Can Data-Driven Accounts Receivable Management Strengthen Client Relationships?

Big data technology has proven to be remarkably useful in the area of accounts receivable management.

accounts receivable management with big data
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Big data is central to financial management. The market for financial data analytics is expected to reach $10 billion by 2025. One of the biggest uses of big data in finance relates to accounts receivable management.

A decent AR (Accounts Receivable) management system is essential for the functioning of any business, no matter what industry you’re in. Making it easy and convenient for clients to pay you is crucial if you want to maintain good relationships with your key customers over the long term. Fortunately, new advances in data technology have made accounts receivable management easier than ever.

The benefits of data analytics in accounts receivable was first explored by a study from New York University back in 2007. More recently, we have seen even more impressive data on its effectiveness.

Big Data is Integral to Modern Accounts Receivable Management

Unsurprisingly, clients are always in the best mood when they’re handing out their hard-earned cash to pay for your invoices. As such, you should consider accounts receivable best practices to make sure that this key interaction with your clients is happening seamlessly without issues.

Here are some tips for AR management that will help you strengthen client relationships through better financial management!

1. Identify routinely tardy customers with predictive analytics

Robert Kugel from Ventana Research has talked about some of the benefits of using big data and AI in finance. One of the factors that he raised was the importance of using predictive analytics to identify customers that regularly missed their deadlines.

Companies can use their predictive analytics models to decide how to resolve issues with tardiness. These include finding ways to expedite payments or lowering lines of credit for customers.

2. Optimize discounts and shorter payment terms as incentives

Predictive analytics can also help you identify the impact of various incentives that you offer to customers, such as discounts and shorter payment terms. You should outline these options beforehand and test them carefully with your big data software after.

Consider offering discounts and short payment terms to your clients, reducing some of the cost if they can pay you quickly. This gives them an incentive to pay your invoices in a timely manner, and while it may mean a smaller profit margin for you, it could save you money in the long-run due to all the working hours spent by employees chasing up invoices.

Payment terms such as 2/10 and N30 are popular with many companies on both sides of the process, as it helps businesses to pay you faster and more easily. Reducing the strain on your customers with these discounts is an excellent business strategy that solidifies your strong relationships with new and existing clients.

Your data analytics interface can assess the impact these incentives have on customer payments. If they don’t work, you can consider omitting them from future customer contracts.

3. A better understanding of customer credit history

Sometimes you need a better understanding of your clients’ credit history and the way that their business is going in order to sympathize with their payment schedule and understand the best way to invoice them for goods and services. This is another very important benefit of using data analytics as part of your accounts receivable strategy.

For instance, a company that makes profit on a seasonal basis might delay their invoice payments until the season rolls around. By understanding this part of their credit history and identity as a company, you can better estimate their invoice payment dates and understand when the money will come. You can use detailed data on customer behavior to reach these insights.

This in turn can lead to better understanding between clients and businesses, making it easy to expect payment dates more accurately. It may also make it easier for you to cut ties with clients who have a poor credit history while fostering relationships with clients who have a good credit history of paying you in full and on time.

Sometimes the best solution for a poor client relationship is to let it go!

4. Explore the Effectiveness of Multiple Payment Options

Decent AR management can mean giving your customers multiple payment options, making it easier for them to pay in the most convenient way for them given their accessible funds at a given time. As well as the classic check, consider accepting ACH, EFT and credit cards, among others.

Giving your client more payment options increases their confidence in your service and allows them to continue the business relationship with the understanding that they have many avenues for paying you. It also makes your company seem innovative and flexible, which is never really a bad thing.

This all sounds good in theory. However, you need to use data analytics to assess the real impact.

Your data analytics interface can tell you how customers respond to the option of using multiple payments. You can use this data to see the cost-effectiveness of different payment options, as well as the likelihood that adding these options really does lead to faster accounts receivable turnaround.

5. Provide a platform for client disputes

Invoice disputes happen all the time. Mistakes are made, items are forgotten, numbers are crunched incorrectly… it happens. This is another issue where big data technology can be useful.

However, when a dispute arises, it’s a good idea to let clients easily raise issues with a platform where they can easily scrutinize invoice amount and line items. Giving your clients an easy platform for discussing issues generally leads to faster conflict resolution, more informed staff members, and improves client satisfaction rates.

From client disputes to better payment options and discounts, there are many ways to strengthen your customer relationships with better AR management.

Sean is a freelance writer and big data expert. He loves to write on big data, analytics and predictive analytics.