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Revenue forecasting in finance: A comprehensive guide for SaaS success

Unlock the power of accurate revenue forecasting for your SaaS business. Explore proven techniques, strategies, and best practices to maximize financial success.
Vanika Achreja
17 min
Table of contents
What is revenue forecasting in finance and why is it so important?
How revenue forecasting fits into cash flow forecasting 
5 different models for revenue forecasting
How to choose the best model to use in your revenue forecast
Challenges and best practices in revenue forecasting
Use technology to level up your revenue forecasting
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Here's what you'll get in this guide:

  • You'll learn the various strategies and methodologies for accurately predicting revenue in SaaS.¬†
  • You will understand the nitty-gritty of integrating revenue forecasting into broader financial planning and cash flow management to achieve sustainable growth and operational stability.¬†
  • And by improving your revenue forecasting, you'll know how to enhance your decision-making, optimize financial performance, and position your SaaS¬†businesses for long-term success.

It wouldn’t be a stretch to say that revenue forecasting is probably the most important skill for survival and success in the big SaaS world. Cash is king after all, getting it right can make or break your business!

This guide will demystify revenue forecasting complexities and offer actionable insights and methodologies that any SaaS business can apply to achieve steady financial stability and growth.

What is revenue forecasting in finance and why is it so important?

Revenue forecasting and planning is all about predicting future monthly recurring revenue (MRR) and, in turn, annual recurring revenue (ARR). It can become quite complex due to churn, upgrades, downgrades, customer acquisition, etc. 

With accurate revenue forecasting, a SaaS company can better manage cash flow and make strategic decisions while keeping long-term viability and growth in mind. 

People often confuse revenue forecasting with revenue projections. Though related, they serve different purposes. For the sake of simplicity, forecasting looks at the past to arrive at a conservative and realistic estimate of the future while projections are forward-looking ‚Äėgoals‚Äô and tend to be optimistic, reflecting the longer-term ambitions of the company.¬†¬†

Every business needs an accurate revenue forecast

Understanding your cash flow is critical to SaaS businesses, so it is necessary to understand revenue forecasting. 

Good revenue forecasting helps you understand your top line growth and lays the groundwork for many strategic exercises, such as budgeting, resource allocation, product development, and market expansion. 

Moreover, forecasts can act as a goalpost and performance metric. Most SaaS companies track them in real-time and make adjustments, when necessary, to align with strategic goals, highlighting their indispensable role in financial and operational planning.

How revenue forecasting fits into cash flow forecasting 

In SaaS companies, revenue forecasting is critical for cash flow forecasting as it estimates future recurring revenue from subscriptions (and service, in some cases) while considering churn rates, upgrades, new customer acquisition, and billing cycles. 

This projection provides a basis for the cash inflow component of cash flow forecasting, which is then combined with forecasts for operational expenses, capital expenditures, and cash outflows to understand the company's future financial position and liquidity. 

This exercise is essential for assessing future funding requirements, hiring, expansion plans, and overall sustainable growth, among others.

To drill down a bit further, in terms of cash flow, there are basically four line items that fall into two basic categories: 

  1. Revenue and expenses
    • These can be forecasted separately with different methods.
    • When you combine the results, you have your income statement.
  2. Assets and liabilities
    • Balance sheet forecasting gives you these numbers.
    • When you combine the results, you have your balance sheet.

These four line items give you everything you need to create your statement of cash flow. 

Of these four, revenue is the hardest to predict whereas expenses are far less variable. This is because you have more control over them. In SaaS, assets and liabilities don’t change much and as such are pretty stable for a given forecasting period. 

5 different models for revenue forecasting

There are a few different models SaaS companies can use to forecast their revenue. Let’s unpack them here.

Total addressable market (TAM) model

This is the pre-revenue model that most SaaS companies start with and is pretty straightforward. It starts by asking what the total available market for a certain product is. Then, it attempts to understand what percentage of that market the company can realistically capture or penetrate. Another obvious driver/indicator that one must account for is whether the target market is growing. 

Tips for using the TAM model

  1. Define the market precisely: It is good to clearly identify and define the specific market segment(s) you are targeting. This helps in accurately estimating the TAM.
  2. Leverage market research: A good place to start understanding the target market is by reviewing existing literature, such as market studies by reputable  organizations. You can also conduct primary research to gather data on market size, growth rates, and customer segments.
  3. Assess market penetration scenarios: To understand potential revenue scales, you must also develop various plausible scenarios for market penetration and demand, from conservative to optimistic.
  4. Validate assumptions: As your early-stage business grows, you must regularly validate and adjust assumptions based on real-world feedback and market dynamics.
  5. ‚ÄćUse TAM model to guide strategy: The TAM model is not just useful for forecasting; it can also guide strategic decisions such as prioritizing product features, marketing, and resource allocation.

Sales rep or quota-based model

The sales rep or quota-based revenue forecasting model predicts the revenue based on the sales quota assigned to individual sales representatives. This model assumes that every salesperson will (at the bare minimum) meet or exceed their sales targets. 

The drivers in this model include the number of sales reps, their assigned quotas, and their sales velocity, which takes into account the conversion rates at various stages in the funnel/pipeline. This revenue forecasting model is usually followed by SaaS companies in their early/growing revenue stage.

Tips for using the sales rep or quota-based model 

  1. Set realistic quotas: Sales leaders must ensure that they set achievable sales quotas based on their level of expertise, product line, sector, and region (whichever is applicable). Unreasonable numbers can impact the forecasts, leading to a negative overall impact on the company. That said, iterating the quotas based on the evolving market realities is important. 
  2. Regular training: Sales reps must receive regular training on product updates and also on the art and science of selling better. Training is also key to ramping up new sales reps faster. 
  3. Create an incentive program: A well-designed sales incentive program is crucial for the success of any SaaS company and can motivate sales teams to work harder and win more deals.
  4. ‚ÄćMonitor performance: Sales leaders must closely monitor individual and team performance metrics to identify improvement areas.

Funnel-based or pipeline-based model

This model is a close cousin of the sales quota approach. In this model, SaaS companies use their sales pipeline to forecast revenue. They track each stage of the sales process‚ÄĒfrom initial contact to final sale‚ÄĒand estimate the likelihood of leads progressing to the next stage and eventually converting into revenue.¬†

For context, in the typical sales pipeline, a lead first becomes a marketing-qualified lead (MQL) and then a sales-qualified lead (SQL). If all goes well in terms of product requirements and pricing, that lead ends up converting to a customer, which of course, means new revenue. 

The funnel-based model provides a detailed view of where each lead and the associated potential revenue is in the sales funnel. This allows for more accurate and dynamic forecasting based on the health and status of the sales pipeline. It is particularly useful for SaaS companies with longer sales cycles and multiple stages in their sales process. Companies in the early/growing revenue stage often use this model.

The drivers are the number of opportunities at each stage in your pipeline and conversion assumptions for each stage. 

Tips for using funnel- or pipeline-based models: 

  1. Maintain accurate data: Your CRM is your holy grail and must be kept accurate and up-to-date to provide a reliable forecast.
  2. Understand conversion rates: Analyzing historical data to understand conversion rates at each sales funnel stage will help you more accurately predict how long it will take to convert leads to revenue.  
  3. Segment the pipeline: To better identify trends and variances, break down the sales pipeline by product line, segment, region, or expertise level. 
  4. Regular review and adjustments: The pipeline must be reviewed regularly to adjust for stalled deals that may be artificially inflating your forecasts. Reacting to changes quickly keeps forecasts realistic.
  5. ‚ÄćAccount for external factors: To the best of your ability, you must account for possible external factors that could impact the sales cycle.

ARR snowball or waterfall model

The ARR snowball revenue forecasting model (also known as a waterfall model) focuses on predicting ARR growth based on existing customer expansion and predicted new acquisitions. The ARR compounds and ‚Äėsnowballs‚Äô as existing customers continue/upgrade their subscriptions and new customers are added.¬†

This model is particularly useful for SaaS companies in their growth and maturity stage as customer retention and upselling become more important. 

The drivers in this model are assumptions regarding new customer acquisition, expansion, and churn. 

Tips for using ARR snowball or waterfall models 

  1. Focus on customer success: When your company is in its growth and maturity stage, it’s important to build a customer success team that can help boost retention and reduce churn.  Retaining and upselling to existing customers accelerates the snowball effect.
  2. Encourage upgrades: It is also good to come up with a playbook to encourage customers to upgrade to higher tiers or try new/additional features.¬†‚Äć
  3. Analyze trends: Continuously analyze revenue trends and customer feedback on your product and keep refining.


The PxQ, or price times quantity model for revenue forecasting in finance is a straightforward approach where you multiply the number of units sold by the price.

In the SaaS context, units are individual subscriptions or services. To generate an accurate forecast, you would need to calculate the combined revenue from each of your product lines in each of the market segments (e.g. SMBs and enterprises). 

This model is most useful for mature SaaS companies. When the company is at a stage where it is offering tiered pricing or multiple service/product packages, this is the simplest revenue forecasting method. 

Generally, the more mature a company is, the simpler it is to forecast because there’s more data to base the forecast on and fewer assumptions to make. 

Tips for using the PxQ model

  1. Continuously evaluate pricing strategy: You must regularly evaluate and adjust your pricing based on market demand, competitor pricing, and customer feedback to keep your forecast as accurate as possible.
  2. Proper segmentation: Proper customer segmentation allows you to optimize and tailor your pricing, upsell high-value bundles/features to the right customer segments, and target product development to meet specific customer segment needs, ultimately maximizing revenue potential. 
  3. Promotions and discounts: Limited-time offers or discounts can often boost sales volume without significantly eroding revenue.¬†‚Äć
  4. Monitor sales trends: Regularly analyze which products or services are clicking with customers and adjust your marketing and sales strategies accordingly.

Choosing the correct model drivers is key

With any financial model, correctly choosing the drivers that impact the model results is key to ensuring that you get the most accurate results. The key drivers for each model are noted in the descriptions above. We've combined them in the table below to make it easier to see what types of information you will need based on the model you choose.

Table showing the typical drivers for the revenue forecasting models commonly used in SaaS. This information is presented in the text of this article.
Typical drivers for the revenue forecasting models commonly used in SaaS.

How to choose the best model to use in your revenue forecast

Every SaaS business goes through three stages‚Äďintroduction, growth, and maturity. Based on the growth stage, companies can choose the right model.¬†

The method you choose can also depend on how accurate you want your forecast to be. You can even combine a couple of models to arrive at a more pinpointed revenue forecast. 

Ultimately, though, the decision often comes down to how much time and resources you are willing to spend on your revenue forecasting exercise. 

Table showing the recommended revenue forecasting models based on a company's maturity. TAM model is typically used for companies in pre-revenue stage because it doesn’t rely on data they would not yet have. Sales rep and funnel-based models work well for early revenue and growth stage companies because they typically have enough data to at least make some broad-level assumptions to support their forecasting. The AR snowball model is typically used by growth-stage and more mature companies focused on expansion because at these stages, a company will have a functioning sales engine to help them forecast more reliably. The PxQ model is simpler in comparison to the other models and works well for companies that have a lot of data to use. More data means fewer assumptions, which in turn makes for a simpler model.
Recommended revenue forecasting models based on a company's maturity.

Challenges and best practices in revenue forecasting

Accurately forecasting revenue can be pretty challenging, but using some best practices can help you get it right.

4 common challenges in revenue forecasting

  1. Determining the level of complexity can be tough: Finance teams have to decide the level of detail they want to explore when it comes to revenue forecasting. The more details one wants to discuss, the longer the exercise will take. The call finance teams have to make is whether the extra effort and time are worth it. 
  2. It’s time-consuming: Revenue forecasting takes a good few months to complete. If the finance team decides to dig deep, it will take even longer.  
  3. Manual data consolidation from multiple data sources is painful: Finance teams have to pull data from multiple systems to create a revenue forecast. If they‚Äôre working in spreadsheets, this can take a huge amount of time to consolidate and validate the data.¬†¬†‚Äć
  4. Scenario planning can be difficult to incorporate into your model: Ideally, CFOs will include scenario planning as a part of their revenue forecasting so they can build game plans for different situations that might occur. However, this can prolong the process significantly when using spreadsheet models.

3 modeling best practices to help you get your revenue forecast right

  1. Create a model map: After you’ve decided on your model, create a model map and whiteboard it. Draw the model to explain which line items are connected to what. This will help get you thinking about how your business works and will ensure your model is aligned with your business. A high-level brainstorm of your model can also help you know if you’re going overboard in terms of complexity. 
  2. Make sure to correctly identify all the drivers for your model: Identifying the right drivers ensures that you account for all the relevant factors influencing your revenue, as you can now understand the relative impact of each driver. You can prioritize and focus on the most significant levers for your revenue growth and optimize for them.‚Äć
  3. Explain the model to your grandma: Try explaining your model to someone else who wasn’t involved in the process. A non-finance person would be ideal. If they can follow it, your model will make sense and you’ll be able to explain it to your stakeholders.

Use technology to level up your revenue forecasting

Drivetrain is an FP&A software solution purpose-built for B2B and SaaS businesses. Its array of features makes it easy for CFOs and finance teams to create highly scalable and reliable financial models. Now, you can build integrated plans, provide budgets and forecasts, track progress in real-time, and resolve bottlenecks that hinder growth. 

  • Native integrations: Drivetrain‚Äôs revenue forecasting solution comes with 200+ out-of-the-box integrations, making it seamless to get the right data required for revenue forecasting. You can ensure accurate forecasts with access to up-to-date information on sales pipelines, customer accounts, billing history, and financial transactions.¬†
  • Enhanced security: With Drivetrain, you have all the control. You can define user permissions and give access to the required information for each user thereby securing sensitive financial data.
  • Audit trails: Drivetrain maintains a detailed history of changes, offering full transparency and accountability required for financial compliance and quickly resolving questions.
  • Spreadsheet familiarity: Think of Drivetrain as a ‚Äėhome away from home‚Äô for Spreadsheet aficionados, except that this home is much easier to maintain.¬†
  • Dynamic reporting and dashboards: You can access data in real-time and also analyze trends and variances quickly with interactive charts. You can also drill down into transaction-level data to rake up actionable insights.‚Äć
  • Scenario analysis: The finance team can come up with various scenarios and test them on Drivetrain. Teams can also run various what-if scenarios to test various hypotheses and make informed decisions.

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