How to Use Data-Driven Strategies to Mitigate AR Collections Recovery

What you’ll learn

  • Learn how customer segmentation can help scale your business with improved collections recovery.
  • Explore the benefits of adopting data-driven strategies to digitalize the existing AR process

For years, mid-sized businesses have been struggling with issues around AR collections to generate working capital and establish their financial health. In today's digital world, companies are searching for alternate solutions to standardize their AR collections management process and bring about effective productivity. Companies are considering strategic initiatives to minimize the Days Sales Outstanding(DSO) and operating costs, secure liquid cash, and support sales by achieving a faster time-to-cash. The biggest bottleneck in the whole AR collections process is the traditional practice of manual collection recovery which is time-consuming and leads to an increase in outstanding receivables. How to Use Data-Driven Strategies to Mitigate AR Collections Recovery

Source: PYMNTS

At HighRadius, we guide our customers to dive deeper into the collection process and figure out the ways and channels by which companies can establish proactive collection dunning to fast-track recovery.

Understanding the Current Process in Play

Companies across the globe follow the traditional route of manually operating their accounts receivables. The conventional accounts receivables process has many loopholes in play, which often leads to issues, some of which are listed down below:

Absence of Customer Segmentation

The absence of customer segmentation and prioritization makes it difficult for small and medium businesses (SMBs) to collect their receivables on time. As finance teams struggle to focus on high-risk accounts, there’s a delay in the overall time-to-cash.

Lack of Centralized System

With an increase in the volume of customer accounts, managing data, recovering collections, and tracking payments becomes difficult for analysts. With the absence of a centralized repository for managing data, the process gets stalled.

Individual Client Engagement

To keep customer satisfaction intact in the whole collection dunning process, every customer is dealt with individually. Most SMBs spend a lot of time on manual correspondence, this includes sending out payment reminders individually. With limited resources and a growing customer base, keeping track of multiple accounts gets difficult.

Error-Prone Processes

Data collection and recovery is a critical process that is affected if there is any missing customer document or false invoice information. With human intervention, the whole AR process becomes prone to human errors.

With the advancement in technology, the AR collections management process has evolved over the years. Due to the increasing number of customers and expanding databases, it is difficult for collectors to slice and dice the data, prioritize their tasks, identify customer risk factors, and recover the due amount.

Businesses seek to adopt a proactive approach for AR collections management to ensure a quality customer experience and better team management. Businesses must make sure that the process is carried out in the most optimal method by prioritizing tasks and identifying priority customers.

Customer Segmentation and the Need for Prioritization

The importance of treating different customers differently is evident. Every customer is distinct, in terms of financial stature, market trends, business relationships, etc. Thus collectors have to rely on customer segments for customized customer communication.

To make the collection dunning process more efficient, it’s necessary to identify the high-risk customers and prioritizing the tasks based on that. Different rules and strategies defined for the buckets can optimize the collection recovery process.

There are many ways in which you can segment your customers, but the most efficient ones are as follows:

Customer Segmentation and the Need for Prioritization

Strategic Importance

Customers are segmented into groups based on their predicted value to the company. Companies should treat their strategically crucial customers with special care and offer flexible options to them.

Payment Performance

Customers are identified and segregated into high-risk and low-risk categories based on their payment behavior.

Credit Risk Category

Customers are segregated based on their credit data and associated risk class. Aging reports play a huge role in identifying the risk category of customers.

Customer Segmentation and the Need for Prioritization

Optimizing the Process With Digitization

Many organizations and businesses have started to opt for adaptive AR automation solutions to achieve a faster time-to-cash.

As per the PYMNTS  Study on B2B Payment Innovation Readiness,

“Businesses that rely on manual AR processes have a 30% longer average DSO than firms that rely on medium or high levels of automated processes for collecting receivables.”

Given the pandemic scenario and resultant changes, technology has benefited SMBs to thrive and grow in a volatile economy. With the amalgamation of technology in the AR process, the process is becoming more streamlined and efficient. Let’s see how automated customer segmentation and prioritization help finance teams focus on at-risk customers:

  • Automated Customer Segmentation: Segregating customers into different categories becomes a tedious task when the customer database is huge. But, with the help of technology, the task of segmenting customers into groups will get automated, and based on the bucket of rules and strategies applied on these brackets, the task can be carried forward, with minimal human intervention. By eliminating the manual aspect,  the room for human error in the process of customer segmentation reduces.
  • Prioritized Worklists for Proactive AR Collections: Collectors spend a majority of their time identifying the high-priority customers, with their intuition, skill, and experience and rummaging through a big pool of customer data. But with technology, tools like AI and ML can take this task one step further by automating the process and creating prioritized worklists based on customer risk categories. This will enable the collectors to pay more attention to the high-risk customer and reduce the possibility of bad debts.


Collections is a strategy-driven process, by focusing more on the performance and functionality of it, companies can manage to minimize DSO and recover payments before they become overdue. For fast payment recovery, the most efficient way is to segregate the customers based on different factors. Businesses need to improve their traditional AR collection process by adapting to the new technologies. Leveraging technologies can make their current process better, more effective, more accurate, and more ideal.

Here’s what you can do next:

  • Analyze your existing AR process and list out the difficulties that you are facing.
  • Discuss all the different pain points with your team members.
  • List down the features that you already have in play.
  • Talk to your clients and get collective feedback to act upon.
  • Analyze your previous investment records and the ROIs.

To learn more about how to optimize your collections process with the help of digitization, check this blog on Enabling Faster Cash Conversion with AR Automation and Digital Payment.

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