- Case study
Engineering a new approach to cash management
How a global manufacturer reinvented collections with predictive analytics
A multinational industrial manufacturer
We ran a detailed analysis and developed a predictive model that forecasts which customers are likely to pay late. We segmented the firm's customer base, and identified where best to deploy collection efforts and resources.
A steep decline in past-due receivables by 40%, which will increase its annual cash flow by over $50 million
Our client has a century-old tradition of producing industrial products that improve the lives of people around the world. But there was a disconnect at play. Both its equipment and service businesses in North America were experiencing payment delays. The reason: it had a traditional approach to collections and cash management strategies that treated every customer identically.
It used dollar thresholds to set collections in motion without a clear understanding of the specific factors influencing the high level of aging invoices. It based collections and credit policies on limited data and historical information that didn't account for evolving payment patterns or customer behavior, making it hard to build a strategy that would reduce past-dues. As a result, days sales outstanding among its North American customers averaged nearly two months. That meant the cost to serve each customer was high, cash flow suffered, and the company risked losing revenue to bad debt.
Having managed the company's finance services, including order to cash, and provided analytics, procurement, and IT services, we had a good overview of its operations, but then it was time to dig down.
Taking a hard look at receivables management
We scoured its accounts receivable and enterprise resource planning platforms for data and intelligence on sales, contracts terms, and payments.
Gathering this data helped us pinpoint factors leading to payment delays. This intelligence allowed us to build a predictive model, powered by advanced analytics, that forecasts which customers are likely to pay late, early, and on time. This model also uses machine learning to help maintain quality collection practices.
Having segmented the company's customers, we could identify reliable payers who need little or no follow-up on their past-due or low-balance accounts. We then automated activities that made collecting from them touchless.
At the same time, we introduced process transformation and enhanced operating rigor to remove bottlenecks in the collections process and reduce late payments.
We also matched and assigned collectors to specific customers while evaluating the skills and people the business needed to fulfill these tasks.
All this means that the company is treating its customers with the individual and nuanced care they deserve – and that will play out to improve its cash management strategies and the bottom line.
In just six months the company realized a range of benefits:
The advantages of an analytics-driven collections strategy and cash management techniques will only grow. As our predictive model learns from experience, it will get even better at identifying clusters of customers with similar payment behaviors. The model will also become more accurate in its late-payment predictions – right down to the number of days it will take the company to collect.
Armed with a predictive-analytics model, the company has access to the insights it needs to continue refining its collections strategy to keep bad debt low and maintain a healthy cash flow. The business has engineered a more resilient future.