In his most recent blog post titled “Collections is about Connections,” Tesorio CEO, Carlos Vega, wrote about the importance of genuine human-to-human relationships in effective B2B collections. As Tesorio's Product Manager for our collections product, I'm amazed at how leading companies gain significant competitive advantages by simply leveraging collections automation (aka, Accounts Receivable Automation) to develop better customer relationships.

Many people don’t think of collections teams as a strategically critical function, as evidenced by the scarce resourcing that they receive, but this is changing. It’s changing because the pace and dynamics of business today demands more efficiency and customer empathy to take control of the lifeblood of your organization, cash.

Challenges With Traditional Collections Processes

The traditional collections process is painful and full of laborious tasks that rely on data located in disparate systems and spreadsheets. Collectors manually cobble together the best customer profile they can, begin calling them, sending emails, calling them again, sending more emails, and on and on.

This approach appears justifiable because it reflects the way collections have always been done. However, there is a better way for any organization to consistently use and disseminate information about each customer’s unique situation: automation. Ironically, the absence of automation in collections leads to less personalization which, in turn, leads to worse customer experiences. Plus, failure to establish rigorous processes creates a higher potential for human error. It’s precisely this type of error that is the main culprit of an otherwise avoidable aging collections portfolio, increasing bad debt, deteriorating working capital metrics, or worse. Remember JP Morgan’s so-called “London whale” fiasco, which led to a $6 billion loss due largely to a spreadsheet error? That is sadly not an isolated incident. Studies show that up to 88% of spreadsheets have errors.

In terms of the efficiency of a manual collections process, a study by Paystream Advisors found that:

Companies who rely on manual processes to manage collections spend 15% of their time prioritizing their activities. Another 15% of their time is spent gathering information to make the collection, and only 20% of their time is spent actually communicating with their customers about payment.

Additionally, according to Credit Today, 25% of credit departments do not have adequate staff to manage their workload leading to low morale and even more mistakes.

Benefits of an Automated Collection Process

Automated collections processes leverage specialized technology, such as Machine Learning or Artificial Intelligence to store customer data, prioritize activities, and automatically send highly customized correspondence to customers about their invoices in the appropriate timetables. This allows collectors to spend their time focusing on engaging more closely with high-profile or struggling accounts.

According to Paystream Advisors, companies who use accounts receivable automation software reduce time spent prioritizing activities from 30% to 12%, and increase valuable time interacting with customers from 20% to 62%. This difference represents a significant shift from lower-value to higher-value activities. The same study also found that about one-third of companies that have implemented collections automation report that they can see the technology impacting at least 60% of their processes.

Key benefits of collections automation include:

• Gain new insights into every step of your collections process
  The overall health of your portfolio is at your fingertips. Understanding your working capital metrics, aging trends, and other KPIs enables you to manage teams effectively and track collector performance.
  
  
• Take action faster with real-time data at your fingertips
  Increasingly, company initiatives and future endeavors are completely data-driven, and debt collection is no exception. Machine Learning provides more insightful real-time reporting, cash flow forecasting, and customer data that allows actionable decisions to be made quickly.
  
  
• Improve team efficiency and morale by eliminating error-prone manual tasks
  An overworked team that loses time to manual processing (especially where it isn’t necessary) is detrimental to an organization’s customer relationships and bottom line. Machine Learning employs fully adaptable debt strategies based on payment and risk performance, paired with electronic delivery of customer statements and invoices. This allows teams to focus on value-add tasks rather than manual activity, which is a win for the team, customers, and the health of the business.

The bottom line is that implementing the right solution for AR and debt collections can fundamentally transform the perception and reality of your collections team from necessary and tactical to value-adding and strategic. Now is the time to join the amazing companies I’ve seen improve the efficiency, efficacy, and morale of collections teams while simultaneously greatly enhancing customer experience.