How to forecast accounts receivable

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When it comes to understanding future cash flow, there is no crystal ball — but there are plenty of major clues hiding in your data.

Cash flow forecasting or the accounts receivable forecasting process is the method most businesses use to predict future payments and ultimately profitability.

Financial forecasting in this way can be frustrating, though, because accounts receivable isn’t a consistent reliable process:

  • There are outstanding invoices that according to the payment terms should’ve been paid and haven’t. 

  • There are bad transactions you’ve written off after several follow-ups and sent to the collections process. 

  • Different clients may have different payment terms and definitely have differing payment behavior. 

With proper forecasting tools and systems, you can forecast accounts receivable with a higher degree of accuracy and certainty, improving your cash flow management and growing your working capital.

How accounts receivable impacts business solvency

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Understanding the realities of your business’s accounts receivable is crucial for your financial health, even for the solvency of your business. That’s because future cash flow is what keeps the lights on, the doors open, and the website online.

Further, accounts receivable forecasting helps you make more informed decisions about all sorts of business functions, like resource utilization, advertising spend, and  purchasing decisions.

In other words, when you know how much money is coming in — and how much is most likely to come in over the next few months — you can make decisions with confidence. When you don’t, you’re forced to make educated guesses, and you greatly increase your chances of hitting a cash flow crisis.

Methods for forecasting accounts receivable

Just like your local meteorologist uses multiple models and competing calculations (and still doesn’t get it right every time), AR forecasting relies on a mix of methods. 

Before we jump into specific methods, let’s level-set a bit:  Just like weather forecasting, accounts receivable forecasting won’t be 100% accurate or generate 100% certainty. Yet we still grab an umbrella when the weather forecaster tells us to, even if the sky is clear and sunny. And we can still make more informed business decisions based on AR forecasting. 

Below are four methods businesses use to forecast accounts receivable. Each method has its uses, and you’ll want to rely on a mix of them depending on the financial model(s) powering your business.

Historical averaging

Historical averaging can get pretty complex in some situations, but at a basic level, it’s more or less what it sounds like: Look at a specific period of history and examine your AR over that time. 

For example, calculate the actual money that came in each month for the last 12 calendar months, then average this amount. This amount is your historical average for the last year and represents what you are likely to bring in, on average, in any given month going forward.

If your business sees a relatively stable pattern of income (for example, a business that sells a SaaS product that brings in consistent monthly subscription fees), then historical averaging can be quite reliable.

This approach is good for charting a general trajectory for your accounts receivable.

Of course, there are limitations to this approach. Historical averaging won’t generate highly specific or variable AR forecasts. And if your business has high seasonality, then this approach won’t help much. It can still show you your overall income trajectory, but the average will be either much too low or much too high depending on where you are within your seasonal sales cycle.

Another angle to historical averaging adds sales forecasting to the mix. By looking at historical sales data (in combination with historical accounts receivable data), it’s feasible to project what accounts receivable could look like in the future. It’s possible to set up quarter-on-quarter and year-on-year comparisons, which (assuming any seasonality is on a yearly cycle) will capture seasonal changes.

Don’t forget about considering macroeconomic variables. History is a great teacher, but market conditions do change (more on this in the next section). And those changes need to factor into future forecasts. 

Market adjustments

The market adjustments method bases its forecasting results on existing figures (such as from the historical average approach) and then manually applies adjustments based on market conditions. Consider whether there is:

  • Inflation changing your AR in real time

  • An industry or economic downturn

  • A surge in business

  • An emerging technology or competitor

Market adjustments can help to knock other forecasts into line with reality, which is generally always a good idea. However, these are manual adjustments that are themselves assumptions. Unless you already have historical data showing the trend or influence, you can’t know how much any market adjustment will affect AR. 

So as you make market adjustments to your AR forecasts, be sure to keep an eye on those adjustments to see how well they track with reality over the coming weeks and months. You’ll likely need to make at least small adjustments to your adjustments before you land on something that works consistently well.

Days sales outstanding

Days sales outstanding (DSO) is a formula that measures the average number of days that a business needs to turn its sales into cash. DSO includes both the conversion of credit charges into liquid money and the time it takes to collect outstanding accounts receivable (such as when sales are made on net 30, net 60, or other delayed payment terms).

DSO matters because one key element of AR forecasting is knowing when money will arrive, not just that money is owed. To oversimplify a bit, a business could have an especially strong sales quarter, but it can’t use that money until it actually hits an account. Knowing the average length of time it takes for transactions to clear in this way will be key to sound financial decision-making.

Here’s the formula for days sales outstanding:

DSO = Accounts receivables / net credit sales x number of days

Rolling forecasts

Commonly used in accounting project management, a rolling forecast is a historical average that moves through time along with you. 

Wall Street Prep defines rolling forecast this way:

“A rolling forecast is a management tool that enables organizations to continuously plan (i.e. forecast) over a set time horizon.”

Instead of looking at January through December of last year, a rolling forecast looks at exactly the last 12 months from today. Then, at whatever cycle or time period you determine, your rolling forecast rolls with you — the oldest month drops off, and your most recent month gets added in.

Rolling forecasts are typically more helpful than static historical averaging. They take a little more work to set up and keep up, but generally, this is worth the investment.

Tips for managing uncertainty

Uncertainty is a part of forecasting, but we still forecast. Because less uncertainty is certainly better than complete uncertainty!

Even as you develop your AR forecasting approach, there are ways to manage uncertainty rather than live in fear of it.

Here are three areas where uncertainty shows up, along with strategies to manage each.

  • Best and worst case scenarios: Businesses need to plan for both. Your project management KPIs can help you determine what the realistic bounds are for good or for bad. Most businesses spend more than their worst-case scenarios as it’s tough to grow with such a conservative approach. 

You’ll have to determine how much risk you’re comfortable with, but keeping spending somewhere toward the middle of the best/worst-case scenario spectrum is generally a good idea.

  • Risks: Any good risk management strategy will identify threats (both internal and external) and develop mitigation plans for each. It’s possible to incorporate those risks into your AR forecasting, assigning a likelihood and a financial impact to each one. You’ll never eliminate all risks, but knowing what their impacts would be can help inform your spending, keeping you solvent.

  • Economic factors: We can’t control the economy or how it affects the industries we serve, but we can plan for economic changes. If an interest rate change is likely to change your clients’ spending patterns, start planning for that change as soon as news of a rate change hits. 

It may also be helpful to evaluate certain economic factors (such as inflation, interest rates, consumer spending, etc.) over various periods (weeks, months, years).

Best practices for predicting accounts receivable

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Each of these accounts receivable forecasting best practices will help you hone your approach, ensuring the ongoing financial health of your company.

Review and adjust forecasts

No forecast is ever set in stone. If you’ve ever watched hurricane forecast coverage, you’ve seen this. Those hurricane path prediction models change their trajectory seemingly by the minute. The broad strokes aren’t in doubt — there is a hurricane and it’s heading toward land — but exactly where it will land and how strong it will be are predictions subject to constant adjusting.

It’s similar for your firm. Your forecasts are helpful in setting a general direction, but you can practically guarantee that they won’t be completely right. 

So build that into your processes. Take time to review your forecasts. How well did they conform to what actually happened? What adjustments could you make that would’ve gotten them closer? Were there abnormalities that the model couldn’t account for (like economic or industry changes)? 

By regularly reviewing and refining your forecasts, you’ll keep improving the accuracy and resilience of your AR forecasting, helping your business move forward with greater confidence.

Keep management and stakeholders informed

AR forecasts are useful tools — but only to the people who know about them.

Make sure to keep management and stakeholders continually updated about your AR forecasts. When changes happen (either to the landscape or to your models), inform those who need that information to guide their broader decision-making.

Integrate forecasting into financial planning 

The results of your AR forecasting should inform decision-making throughout your firm, including your broader financial planning. If you can see a downturn in AR coming, then this might not be the time to plan that high-spend project. But if AR is stable or projected to increase, then this could be the perfect time for it. can help your business manage complex financial projects — keeping everyone on time and on budget 

The accounts receivable process is a key part of financial management and budget forecasting, and for financial firms, it’s only one portion of a much more complex financial picture.

As your financial projects grow in complexity, your firm may find the need for a more elegant solution for tracking it all. Excel spreadsheets have their place, but they may not be the best way to track all the metrics that feed into your forecasting. is a better approach. With you can consolidate your data analysis, turning your historical data sources into greater forecasting accuracy.

You can also track every other aspect of projects within your business, from short-term one-off projects to long-ranging highly complex projects.

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