SaaS modeling: 5 revenue model types that clarify your data

Planning finances for a subscription business is different from traditional business planning. You need the framework of SaaS modeling to handle recurring revenue, customer churn, and long-term growth.

What is SaaS modeling?

SaaS modeling is financial modeling purpose-built for subscriptions. In practice, SaaS financial modeling forecasts revenue, costs, and cash. They do so using inputs like MRR, ARR, churn, and expansion.

The goal is clarity. You create a single model that ties projections, pricing, and operations into actions.

Why SaaS modeling matters: Recurring revenue behaves differently from one-time sales. Churn and retention shape customer lifetime value (LTV) and drive hiring, spend, and cash runway.

Note: For a deeper primer, see our guide to the SaaS financial model.

How to build a SaaS financial model

Here’s a step-by-step explaining how to build a SaaS financial model:

1. Set the model’s purpose: Fundraising, board planning, or operating cadence. Define the decision you need to make in the next 90 days.
2. Collect baselines: Bookings, MRR/ARR, cohort data, active users, upgrades, downgrades, and churn. Keep raw exports in a separate tab.
3. Map revenue drivers: Split growth into new, expansion, contraction, and churn. Model them separately to see which lever moves ARR.
4. Choose a forecast method:
   
   1. Bottoms-up: Build pipeline capacity, conversion, pricing, and ramp for a granular B2B SaaS financial model.
   2. Top-down: Project momentum rates (new, expansion, churn as a % of beginning ARR) from history, then adjust for strategy changes.
5. Translate ARR to revenue: ARR is a point-in-time metric. For conservative planning, use beginning-of-month ARR ÷ 12. Then, adjust for go-live lags when contracts start later. Track CARR (contracted ARR) vs. LARR (live ARR) if your implementations delay revenue.
6. Layer your operating model: Headcount plan, hosting, support, R&D, sales, and marketing. Separate COGS vs. OpEx to keep gross margin visible.
7. Build scenarios: Base, upside, downside. Stress churn, price, and sales productivity. Add SaaS revenue projections by cohort to test retention programs.
8. Close the loop monthly: Import actuals, run a variance check, and update assumptions. Keep notes on why each change was made.

Note: Read our explanatory guide for a walkthrough on SaaS revenue projections.

Core components of a SaaS financial model

A solid SaaS financial model integrates many key metrics that are key to a firm's trajectory. These metrics interact with each other and provide a view of the company's health. Here’s a summary of these vital components:

• Annual recurring revenue (ARR): Annualized recurring contract value from active subscriptions. Use it to track the revenue base and momentum.
• Monthly recurring revenue (MRR): Subscription revenue per month. It shows short-term trends and seasonality.
• Customer acquisition cost (CAC): All sales and marketing costs to get an average user. Use it with payback and LTV.
• Customer lifetime value (LTV): Total gross profit per customer over their relationship. Tie it to churn and expansion for unit economics.
• Churn rate: Percentage of customers or revenue lost in a period. Track logo churn and revenue churn separately.
• Retention rate: Percentage of customers or revenue kept.
• Payback period: Months to recover CAC from gross margin. This shapes growth pace and burn.

These metrics are interconnected. For example, a high churn rate negatively impacts LTV. It makes it necessary to either reduce CAC or increase ARPU to maintain a healthy LTV to CAC ratio.

Note: Brush up on SaaS metrics that feed these calculations.

Types of SaaS revenue models

Below are some common SaaS revenue models.

Flat-rate

A flat-rate revenue model offers a single, fixed price for all features of the software. All customers pay the same amount, regardless of usage or the specific features they use.

Pros

1. Simplicity: Pricing is easy to understand for both the business and the customer.
2. Predictable revenue: Consistent pricing leads to more predictable recurring revenue.
3. Simpler to market: A single price point simplifies marketing and sales efforts.

Cons

1. Limited flexibility: May not suit customers with varying needs or usage.
2. Under-utilization: Some customers may pay for features they don't use.
3. Potential for churn: Lack of options can lead to churn if needs aren't met.

Tiered

The tiered revenue model offers many pricing packages with other features and usage limits at varying price points. This allows customers to choose a plan that best fits their needs and budget.

Pros

1. Customer choice: Offers options for different customer segments and needs.
2. Upselling chances: Customers can upgrade to higher tiers for more features or usage.
3. Increased ARPU: Tiered pricing can lead to a higher average revenue per user.

Cons

1. Complexity: More complex to manage and market than a flat-rate model.
2. Feature gaps: Customers might find that no single tier perfectly meets their requirements.
3. Decision paralysis: Too many tiers can make it difficult for users to choose.

Per-user

In a per-user revenue model, pricing is based on the number of users who can access the software. Each user pays a set fee per month.

Pros

1. Directly tied to value: Revenue scales with the number of active users.
2. Easy to track: Usage and billing are straightforward to track.
3. Potential for viral growth: As teams grow, revenue automatically increases.

Cons

1. Discourages collaboration: Can penalize teams that need many users.
2. Churn risk with small teams: If a small team shrinks, revenue can decrease.
3. Management overhead: Tracking individual users can become complex for large organizations.

Usage-based

The usage-based revenue model charges users based on their usage of the software's features. Examples include the number of transactions, data processed, or API calls made.

Pros

1. Fairness: Customers only pay for what they use.
2. Scalability: Revenue can grow, especially with increased usage.
3. Attracts price-sensitive users: Lower initial costs can attract a wider audience.

Cons

1. Revenue unpredictability: Usage can fluctuate, making revenue forecasting challenging.
2. Customer anxiety: Customers may be concerned about unexpected high bills.
3. Complex tracking: Requires full systems to track and bill usage.

Hybrid

A hybrid revenue model combines elements of two or more of the other models. For example, a company might offer tiered pricing with usage-based add-ons or a flat-rate plan with per-user fees for extra features.

Pros

1. Flexibility: Can cater to a wider range of customer needs and usage patterns.
2. Optimized revenue: Allows for capturing value in different ways.
3. Competitive advantage: Can offer unique pricing structures that differentiate the business.

Cons

1. High complexity: Can be very complex to design, implement, and communicate.
2. Customer confusion: Many pricing components can confuse potential customers.
3. Management challenges: Requires refined billing and tracking systems.

SaaS operating models and cost structures

Costs influence strategic decisions on pricing, customer acquisition, and business growth. Here's a breakdown of typical SaaS operating costs.

Common mistakes in SaaS revenue forecasting

Several typical missteps can undermine the accuracy of SaaS modeling and lead to flawed business decisions.

Underestimating churn

Many SaaS businesses in their early stages misjudge the rate at which users will churn. This leads to overly optimistic revenue projections.

Solution: Use churn tracking and analysis. Segment churn by customer type and reason to identify patterns. Incorporate churn rates based on historical data and industry benchmarks into your SaaS modeling. Regularly review and adjust churn assumptions.

Overestimating acquisition

Another mistake is overestimating how many new users can join within a specific timeframe. The issue often stems from unrealistic marketing or sales effectiveness assumptions.

Solution: Base acquisition forecasts on historical conversion rates from different marketing channels. Conduct thorough market research and factor in seasonality and competitive pressures.

Ignoring the impact of pricing on model accuracy

Failing to model the effects of different pricing strategies or potential price changes can lead to forecast issues.

Solution: Conduct a price sensitivity analysis and model different pricing scenarios. Understand the relationship between price, value proposition, and customer willingness to pay. Review and adjust pricing based on market feedback and competitive dynamics.

Not accounting for customer expansion and contraction

SaaS revenue isn't just driven by new customer acquisition. Existing customers can upgrade their plans or downgrade/reduce usage. Ignoring these dynamics skews revenue forecasts.

Solution: Track and analyze historical expansion and contraction rates. Model these trends based on customer cohorts, product adoption, and customer success initiatives.

Overlooking the payback period

Focusing on top-line revenue growth can be a problem. Failing to consider the cost of acquisition and the time it takes to recoup that cost can lead to unsustainable growth. A long payback period can strain cash flow.

Solution: Calculate and track the CAC payback period. Set targets for payback based on your business model and funding. Analyze the impact of different acquisition channels and user segments on payback.

Note: For a deeper dive into revenue forecasting structure, start here.

Pricing experiments in SaaS financial models

Testing new pricing strategies is vital for SaaS businesses to improve revenue, customer acquisition, and retention. SaaS modeling should incorporate the ability to simulate and analyze the impact of these experiments. Let’s zoom in on some experiments to run.

Isolating variables in pricing tests

When testing new pricing, isolate one variable at a time, such as the price point of a specific tier or the inclusion of a new feature in a plan. This isolation allows for clear attribution of any changes in key metrics.

Tracking key metric changes

Track changes in ARPU, churn rate, and LTV closely during and after a pricing experiment. An increase in ARPU with a minimal rise in churn suggests a successful price adjustment. A marked drop in LTV due to increased churn might indicate a problematic pricing change.

The importance of version control

Maintaining version control of your pricing logic within your SaaS financial models is crucial. Doing so allows you to revert to previous pricing structures if an experiment yields negative results and to compare the performance of different pricing iterations over time.

Orb's role in pricing experimentation

Orb, the done-for-you billing platform, offers valuable capabilities for managing pricing experiments. Its plan versioning feature allows you to create and track different pricing iterations without the messiness and manual work that spreadsheets require.

Orb Simulations enables you to simulate the impact of pricing changes using historical data. This helps you predict how these changes might affect key metrics like ARPU, churn, and LTV before changes are rolled out to customers, and reduces the risk of pricing experiments.

From spreadsheet to system: When should you upgrade?

While spreadsheets are often the starting point for SaaS modeling, they present significant limitations as a business grows and its pricing becomes more complex. Here are some signals you should pay attention to.

Limitations of spreadsheet modeling

Spreadsheet-based models are often error-prone due to manual data entry and formula management. Collaboration can be challenging, with multiple versions and difficulties in tracking changes.

Plus, forecasts in spreadsheets tend to be static, lacking the real-time data integration needed for accurate analysis of dynamic pricing experiments.

The need for purpose-built infrastructure

Once pricing complexity increases (e.g., multiple tiers, usage-based components, frequent experimentation) or the scale of customer data grows, it becomes vital to upgrade to purpose-built infrastructure.

Platforms like Orb provide a scalable solution for managing pricing, billing, and revenue data. Orb ingests raw event data and decouples it from pricing metrics, which allows for easy experimentation and accurate billing.

Advantages of dedicated systems

Moving to a system like Orb offers several advantages over spreadsheets. It reduces the risk of errors through automated data ingestion and calculation, version control, and audit trails.

Orb also enables dynamic analysis by providing real-time usage data, allowing for more accurate forecasting of pricing experiments and their impact on key SaaS metrics.

Key takeaway: The transition from spreadsheets to a dedicated system supports agility, accuracy, and extensibility in your monetization strategy, ultimately driving faster growth.

FAQs

How do you forecast revenue for a SaaS company?

You forecast MRR/ARR using separate lines for new, expansion, contraction, and churn, then translate ARR to revenue with timing rules. Tie assumptions to funnels, cohorts, and pricing plans.

How do pricing changes impact financial forecasts?

Pricing shifts change ARPU and churn, which cascade into ARR, LTV, and payback. Model ranges, test, then lock the winner.

What’s the best way to model usage-based pricing?

Pick a clear metric (events, data, API calls). Model historical usage per account, apply unit rates or tiers, and stress-test high-usage cohorts for SaaS revenue forecast sensitivity.

How do I integrate billing data into my financial model?

Connect your billing platform so your model pulls actual invoices, statuses, and usage each month. You’ll reconcile faster and adjust assumptions sooner.

SaaS modeling helps you plan, and Orb helps you execute

Orb is a done-for-you billing platform that helps you move beyond static billing and gain the insights needed to put your SaaS modeling plans into action. Here's how Orb helps you execute your SaaS modeling:

• Define billing metrics without code: Orb ingests raw usage events so you can define your own billable metrics in the Orb SQL Editor or a visual editor.
• Forecast before you ship: Orb Simulations uses your historical data to preview revenue and usage outcomes.
• Reduce billing errors: Orb turns raw event data into fully auditable invoices. As pricing models change, invoices are automatically recalculated to remain accurate.
• Operate at scale: Its scalable API ingests high-volume event data, and integrations keep usage and revenue data synced across systems.

Ready to transform your billing execution into a strategic asset for growth, bringing your SaaS modeling plans to life? Explore our flexible pricing tiers for a solution tailored to your needs.