Revenue forecasting is critical for any business, especially when it comes to B2B SaaS. The immense speed of progress in this industry requires keeping up with trends, continuously experimenting with fresh channels, and adjusting budget allocation based on future predicted revenue.
Accurate revenue forecasts help organizations make data-driven growth decisions.
This comprehensive guide will cover everything you need to know about revenue forecasting models.
Revenue forecasting is the process of predicting future revenue for a company using historical performance data, predictive modeling, and qualitative insights. Revenue forecasts provide an estimated projection of the total revenues expected in a future period.
Forecast time horizons can range from next month to next quarter to five years from now. Short-term forecasts may focus on immediate sales pipeline conversion, while long-range forecasts take a broader market-based approach.
With revenue forecasting, the goal is to provide the most accurate prediction of future revenue based on current insights. These reports can also be improved by leveraging attribution data so you know exactly what functions of marketing or sales bring in real revenue.
Revenue forecasting helps answer questions like:
When done right, revenue forecasting can power key business functions:
While revenue forecasting attempts to predict future revenues, it differs from a revenue projection which is typically more aspirational. Let’s understand the differences.
These three terms are used quite often when it comes to budgeting and strategic planning but they mean different things.
Now, let’s understand the types of revenue forecasts that you may come across.
There are also different types of revenue forecasts based on methodology and time span:
Now, let's examine some key business uses and benefits of revenue forecasting.
Accurate revenue forecasts can be the difference between success and failure for a business. Here are a few ways forecasting powers planning across the organization:
For finance teams, the single biggest use of forecasts is to build organization-wide budgets.
Budgets dictate how much gets spent on everything from R&D investments to marketing programs and payroll. Without reliable revenue forecasts, budgets devolve into guesswork.
For example, assume a company's revenue was $5M last year. Now the CFO needs to build next year's budget.
With intelligent forecasts, finance can model that based on new product launches, a 10% industry growth rate, and sales team expansions, revenues are likely to reach around $7.5M next year.
The CFO can now budget for expenses accordingly - say $1M for new engineering hires, $500K for more marketing, $150K for sales operations software etc.
Without forecasts, the CFO is flying blind. Maybe she pads the budget with a 20% increase to $6M. But if actual revenues only end up at $5.5M, suddenly there's a multi-hundred thousand dollar budget shortfall, requiring drastic cuts.
Conversely, if revenues actually reach $8M but budgets are based on last year's numbers, the company is now missing key growth opportunities due to under-investment.
2. Optimize Operations Management
Beyond budgets, forecasts guide operational decisions across departments:
Across the board, teams depend on forecasts to optimize operational management for future success amid constraints.
Forecasts also provide the quantified confidence executives need to drive growth through major strategic moves:
Creating reliable revenue forecasts empowers executives to place decisive strategic bets amid uncertainties, as opposed to shooting blind.
Revenue forecasts also provide a scorecard against which actual results can be monitored. Comparing real revenue performance vs. forecasted expectations then allows deviations to be easily flagged. With this information at hand, teams can course-correct before small misses snowball into major disasters.
Without forecasts as the reference point, there is no way to reliably track progress against potential. Revenue actuals in a vacuum don't reveal whether performance is on-target or off-course.
Now that we understand the fundamentals of revenue forecasting, let's examine some of the most common revenue forecasting models and techniques.
Broadly, forecasting approaches can be divided into two families:
There are four common forecasting models namely linear regression, time series, bottom-up, and top-down. The best way to perform revenue forecasting is by combining multiple models to benefit from each of them.
Let's explore some of these popular models.
Linear regression analyzes historical data to model how changes in key variables impact revenue.
Regression provides a data-backed view into drivers of revenue growth and contraction.
However, regression models are only as good as the input data. They may miss complex real-world dynamics that are not reflected in historical data. Approaching them as helpful guiding tools rather than absolute truth is important.
Simple linear regression uses one variable, often time, to predict revenue.
For example, it can help a business quantify how much additional revenue every $1 increase in marketing spend has historically generated. This insight can be used to forecast revenue under different scenarios.
Multiple linear regression incorporates additional factors simultaneously like marketing spend, sales activities, market dynamics etc.
The model examines historical data to calculate coefficients measuring each variable's unique relationship with revenue. These insights feed the predictive model to forecast expected revenue under different scenarios.
Regression provides a data-backed view into drivers of revenue growth and contraction. It brings statistical rigor to projecting the top and bottom-line impact of decisions around pricing, hiring, product launches, geographical expansion and more.
However, these models are only as good as the input data. They may miss complex real-world dynamics that are not reflected in historical data. Approaching them as helpful guiding tools rather than absolute truth is important.
Time series analysis detects historical patterns in data over time. This helps tease out seasonal and cyclical trends from broader growth trajectories and random noise.
It decomposes revenue time series into:
Time series models maximize signals and minimize noise in historical data for sophisticated revenue projections tailored to the business. These models can incorporate recent data, balancing responsiveness to change with smoothing noise and help you extract actionable insights for reporting and forecasting.
Time series techniques like moving averages, exponential smoothing, and ARIMA modeling analyze a revenue time series to optimize the predictive modeling of its components.
For example, enterprise software revenues may spike every fourth quarter due to a year-end budget flush. Media subscriptions may dip in the summer months when travel is high. Understanding these nuances helps make more contextual and accurate forecasts.
You can then use the insights generated from the time series forecasts to smoothen the growth curve giving you more predictable revenue.
Time series models need sufficient history to detect reliable patterns. They may miss entirely new market dynamics or one-off events, unlike the past. Hence, combining them with human judgment is important.
Bottom-up forecasting taps insights from sales, account management and other frontline teams to build projections. They incorporate pipeline health, competitive threats, and market mood along with historical data.
Let’s take an example organization with sales, marketing, finance, and leadership teams. Here’s how bottom-up forecasting would work:
Inconsistent assumptions between teams can skew the overall forecast. Guidance from leadership on industry outlook, macroeconomic factors and growth objectives helps align assumptions and methodologies.
Top-down forecasting starts with the big-picture view of the total addressable market, growth trajectories, economic conditions and business strategy. Leadership sets goals and divides revenue targets across functions.
This ensures strategic alignment between long-term goals and short-term operations. However, seemingly arbitrary targets could demotivate teams without context on the rationale so with top-down forecasting, you need to ensure two-way communication and transparency from leadership.
Let’s look at top-down revenue forecasting through an example.
Blending both top-down and bottom-up approaches for revenue forecasting can help set realistic targets based on market conditions while aligning activities to growth objectives.
The best forecasting method depends on your use case. Let’s understand this with two examples.
A SaaS company with recurring subscription revenue may find time series analysis to be very effective. That’s because, studying historical revenue patterns over time, seasonal cycles and trends become apparent. Statistical time series models can help quantify these patterns to accurately predict recurring revenues.
On the other hand, for a retail chain opening new store locations, a bottom-up approach could prove more useful. Each new store manager could prepare detailed forecasts for their location based on demographics, nearby competitors, marketing plans etc. Aggregating these bottom-up projections provides a realistic the overall revenue forecast.
The point is, every business is situated differently. The ideal approach depends on:
Leaders need to understand revenue drivers in their industry and business and use the insights to tailor the forecasting methodology to their specific situation and objectives.
Combining methods can also be beneficial. For example, a short-term quarterly forecast may use time series analysis to leverage recent revenue trends. And for the annual budget, a bottom-up approach could then add local market perspectives for a comprehensive view.
The key is adapting forecasting approaches to match business realities which provides the accuracy and insights required for confident decision-making across the organization.
What are some of the best practices for ensuring accurate revenue forecasting when using these revenue forecasting models? Let’s look at 4 of the best practices that you should consider following.
Remember this—garbage in, garbage out. Even the most advanced model cannot compensate for poor-quality data. Invest in processes and systems to collect accurate, complete revenue data, with proper change logs and auditing.
Stale data loses relevance quickly. Establish mechanisms to continually gather the latest data on revenue drivers. This could involve surveys, sales team feedback, customer interviews etc.
Annual plans using old assumptions miss market shifts. Re-forecast more frequently using the latest data to stay agile. Quarterly or even monthly cycles are preferable.
Obsessing over tiny accuracy improvements is counterproductive beyond a point. Focus on balancing usefulness and cost when selecting model sophistication.
Let's face it—optimizing your GTM strategy is tedious, and time-consuming without having all the right data in one place.
You have your metrics in different silos across marketing, sales, and revenue and piecing together a complete picture feels impossible. You could have leaks in your funnel, but cannot find the exact pages. Attribution has become a shot in the dark. And you're pouring money into campaigns without knowing if they’re working or not.
This is where Factors comes in.
Factors integrates all your disparate data sources—CRM, MAP, web analytics, social media, ad platforms—into one unified view.
You can quickly pull custom reports to get insights and answers on the fly. Factors also leverages leading IP resolution technology to reveal anonymous website traffic. Helping you discover up to 64% of untapped traffic and turn them into known, sales-ready accounts. More accounts to market means more pipeline and revenue.
With unified data and a complete view of your funnel, you gain the power to make strategic decisions that move the revenue needle. Scale what works, fix leaks, attribute MQLs to campaigns, analyze account journeys—Factors has you covered.
Don’t shoot in the dark. Book a demo with Factors to see how we can help you get better insights and data to power your forecasting models and make data-driven decisions to boost pipeline and growth
Revenue forecasting is the process of predicting future revenue for a company using historical data, predictive modeling, and insights. Accurate forecasts empower data-driven planning and growth decisions across functions like finance, sales, marketing and operations. Reliable revenue forecasts are mission-critical for budgeting, managing operations, fueling strategic growth moves and tracking performance.
Popular models include linear regression to model revenue drivers, time series analysis leveraging historical patterns, bottom-up forecasting aggregating projections from frontline teams, and top-down forecasting starting with leadership’s total target. Combining approaches provides flexibility to tailor models to business needs and data availability.
Outdated assumptions lose relevance quickly, so forecasts should be refreshed frequently. Quarterly or monthly re-forecasting cycles are preferable to stay agile versus annual plans. Access to latest revenue driver data enables more responsive modeling.
Potential pitfalls include unpredictable market shocks, limitations of available data, human errors in model assumptions, and finite resources to build sophisticated models. Perfection is unrealistic but maximizing useful accuracy is key.
Quality historical revenue data is the foundation. Relevant drivers like market trends, sales activities, product changes, and economic indicators help explain revenues. Updated inputs prevent stale assumptions. Data challenges need pragmatic solutions.
Tools like CRM, account intelligence and analytics tools like Factors, etc. provide key sales and marketing data inputs. Purpose-built FP&A software centralizes data for modeling and reporting. Technologies like AI and machine learning can boost forecasting sophistication.
Best practices include maintaining high-quality data, eliminating outdated information, shortening planning cycles, combining modeling approaches, and focusing models on business needs. Avoid needless complexity but leverage enough sophistication to meet objectives.
Get the latest best practices in Marketing Analytics
delivered to your inbox. You don't want to miss this!!