How to Analyse Accounts Receivable

Your UK Business produces a lot of data, but how can you make sense of it? Accounts receivable analysis helps you understand what your data is saying and make decisions that grow your business.

Chances are, you are reading articles on accounts receivable analytics because you want to:

• Maintain an accurate record of your accounts receivable (AR)
• Streamline your AR processes to improve cash flow
• Have a clear picture of your company’s financial standing

This article will explore how accounts receivable analytics can help you achieve these goals, especially for businesses in the UK. It covers why accounts receivable analytics are important, how to perform an accounts receivable analysis, how to create an accounts receivable analysis report, and FAQs on accounts receivable analytics.

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Why is accounts receivable analytics important?

Here are key reasons every small business should consider using AR analytics:

Streamline cash flow management: AR analytics gives you real-time visibility into all your AR processes, from invoices to payment status. This ensures that you effectively monitor and manage your cash flow.

Helps understand customer behaviour: With AR analytics, you can understand your customers' behaviour to pinpoint early payers and track how quickly customers settle invoices. Additionally, you can segment your customers into groups and distinguish your best customers from the high-risk accounts.

Improve operational efficiency: AR analytics help you uncover bottlenecks in your credit and collection processes. Additionally, it provides insights on how you can improve your AR operations to make it run smoothly and efficiently. For instance, if your AR team heavily relies on manual processes, AR analytics can pinpoint areas where automation is needed.

Mitigate bad debts: With AR analytics, businesses can pinpoint and identify high-risk accounts and take proactive steps to avoid extending credit to them. By doing so, AR teams can minimise bad debts.

Review your payment terms: Analysing your AR data also gives insight into how your customers respond to different payment terms to find the payment terms that work best for you and your customers.

Accounts receivable analysis types

When conducting AR analysis, here are some important types of reports and KPIs to look out for:

Days Sales Outstanding (DSO)

This metric measures how long it takes for a company to collect receivables from its customers after sales. A lower DSO usually indicates a company’s collection process is working well, while a higher DSO signifies delays in payments. Day Sales Outstanding is also known as the average collection period.

AR Turnover Ratio

This financial metric measures the efficiency of a company’s collection process. It calculates how often a company can collect its receivables within a specific period, usually a year. A higher turnover ratio indicates a company has a better collection process, and a lower turnover ratio indicates the opposite.

AR turnover ratio gives companies an insight into whether or not they are extending credit to the right customers.

Collection Effectiveness Index (CEI)

The Collection Effectiveness Index (CEI) measures the percentage of receivables a company has collected over a given period. A higher CEI indicates that your credit and collection teams are doing an impressive job.

Average days delinquent (ADD)

Average Days Delinquent (ADD) refers to the average number of days that pass between due dates for invoices and the receipt of payment. This metric helps businesses know the average days outstanding and delinquent for credit payments.

Number of revised invoices

This is the total number of revisions done by your AR team over a specific period. These revisions might occur because of an administrative error or due to a customer dispute.

Bad debt

This is the amount that a company will write off as uncollectible. It’s categorised as an expense on a company’s balance sheet. Doing this lets a company forecast how much bad debt to expect and how that would impact their bottom line.

How to perform an accounts receivable analysis

Here’s a quick walkthrough of how you can perform an accounts receivable analysis:

Prioritise decision-making metrics

The bottom line of AR analysis is better decision-making. It's possible to obsess over numbers without improving your collections process. To make the most of AR analytics, spend more time on the metrics that help you make decisions to improve collections and cashflow. This differs from business to business.

Understand context

To make sense of your AR data, you need to understand the context behind the numbers. A metric can be good or bad depending on your AR practices. There's no overarching basis to assess what a metric means for your business.

For example, to interpret your Day Sales Outstanding (DSO) accurately, consider the average payment terms you offer to customers.

If your DSO is 65 days and your average payment terms are 60 days, this indicates your collection period is reasonable. However, if another company has the same DSO of 65 days but their average payment terms are only 30 days, this suggests their collection process is inefficient and slower than expected.

Analyse customer segments

Analyse accounts receivable metrics by different customer segments. This analysis helps you to understand and differentiate your best-paying customers from the ones that aren’t.

You can segment them by location, industry, or region. For instance, segmenting your customers can reveal to you that customers in the manufacturing industry pay faster than those in hospitality.

Conduct a trend analysis

Monitor trends in your AR analysis to determine whether your AR team's performance is improving or declining over time.

Say you evaluate the Collections Effectiveness Index (CEI) of your company over six months, it'll be clear if the effectiveness of your collections is improving or deteriorating.

How to create an accounts receivable analysis report

After you’ve conducted an AR analysis, you need to create a detailed report of your findings. Here’s a step-by-step process on how to create an AR analysis report:

1. Data collection: Gather all the relevant information in your accounts receivable, such as invoices, amount range, aging report, payment terms, customer details, and due dates.
2. Data classification: Classify the data in your AR by different parameters. This can be by the due date, customer, industry, or type of AR.
3. Analyse key metrics: Analyse important metrics like Days Sales Outstanding (DSO), AR Turnover ratio, and bad debts.
4. Visualise: Use AR analytics tools or Excel to create a visual accounts receivable analysis report.
5. Trend analysis: Identify recurring trends and patterns in your AR report.
6. Interpretation and reporting: Present your report to stakeholders to make informed decisions.

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FAQs - accounts receivable analytics

Here are some commonly asked questions:

Why is accounts receivable analytics important?

AR analytics helps companies evaluate their AR data to see how effective it is and uncover ways to make its process even better.

What are key metrics in accounts receivable analysis?

Some of the key metrics to consider during AR analysis include Days Sales Outstanding (DSO), Aging Reports, and AR Turnover Ratio.

What is the role of automation in AR analytics?

Automation streamlines AR analytics in the following ways:

• Streamline invoicing, payment reconciliation, and sending reminders.
• Provide real-time insight to KPIs such as AR aging, payment status, and days sales outstanding (DSO)
• Reduce errors that might occur from manual data collection

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