Ian Elam, Business Development Director at Capita Customer Management, explains how data allows you to engage with your customers in a very personal way and reduce debt write-offs.
It’s more important than ever to truly understand your customers, but doing that can often involve the hard slog of manually collecting and analysing data about them – a task that has historically too often ended up on the ‘too hard’ pile. The result? Customers end up with a ‘one-size-fits-all’ experience because collections teams default to generic data points when planning the journey their customers will take with them.
The tell-tale sign of this approach is a lack of engagement, which leads to lost customers, a lot of complaints and disputes and, ultimately, companies failing to collect arrears and having to write off bad debt.
But now, advancements in Artificial Intelligence (AI) mean there’s no excuse for not digging into the trove of customer data that companies hold.
AI is no longer science fiction but a credible business tool that many organisations are embracing, as readers of the trade press will have seen: organisations such as Northumbria Water Group (NWG), Utilita and Shawbrook Bank have all announced recent projects involving the technology.
A recent report from Utility Week1 describes the benefits that NWG is seeing from its first foray into AI. It’s using machine learning and historical company data to intelligently predict the best way, at different stages of the debt recovery cycle, to encourage a customer to make payments.
The solution enables NWG to look at hyper-personalised ‘next-best-path’ models that suggest the most effective action to collections agents, and a subsequent path for each customer and circumstance. According to its estimates, if the models were implemented, a collection would happen a significant 22 days earlier, on average, and remove two steps from the process, saving resource and time.
Energy company Utilita, which has already achieved operational efficiencies with Robotic Process Automation, is now exploring how AI can make managing debt easier, focusing on the potential to set collection rates and identify the most effective collection methods and the best time and channel to communicate with customers.
And small enterprise lender Shawbrook Bank has signalled its intention to focus on data-led products and solutions by strengthening its partnership with Quinnox2, an organisation specialising in data-driven technology solutions, and appointing its first head of fintech strategy3.
But this data-driven approach to customer engagement isn’t only based on the latest technology. It also reflects customer opinion. As our Fairness in Collections report4 shows, customers are willing for operators to use their information if they can see a benefit. They support the use of data and AI if it can enable features such as warnings, when they may be about to slip into problematic debt, or convenient engagement channels, such as chat bots.
So how can you make the most of your data using AI and take that first step towards improving your customers’ collections experience? You can:
- crunch the vast amounts of data in real time that are needed to drive agile collections at the level of individual customers, or at least of highly customised segments
- create more realistic forecasts for your customer portfolios by using AI to collate and analyse all historic customer information
- reduce the cost of serving your customers by determining the best time and channel to engage with them.
If you get it right, your customers will become more engaged and repay their debts more quickly. Your agents will also become more satisfied, because they’ll be talking to customers at the right time in the debt journey – increasing the chances of a better conversation and outcome.
The time to act is now. AI is a reality and is having a positive effect on collections for many organisations. And, most importantly of all, it’s re-setting your customers’ expectations of the collections experience.