Collections optimisation: balance the books and treat vulnerable customers with patience

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With unemployment at a three-year high, the socio-economic impact of the COVID-19 pandemic has hit the UK hard. While significant progress has now been made on the clinical side of the crisis, and the success of the vaccination programme is a cause for optimism, the impact on the economy will take longer to heal.

For energy and utilities, this poses a pair of related problems. Operating in a highly regulated environment and providing essential services to the population, these organisations have an obligation to supply customers and a moral imperative to support society’s most vulnerable people. At the same time, to remain in business, they must be able to pay for the electricity and gas that they supply, which means they must be able to collect the money that customers owe them.

With more accounts entering the collections process, utilities need to walk the tightrope between balancing their own books and treating their most vulnerable customers with patience and empathy during this difficult time.

Taking a deep dive into the consumer experience

To its great credit, Ofgem reacted quickly to the crisis, commissioning a study into “Consumers’ Experiences with Energy During the Covid-19 Pandemic – Summary of Research Findings | Ofgem” in April/May 2020, and publishing the results in August. However, we’re now nearly a year on from that survey, and it’s a good time to review what we as an industry have learned.

Have things got better for customers since the start of the crisis, or worse? Has their behaviour changed as a result? Do we actually know which customers are in trouble? And have the measures we’ve taken to improve collections actually worked?

To answer these types of questions, we need better information on who customers are, how they behave, and how we interact with them during the collections process. In fact, the Ofgem study is a  good example of the kind of analysis that’s possible when you’re able to take a deep dive into customer data.

For example, the report shows that in May 2020, 22% of customers with prepayment meters (PPMs) had used emergency credit, and 11% could not afford to top up their meters. That’s a third of PPM customers who were already in the vulnerable category at an early stage in the crisis. What has happened to these customers since? Do their utilities providers know who they are? Do they know how best to support and communicate with them?

Key dimensions for collections analytics

There are three things about the report that I find especially interesting. First, it identifies customer cohorts in much finer detail and across more dimensions than most collections teams’ current approach to segmentation. Instead of using simplistic measures such as ‘number of days delinquent’ to divide customers into large buckets, it looks at demographic factors such as age, and at different types of accounts (PPM versus post-pay, for example).

Second, it also examines the relationships between these customer cohorts and their behaviour. For example, one of the most enlightening pages of the study identifies nine different behaviours related to customers topping up their PPMs during a period of social distancing. By segmenting customers not only by their static attributes such as demographics and account type but also by more dynamic attributes such as changes in behaviour over time, it’s possible to get a much deeper understanding of the individual customer’s situation—which in turn enables much better decision-making during the collections process.

Third, the report analyses the relationship between consumers and suppliers in terms of communication channels: what information have consumers received from their supplier during the lockdown period, whether they have tried or been able to contact their supplier, and whether they were satisfied with the outcomes of that contact. This is another key dimension for collections teams because there’s significant evidence that picking the right channel of communication has a huge influence on the success or failure of a collections campaign.

In our work with clients in the financial services sector, we’ve developed a set of collections optimisation capabilities that have helped one of the world’s largest banks significantly increase the returns from its collections campaigns—yielding an additional £5 for every £1 invested.

From one-off research to continuous monitoring

However, to optimise collections processes, it’s not enough to do this kind of deep-dive analysis as a one-off research project. Customers’ situations are changing all the time, and a single snapshot isn’t good enough as a guide to action. The problem is that most utilities are currently unable to do more than basic analysis of their collections data on a regular basis, which means they can’t effectively monitor dynamic changes in customer situations, behaviour or interaction patterns over time.

In our work with clients in the financial services sector, we’ve developed a set of collections optimisation capabilities that have helped one of the world’s largest banks significantly increase the returns from its collections campaigns—yielding an additional £5 for every £1 invested. By providing both the data intelligence to understand customers as individuals and the workflows to manage each case efficiently across multiple channels, we can help utilities’ collections teams operate at a much greater scale while offering each customer the appropriate treatment for their specific circumstances.

In the big picture, if utilities can help their customers get through this difficult period, they will lay down a firm foundation for a loyal and profitable long-term relationship as the economy restabilises and personal finances recover. To make that happen, utilities companies need to take action now—and we want to make it as easy as possible to get started. Our collections optimisation capabilities can be delivered as a service, so you can harness credit risk analytics to create a more customer-centric business quickly.

 

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About Author

Emma McDonald

Emma joined SAS in 2019 as an Engagement Manager as part of the Professional Services Team and then moved into a Client Director position, responsible for UK&I Utilities and Life Sciences organisations. She joined SAS with a background in cloud-based, enterprise analytics and has led several multi-national analytical research and digital transformation initiatives. During her time at SAS, Emma has been involved in numerous projects, driving innovation and AI adoption in various organisations. Emma holds a BSc in Neuroscience and an MSc in Stratified Medicine and Pharmacological Innovation from the University of Glasgow. She is an open source enthusiast and has a passion for helping organisations use data to solve complex challenges and operationalising analytical innovation from concept into production.

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