Revamped collections processes. A predictive model for better customer care
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.
- We performed in-depth analytics and risk assessments on customer behaviors and characteristics
- We took a detailed look at the company's payment and credit terms, reviewed the products customers bought, examined collection processes, and more
- We assessed a list of more than 10,000 active customer accounts for delinquency figures and invoice volumes to distinguish the dependable payers from the rest
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.