Introduction

The software industry is in the midst of a fundamental transformation in how value is monetized. Traditional software-as-a-service (SaaS) revenue models, rooted in fixed pricing are increasingly giving away to usage-based and even outcome-based pricing. Driving this shift is customer demand for flexibility, better alignment with consumption, and the need to reduce long-term contractual risk. Additionally, the evolution of GenAI has shifted more SaaS companies to usage-based and outcome-based models. AI-enabled tools are generally built upon foundation models that charge based on usage, thus changing the unit economics compared to traditional SaaS products. Companies must therefore better align their revenues with their variable cost structure.

While subscription has not lost its dominance as the primary pricing model, hybrid and alternative models are gaining popularity. About two-thirds of SaaS providers are now leveraging usage and consumption-based pricing, up from just over half in 2022[1]. This shift has significant implications for financial due diligence in the software sector. In this article, we explore the benefits and challenges of usage-based and outcome-based models, highlight key regional and industry trends, and provide key recommendations for investors evaluating software and technology providers.

Benefits of usage- and outcome-based models and implications for financial due diligence

There are several strategic advantages to usage-based models over traditional SaaS models, such as scalability and cost alignment.

Usage-based pricing allows vendors to “land and expand” more effectively, scaling revenue in line with customer adoption. Such models also align better with cloud infrastructure costs such as compute time and storage, improving visibility into margins. These models also enable more opportunities for upselling, particularly in this era of rapidly growing data consumption. As customers increase usage, vendors can capture more value without having to renegotiate contracts, preserving margins.

However, as GenAI evolves, pricing-based volume or usage may create business challenges if the software helps drive efficiency. For example, a company may offer an AI tool that drives efficiency and is priced based on the volume it processes. If the tool improves efficiency, customers will need to purchase less and companies will see a customer’s revenue shrink over time. To solve for this paradox as AI evolves, companies may need to rethink their pricing strategy simultaneously.

Outcome-based software models have also been an increasingly common model for software companies as a potential solution for the deflationary effect of efficiency gains under a usage-based model. In these models, pricing is structured such that the fee charged to a customer is tied directly to the business results or success metric they achieve using the software. Customers often prefer these models as this pricing strategy can help manage costs by linking them directly to positive achievements. Outcome-based models also incentivize software vendors to produce real results for their clients, thus aligning incentives on both sides to create a partnership. Additionally, customers can directly see the link between their desired outcome and the cost of the desired outcome, creating a transparent ROI.

Despite these advantages, this model introduces additional complexity. Most traditional SaaS models rely on fixed-fee contracts, which have simple revenue recognition guidance. When moving to an outcome-based model, the fee becomes variable consideration, and the related tracking and revenue recognition becomes more complex. In some instances, companies may need to develop models to track achievement of outcomes real-time. In other instances, the amount of consideration ultimately received may not be known for an extended period of time. This may require companies to estimate the variable consideration they will receive from a contract and true-up revenue once the amount is known.

Usage-based and outcome-based models also introduce complexity when it comes to due diligence and analysis, and investors consider the following implications while evaluating businesses:

• Revenue clarity: Investors must separate recurring revenue from transactional usage as these models complicate analysis. In a variable-fee arrangement, the line may be blurred, and more revenue could be considered re-occurring vs. the traditional recurring nature of a fixed-fee SaaS offering. Hybrid models are increasingly common, whereby customers commit to an annual/quarterly/monthly volume with overage fees for excess use above the minimum volume. It is important for investors to analyze trends with and without the excess usage and understand customer-level trend drivers for changes driven by price versus volume.
• Data gaps: Investors are still demanding the same level of detail on revenue drivers and traditional SaaS key performance indicators such as annual recurring revenue (ARR), churn and retention, but such data is often either lacking or of poor quality when it comes to usage-based models. Companies may include assumptions of customer growth, adoption, or usage in their ARR figures. Companies may present customers operating a short-term pilot as recurring revenue.
• Contract complexity: Contracts often include base fees, usage tiers and renegotiation clauses. Understanding these structures and tracking lifecycle and usage patterns of top customers is essential to assess revenue durability.
• Dependence on analytics: Diligence teams increasingly rely on analytics to unpack customer-level data such as usage patterns and lifecycle trends. Furthermore, to gain comfort over bookings, backlog, and related revenue forecasting, it is vital to perform a bookings realization analysis, assessing annual contract value and expected volumes at the time of booking versus how long it takes to actually realize that revenue value.
• Predictability: The predictability of a traditional fixed-fee SaaS models has been attractive to investors. Investors must grapple with the lack of predictability of variable fee models which may make valuation more difficult.

What investors should know about due diligence for usage-based and outcome-based models

Usage-based pricing is gaining traction across industries and geographies. In our experience, consumption-based pricing is increasingly showing up in sectors like logistics, shipping and transaction-heavy sectors.

Looking at regional trends, the model is showing strong growth in mature markets such as the US and Europe. In the latter, usage-based pricing is emerging as a differentiator in saturated software markets, with notable adoption in Germany and the Nordics. Less mature markets like Latin America have lagged in adoption, but investor interest is growing, especially for specialized tech funds.

As more companies embrace usage- and outcome-based models, it is vital for investors to be aware of their unique considerations while performing due diligence.

1. Get Granular

• Don’t just rely on simple “last quarter × 4” or “last month x 12” ARR calculations.
• Examine customer-level trends, contract structures, usage volatility, and renewal/renegotiation dynamics.
• As new revenue models are rolled out, prospective customers may be in an experimentation phase, trying products on a short-term pilot basis with “kill switches,” creating high churn risk.
• Companies may claim bookings at a number they expect customers to pay in the future, and not based on actual current usage or contracted amounts. Investors must understand assumptions baked into ARR calculations.

2. Metrics & Analytics

• While traditional SaaS KPIs remain valid, perform granular analysis of the data.
• Separate revenue drivers into more detailed buckets such as price increases, new users, cross-sell, and transaction volume.

3. Profitability & Valuation

• Focus is expanding from just ARR growth to include profitability and unit economics.
• Expect different valuation multiples for secure recurring revenue versus volatile, usage-driven revenue.
• Revenue and EBITDA may be impacted by estimates if an outcome-based model is variable consideration. Reported revenues may be a product of the company’s estimate, which may be more or less than the amount realized. Further, reported revenues may be impacted by true-ups to prior estimates.
• Under GAAP, when revenue includes variable consideration, companies must assess whether to apply a “constraint” to prevent a potential future reversal of revenue. A constraint may may be necessary when the outcome is not known for a long period of time, or if companies have limited experience to reliably predict outcomes, such as when an outcome-based arrangement is newly implemented. If a constraint is applied, the conservatism from applying a constraint may not fairly reflect the true value of an arrangement.

Conclusion

As software revenue models evolve from traditional SaaS with fixed pricing to usage- and outcome-based pricing models, investors must adapt their due diligence approaches and prepare for the transition accordingly. This means digging deeper into customer-level data, reassessing revenue drivers and valuation frameworks for better outcomes.

The investors best placed to benefit are those who can embrace the opportunities of this pivotal period in the software industry, while planning for the complexity it brings to evaluating businesses.

[1] https://www.maxio.com/blog/consumption-based-billing