Failed payments can cost a business in many ways, from both loss of income to time in chasing the payment. As many as 15 per cent of transactions fail across all payment mechanisms. To help combat this, GoCardless launched a new payments intelligence tool called Success+, which helps credit control improve their payment success.
The tool uses machine learning from nine years of payment behaviours gathered by GoCardless insights to analyse trends from recurring payments data. It not only helps to identify the ideal time to retry a failed payment but then automatically schedules payment retries on an optimal day for each payer.
Until now, it has been difficult for merchants to know when is the best time to retry a failed payment. This has meant businesses have relied on retries that take place on random dates, often with limited success. Using previous insights, the Success+ tool allows businesses to find the optimum day for payments via merchant data and takes into account the industry of the business and payer data – such as a history of failed payments. This will also let a business know if the tool thinks that a payment is very likely to fail, so it can decide if it’s worth retrying the payment anyway or handling the situation differently.
Out of the 2,000 GoCardless customers that trialed Success+, 15 per cent are seeing a decrease in payment failures and 63 per cent have seen an improvement in cashflow. With the payment tool taking care of recovering the failed payments, businesses are saving time and can focus on the day-to-day running of their business and maintaining their customer relations.
So, are payments the next industry that can rely on machine learning for insights? Duncan Barrigan, Chief Product Officer at GoCardless thinks so. He said, “Failed payments is a widespread business issue. They are costly – both in time and money, can impact customer relationships and increase the risk of bad debt and customer churn. We created Success+ to combat this.
“An interesting insight we spotted when looking at the data is that many specific factors need to be taken into account to get to the best results. For example, the industry of the business taking the payment and the country of residence of the customer making the payment can make a meaningful difference.
“We found a significant proportion of customers are willing to remake a payment that has failed and the failure is down to something that has gone wrong temporarily, so it’s important to give them the chance to address it. Finally, we had some fascinating insights in our wider research into how customers get paid such as amazingly, 11% of companies in the UK operate a lunar payroll!”
12-months ago, the smart home-insurance provider Neos became one of the first businesses to test GoCardless’ payment intelligence product, Success+.
The company has a current failure rate of between 4 and 5%, and most of these are due to customers having insufficient funds in their bank account. But the company is impressed with the tool. Head of finance, Monsur Alam at Neos said: “Before using Success+, we had one person spending 60% of their time on credit control. By automatically retrying the payments, that’s now down to 20% of their time, as it’s far more likely the payment will be collected before we need to intervene.
“By automatically retying failed payments, it gives our customers the time to make sure there is money in their account. Because of this, almost 90% of retried payments are now successfully collected, where before this was 30-40%.”
The Success+ tool is currently focussed on using macro trends, merchant, and payer data to figure out the optimal retry schedules for individual payments and customers; although given it is operated by machine learning, this analysis doesn’t directly produce high-level insights into trends over time.
This is something that the company hopes to improve in the future. Barrigan added, “As Success+ continues, GoCardless will continue to look for ways to help businesses learn more about customer behaviour, payment performance and how to reduce failed payments.”
