AR Intelligence: The Key to Navigating Bad-Payer Customer Relationships

You value your customers—you couldn’t do business without them—but there are always bad payers in the bunch, customers who chronically pay late or worse, not at all. For a business trying to manage risk, navigating the bad payer customer relationship can be challenging, and AR is on the front lines, trying to monitor erratic payers and continually chasing down payments. The job becomes nearly impossible when AR teams are working with manual processes.

When payments are due, it’s not time for guesswork. For a business to run efficiently and maintain cash flow, AR teams need to accurately monitor the risk of every customer account. When payments are late, AR needs to evaluate customer behaviors on a granular, nuanced level to determine the next course of action, such as requesting that the controller reach out to the customer’s CFO or changing a customer’s payment options.

But how can AR deftly navigate these relationships when the behaviors of bad, erratic, or slowly worsening payers are so difficult to track and predict? The answer is for businesses to make more use of the operational data captured in the process so they can make smart decisions around customer payment behaviors and payment forecasting, as well as the need to borrow money. Informed decisions lead to improved cash flow, more efficient AR operations, and stronger customer relationships.

When AR teams rely on manual processes to track customer behaviors, they suffer from a host of problems, such as:

• Working with inaccurate, unreliable data

• Poor quality invoice-remittance matching

• Wasting time chasing late payments

• Making unnecessary contact with customers

Consider how two scenarios play out for a business whose AR team relies on manual processes:

Scenario 1

Customer A is on 30-day terms but consistently pays on day 32. Seeing the invoice become overdue on day 31, your AR team contacts the customer to remind them that their payment is overdue. This contact occurs for several months until the perturbed customer points out that they are a good payer, one who pays regularly, but must wait to be paid themselves before paying you and, therefore, shouldn’t be bothered every month.

The result: Your AR team members have wasted their time making unnecessary customer contact and possibly harmed the customer relationship.

Scenario 2

After a year of Customer A consistently paying two days late without touch points, their payments slip by two days. It’s a slight change—and maybe one or two additional days aren’t a problem—but it’s one that a manual process wouldn’t highlight. Months go by without the customer being contacted. Their payments continue to slide later by a day here and there, until they stop completely. The collections team goes into panic mode, trying to contact the customer who doesn’t respond.

The result: AR missed an opportunity to make a timely call to the customer when the aberrational behaviors began and now has an increased amount of bad debt to write off due to the customer not paying.

As most parents will attest, the child who gets the most attention often isn’t the one who follows rules but rather the one who’s always getting into trouble. In a similar way, many AR teams today spend most of their time chasing down overdue invoices and contacting late payers, instead of spending more time with good payers, building those relationships.

Imagine if AR could predict bad payers’ propensity for issues early in the payment cycle? If they could, AR could skillfully manage risk of all accounts, minimize late payments, and improve profitability. That’s where AR intelligence comes in.

Teams that employ AR intelligence effectively monitor risk by efficiently and accurately evaluating customer behaviors. They receive automatic alerts that tell them exactly how and when to reach out to a customer whose payments are falling behind, thereby saving an untold number of hours and allowing teams to focus on more meaningful, strategic tasks.

Using AR intelligence, businesses can take operational data out of the AR process to gain advantages across the company. For example, they can:

• Leverage data insights to be able to quickly be alerted to problem accounts

• Identify good-paying customers who should be extended credit

• Make the right borrowing decisions with payment forecasts based on customers’ actual payment trends

• Strengthen the customer relationship by improving communication

When businesses adopt AR intelligence solutions, they not only meet the aforementioned objectives but find such tasks easy to perform. For example, BlackLine’s AR Intelligence module accurately analyzes customer behaviors while also tracking DSO and allowing customers to apply a “customer attractiveness” score based on a number of metrics, such as days to pay, age of outstanding debt, and more.

Revisiting the two scenarios discussed earlier, a business with keen AR insights and system-wide visibility would respond quite differently:

Scenario 1

When Customer A is shown to be a late but consistent payer, the system alerts the AR team but doesn’t recommend the customer be contacted.

The result: The AR team saves time on monthly contacts (leaving a slightly tardy but reliable customer inviolate) and can focus on truly problematic accounts.

Scenario 2

As soon as the customer’s late payment slips more than its usual two days, the system alerts the user who then can decide whether to contact the customer.

Here is what this process might look like if the organization uses BlackLine’s AR management solution. In the screenshot below, AR management alerts collectors on daily actions required based on recovery sequences defined by the customer.

The result: The AR team proactively gets in touch with the customer early in the process and has a meaningful conversation about improving the timeliness of future payments. The team also tracks customer behaviors that reflect a change in the business, one that could require that the team reduce credit limits, limit how much the customer can trade with them, or adopt other avenues of controlling and mitigating the risk.

The key to handling a bad-payer relationship is to proactively identify legitimate customer issues at the earliest possible moment. These conversations—which should be done in the spirit of being supportive (not admonishing)—can help the AR team gauge the severity of the problem and inform strategies for moving forward, such as changing credit limits or payment terms.

Through these conversations, an intelligent AR system can dramatically improve the dynamic of the customer relationship, because a controller or rep sharing revealing insights with the customer can lead to that business improving the way it makes payments. It could even decide to prioritize its payments to the AR team’s organization, knowing that it will be more proactive than others when payments are late.

Armed with a solution that’s empowered with AR intelligence, AR teams aren’t mired in processing day-to-day transactions. Instead, they work efficiently, saving countless hours, and feel confident about the data they process. They can improve the predictability of the business’s cash flow, build stronger customer relationships, and even turn problematic customers into good payers.

Get your copy of this white paper to see how BlackLine’s unified AR solutions can raise the bar for you and your organization. You will learn how AR automation can transform how AR processes and tasks are performed and completed; increase capacity and time so AR teams can focus on activities that drive results and business outcomes; and maximize the expertise and skills of your AR professionals to ensure cash keeps flowing and risk is managed effectively.