Shopping Features. These features include frequency of ecommerce spends(platforms like jumia), clothing and fashion spends etc. They are returned in the srilanka/features/shopping endpoint.Definitions of these features generated by Pngme are:
| Feature | Feature Definition | Use Case | Value Proposition | Return Value |
|---|---|---|---|---|
| count_shopping_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through shopping events, where shopping events are broken down into general shopping ,retail store,supermarket. 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} |
| 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_ecommerce_platform_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through ecommerce platform 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 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_retail_store_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending retail store. 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. | • 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 | • 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} |
| sum_of_shopping_debits_{t0_days}_{t1_days} | The sum of debits where user engaged in spending through shopping events, where shopping events are broken down into general shopping ,retail store,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 _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_ecommerce_platform _debits_{t0_days}_{t1_days} | The sum of debits where user engaged in spending through ecommerce platform. 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_retail_store _debits_{t0_days}_{t1_days} | The sum of debits where user engaged in spending through retail store, 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} |