Improving resource allocation
Using predictive modelling to segment customers based on their payment history
Duration
Days
Industry
Public
and government
Services
Customer
Segmentation
Company Background
The client faced several inefficiencies, including a lack of comprehensive customer segmentation, difficulty in predicting customer behavior, and challenges in managing electricity services efficiently. The need for automation and data-driven insights became clear as the client struggled to forecast and manage late payments and defaulters.
Challenges
Unable to Segment Customers
The client’s approach of segmenting its customers based on their payment behaviors was ineffective as it overlooked the diverse circumstances and behaviors of individual customers. It couldn’t differentiate between a consistently punctual bill payer and someone experiencing temporary financial difficulties.
Difficulty in allocating customers
As they were unable to segment their customers based on their bills, the client could not effectively allocate its services to its customers. This led to discrepancies, such as cutting off of electricity to customers who had already paid or giving more electricity to those who had not paid at all.
Unable to forecast late payments
The client were unable to predict the actions of their customers e.g. who is likely to complete their payment on time, who will delay it, or who will not pay it. As a result, the client faced delayed payments, inefficiencies in targeting at-risk customers, and a lack of proactive resource allocation for collecting debt.
Solution
Data Pilot built a predictive model that provided detailed insights into customer payment behaviors across a dataset of 102.1k customers with a two-year payment history. This model would also forecast the likelihood of customers paying their bills on time by virtue of metrics like dates and segments.
- ETL for customer payment data consolidation
- Predictive analytics using historical payment data
- Mapping customer payment behaviors.
The platform consolidated data from over 102.1k customers and applied advanced ETL (Extract, Transform, Load) technologies to manage the large datasets. After consolidating the data, new columns were added to add more depth to the analyses: ‘Days’ calculated the number of days a payment was made before or after the due date, ‘Paid Type’ categorized payments as “Timely, Late, or Not Paid”, and ‘Min/Max Late Days’ recorded the minimum and maximum number of days late for each payment within the customer segments.
- Segment customers by their payment patterns so that the company can identify which customers are more likely to pay on time, pay late, or default.
- Integrate data from various customer interactions, so that the company can access a holistic view of customer behavior. This enabled an effective means of allocating resources for timely follow-ups and personalized reminders.
- Forecast payment behaviors using key metrics (days late, paid type) so that the company can proactively manage customer engagement and improve satisfaction.
- Visualization tools were integrated with the model to provide a holistic view of customer behavior, enabling enhanced resource allocation for timely follow-ups and personalized reminders.
- This helped the model forecast payment behaviors using key metrics (days late, paid type, and customer segments) so that the company can proactively manage customer engagement and improve satisfaction.
Through analytics, 4 customer segments were created:
Stars
Accounts with 90% or more of their payments made on time (e.g., at least 22 out of 24 payments made before the due date).
Potential Stars
Accounts with 80% to 90% timely payments.
Irregulars
Accounts with 50% to 80% timely payments.
Defaulters
Accounts with less than 50% timely payments.
The Impact
70-90% decrease in collection costs
Improved revenue collection through a 70-90% decrease in collection costs per loan. The model helped provide accurate forecasts of late and default payments, enabling the company to target at-risk customers effectively.
20-40% increase in recovery rates
Optimized resource allocation by identifying high-risk customers and allocating follow-up resources accordingly. This led to a more proactive approach in addressing payment delays and defaults. This strategy resulted in a 20-40% increase in recovery rates, ensuring more effective debt collection and minimizing losses.
Improved existing practises
The project’s success was mainly measured by client feedback
and their acceptance of our gap analysis and
recommendations.
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Industry Applications
Finance
Solution can analyze the payment history and financial behavior of its customers. It can predict which loan customers are at risk of not paying back their owed money. The solution can also allow banks to offer personalized repayment plans or alternative payment plans before customers default.
Industry Applications
Subscription-based Businesses
Companies offering subscription plans to their customers can employ this solution to maintain a stronger relationship with their subscribers and reduce revenue loss from unpaid fees. By using insight gathered from the solution, it can send timely reminders or offer flexible payment options to prevent customers from canceling their subscription.
