Financial Features. These features include insurance debits, investment frequency etc. They are returned in the kenya/features/financial endpoint.Definitions of these features generated by Pngme are:
| Feature | Definition | Use Case | Value Proposition | Response value | |
|---|---|---|---|---|---|
| count_financial_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through financial events, where financial events is broken down into general financial spending events eg sacco, insurance, investment. 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 for financial events such as insurance, investment and savings services, helping them make cost-conscious decisions and manage their finances better. • Improve data quality and reliability by using SMS data as a source of truth for verifying financial events of customers. | • This feature may be indicative of an individual’s interest in financial growth through investments and corporate savings. It may also point to individuals owning high-valued properties that necessitate insurance. • Understanding a user’s activity related to financial events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_insurance_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through insurance 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 product recommendations for insurance helping them manage their finances better. • Improve data quality and reliability by using SMS data as a source of truth for verifying financial events of customers. | • This feature may point to individuals owning high-valued properties that necessitate insurance. • Understanding a user’s activity related to insurance can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_investment_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through investment. 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 for investment helping them make cost-conscious decisions and manage their finances better. • Improve data quality and reliability by using SMS data as a source of truth for verifying financial events of customers. | • This feature may be indicative of an individual’s interest in financial growth through investments • Understanding a user’s activity related to financial events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_sacco_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through saccos. 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 for financial events such as microloans expanding access to credit. • Improve data quality and reliability by using SMS data as a source of truth for verifying financial events of customers. | • This feature may be indicative of an individual’s interest in financial growth through saccos. It may suggest saving habits and long-term financial planning • Understanding a user’s activity related to financial events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_salary_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user received income through salary. 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. | • Use the data to build predictive models that forecast the likelihood of a user defaulting on a loan based on estimated monthly income • Improve data quality and reliability by using SMS data as a source of truth for verifying salary income of customers. | • This feature may be indicative of the inflow an individual receives on a monthly basis, serving as useful data for estimating income. • Understanding a user’s activity related to Salary income can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_service_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending credit services is broken down into general financial spending 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 product recommendations for microloans. • Improve data quality and reliability by using SMS data as a source of truth for verifying financial events of customers. | • Understanding a user’s activity related to financial events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| sum_of_financial_debits _{t0_days}_{t1_days} | The amount of money spent through financial events, where financial events is broken down into general financial spending events eg sacco, insurance, investment. 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 for financial events such as insurance, investment and savings services, helping them make cost-conscious decisions and manage their finances better. • Improve data quality and reliability by using SMS data as a source of truth for verifying financial events of customers. | • This feature may be indicative of an individual’s interest in financial growth through investments and corporate savings. It may also point to individuals owning high-valued properties that necessitate insurance. • Understanding a user’s activity related to financial events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} | |
| sum_of_insurance_debits _{t0_days}_{t1_days} | The amount of money user spent on insurance events. 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 for insurance helping them manage their finances better. • Improve data quality and reliability by using SMS data as a source of truth for verifying financial events of customers. | • This feature may point to individuals owning high-valued properties that necessitate insurance. • Understanding a user’s activity related to insurance can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} | |
| sum_of_investment_debits _{t0_days}_{t1_days} | The amount of money user spent on investment. 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 for investment helping them make cost-conscious decisions and manage their finances better. • Improve data quality and reliability by using SMS data as a source of truth for verifying financial events of customers. | • This feature may be indicative of an individual’s interest in financial growth through investments • Understanding a user’s activity related to financial events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} | |
| sum_of_sacco_debits _{t0_days}_{t1_days} | The amount of money user spent on saccos. 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 for financial events such as microloans expanding access to credit. • Improve data quality and reliability by using SMS data as a source of truth for verifying financial events of customers. | • This feature may be indicative of an individual’s interest in financial growth through saccos. It may suggest saving habits and long-term financial planning • Understanding a user’s activity related to financial events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} | |
| sum_of_salary_credits _{t0_days}_{t1_days} | The amount of money user received through salary. The credits 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. | • Use the data to build predictive models that forecast the likelihood of a user defaulting on a loan based on estimated monthly income • Improve data quality and reliability by using SMS data as a source of truth for verifying salary income of customers. | • This feature may be indicative of the inflow an individual receives on a monthly basis, serving as useful data for estimating income. • Understanding a user’s activity related to Salary income can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} | |
| sum_of_service_debits _{t0_days}_{t1_days} | The amount of money spent on credit services. 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 for microloans. • Improve data quality and reliability by using SMS data as a source of truth for verifying financial events of customers. | • Understanding a user’s activity related to financial events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} | |