SaaS modeling: A guide to revenue, forecasting, and execution

What is SaaS modeling?

SaaS modeling, also known as SaaS financial modeling, is the process of building financial models specifically for SaaS businesses. Unlike basic financial reporting, which mostly looks at past performance, SaaS modeling focuses on forecasting future financial outcomes. 

It takes the unique characteristics of the SaaS business model into account, such as recurring revenue, customer churn, and distinct cost structures.  

A SaaS forecast model combines revenue projections, pricing strategies, and operational assumptions into a single, coherent framework. This framework allows SaaS companies to understand their financial health, plan for the future, make educated decisions about pricing and operations, and attract investors by demonstrating growth potential and financial stability. 

By simulating different scenarios and analyzing key performance indicators (KPIs), SaaS modeling provides valuable insights for strategic planning and risk mitigation. 

Core components of a SaaS financial model

A solid SaaS financial model incorporates several key metrics that are crucial for the business's performance and future trajectory. These metrics interact with each other and provide a comprehensive view of the company's health. Here’s a summary of these vital components:  

• Annual recurring revenue (ARR): It’s the total value of all recurring revenue normalized to a yearly basis. It excludes one-time fees and provides a clear picture of the subscription-based revenue.  
• Monthly recurring revenue (MRR): This is the total revenue generated from subscriptions each month. It's a key indicator of short-term revenue trends and growth.  
• Customer acquisition cost (CAC): A metric that represents the average cost of acquiring a new customer, including all sales and marketing expenses.  
• Customer lifetime value (LTV): This is the total revenue a business can expect from a single customer over the entire duration of their subscription.
• Churn rate: The percentage of customers who cancel their subscriptions within a specific period (monthly or annually). It directly impacts recurring revenue and growth potential.  
• Retention rate: It’s the percentage of customers who continue their subscriptions over a specific period. It's the inverse of the churn rate and highlights the ability to keep customers.
• Payback period: This is the time it takes to recoup the cost of acquiring a new customer (CAC) from the revenue generated by that customer.

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

Note: SaaS modeling insights, particularly CAC and LTV, are key for competitive pricing. The forecasting aspect of these models can also be leveraged for dynamic and AI pricing strategies to optimize revenue.

Types of SaaS revenue models

Familiarizing yourself with SaaS revenue models is crucial for effective SaaS financial management and accurate financial modeling for a SaaS company. The chosen model directly impacts revenue streams, customer acquisition, and long-term growth

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 utilize.

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 multiple pricing packages with different 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 individual users who can access the software. Each user typically 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 monitor.
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 significantly.
3. Management overhead: Tracking individual users can become complex for large organizations.

Usage-based

The usage-based revenue model charges customers based on their actual usage of the software's resources or 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 accurately 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 additional 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: Multiple pricing components can confuse potential customers.
3. Management challenges: Requires refined billing and tracking systems.

SaaS operating models and cost structures

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

Common mistakes in SaaS forecasting

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

Underestimating churn

Many SaaS businesses, especially in their early stages, underestimate the rate at which customers will cancel their subscriptions (churn). This leads to overly optimistic revenue projections.

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

Overestimating acquisition

Another frequent mistake is overestimating the number of new customers that can be acquired within a specific timeframe and at a given cost. The issue often stems from unrealistic marketing or sales effectiveness assumptions.

Solution: Base acquisition forecasts on historical conversion rates from different marketing channels and realistic sales capacity. Conduct thorough market research and factor in seasonality and competitive pressures. Use cohort analysis to understand the long-term acquisition trends.

Ignoring the impact of pricing on model accuracy

Pricing decisions have a direct and significant impact on revenue, customer acquisition, and churn. Failing to accurately model the effects of different pricing strategies or potential price changes can lead to significant forecast inaccuracies.

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

Not accounting for customer expansion and contraction

SaaS revenue isn't solely driven by new customer acquisition. Existing customers can upgrade their plans (expansion revenue) or downgrade/reduce usage (contraction revenue). 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. Incorporate assumptions about future expansion and contraction based on planned features and customer engagement strategies.

Overlooking the payback period

Focusing solely on top-line revenue growth without considering the cost of acquisition and the time it takes to recoup that cost (payback period) can lead to unsustainable growth. A long payback period can strain cash flow.

Solution: Calculate and closely monitor 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. 

Disregarding the nuances of different revenue models

Applying a generic forecasting approach across different revenue models (e.g., flat-rate vs. usage-based) will lead to inaccuracies. Each model has its own drivers and requires a tailored approach to forecasting.

Solution: Understand the specific drivers for each revenue model you employ. Develop distinct forecasting methodologies for each, considering metrics like usage patterns for consumption-based models and seat growth for per-user models.

Remember: It’s key to integrate billing and revenue data directly into your forecasts. This helps make sure that your SaaS modeling is based on actual performance and provides a more accurate picture of your business's financial trajectory. 

Plus, using SaaS financial software that automates this integration can improve forecast accuracy and efficiency. 

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 robust and 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?

Revenue forecasting for SaaS companies typically means projecting future recurring revenue (MRR/ARR) based on new customer acquisition, churn rates, and expansion revenue from existing customers. This projection often considers different growth scenarios and historical performance.

How do pricing changes impact financial forecasts?

Pricing changes directly affect financial forecasts by altering the ARPU, which in turn influences overall MRR and ARR projections. SaaS modeling allows you to simulate these impacts on revenue, churn, and customer lifetime value before implementation.

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

Modeling usage-based pricing requires tracking and projecting consumption metrics, such as data volume or API calls, and applying the pricing tiers or per-unit costs. Integrating historical usage patterns and anticipated growth in usage is key for proper forecasting.

How do I integrate billing data into my financial model?

Integrating billing data into your financial model involves connecting your billing platform, like Orb, to your forecasting tools to pull in actual revenue, subscription status, and usage data. Direct integration helps make sure your model reflects real-world performance and improves forecast accuracy.

SaaS modeling helps you plan, and Orb helps you execute

Orb empowers SaaS and GenAI companies to unlock their usage data, allowing for adaptable pricing and smooth billing for faster growth. You can 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 for billing and invoicing:

• Real-time usage monitoring: See exactly how your customers are using your product as it happens. This visibility into usage patterns allows you to understand what drives your billing and identify opportunities to adjust pricing based on the value delivered.
• Instant revenue impact predictions with Orb Simulations: Put your pricing models to the test using your actual product usage data. Orb Simulations lets you model different scenarios side-by-side, helping you confidently choose the pricing approach that best aligns with your growth goals and financial forecasts, before you make any live changes.
• Precise billing calculations with Orb RevGraph: Ensure accurate and auditable billing by leveraging Orb RevGraph, which ingests and processes all raw event data. This data is combined with pricing logic, resulting in precise billing that builds customer trust and provides a reliable foundation for your revenue reporting and pricing analytics.
• Dynamic pricing model analysis: Understand how your current pricing models perform, whether usage-based, tiered, or hybrid, with up-to-the-minute data. Orb's platform allows you to analyze which models are most effective for various user segments.
• Integration for unified data: Connect Orb with your existing financial stack, including accounting systems and data warehouses, to create a unified view of your revenue and customer data. These integrations ensure your pricing execution is based on a complete and consistent dataset.
• Customizable dashboards and reporting: Gain clear, real-time visibility into key pricing metrics and trends through Orb's customizable dashboards. These tools allow you to monitor performance and generate reports that provide actionable insights for your pricing decisions.

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.