Analytics

A Leading Utility Increases its Debt Collection by 50 Percent with Predictive Analytics

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Key Points

Challenge
The client wanted to optimize collection processes to improve recovery and create strategies to manage customer write-offs more effectively
Solution
WNS transformed the client's collections process by leveraging predictive analytics and enhancing the customer interaction strategy
Benefits
The client achieved a 50 per cent improvement in debt collection in three months

The Client

One of the leading energy and utilities companies

The Challenge

The client had a dire business need to re-balance its energy final debt portfolio. On one hand, its debt recovery rate was 4 percent, compared to 14 percent achieved by its competitors. On the other hand, the commissions charged by the client's debt collection agencies were as high as 50 percent of the collected amount, which resulted in high operational costs. Consequently, profit margins were dented. The client intended to optimize its final debt collection processes to improve recovery of receivables. The client also wanted to formulate focused debt management strategies for different customer segments to manage customer writeoffs more effectively and decrease operational costs.

The WNS Solution

WNS concentrated on transforming the client's collections process by embedding predictive analytics and making changes to the customer interaction strategy. Key aspects of the WNS Solution:

A Propensity-to-Pay Predictive Data Model exclusively for residential customers. This model predicted the likelihood of customers being able to pay their dues after their accounts were finalized. The model assigned a propensity-to-pay score to every customer.
Customer classification into high, medium and low propensity-to-pay segments based on their scores.
Focused delinquency management strategies for every segment.
Customer segment prioritization. Customer segments were prioritized on the basis of the propensity-to-pay scores and the amount of outstanding debt.
Rigorous cost-benefit analysis to streamline operational, financial and human resource activities. This exercise was instrumental in optimizing the debt management process.
Engaging with customers. The customer service executives used customized call scripts and pre-determined verbiage to conduct settlement negotiations and provide debt management advice to customers.
Performance monitoring of pilot strategies against critical tactical and quality indicators and also against the parameters set by the standard process.

Benefits Delivered

By deploying predictive analytics, WNS was able to fulfill the client's business objectives and helped achieve the following outcomes:

Debt collection increased by 50 percent within 3 months
The modified process recorded an 8 percent rise in conversion rates compared to the standard process
Operational expenses decreased by 20 percent

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