SaaS Revenue Forecasting: Building Predictable Growth Models for Your Startup

According to various studies, the global SaaS market is expected to grow from $317 billion to $1.23 trillion from 2024 to 2032, representing an annual compound growth rate of nearly 20 percent. Startups specifically are poised to take advantage of this rapid growth, but it’s important to grow and evolve smartly, underscoring the importance of accurate forecasting.

Inaccurate forecasting has the potential to derail a SaaS startup before it even begins, possibly negatively impacting average revenue, strategic planning, sales pipeline goals and more. It can lead to a snowball effect of missed milestones, budget cuts and reduced investment, which can disrupt your startup, lead to excessive waste and inefficiency, and impact customer retention and stakeholder trust.

Yet, SaaS startups have unique revenue forecasting challenges due to the complexity of their subscription-based business models. In this post, we’ll provide a comprehensive guide to the models, metrics and implementation strategies for proper SaaS revenue forecasting.

Core Components of SaaS Revenue Forecast Models

Various core components create the building blocks of any good SaaS revenue model. Key metrics like monthly recurring revenue (MRR) and annual recurring revenue (ARR) track subscription-based income, while models also need to account for customer churn and expansion, customer acquisition cost (CAC), customer lifetime value (LTV), income statements, balance sheets and cash flow management related statements to create a comprehensive model.

MRR and ARR are the two most fundamental metrics for forecasting, representing predictable monthly and annual revenue. MRR provides a short-term outlook on your startup’s financial health, while ARR represents a more high-level, long-term outlook. Together, they’re both key to creating accurate forecasts and measuring overall growth.

Other more advanced metrics, such as churn, expansion revenue and customer acquisition, also play a key role in forecast models, notably when calculating net MRR. Good forecasts should model each component and then combine them into growth projections. Cohort analysis is also important for SaaS revenue forecasting, as it can help reveal patterns over time, assist leadership with calculating true LTV and measure the impact of product changes and marketing efforts.

Recurring Revenue Metrics That Drive Accurate Forecasts

There are various subsets of MRR that help predict future monthly recurring revenue. These include:

• New MRR, or predictable, recurring revenue that’s added from new customers.
• Expansion MRR, or additional recurring revenue that’s generated from existing customers.
• Contraction MRR, or any recurring revenue that is lost from existing customers. This is usually due to downgrades to a cheaper plan.
• Churned MRR, or total recurring revenue lost when customers cancel their subscriptions.

Another recurring revenue metric you’ll want to know is net revenue retention, or NRR. NRR measures how much revenue your startup retains and grows from its existing base over time, taking into account expansion, contraction and churn. It’s calculated using this formula:

• NRR = (Starting recurring revenue + expansion revenue – downgrades – churn) / Starting recurring revenue

SaaS companies should strive for an NRR above 100 percent, which signals sustainable growth and loyalty. Anything below is indicative of contraction and higher churn rates.

Customer Behavior Patterns in Revenue Projections

Customer behavior patterns, such as lifecycle data, can help improve forecast accuracy by helping predict churn and retention, anticipate expansion revenue and optimize CAC. These patterns examine each stage of the customer journey and assess their impact on financial models, providing insight into future revenue.

For instance, customers tend to increase product usage and adoption after they demonstrate a high level of engagement and experience success with their initial purchase. Seasonal trends also play a role in SaaS revenue cycles. For instance, many SaaS startups may see higher demand during the holiday season or around the start of the new year when discounts are offered to entice consumers. These factors and behavior patterns all must be accounted for to ensure accurate revenue projections.

Revenue Forecasting Methods for Different Growth Stages

Your SaaS startup should forecast growth based on its maturity level. Choosing the right revenue forecasting models is critical to ensure accuracy at each stage.

For instance, most early-stage startups practice top-down forecasting, which starts with a broad picture of the overall market and works its way down to determine specific departmental forecasts. Top-down forecasting is ideal for long-term strategic planning, is fast and simple, and tends to be investor-friendly. However, it can also be less accurate and unrealistic. Bottom-up forecasting begins at the lowest level of your startup and builds an aggregate forecast. It tends to be a more accurate forecast that’s used for more detailed operational planning purposes. However, it is also laborious and can be too conservative. Many experts agree that a hybrid model that mixes both top-down and bottom-up forecasting is ideal for startups.

Most startups also begin with simpler forecasting models that rely on historical sales data. However, as your startup grows, it will find that more complex models help improve overall accuracy. Some signs you need to move beyond basic forecasting models include poor forecasting accuracy, more complex seasonal trends and rapid startup growth.

Pre-Series A Revenue Modeling Strategies

Pre-Series A revenue modeling should rely more on market research, assessing industry benchmarks and making reasonable assumptions to help offset the lack of historical data. Start by defining your business model and identifying key metrics. From there, you’ll want to conduct your own market research and create a financial projection accordingly, which is essential for predicting future revenue. Investors are more interested in your startup’s potential than they are in returns in the early stages. Demonstrating that you have a roadmap and a product-market fit tends to be more key than robust forecasts at this stage of your startup’s journey.

Scaling Stage Forecast Optimization

Growth-stage startups should move from simple assumptions to more data-driven ones that leverage historical data in addition to market trends. More advanced forecasting techniques, such as time-series analysis and cohort analysis, are often implemented, and forecasts should be updated more frequently to forecast revenue accurately and reflect current conditions.

Building Your SaaS Sales Forecasting Framework

What are some of the best practices for implementing a robust forecasting system? It consists of five key components: data collection, storage, processing, governance and analytics, all of which play a key role in creating high-quality and reliable predictions.

Don’t implement forecasting in a bubble. In addition to accessing historical data and assessing market conditions, it’s also important to involve other departments, such as your sales, marketing and finance teams, to ensure strategic alignment and receive valuable input on market realities.

Consider creating both rolling forecasts that are continuously updated and an annual plan, or a static blueprint.

Data Collection and Pipeline Integration

Data collection is a key component for implementing a robust forecasting system, yet gathering the right data can be a challenge. Your startup should seek data from both internal and external sources, including CRM and ERP systems and sales platforms. Market data should make up the majority of your external data collection. Keep data consistent by implementing the technologies that can keep it up-to-date so you have an accurate, real-time data picture.

Forecast Validation and Continuous Improvement

You should always strive to improve your forecasts so they become even more accurate over time. Some tips for continuous improvement include:

• Develop variance analysis processes to track your forecast versus your actual performance.
• Implement regular feedback loops to review and adjust forecasts as necessary.
• Build advanced learning mechanisms to improve future projections.

Investor-Ready Revenue Projections and Reporting

Forecasts can be an important tool during fundraising rounds and for building stakeholder trust. To build investor-ready models, structure them logically and use visuals to tell a story about your startup. Anticipate questions about your assumptions and back up your answers with historical data, key metrics and insight on market trends. Additionally, don’t be over-optimistic with your reporting. Too much optimism can squash credibility with your projections.

Accelerate Your Revenue Forecasting with Expert Support

For more information on the importance of revenue forecasting and how Graphite Financial can serve as your startup’s trusted financial planning partner, contact us today. As a full-service financial firm dedicated to working with startups, we have the industry-specific knowledge and expertise to build accurate financial projections and help your startup reach its fundraising and financial goals. Contact us today for more information and to schedule a consultation.

Frequently Asked Questions

How far into the future should SaaS revenue forecasts extend?

While this varies depending on the state of your SaaS startup, it’s typically best practice to set long-term and short-term SaaS revenue forecasts. Longer-term forecasts should extend out several years, while short-term forecasts should extend anywhere from three months to a year.

However, early-stage startups often lack robust historical data to forecast, so simple models tend to be preferred to create a 12-18 month forecast. As your startup enters growth and mature phases, you should look to create longer-term forecasts. Your forecasts should be routinely updated, driven by data and focus on a foundation of key SaaS metrics.

What’s the difference between bookings, billings, and revenue in SaaS forecasting?

Bookings, billings and revenue are all important to SaaS forecasting — but they’re all very different from each other. Bookings are the total value of a signed customer contract, while billings indicate the sum of money that your startup invoices to a customer for a specific period or service. Revenue is the total income earned at the end of a specific period after providing services to a customer.

All play an important role in forecasting. Bookings help predict future income, billings measure short-term cash flow and revenue is representative of the real, recognized income of your startup.

How do we account for customer churn in long-term revenue projections?

Calculating your historical churn rate and using this metric as a guide is the best way to factor in customer churn in long-term revenue projections. Using historical data can help predict future customer loss and make more educated financial projections. There are some key considerations to weigh when factoring in customer churn. For instance, different segments of your customers may experience different churn rates. It’s also important to track your customer churn rates to reflect behavioral changes, market dynamics and any strategies you’re implementing to reduce churn.

When should startups transition from spreadsheet-based to automated forecasting?

It’s common for startups to use manual, spreadsheet-based accounting in their early stages. However, when your startup becomes more complex or experiences rapid growth that overwhelms these manual processes, it’s time to upgrade your infrastructure to more robust automated forecasting. Automated tools and programs can help streamline forecasting, provide real-time visibility into your startup’s overall financial dynamics, facilitate more collaboration between employees and ready your startup for future fundraising rounds. Automated forecasting can also help facilitate better decision-making.

How do product launches impact SaaS revenue forecast models?

New product launches introduce several variables into your forecast models, such as customer acquisition and churn, expansion metrics and more. New product launches must also factor in marketing relative to sales performance, and weigh upsells, cross-sells and contraction within your existing customer base. The best way to adapt your forecasting model to a new product launch is to forecast from the bottom up, segment your revenue streams, run multiple scenarios to anticipate several situations, and then document and regularly refine your assumptions.

What forecast accuracy rate should growing SaaS startups target?

Growing SaaS businesses should target a quarterly accuracy rate of around 10 percent, or 85 to 94 percent accuracy. However, like all of your startup’s metrics, this will fluctuate as it grows and matures. When your startup is in the early stages, it should be more concerned about finding the right product-market fit, especially when data is limited and forecasting is more unreliable. As your startup enters the growth stages, aim for 85 to 94 percent accuracy. Mature startups should strive to achieve an accuracy rate of 95 percent or higher.

How do we incorporate market expansion plans into revenue forecasts?

If you’re planning to expand, it’s important to build a detailed financial model that includes variables such as assumptions for customer growth, projected revenue growth per customer and customer conversion rates. Additionally, it’s crucial to factor in expansion-related costs and resources. Like all models and forecasts, it’s important to model various scenarios, gauge performance and make adjustments as necessary.

What role does unit economics play in SaaS revenue projections?

Unit economics are key to building accurate and reliable revenue projections by analyzing the revenue and costs associated with a single customer. By incorporating unit economics, your startup’s SaaS revenue projections become more high-level and can result in more precise, data-driven assessments. Unit economics can help your startup achieve predictive accuracy and result in more strategic decision-making. They can also help build relationships with investors and key stakeholders.