchases. • Enhance customer engagement and retention by offering tailored product recommendations, enhancing their shopping experience and increasing the likelihood of purchase • Improve data quality and reliability by using SMS data as a source of truth for verifying ecommerce activities of customers. | • These features may be indicative of an individual’s spending capacity across a broad spectrum of goods and services, offering insights into their overall financial behaviour and preferences. • Understanding a user’s activity related to shopping events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} |
| sum_of_supermarket _debits_{t0_days}_{t1_days} | The sum of debits where user engaged in spending at a supermarket. The debits are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | • Improve customer satisfaction and loyalty by offering customers access to exclusive shopping platform events, such as flash sales, early access events, and member-only discounts, rewarding their loyalty and encouraging repeat purchases. • Enhance customer engagement and retention by offering tailored product recommendations, enhancing their shopping experience and increasing the likelihood of purchase • Improve data quality and reliability by using SMS data as a source of truth for verifying shopping spends of customers. | • This feature may be indicative of an individual’s spending capacity across a broad spectrum of goods and services, offering insights into their overall financial behavior and preferences. • Understanding a user’s activity related to shopping events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} | |
| sum_of_[supermarket categories] _debits_{t0_days}_{t1_days} | sum_of_carrefour_debits_{t0_days}_{t1_days} sum_of_naivas_debits_{t0_days}_{t1_days} sum_of_quickmart_debits_{t0_days}_{t1_days} | The sum of debits where user engaged in spending at various supermarkets. These supermarkets are carrefour, naivas and quickmart. The debits are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | • Improve customer satisfaction and loyalty by offering customers access to exclusive shopping platform events, such as flash sales, early access events, and member-only discounts, rewarding their loyalty and encouraging repeat purchases. • Enhance customer engagement and retention by offering tailored product recommendations, enhancing their shopping experience and increasing the likelihood of purchase • Improve data quality and reliability by using SMS data as a source of truth for verifying shopping spends of customers. | • These features may be indicative of an individual’s spending capacity across a broad spectrum of goods and services, offering insights into their overall financial behavior and preferences. • Understanding a user’s activity related to shopping events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} |