Shopping Features. These features include frequency of ecommerce spends(platforms like jumia), clothing and fashion spends etc. They are returned in the kenya/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 spending, clothing and fashion, ecommerce platform, entertainment and subscription, professional service, retail store, supermarket, wholesale and distribution , naivas, quickmart, carrefour. 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_clothing_and_fashion_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending clothing and fashion. 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. | • This feature may be indicative of an individual’s spending capacity on clothing and fashion, offering insights into their preferences. • Understanding a user’s activity related to clothing 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_professional_service_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through professional service eg car wash, salon 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} |
| count_wholesale_and_distribution_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending on wholesale merchants or distributors. 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 | • 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. Could also indicate cost effeciency or a smaller business | {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 spending, clothing and fashion, ecommerce platform, entertainment and subscription, professional service, retail store, supermarket, naivas, quickmart, carrefour, wholesale and distribution . 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_clothing_and_fashion _debits_{t0_days}_{t1_days} | The sum of debits where user engaged in spending through clothing and fashion. 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_professional_service _debits_{t0_days}_{t1_days} | The sum of debits where user engaged in spending through professional service eg car wash, salon. 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} |
| sum_of_wholesale_and_distribution _debits_{t0_days}_{t1_days} | The sum of debits where user engaged in spending through wholesale and distribution, 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. Might also indicate smaller business | {float, null} |