Utilities Features. These include features on utility bills, electricity, water, rent etc. They are returned in the kenya/features/utilities endpoint.Definitions of these features generated by Pngme are:
| Feature | Feature Definition | Use Case | Value Proposition | Return Value |
|---|---|---|---|---|
| count_utilities_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through utilities, where utilities are broken down into general utilities spending, airtime, energy, housing, _kplc, **_internet, and water** 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 enabling customers to track their spend and usage patterns across different utility events in order to manage consumption. • Improve customer satisfaction by recommending platforms that provide ease of payment for utility bills • Improve data quality and reliability by using SMS data as a source of truth for verifying utility events of customers. | This feature may be indicative of individuals who earn regularly and possess sufficient resources to cover short-term bills monthly or weekly. Such individuals are likely to be relatively stable financially, as evidenced by their consistent bill payments. • Understanding a user’s activity related to utilities events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} |
| count_energy_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through energy/electricity. 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 enabling customers to track their spend and usage on electricity events in order to manage consumption. • Improve customer satisfaction by recommending platforms that provide ease of payment for electricity bills • Improve data quality and reliability by using SMS data as a source of truth for verifying utility events of customers. | This feature may be indicative of individuals who earn regularly and possess sufficient resources to cover short-term bills monthly or weekly. Such individuals are likely to be relatively stable financially, as evidenced by their consistent bill payments. • Understanding a user’s activity related to utilities events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} |
| count_housing_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through housing/rent. 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 enabling customers to track their spend on rent in order to manage consumption. • Improve customer satisfaction by recommending platforms that provide ease of payment for utility bills • Improve data quality and reliability by using SMS data as a source of truth for verifying utility events of customers. | This feature may be indicative of individuals who earn regularly and possess sufficient resources to cover short-term bills monthly. Such individuals are likely to be relatively stable financially, as evidenced by their consistent bill payments. • Understanding a user’s activity related to utilities events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} |
| count_kplc_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending to kplc. 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 enabling customers to track their spend and usage patterns on kplc in order to manage consumption. • Improve customer satisfaction by recommending platforms that provide ease of payment for utility bills • Improve data quality and reliability by using SMS data as a source of truth for verifying utility events of customers. | This feature may be indicative of individuals who earn regularly and possess sufficient resources to cover short-term bills monthly or weekly. Such individuals are likely to be relatively stable financially, as evidenced by their consistent bill payments. • Understanding a user’s activity related to utilities events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} |
| count_water_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending for water. 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 enabling customers to track their spend and usage patterns on water bill events in order to manage consumption. • Improve customer satisfaction by recommending platforms that provide ease of payment for utility bills • Improve data quality and reliability by using SMS data as a source of truth for verifying utility events of customers. | This feature may be indicative of individuals who earn regularly and possess sufficient resources to cover short-term bills monthly or weekly. Such individuals are likely to be relatively stable financially, as evidenced by their consistent bill payments. • Understanding a user’s activity related to utilities events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} |
| sum_of_utilities_debits _{t0_days}_{t1_days} | The sum of debits indicating that the user engaged in spending through utilities, where utilities are broken down into general utilities spending, airtime, energy, kplc, housing, internet, and water bill 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 enabling customers to track their spend and usage patterns across different utility events inorder to manage consumption. • Improve customer satisfaction by recommending platform that provide ease of payment for utility bills • Improve data quality and reliability by using SMS data as a source of truth for verifying utility events of customers. | This feature may be indicative of individuals who earn regularly and possess sufficient resources to cover short-term bills monthly or weekly. Such individuals are likely to be relatively stable financially, as evidenced by their consistent bill payments. • Understanding a user’s activity related to utilities events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} |
| sum_of_energy_debits _{t0_days}_{t1_days} | The sum of debits indicating that the user engaged in spending through energy/electricity bill 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 enabling customers to track their spend and usage on electricity events in order to manage consumption. • Improve customer satisfaction by recommending platforms that provide ease of payment for electricity bills • Improve data quality and reliability by using SMS data as a source of truth for verifying utility events of customers. | This feature may be indicative of individuals who earn regularly and possess sufficient resources to cover short-term bills monthly or weekly. Such individuals are likely to be relatively stable financially, as evidenced by their consistent bill payments. • Understanding a user’s activity related to utilities events can help tailor marketing strategies and develop products that cater to this specific customer | {float, null} |
| sum_of_kplc_debits _{t0_days}_{t1_days} | The sum of debits indicating that the user engaged in spending through kplc bill 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 enabling customers to track their spend and usage patterns on kplc in order to manage consumption. • Improve customer satisfaction by recommending platforms that provide ease of payment for utility bills • Improve data quality and reliability by using SMS data as a source of truth for verifying utility events of customers. | This feature may be indicative of individuals who earn regularly and possess sufficient resources to cover short-term bills monthly or weekly. Such individuals are likely to be relatively stable financially, as evidenced by their consistent bill payments. • Understanding a user’s activity related to utilities events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} |
| sum_of_housing_debits _{t0_days}_{t1_days} | The sum of debits indicating that the user engaged in spending through housing/rent bill 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 enabling customers to track their spend on rent in order to manage consumption. • Improve customer satisfaction by recommending platforms that provide ease of payment for utility bills • Improve data quality and reliability by using SMS data as a source of truth for verifying utility events of customers. | This feature may be indicative of individuals who earn regularly and possess sufficient resources to cover short-term bills monthly. Such individuals are likely to be relatively stable financially, as evidenced by their consistent bill payments. • Understanding a user’s activity related to utilities events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} |
| sum_of_water_debits _{t0_days}_{t1_days} | The sum of debits indicating that the user engaged in spending through water bill 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 enabling customers to track their spend and usage patterns on water bill events in order to manage consumption. • Improve customer satisfaction by recommending platforms that provide ease of payment for utility bills • Improve data quality and reliability by using SMS data as a source of truth for verifying utility events of customers. | This feature may be indicative of individuals who earn regularly and possess sufficient resources to cover short-term bills monthly or weekly. Such individuals are likely to be relatively stable financially, as evidenced by their consistent bill payments. • Understanding a user’s activity related to utilities events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} |