Financial forecasting is the backbone of strategic growth, helping businesses set precise targets and align teams for the road ahead. By outlining clear objectives and setting the right benchmarks, any organization can create a unified, overarching vision for their business to follow. 

When analyzing the current economic environment, startups especially have to pay close attention to the direction that their business is heading.

Whether a startup is raising capital or facing uncertain economic conditions, financial forecasting ensures that all senior management has greater visibility and guidance on the overall progression of the organization. 

By analyzing current and historical data, financial analysts can use a forecast model to create a holistic report for management. This type of financial report is often used to plan for the future and help guide strategic decision-making among the executive team. 

One of the biggest challenges finance teams face when creating forecasts today is the accuracy of their data. Traditional finance teams have to pull data from different platforms in order to create their forecasts. However, juggling multiple data sets from several sources is a painstaking process that's both time-consuming and prone to human error. Companies using machine learning algorithms that analyze 200+ variables (including social sentiment, weather patterns, and logistics data) improve forecast accuracy by 12-25% versus traditional manual methods.

So, how can CFOs, FP&A managers, analysts, and financial controllers ensure that their data is accurate and up-to-date?

If your team is looking to improve its forecasting accuracy, our experts are here to help. In this guide, we'll be explaining how to improve forecast accuracy and provide best practices to follow so your finance team can fine-tune its growth strategies.

What is Classified as Forecasting Accuracy?

Forecasting accuracy measures how closely financial predictions match actual results. This metric is essential for finance teams aiming to improve predictability and support better decision-making.

According to FP&A trends, only 40% of organizations report high or good forecast accuracy, a 13% decline from 2021 levels when 53% achieved satisfactory performance.

Slight variations between forecasts and actuals are completely normal. However, the closer a finance team can get to their forecasted estimates, the better their financial planning will be. By having the right processes in place, finance teams can conduct variance analysis to study these differences and ultimately reduce inaccuracies within their forecasting efforts.

Why Forecast Accuracy Matters

Forecast accuracy is critical for effective financial planning and decision-making. Accurate financial forecasting helps CFOs and finance teams make informed decisions about resource allocation, investment opportunities, and risk mitigation.

How to measure forecast accuracy

Finance teams often measure accuracy through variance analysis. By comparing forecasted figures with actual results, teams can identify consistent patterns of over- or underestimation and refine their models accordingly.

Common factors affecting forecast accuracy

Several factors can impact the accuracy of financial forecasts. Changing market conditions, data silos, and unverified assumptions are among the main drivers of inaccuracy. Identifying these factors helps finance teams adjust their models proactively and improve future forecasts.

Key Metrics for Measuring Forecast Accuracy

Finance teams use several metrics to evaluate the accuracy of their forecasts. These metrics help quantify the difference between forecasted and actual results, allowing teams to identify the magnitude and direction of forecast errors.

Mean absolute percentage error (MAPE)

Mean absolute percentage error (MAPE) measures the average percentage difference between forecasted values and actual outcomes. It is one of the most common metrics because it provides a simple, relative measure of error. A lower MAPE indicates a more accurate forecast.

Mean absolute deviation (MAD)

Mean absolute deviation (MAD) calculates the average absolute difference between the forecast and the actual results. Unlike MAPE, it is not a percentage and is expressed in the same units as the forecast. This metric is useful for understanding the magnitude of errors in absolute terms.

Forecast bias and tracking signal

Forecast bias reveals whether your forecasts are consistently too high or too low. A tracking signal is a metric used to monitor this bias over time. Consistently positive or negative signals indicate a systematic error in your forecasting process that needs correction.

How to Detect and Correct Forecast Bias Using Software

Modern FP&A software helps finance teams identify and correct forecast bias. Forecast bias can silently undermine financial planning, leading to poor resource allocation and missed targets. Today’s platforms transform forecasting from a manual chore into a strategic, data-driven process.

Platforms like Abacum empower finance teams to identify and resolve bias by centralizing data and automating analysis. Software can help by:

• Automating data consolidation: By integrating with your CRM, ERP, and HRIS systems, software creates a single source of truth. This reduces manual data entry errors and eliminates the bias that comes from working with outdated or siloed information.

• Visualizing trends with dashboards: Interactive dashboards allow you to track forecast vs. actuals in real time. This visualization helps you quickly spot consistent over- or under-forecasting patterns across different departments or product lines.

• Using scenario planning to test assumptions: Software enables you to run multiple scenarios based on different assumptions. This helps counteract optimism or pessimism bias by modeling best-case, worst-case, and most-likely outcomes, leading to more resilient plans.

6 Ways to Improve Your Forecasting Process

Improving forecast accuracy reduces the need for corrective actions later on. If revenue predictions continuously miss the mark, it can lead to resource misallocation, missed growth opportunities, or compromised stakeholder trust.

By prioritizing accurate forecasting, finance teams can allocate budgets effectively, plan for unexpected shifts, and set realistic goals. Building the right data processes enhances forecasting accuracy, enables variance analysis, and provides greater visibility to senior management. In practice, this can involve cross-functional alignment with Sales to refine pipeline data or leveraging scenario planning tools to test multiple possible outcomes.

All of this can be achieved through the use of planning and forecasting software, which enables teams to unify data, automate calculations, and streamline reporting for better decision-making.

1. Keep your financial forecasts flexible

Financial forecasts should adapt as your company evolves. Relying on a single annual forecast can lead to outdated decisions and missed opportunities. Building flexibility within your processes allows you to respond quickly to changes.

Switching to a rolling forecast model enables management to align on up-to-date data and support strategic decision-making.

2. Maintain data integrity

High data integrity is essential for accurate forecasting. Almost every decision the management team makes is based on data, so finance teams must prioritize data quality.

By auditing changes and establishing permissions, finance teams can improve their data integrity and ensure reliable forecasts.

3. Take into account external influencers

Finance teams often focus on internal data, but external factors also impact forecasts. Considering both the micro and macro environments helps create more realistic projections.

Analyzing competition, market conditions, and economic trends allows your finance team to adopt a holistic approach to financial forecasting and planning initiatives.

4. Consider all outcomes with scenario planning

Scenario planning broadens your perspective and improves organizational agility. By making assumptions about the future and predicting how your business will be affected, you can prepare for uncertainty.

Scenario planning helps teams plan for the unexpected. Creating multiple forecasts based on different scenarios enables senior management to pivot strategy and maintain momentum.

Learn more: Scenario planning: Strategic forecasting for finance teams

5. Take a lateral approach to forecasting

Modern finance teams seek input from all functions, not just top-down directives. Engaging with leaders from Sales, Marketing, and Operations provides greater insight into revenue projections and demand.

Many organizations prioritize a corporate performance management strategy to understand how each team supports one another.

Learn more: Ranking the best Corporate Performance Management Software: our top picks for 2023

6. Benchmark against competitors

Analyzing competitor performance uncovers trends, strategy shifts, and opportunities that can inform your own forecasting. A side-by-side comparison helps you see how your company stacks up and adapt your strategy accordingly.

Additionally, analyzing top-ranking competitor content for targeted keywords can help your finance team identify content gaps and new opportunities to enhance forecasting accuracy. By studying their structure, level of detail, and user engagement tactics, you can incorporate proven strategies such as data visualizations, industry-specific examples, or interactive tools that resonate with readers. Implementing these insights in your own content can drive better search rankings and provide a richer, more practical experience for users seeking guidance on forecasting accuracy.

Taking a Modern Approach to Improve Financial Forecasting Accuracy

Adopting modern tools and processes can significantly improve forecasting accuracy. With the proper technical infrastructure and workflows in place, any finance team can streamline their forecasting efforts and set their company up for success.

For start-ups seeking funding, improving financial forecast accuracy also provides stronger investor reports and builds trust among future investors. By upgrading your tech stack and using a centralized platform for all your data, your financial analysts will be able to create reliable and accurate forecasts from a single source of truth.

Financial Planning and Analysis Job Profile

The financial planning and analysis (FP&A) role is central to driving forecasting accuracy and strategic alignment. For many organizations, the FP&A job profile entails overseeing core budgeting functions, forecasting accuracy, and overall financial strategy alignment.

The FP&A professional is responsible for gathering data from multiple departments, analyzing cost structures, projecting cash flow, and delivering actionable insights to management. By bridging the gap between specific departmental needs and high-level strategic goals, FP&A experts ensure that businesses stay on track for sustainable growth while effectively managing risks.

Looking to improve your business forecasting method? Talk to one of our experts today to see how Abacum can help improve your team's operational efficiency.