How to Optimize AR Collections Management to Improve Cash Flow?

With the rise in customer numbers, it is difficult for SMBs to optimize accounts receivables. The huge variance in cash inflow and outflow will hinder business growth and profits. Moreover, the lack of a single centralized repository of customer information will lead to time spent collaborating rather than on strategic dunning communication.

As an effective measure to speed up the process and cut down on days to collect accounts receivable, companies could group their customers into different categories and modify the dunning approach.

Customer Segmentation:

Dividing customers into different brackets and defining strategies for each bracket could enhance the AR collections process and optimize it. By adopting this method, SMBs could easily identify their high-risk customers and prioritize the required task for a better collections recovery. Let’s look at some effective strategies for customer segmentation:

• Aging Buckets: You can categorize customers based on the time periods for which the payments are due. For eg., an aging bucket can be defined for the period of 1 to 60 days which will include all the debited accounts that are 1 to 60 days past due. This could help businesses identify the accounts that are overdue for a long time and collect them as early as possible to avoid bad debts.

• Value to the Company: Based on the invoice amount, the customers can be segmented into different dollar amounts. This could help the analysts to identify the large paying customers and avoid the possibility of missing out on hard dollar payments.

• Payment Performance: The customers can be divided into high-risk and low-risk categories based on their past payment performance. This could help the analysts to identify the on-time payers and consistent late payers.

• Credit Risk Category: Customers are separated based on their credit data and the associated risk class. The customers are classified into high-risk class, medium-risk class, and low-risk class. This could help the analysts to identify the payable customers, likely-to-pay customers, and the deny-to-pay customer.

Prioritizing the Collections Task:

Based on the customer segmentation, analysts can prioritize their tasks to optimize the collections process. Analyzing aging reports, credit scores, risk categories, and dollar value could help analysts recognize the priority accounts to focus on, thus saving resources and time.

Applying different brackets on customer sets based on available data such as invoice details, credit scores, days past due, etc., helps create worklists to highlight the immediate-attention customers to low-risk customers.

However, this task becomes painstaking when businesses deal with a large customer base. SMBs often find it hard to recover the accounts receivables due to higher customer numbers and a small workforce.

To resolve this issue, SMBs can turn to technology to automate their process and increase efficiency.  With the intervention of technology and next-gen tools such as artificial intelligence, priority worklists can be automatically created.  This could help analysts get started with sending and tracking en masse collections correspondence to scale collections outreach.

Sources: PYMNTS

The technology will help increase the effectiveness of accounts receivables and reduce operational and functional costs.