Entertainment Features. These features include netflix subscriptions, spend on sports clubs. They are returned in the kenya/feature/entertainment endpoint.Definitions of these features generated by Pngme are:
| Feature | Definition | Use Case | Value Proposition | Response value | |
|---|---|---|---|---|---|
| count_dstv_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending to dstv. 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 easier payment to dstv. • Enhance customer engagement and retention by offering tailored product recommendations • 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 Indicates disposable income and entertainment preferences • Understanding a user’s activity related to subscriptions and entertainment can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_entertainment_and_subscription_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through entertainment and certain subscriptions. 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 easier payment to subscriptions rewarding their loyalty and encouraging repeat purchases. • Enhance customer engagement and retention by offering tailored product recommendations | • These features may be indicative of an individual’s spending capacity • Understanding a user’s activity related to subscriptions and entertainment can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_golfclubs_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending to any golf club captured in sms. 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 •Could be used to drive premium product adoption, and strengthens brand loyalty through lifestyle alignment | • Might signal high-income or affluent customers for premium banking products Identifies lifestyle-driven clients • Understanding a user’s activity related to golf clubs can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_gotv_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending to gotv. 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 easier payment to gotv. • Enhance customer engagement and retention by offering tailored product recommendations • 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 Indicates disposable income and entertainment preferences • Understanding a user’s activity related to subscriptions and entertainment can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_netflix_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in netflix subscriptions. 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 easier payment to netflix. • Enhance customer engagement and retention by offering tailored product recommendations • 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 Indicates disposable income and entertainment preferences • Understanding a user’s activity related to entertainment can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_only_fans_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in only fans. 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 the ability to manage risk by providing additional data points for assessing customer's financial stability • Offer personalized services such as financial planning advice or tools to customers who frequently engage in only fans to help them manage their finances better | • This feature may be indicative of an individual’s spending capacity Indicates disposable income and entertainment preferences | {int, null} | |
| count_padel_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending on padel(sports club). 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. | • Enhance customer engagement and retention by offering tailored product recommendations • Improve data quality and reliability by using SMS data as a source of truth. | • Might signals high-income or affluent customers for premium banking products Identifies lifestyle-driven clients • Understanding a user’s activity related to padel can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_showmax_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending to showmax subscriptions. 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 easier payment to subscriptions. • Enhance customer engagement and retention by offering tailored product recommendations • 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 Indicates disposable income and entertainment preferences • Understanding a user’s activity related to entertainment can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_sports_clubs_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending on sports clubs. These include golf clubs, padel and general sports club. 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. | • Enhance customer engagement and retention by offering tailored product recommendations • Improve data quality and reliability by using SMS data as a source of truth. | • Might signals high-income or affluent customers for premium banking products Identifies lifestyle-driven clients • Understanding a user’s activity related to sports clubs clubs can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_spotify_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending to spotify. 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 easier payment to subscriptions rewarding their loyalty and encouraging repeat purchases. • Enhance customer engagement and retention by offering tailored product recommendations • 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 Indicates disposable income and entertainment preferences Signals digital engagement • Understanding a user’s activity related to subscriptions and entertainment can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_viusasa_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through viusasa. 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 easier payment to viusasa. • Enhance customer engagement and retention by offering tailored product recommendations • 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 Indicates disposable income and entertainment preferences • Understanding a user’s activity related to subscriptions and entertainment can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_wines_and_spirits_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through purchasing wines/spirits. 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 • 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 Might signal high-income or affluent customers for premium banking products On the other hand might also signal risky profiles where there is excessive spending on wines and spirits | {int, null} | |
| sum_of_dstv_debits _{t0_days}_{t1_days} | The amount of money a customer has spent on DStv subscriptions over a defined period. 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 easier payment to dstv. • Enhance customer engagement and retention by offering tailored product recommendations • 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 • Understanding a user’s activity related to subscriptions and entertainment can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| sum_of_entertainment_and_subscription_debits _{t0_days}_{t1_days} | The amount of money a customer has spent on entertainment and certain subscriptions. 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 easier payment to subscriptions rewarding their loyalty and encouraging repeat purchases. • Enhance customer engagement and retention by offering tailored product recommendations • Improve data quality and reliability by using SMS data as a source of truth. | • These features may be indicative of an individual’s spending capacity • Understanding a user’s activity related to subscriptions and entertainment can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| sum_of_golfclubs_debits _{t0_days}_{t1_days} | The amount of money a customer has spent on golf clubs. 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. | • Enhance customer engagement and retention by offering tailored product recommendations • Improve data quality and reliability by using SMS data as a source of truth. | • Might signal high-income or affluent customers for premium banking products Identifies lifestyle-driven clients • Understanding a user’s activity related to golf clubs can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| sum_of_gotv_debits _{t0_days}_{t1_days} | The amount of money a customer has spent on gotv. 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 easier payment to gotv. • Enhance customer engagement and retention by offering tailored product recommendations • 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 • Understanding a user’s activity related to entertainment can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| sum_of_netflix_debits _{t0_days}_{t1_days} | The amount of money a customer has spent on netflix subscriptions. 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 easier payment to netflix. • Enhance customer engagement and retention by offering tailored product recommendations • 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 • Understanding a user’s activity related to entertainment can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| sum_of_only_fans_debits _{t0_days}_{t1_days} | The amount of money a customer has spent on only fans. 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. | • Enhance the ability to manage risk by providing additional data points for assessing customer's financial stability • Offer personalized services such as financial planning advice or tools to customers who frequently engage in only fans to help them manage their finances better | • This feature may be indicative of an individual’s spending capacity Indicates disposable income and entertainment preferences | {int, null} | |
| sum_of_padel_debits _{t0_days}_{t1_days} | The amount of money a customer has spent on padel(sports club). 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. | • Enhance customer engagement and retention by offering tailored product recommendations • Improve data quality and reliability by using SMS data as a source of truth. | • Might signals high-income or affluent customers for premium banking products Identifies lifestyle-driven clients • Understanding a user’s activity related to padel can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| sum_of_showmax_debits _{t0_days}_{t1_days} | The amount of money a customer has spent on showmax subscriptions. 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 easier payment to subscriptions. • Enhance customer engagement and retention by offering tailored product recommendations • 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 • Understanding a user’s activity related to entertainment can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| sum_of_sports_clubs_debits _{t0_days}_{t1_days} | The amount of money a customer has spent on sports clubs. These include golf clubs, padel and general sports club. 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. | • Enhance customer engagement and retention by offering tailored product recommendations • Improve data quality and reliability by using SMS data as a source of truth. | • Might signals high-income or affluent customers for premium banking products Identifies lifestyle-driven clients • Understanding a user’s activity related to sports clubs clubs can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| sum_of_spotify_debits _{t0_days}_{t1_days} | The amount of money a customer has spent on spotify. 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 easier payment to subscriptions rewarding their loyalty and encouraging repeat purchases. • Enhance customer engagement and retention by offering tailored product recommendations • 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 • Understanding a user’s activity related to subscriptions and entertainment can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| sum_of_viusasa_debits _{t0_days}_{t1_days} | The amount of money a customer has spent on viusasa. 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 easier payment to viusasa. • Enhance customer engagement and retention by offering tailored product recommendations • 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 • Understanding a user’s activity related to subscriptions and entertainment can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| sum_of_wines_and_spirits_debits _{t0_days}_{t1_days} | The amount of money a customer has spent on purchasing wines/spirits. 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 • 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 Might signal high-income or affluent customers for premium banking products On the other hand might also signal risky profiles where there is excessive spending on wines and spirits | {int, null} |