Health Features. These features include hospital bills, spend on pharmacies etc. They are returned in the india/features/health endpoint.Definitions of these features generated by Pngme are:
| Feature | Feature Definition | Use Case | Value Proposition | Return Value |
|---|---|---|---|---|
| count_health_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through health events, where health events are broken down into general health, hospital, pharmaceutical and specialist events. 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 recommendations on healthy living, tips and HMOs. • Improve data quality and reliability by using SMS data as a source of truth for verifying health events of customers. | • This feature may be indicative of the amount spent on health-related services, potentially highlighting individuals facing health challenges who could be targeted for health product campaigns. • Understanding a user’s activity related to health events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} |
| count_hospital_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending to hospital. 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 recommendations on healthy living, tips. • Improve data quality and reliability by using SMS data as a source of truth for verifying health events of customers. | • This feature may potentially highlight individuals facing health challenges who could be targeted for health product campaigns. • Understanding a user’s activity related to health events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} |
| count_pharmaceutical_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through pharmaceutical events. 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 recommendations on healthy living. • Improve data quality and reliability by using SMS data as a source of truth for verifying health events of customers. | • This feature may be indicative of the amount spent on health-related services. • Understanding a user’s activity related to health events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} |
| sum_of_health_debits _{t0_days}_{t1_days} | The amount of money spent on health events, where health spends are broken down into general health, hospital, pharmaceutical, healthcare and specialist spends. 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 recommendations on healthy living, tips and HMOs. • Improve data quality and reliability by using SMS data as a source of truth for verifying health spends of customers. | • This feature may be indicative of the amount spent on health-related services, potentially highlighting individuals facing health challenges who could be targeted for health product campaigns. • Understanding a user’s activity related to health events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} |
| sum_of_hospital_debits _{t0_days}_{t1_days} | The amount of money spent on hospital bills. 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 recommendations on healthy living, tips. • Improve data quality and reliability by using SMS data as a source of truth for verifying health events of customers. | • This feature may potentially highlight individuals facing health challenges who could be targeted for health product campaigns. • Understanding a user’s activity related to health events can help tailor marketing strategies and develop products that cater to this specific customer segment | {float, null} |
| sum_of_pharmaceutical_debits _{t0_days}_{t1_days} | The amount of money spent on pharmacies. 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 recommendations on healthy living. • Improve data quality and reliability by using SMS data as a source of truth for verifying health events of customers. | • This feature may be indicative of the amount spent on health-related services. • Understanding a user’s activity related to health events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} |