WNS partners with Energy & Utilities (E&U) companies to transform the collections process and helps them navigate uncertainties by embedding predictive analytics.
It is crucial for E&U companies to deeply understand their debt books and prepare their businesses to combat potential future uncertainties. WNS’ predictive analytics-led collection strategies enable our clients to identify the propensity of customers to pay back the outstanding debt and prioritize customers on the basis of their delinquency behavior. Our mission is to fast-track the freeing up of our clients’ working capital.
Improved recovery rates and increased cash flow by 50 percent for a leading regulated utility in the US
Reduced collections costs and time by 20 percent for a leading regulated utility in the US
Reduced bad debt write-offs to improve capital availability (about USD 3-5 Million in debt improvement for various utilities)
Energy & Utilities (E&U) companies, typically, have a sizable number of C&I customers who bring considerable revenues. But when these customers find themselves in financial trouble, it could translate into defaulted payments and potentially into bad debts worth millions of dollars. This is a situation that every E&U company would like to avoid.
It would be impractical to pursue each of the commercial accounts in the company’s portfolio. It is thus imperative for companies to identify vulnerable commercial accounts leveraging data, and build quick and robust communication strategies for their troubled C&I customers to lessen the risk impact on their revenues.
If companies choose to merely rely on their internal interactions (with their commercial customer accounts) to identify vulnerabilities, that would be grossly insufficient. Rather, WNS can help with its holistic approach, underpinned by the analysis of the C&I customer’s interaction with the E&U company, its financial metrics, stock market data, press coverage and macro-economic data impacting the overall industry, to gauge the customer’s vulnerability to insolvency.
WNS’ Bankruptcy solution model served as an early alert for a leading U.S. utility company, which could develop customized business strategies for its commercial customers that were vulnerable to insolvency. The analysis helped accurately identify one such account (and categorize it into the ‘high-risk’ bracket), which filed for bankruptcy, despite showing no clear signs of risks.
In the event of market-wide financial challenges, low-income customers pose the highest risk for E&U companies. Therefore, it is crucial to understand their needs and offer right-fit solutions that are best-suited to their situation. However, it is not advisable to club all low-income customers into the same bracket – there’s no one-size-fits-all. It is important to segment these customers appropriately to understand their unique situations, needs and capabilities.
This focused understanding will help the utility company to create tailor-made campaigns such as energy efficiency, budget billing, payment arrangements and target the customers appropriately.
WNS can help E&U companies create low-income customer segmentation using various demographic, transactional and smart meter usage data to improve conversion rates, increase stickability for programs and help quantify impact of programs on debt curation.
Even before COVID-19, understanding which customers would come under the broad umbrella of ‘vulnerable’ was a key challenge for utilities companies. During the current uncertain climate, utility companies are playing their part by communicating clearly and supporting their customers as much as possible. Suppliers are therefore putting in place support measures for people on prepayment meters, people and families who need to self-isolate or take steps to reduce social contact, and people who may otherwise be in vulnerable situations.
The problem, however, is how to identify who is vulnerable?
WNS can help utilities by leveraging text and speech analytics solutions that are designed to uncover customer insights coming through the contact center – and this can be done at scale. Unstructured conversational data is transformed into structured data that can be searched, tagged and safely stored for short and long-term analysis. Analyzing 100 percent of customer conversations with automated tagging can provide energy and water companies with a view into the scale and common needs of vulnerable customers, and any trends surrounding those needs.