Collections management is a central part of the credit-to-cash process. It encompasses all the activities that happen after an invoice is sent to a customer, to the moment when the money is accounted for in your bank account.
Traditionally, this is both a labor intensive and people-intensive process. You are trying to balance maintaining happy customer relationships and open communications while asking your customers to please pay their bills. On time. Through one your preferred payment channels.
While certainly you want to maintain a personal touch when connecting with your customers on the topic of money, you also want to manage the workflow so that your teams are not overwhelmed and under-supported. This is where implementing automation and machine learning through a smart AR platform can make a world of difference. To everyone, including your customers.
Collections management consists of four core activities, in which we can deploy smart automation/machine learning (AI/ML) tools to create a smooth and productive process:
This is your communications process for reminding customers to pay you, either through email, phone or other means. There are a few areas where AI/ML can help:
- Automated communications can be sent according to payment due dates, past due dates and other triggers your team identifies. Each communication can be customized through your AR system with the appropriate account information, freeing your team from the daily, repetitive tasks of sending out reminder emails.
- Optimal email text suggestions, based on a customer profile and the way a customer responds to communication, can help an AR manager determine the best way to approach a delinquent customer and estimate if the email text is going to trigger a customer to pay.
- Optimal email delivery time means your AR system looks at the way specific customers respond to their communication and establishes the best time to email then to grab their attention. This feature will significantly increase the response rate, which will result in solving issues and getting paid in a much more timely manner.
Inbound Request Processing
Responding to inbound requests over the email or through your AR system. This includes things like processing disputes, finding solutions to problems, and giving customers what they need to pay you.
- Prioritization of inbound requests can be automated so that your AR team is focused on the highest value, most urgent requests first. A smart, automated system will look at the type of request, the customer’s open balance, customer score or credit with your business, and the sentiment of the communication (positive or negative) among other characteristics to score it and put it at the top of your AR team’s to-do list.
- Classification of inbound requests makes it easy to sort through which requests require personal attention (like the prioritization above) and which can be simply resolved with an automated response. Simple requests that could be managed through automation include things like unsubscribe requests, payment link requests, invoice copy requests or PO number requests.
Communication and support from sales, customer services and other teams is often required to help solve customer issues, understand challenges with delivery or products, or simply to know the history behind a prior customer agreement.
- Detecting customer anomalies can help your teams piece together a customer story and get a full picture of both your customer’s situation and the impact to your business. Anomalous customer behaviour is when customers change the way they pay a business or communicate with it, which might be a leading indicator of the potential payment issues in the future, or in general, decreasing credibility of a customer. If those anomalies go unnoticed (which happens in most of the cases) - it results in significant payment delays or bad debt for the company. Once you know what’s truly going on with a customer, you can determine who is the best contact inside your company (sales, customer support or finance) to have the next conversation.
A smart AR system can look at things like:
- Customer was always paying 7-10 days after the due date,consistently. Now they are starting to delay the payment to day 15.
- Customer was consistently paying with one monthly payment with a check, and now they are making smaller payments with a credit card.
- Customer was always responding to collection emails, but now they are ignoring incoming communication.
- Customer had disputes on average once a quarter, and those disputes were related to product/service issues, and now they are disputing every invoice for any reason they can find.
- Offering new incentives for payment is something your broader teams can also help communicate to a customer, and support the collections process. Your AI/ML tools can bring valuable information to the forefront of these discussions by suggesting which would have the most impact. There are many levers that you can use to encourage a customer to pay, for example:
- Dynamic Discounting
- Late payment penalties
- Payment with a credit card
- Sending account to collections
- Terminating Service
Some of these are positive incentives, and some are negative ones. The algorithms analyze historical data on how the customer (or other customers, who are similar to that) reacted to each of these incentives at different times, and suggest the best times to use them to maximize collections and minimise DSO.
Transparency of the collections process, including cash forecasting, analytics of the AR specialist’s performance, are all key to evaluating the health of your collections management process. If you have a smart AR platform that is leveraging AI/ML to help you manage the prior three steps, then you will have detailed dashboards available to help you understand your business health. Having reports generated automatically for your team’s review ensures you are monitoring the success of your collections strategy, identifying areas for improvement, and proactively maintaining happy customer relationships. Some reports to evaluate regularly include:
- Collections Effectiveness Index (CEI)
- AR aging report
- Days Sales Outstanding (DSO)
Keeping your customers happy throughout the collections cycle is about clarity and consistency. Have clear and simple guidelines to help your teams manage the process both prior to and during collections. Automation and machine learning can ensure that you provide consistency in communications and have valuable data available to help you make smart, flexible decisions in handling your outlier cases. Using technology to offer the best possible options, at the right time, keeps both your customers and your AR teams happy and working together.