Collection Segmentation Clusters and Strategies


Segmenting customers for Collections into buckets of strategic accounts and providing an insight into Collections process for faster operations

Contents

Chapter 01

Executive Summary

Chapter 02

What is Customer Segmentation in Collections?

Chapter 03

Benefits of Customer Segmentation in Collections

Chapter 04

Segmentation Dimensions

Chapter 05

Collection Segmentation Clusters and Strategies

Chapter 06

Tools to Deploy: Key Criteria

Chapter 07

The Scalable Solution

Chapter 08

About HighRadius
Chapter 05

Collection Segmentation Clusters and Strategies


The subsequent sections detail how the customer segments can be clubbed to generate customer portfolios and strategies. These segment clusters serve as the underlying foundation for any collections action. Some sample segmentation clusters are listed below. By using a combination of all of the above variables, an organization could come up with many more collection clusters and strategies.

Digital Enablement

This strategy illustrates the level of digitalization in the A/P processes of the customers. Customers who have e-invoicing options and opt for e-payment like ACH, Wire or Credit Cards are considered digitally enabled.

Collections Risk: High or Low

Collections risk is based on three factors:

  • Credit Risk
  • Slow Paying Vs. Fast Paying Customer
  • Percentage of invoice dollars greater than 30 days past-due

The fast-paying customers with low credit risk and a small percentage of invoice dollar value in 30 days + past- due bucket can be deemed as customers with low collections risk. On the contrary, the slow-paying customers with high credit risk and high percentage of invoice dollar value in 30 days + past-due buckets could be characterized as customers with high collections risk. The business rules and priority for different segmentation dimensions decide the collection risk category for customers with other combinations of these factors.

Large and SMB Accounts

This strategy takes into account whether it?s a large customer or a small to medium business enterprise. Different companies have a different perspective on the classification of accounts into large or SMB accounts. Dividing the segments into strategic and SMB accounts helps collectors in choosing either the high-touch collections call approach or low-touch email correspondence approach.

Disputes

The number of disputed invoices is another crucial and dynamic component that could prove to be vital in analyzing the collections’ actions based on customer behaviour. One indicator of customer satisfaction is the number of short-payments or invoice disputes that the customer has. For a high dispute-prone account it makes more sense for the collections team to form a tag-team with key account managers and use a high-touch personal approach for collecting open A/R. An example here could be a retailer such as Walmart or Target who tend to order goods from say P& G in millions of dollars. Given the size of the orders, there tend to be a lot of deductions too. Including the volume of deductions is also critical for segmenting customers better. The next chapter probes into collections approach and actions based on clusters of collection strategies and segments. There can be many combinations and permutations of these clusters and the following explores a few of them.

Sample Strategies Based on Segment Clusters

These sample strategies are based on whether the customer is digitally enabled, the level of collections risk the customer poses, the type of account and the average number of disputes raised by the customer. Example 1: The following illustrates the customer position with respect to the given collections segmentation dimensions: Advanced Drill-Down Functionality This sample cluster scrutinizes the large, digital-ready accounts which have low collections risk but a larger share of disputes. As these customers constitute a major chunk of the total invoice value of collectables, the collectors must ensure the quality of customer experience. For this portfolio, the collections process should kickoff early for with concentrated efforts on identifying pre-deductions, confirming A/P invoice approval and addressing invoice-related queries. Since the large customers are seamlessly integrated with automation and e-trends, a ?self-service portal with a grievance dashboard? can serve well to ensure early tagging of disputed invoices, in turn, accelerating the collections process. Here the customers could view all their invoices, and create and share disputed items. The action for these customers should follow ?Key Accounts Management with Low Touch? maxim. This customer segment is vital and challenging to the business, yet due to low risk and digitization, automation coupled with prompt correspondence can be leveraged as a strategy to overcome a large number of deduction items generated by these customers. Example 2: The following illustrates the customer position with respect to the given collections segmentation dimensions: Process And Team Performance Tracking The above represents one of the worst-case scenarios where a large customer has high collections risk and a large number of disputes. Owning a big chunk of receivables, these customers need focused collections efforts as they come under the crucial or ?at-risk? category. The collectors should indulge in sincere follow-ups via emails and phone calls while ensuring satisfactory customer experience. These accounts should be prioritized in the worklist and dedicated collections efforts should be made towards these customers. The collectors should implement a ?High Touch Key Accounts Management? strategy for these customers. Example 3: The following illustrates the customer position with respect to the given collections segmentation dimensions: Filters For Customized Data Visualization SMB customers who are digitally adaptive, pose low collections risk and have fewer disputes, enable the collectors to leverage ?no-touch collections automation? to process their collections operations. With e-invoicing and ACH payments, the burden of pro-active correspondence for collectors is significantly trimmed down. The collections process can further be expedited and made proactive, with a self-service payment portal where the customers can submit payment commitments, make payments and get real-time notifications and reminders about their invoices. Moreover, the collectors could dispatch templatized bulk emails to these customers, improving account coverage. This customer segment essentially narrates the best-case scenario, and engaging in the above-said actions can strategically reduce the burden on collectors, increase their customer reach and enforce ?doing more with less? maxim in the collections process. Example 4: The following illustrates the customer position with respect to the given collections segmentation dimensions: HighRadius Integrated Receivables Platform This cluster explores the customer segment with SMB account, high collections risk, few disputes and where the customers have not yet embraced digitalization. Due to the absence of e-invoicing and e-payments, the collectors need to enforce proactive correspondence strategies like early invoicing, telephonic confirmation of invoice delivery and advanced reminders ahead of due-date. Moreover, as the dunning process would be paper-based (print and mail/fax), extensive manual collections efforts would be needed for collectors to be on top of these accounts. However, since this segment includes small to medium businesses, they would not land a huge impact on the bottom line or past-due A/R. So, the collections operations should be optimized based on the worklist and volume of customers in other strategies such that the collectors are ?proactive but not unduly invested? in this segment.

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HighRadius Integrated Receivables Software Platform is the world's only end-to-end accounts receivable software platform to lower DSO and bad-debt, automate cash posting, speed-up collections, and dispute resolution, and improve team productivity. It leverages RivanaTM Artificial Intelligence for Accounts Receivable to convert receivables faster and more effectively by using machine learning for accurate decision making across both credit and receivable processes and also enables suppliers to digitally connect with buyers via the radiusOneTM network, closing the loop from the supplier accounts receivable process to the buyer accounts payable process. Integrated Receivables have been divided into 6 distinct applications: Credit Software, EIPP Software, Cash Application Software, Deductions Software, Collections Software, and ERP Payment Gateway - covering the entire gamut of credit-to-cash.