Inaccurate revenue forecasting can have devastating consequences for businesses, from potential decreases in company stock and less cash flow to inadequate inventory. According to Cherry Bekaert's inaugural CFO Survey, more than one third (36%) of middle market CFOs rank forecasting among their top pain points.
By implementing key steps and aligning operational forecasting with other key departments, such as accounting, companies can streamline their forecasting models and gain access to valuable business insights.
What is Revenue Forecasting?
Revenue forecasting is an attempt to estimate the future sales of a company over a specific period, such as a quarter or a year. To predict future revenue, a company will analyze a variety of factors, including:
- Historical data
- Current economic and market trends
- Sales pipelines
- Market competition
- Consumer behavior
- Product pricing
Revenue forecasts guide data-driven decisions, from advertising budgets to hiring limits to sales projections. The more reliable the forecast, the smoother the company can run.
Operational Forecasting: Supply and Demand
Operational forecasting is a vital component of revenue forecasting and is used to predict production scheduling and future operational conditions of an organization. Operational forecasts can be divided into supply and demand.
Supply forecasts address inventory and production needs to meet demand. They rely on internal data like inventory and production capacity. Demand forecasts estimate future customer purchases to guide marketing and sales strategy using market and consumer data.
By aligning supply and demand forecasts with the company's accounting, businesses can create a cohesive strategy that aligns production, sales and financial goals.
Revenue Forecasting Models
Most revenue forecasting models fall into two categories: quantitative, which relies on a data-driven approach to trend identification, and qualitative models, which utilize market analyses and broad business insights to facilitate predictions.
Below, we define the most commonly used forecasting models within these two categories.
Linear Regression
Linear regression is a quantitative model that uses historical data to uncover how key variable changes influence revenue. This model is useful for its ability to quantify the dynamic between revenue drivers and outcomes, but can fall short when complex market dynamics are not reflected in the data.
Moving Average
A moving average model averages results over a fixed number of periods, allowing for steadier forecast trends that aren't heavily influenced by short-term fluctuations. This method is beneficial for companies that do not have strong seasonal patterns or those working with limited data.
Time Series
Time series modeling analyzes revenue data over a period of time to detect seasonal or cyclical trends. A benefit of this forecasting method is its ability to differentiate random, unexplained variations from true cyclical patterns. However, this model can miss new market dynamics since it is relying on historical data.
Straight Line
The straight line method is a simple quantitative approach that assumes a company's financials will continue to grow or decline at the same rate of historic periods, regardless of market conditions or seasonal trends. This model is best suited to companies with predictable growth patterns, rather than those in rapidly evolving industries.
Bottom-up
This method aggregates revenue projections from various departments, such as sales, marketing and team leads, to create a company-wide forecast. While the bottom-up method can promote buy-in across the organization due to including a variety of team inputs, issues may arise if teams have inconsistent assumptions that skew the predictions.
Top-down
In contrast to the bottom-up method, top-down models begin with big-picture goals and business strategies. Taking economic conditions and the addressable market into consideration, leadership sets overall business goals and then delegates a revenue target to each team, allowing them to build their own execution plans.
Pipeline-based
Pipeline-based forecasting is beneficial for companies with detailed customer relationship management (CRM) data, as it uses a company's active sales pipeline to predict future revenue. Conversion probabilities are applied at each deal stage to provide real-time updates and allow leaders to better identify pipeline gaps.
Artificial Intelligence (AI) Forecasting
AI and machine learning (ML) forecasting models use algorithms to analyze large datasets and identify trends. These models work well for businesses with an abundance of accurate data and can process more variables than traditional models.
Forecasts from ML and AI methods can be harder to interpret, as the process and methodology are not always as clear. For this reason, companies may choose to combine traditional models with ML insights.
How To Revenue Forecast
By using historical data, external factors and internal factors of the business, leadership can make educated predictions on future revenue. Most revenue forecasting methods follow these steps:
- 1. Gather Accurate Financial Data: Income statements, cash flow statements and balance sheets all offer historic and current data on a company’s financial situation. Gather all relevant information to gain a deeper understanding and produce a more accurate forecast.
- 2. Choose the Time Period: A company may only be undergoing an annual revenue forecast or may choose smaller periods, such as quarterly predictions. Additionally, your forecasting model will influence what time periods are used to pull and analyze data.
- 3. Consider Internal and External Factors: Internal factors may include industry, product type or production logistics. External factors may include demand, market conditions and seasonality, among others. Both types of factors will influence growth.
- 4.Select Your Model: Accounting for the unique needs of the business, choose a revenue forecasting model and apply it. Some companies see success in combining multiple methods to provide a more comprehensive forecast.
- 5. Monitor the Forecast: Set up dashboards to track the forecast against actual results. Adjust model inputs as needed, and as new data becomes available.
Benefits of Revenue Forecasting
Accurate forecasting provides essential insights into the financial health of a business and allows for enhanced risk and opportunity identification. Additionally, it allows for better market demand prediction, allowing companies to effectively plan their production and marketing strategies.
When done well, revenue forecasting can also optimize:
- Operations management: Prioritizing specific services and products for supply and demand planning, setting realistic sales quotas, hiring the right amount of talent to avoid shortage or surplus
- Strategic decision-making: Executing market expansion, launching new products, undergoing mergers and acquisitions (M&A) valuation
- Performance tracking: Flagging deviations, correcting operations, ensuring sustained growth
Revenue forecasting allows companies to gain a competitive edge by adequately meeting consumer needs and strategically planning for growth opportunities.
Tips for Enhanced Revenue Forecast Accuracy
While revenue forecasting is a vital business tool, 49% of CFOs say poor data quality is preventing them from making business critical decisions, as reported in our CFO survey. Data can originate from multiple sources within a company, and different departments might be asked to contribute to a forecast.
Accounting and operations teams must align for an accurate forecast. For example, companies using the accrual method with long order-to-cash timelines will not have a sales team’s forecast that aligns with the financials unless it takes into account things like contract terms, billing efficiency, etc.
If multiple teams are providing a forecast, they need to work together to ensure their projections align and the business is utilizing a data-driven forecast with accurate source data. Cross-department collaboration helps increase the accuracy of a sales forecast. Compiling insights from multiple teams offers a more holistic view of the organization and provides more opportunities to spot issues and opportunities others may miss.
Additional techniques for forecasting success include:
- Leveraging automation tools for data collection
- Starting with simple models
- Validating data
- Analyzing historical data to spot patterns
- Revisiting assumptions
While a 100% accurate forecast is unlikely, leveraging data and reforming processes can help teams gain greater precision.
Risks and Challenges of Revenue Forecasting
Costs of Inaccurate Forecasts
Inaccurate, inefficient and data-deficient forecasting can lead to numerous hidden costs for companies. When business revenue comes in under the forecast, the company faces potential layoffs, decreased stock value and less overall cash flow.
Additionally, investor confidence may wane, while employees face lowered morale, and the brand risks being perceived as less trustworthy by consumers, stakeholders and third-party partners.
When business revenue exceeds the forecast, it may not have enough inventory to meet demands and could encounter wasteful, last-minute spending to meet new fiscal period requirements. Underestimating revenue also costs businesses missed growth opportunities, under-resourced teams and the premature increase of future quotas.
Revenue Projection Pitfalls To Avoid
Without effective strategies in place, companies are more likely to make forecasting mistakes and calculate inaccurate projections. Newer companies may struggle due to the lack of historical data available. Companies can use competitor information and industry benchmarks to create predictions if there is not enough business-specific data to utilize for a forecast.
Other mistakes businesses should work to avoid include:
- Overreliance on qualitative methods: Companies should not rely solely on qualitative methods, such as expert opinion and market research, for forecasting. Quantitative methods that use hard data and statistical techniques must also be used for accurate projections.
- Ignorance of external factors: Businesses must monitor current market trends, regulatory changes and competitors in order to accurately forecast future outcomes and revenues.
- Irrelevant forecasts: Projecting revenue is an ever-evolving task. Regularly update forecasts to maintain accuracy and relevance in changing business environments.
Challenges of Revenue Forecasting for Professional Services
Professional services firms may experience industry-specific challenges regarding revenue forecasting because they sell experience and expertise rather than physical products. The project-based nature of professional services can mean irregular revenue streams and variability of projects, which often makes projections difficult. Using forecasting models that account for project flexibility will help offset these challenges and refine predictions.
Revenue Forecasting FAQs
Sales forecasting predicts how much a company expects to sell, while revenue forecasting estimates how much money those sales will generate. Revenue forecasting tends to be a more comprehensive prediction because it incorporates pricing, timing, renewals and revenue recognition.
Companies can improve accuracy by using clean historical data, standardizing forecasting processes, incorporating external factors, and continuously reviewing forecasts against actual results. Combining data-driven models with regular updates helps refine assumptions and reduce error over time.
Forecasting cadence depends on business needs, but most organizations use a combination of frequent updates for operational planning and quarterly or annual forecasts for strategic decision-making.
Revenue forecasting is typically supported by CRM systems, financial planning and analysis platforms, and specialized forecasting software. Tools such as Salesforce, HubSpot, Anaplan, and Clari use historical data, pipeline activity and AI-driven insights to predict revenue and track performance
The best forecasting method depends on your business model, data maturity and forecasting goals. Companies with strong historical data often use quantitative methods, while newer or rapidly changing businesses may rely more on qualitative approaches or hybrid models. Many organizations combine top-down and bottom-up methods to balance strategic perspective with operational detail.
Your Guide Forward
Cherry Bekaert's CFO Advisory and Financial Planning & Analysis (FP&A) Services practice is equipped to help your business execute a streamlined, successful revenue forecast. Our industry-focused team makes your data digitally accessible, interactive and easy to understand. Our solutions help you think differently about your data, resulting in smarter decisions and more impactful business outcomes.
From technical accounting advisory and finance transformation to co-sourcing services, Cherry Bekaert's experienced professionals provide solutions tailored to the unique needs of your business.
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