Returns features on communication, spend on airtime and internet and frequency at which they spend on airtime and internet. They are returned in the kenya/features/communication endpoint. Definitions of these features generated by Pngme are:
| Feature | Definition | Use Case | Value Proposition | Response value | |
|---|---|---|---|---|---|
| count_airtime_purchase_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user purchased airtime with any telco. 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. | • Increase customer engagement and retention by offering mobile-related products and services, such as mobile banking, mobile money, or mobile insurance • Enhance customer segmentation and targeting by understanding the mobile preferences and behavior of customers, such as their preferred telco, airtime amount, and frequency of purchase • Improve data quality and reliability by using SMS data as a source of truth for verifying the mobile transactions and activities of customers | • Understand the mobile usage and spending patterns of their customers • Gauge the customer satisfaction and loyalty with their current telco provider. | {int, null} | |
| count_airtime_received_events _{t0_days}_{t1_days | The number of SMS received indicating that the user received airtime from someone. 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. | • Increase customer engagement and retention by offering mobile-related products and services, such as mobile banking, mobile money, or mobile insurance • Improve data quality and reliability by using SMS data as a source of truth for verifying the mobile transactions and activities of customers | • Understand the mobile usage and spending patterns of their customers Might indicate financial reliance | {int, null} | |
| sum_of_airtime_credits _{t0_days}_{t1_days} | Total of credit transactions across airtime accounts, over the previous t0 to t1 days, where the time windows are 0-30, 31-90, or 0-90 days history prior to the prediction date. | • Implement targeted marketing campaigns for communication products by leveraging insights from the total of airtime credit transactions, ensuring that promotions are relevant to users' communication habits | • Gain valuable insights into users' behavior by calculating the total of credit transactions across airtime accounts, allowing for a deeper understanding of their communication and spending patterns • Improve customer engagement strategies by analyzing the total of credit transactions in airtime accounts, allowing for targeted promotions, discounts, or loyalty programs • Tailor airtime offers based on the total of credit transactions, providing users with personalized promotions that align with their communication needs and usage. • Inform user-centric product development by considering the total of credit transactions, helping in the creation of new services or features that meet users' communication and financial needs • Optimize revenue streams by understanding the total of credit transactions in airtime accounts, allowing for strategic pricing, bundling, and monetization of communication services | {float, null} | |
| sum_of_airtime_debits _{t0_days}_{t1_days} | The sum of SMS received indicating that the user engaged in spending through airtime 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 by enabling customers to track their spend on airtime inorder to manage consumption. • Improve data quality and reliability by using SMS data as a source of truth for verifying airtime events of customers. | 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_internet_debits _{t0_days}_{t1_days} | The sum of SMS received indicating that the user engaged in spending through internet 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 by enabling customers to track their spend on internet inorder to manage consumption. • Improve data quality and reliability by using SMS data as a source of truth for verifying airtime events of customers. | 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} |