Predicting the future is no small feat. Unless you're armed with a crystal ball, your chances of accurately forecasting what's to come can seem slim. Yet, in the world of finance, we often find ourselves relying on Excel to forecast, track, and analyze collections, striving to get as close as possible to the crystal-clear insights a soothsayer might provide.

You may often find yourself asking: What is the best method for Accounts Receivable (A/R) forecasting?

In this article, we'll dive into the quest for the most reliable forecasting method. We'll compare different techniques, discuss the factors that can affect their reliability, and highlight the importance of choosing the most reliable method for your business. We'll also share examples of successful forecasting using these reliable methods & best tools.

Understanding Forecasting Techniques and Methods

First of all, it should be made clear that there is no one-size-fits-all method for predicting A/R. There are many available techniques and methods, each with advantages and disadvantages.

Let's examine some of the most popular ones in more detail:

Time Series Analysis: is a technique that examines past data to find trends or patterns that can be extrapolated into the future.

Regression Analysis: Regression analysis is a statistical technique that looks at how various variables relate to one another. In AR management, this could mean looking at how factors like economic conditions or customer behavior affect payment times.

Machine Learning Methods: These methods leverage artificial intelligence to analyze large datasets and make predictions. They can adapt and learn from new data, making them highly flexible and accurate. However, they require significant computational resources and expertise to implement. By the way, Tesorio has been developing its AI capabilities, as showcased in our AR Forecast tool. This tool leverages AI-powered predictions to provide highly accurate forecasts of a company's accounts receivable balance, cash collections, and other key metrics. This approach eliminates guesswork and reduces reliance on manual Excel-based processes, increasing the efficiency and effectiveness of cash management and forecasting. The tool is able to adapt to a company's unique historical A/R and cash collection trends, analyze seasonality, customer-specific data, and more. This result: highly accurate, customizable forecasting on a daily, weekly, and monthly basis.

Qualitative Forecasting: This method relies on expert judgment rather than numerical data. It's often used when historical data is limited. E.g. you may use this method to forecast new products or markets.

Scenario Analysis: If you’re faced with a lot of “What If” questions you may want to pick scenario analysis and play with different assumptions. Analyzing potential outcomes through the creation of various scenarios based on different assumptions is known as scenario analysis. This is a helpful tool for determining the potential effects of various tactics or choices.

Each of these methods has its place in A/R management. The key is to understand their pros and cons and to choose the one that best fits your specific needs and circumstances. In the next section, we'll delve into the factors that can affect the reliability of these forecasting methods.

Here’s an overview of all of them:

In the next section we’ll take a look at how to pick them and the situations in which each of them may be better than the others:

Factors Affecting the Reliability of Forecasting Methods

No matter which model you choose, you’ll want to keep in mind the below factors that will inevitably affect the reliability of your forecasts:

• Data Quality and Accuracy: How accurate is your data? If your data is flawed or incomplete, your forecasts could lead you astray. To maintain the integrity of your data, regular cleaning and validation are essential.

• Forecasting Horizon: The length of your forecasting horizon can also impact reliability. As a rule of thumb, short-term forecasts tend to be more accurate. This is because uncertainty increases over time, making long-term forecasts less reliable.

• Industry Volatility: Industries with high volatility may experience more unpredictability, making forecasts less reliable. You’ll always want to factor in industry-specific trends and changes.

• Economic Factors: Changes in the economy, such as inflation rates, interest rates, and market trends, can influence your forecasts.

• Technological Changes: Advances in technology can disrupt industries and change customer behavior, affecting how reliable your forecasts are.

Importance of Choosing the Most Reliable Forecasting Method for Your Business

Choosing a reliable forecasting method that fits your company and unique situation can not only affect cash flow and budgets but also decision-making across departments. Reliable forecasts offer valuable insights that can help with strategic decisions, such as allocating resources and managing risks.Business Growth and Development: By providing a clear view of the future, good forecasting supports business growth and development strategies.

Picking the Best Method & Forecasting Tool

In order to pick the most reliable forecasting method you’ll want to consider the pros and cons inherent to certain forecasting methods listed above as well as key considerations listed such as data quality, industry volatility and economic factors.

For example, if you’re dealing with a stable business with lots of historical data in a stable economic climate you may pick time-series analysis.

If you’re projecting A/R for a completely new product with access to no data you may choose a qualitative model.

In general, it's worth noting that advancements in technology are increasingly pointing towards AI-supported models like Tesorio’s AR Forecast Feature. Keeping track of finances on Excel sheets and creating one’s own unwieldy models seems more and more outdated in a world where AI can sift through data in micro-seconds.

Tesorio’s forecast feature aims at leveraging the advantages of most other forecasting techniques, such as Time Series Analysis and Regression Analysis, by incorporating these aspects within our Machine Learning Methods.

The AR Forecast tool provides highly accurate predictions about a company's accounts receivable balance, cash collections, days sales outstanding, and net days beyond terms. It achieves this by analyzing a vast array of data, including the company's historical A/R and cash collection trends, aging profile, days beyond terms ratios, credit terms, and growth projections. Plus, it considers factors such as seasonality, trends, and customer-specific data.

A testament to the tool's effectiveness comes from Sarah McCauley, Director of Billing & Accounts Receivable at Gitlab. She says, “I’m in and out of AR Forecast daily. I love being able to drive reporting from this, and not having to build these out manually for each week and agent. Tesorio empowers my team to get the information they need without the spreadsheets.”

Harnessing The Power Of Accurate Forecasting with Tesorio

Choosing a forecasting method is more than a selection; it's an understanding of the technique that suits your needs best. However, while every forecasting method brings something to the table, the technological evolution is undeniably leaning towards AI-supported models. Tesorio's AR Forecast, employing Time Series and Regression Analysis in its Machine Learning methods, adapts to new data and maintains its accuracy, providing a reliable, efficient, and streamlined approach to the complexities of forecasting.

To harness the power of accurate and adaptable forecasting tailored to your unique business needs, connect with Tesorio today. Our team of experts is ready to guide you through the process and help you discover the forecasting method that aligns perfectly with your organization. Don't let market fluctuations catch you off guard—take control of your financial future. Contact Tesorio now or explore the possibilities yourself with the interactive self-guided demo. Together, let's stay one step ahead of the competition.