Missing From the Conversation About Accounts Receivable: Decision Intelligence

There are two things that credit teams know full well: 1) they must find ways to get customers to pay their bills, and 2) to do that, they have to understand what motivates customers to pay and keep paying on a timely basis.

At the top of any team’s wish list is having real-time insights derived from their customers’ behaviors. Just because a customer paid on time last month is no assurance they’ll pay on time next month, or next quarter. The goal is to elevate customer engagement in ways that improve relationships and support future revenue.

But many AR teams aren’t anywhere close to achieving those objectives. Often, they find themselves in the trenches, unsure of which of their efforts to collect payments are working and which are necessary. Without those real-time insights, they settle for simply looking to collect.

What should be taking center stage? The benefits that can be gained by employing decision intelligence.

Decision intelligence, according to Google data scientist Cassie Kozyrkov, is the discipline of turning information into actions at any scale. By 2023, Gartner projects that more than one-third of large organizations will be practicing decision intelligence. AR functions are particularly well-suited to benefit from this trend.

For the AR collector, it means receiving critical information to better perform tasks easily and efficiently. Specifically, decision intelligence enables AR teams to:

• Identify patterns related to how customers are paying

• Automate the process of keeping up with and being alerted to changing behaviors

• Anticipate early that customers may be unable to make timely payments

• Determine and set in motion the best strategies to maintain or improve payment performance

• Analyze how collection strategies are working

The path toward achieving decision intelligence excellence for AR means harvesting insights from customer data so that AR teams are equipped to take the right actions at the right time and dramatically improve customer payment performance.

Think about the way you might use GPS navigation in your car. While you certainly use this tool to get somewhere you’ve never been before, many people also find it helpful to engage GPS even when they’re traveling from home to work. Why? Because this process adds an extra layer of intelligence that informs the driver on important things, such as how to avoid delays caused by road construction or accidents.

In the world of AR, credit teams can benefit from this added layer of intelligence to make the best decisions. And they need to be informed as soon as possible. That is, not when a customer is on the brink of bankruptcy and unable to make payments, but when there are yellow flags that point to the likelihood that a customer’s business is in trouble. Back to the GPS analogy, you want to know that there’s an overturned truck causing traffic five miles ahead so you can quickly reconfigure your route before you’re stuck.

In a similar way, credit teams can put decision intelligence to work for them in myriad ways when they need to be alerted to payment shifts that might impact the business.

Let’s first look at how this process typically works using conventional methods.

At many companies, the manual nature of AR monitoring limits the team so they only have the bandwidth to contact and interact with a fraction of customers, say, the largest 10% because that’s all they can get to. They don’t have time to look at smaller accounts and notice signs that point to an increasing risk of non-payment, such as when customers start to pay later or use a new payment method or account.

Compounding this issue, most AR systems employ a one-size-fits-all type of type of monitoring, such as those that flag a customer account based on a fixed due date. This can be problematic in the case of a customer who consistently pays a couple of days after the invoice is due. This means that, each time, they’ll be flagged as late and receive a phone call reminding them of their late payment.

These limited processes and mechanisms create extra work for account teams and cause them to miss critical nuances due to a missing layer of intelligence that would spot yellow flags before things get serious.

AR solutions built to enable decision intelligence, on the other hand, provide those critical insights and flag payment issues early, because they can detect customer behavior changes, such as:

• Shifting payment dates including those that are not considered delayed

• Changes to payment methods

• Declining credit scores

Take, for example, a customer who has always paid two days after their invoices are due, but over time, their payments are slipping. One month, they’re paying four days after the due date. The next month, seven days after the due date, and so on. Whereas a conventional AR system might not flag this customer’s change in behavior, one that’s optimized for decision intelligence will notice this nuanced change and alert the team so they can consider actions to take to ensure payments continue to be received and check in with the customer to ensure all is well.

Decision intelligence for AR has benefits beyond indicating when a customer shows signs of a worsening situation. Operational efficiencies can be realized when analytics indicate that AR should not contact a customer who has an acceptable track record. For example, perhaps a customer’s payments are coming in late, but the company is consistently paying four days after the due date. A system built for decision intelligence would inform AR of this nuance rather than requiring action, which in this case, probably isn’t needed. The end result is that AR doesn’t waste its time on an account that doesn’t need attention or unnecessarily bother a customer.

BlackLine’s AR Intelligence module—which is built from information collected in the Cash Application process—puts critical intelligence at users’ fingertips so they can:

• Gain detailed insight into how their customers are working with, or against, them. Users can analyze how individual customers pay against their terms rather than a default figure applied to all customers, track where customers are taking their own extended terms (beyond what has been reasonably agreed), and tailor their interactions accordingly.

• Build on the collected data. Companies can create a six-week payment forecast based on how customers have previously paid. This can then be tracked against real-world collections (from the payments received in the Cash Application module) to accurately see where customers are paying as predicted or not, allowing users to quickly curate a customer contact list of actions.

• Enable payment, invoice, and customer data to be aggregated across multiple ERP systems. This allows users to immediately see the big picture rather than a siloed view that must be manipulated, adding further effort and delay into the process. This ability also allows data to be shown at the global and granular entity level which can be useful for tracking DSO and detailed sales ledger analysis and use this data across a greater number of business teams.

Decision intelligence improves AR efficiencies on the ground level, but there’s a business case for adopting decision intelligence best practices that go beyond improving AR processes and collections. Two additional functions that can be positively impacted are sales and treasury.

Sales teams can make use of this intelligence to create business growth opportunities by accurately tracking customer payment trends and being alerted to customers whose performance suggest a review of current trading levels.

AR decision intelligence is also invaluable for treasury departments whose actions concerning cash flow and borrowing have an enormous impact on a business’s pooling of cash and ensuring the necessary levels of working capital. Whereas AR might be looking at how to collect by the end of the month, treasury benefits from having an accurate understanding of the bigger picture and can make adjustments based on when money is forecast to be received and how much is expected based on how customers have paid them previously. These insights can enable treasury to determine whether they need to borrow to cover cash flow or deploy working capital.

Customers' circumstances are changing all the time, and their behaviors can shift on a dime. Businesses need to be able to analyze and react to those changes quickly and in real time. Decision intelligence is an essential discipline that AR teams must harness so that they can take the right actions to improve payment performance and profitability.

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