What is revenue forecasting and how does it work?

61% of enterprise companies (1,000+ employees) missed their revenue target last year, largely due to the complexity introduced by modern pricing models. Revenue forecasting—the process of estimating future sales to enable informed business decisions—has become significantly more challenging with the widespread adoption of consumption-based pricing models and usage-based billing structures.

Companies adopting consumption models report up to 38% higher year-over-year revenue growth, but this growth comes with forecasting complexity that traditional methods cannot address. The prevalence of consumption or usage-based pricing models in the B2B SaaS sector has nearly doubled in the past 5 years, with 60% of companies having implemented or experimenting with a consumption model.

The Evolution of SaaS Pricing Models

Modern SaaS pricing has evolved beyond simple subscription tiers. 39% of SaaS companies have adopted some form of usage-based pricing, creating hybrid pricing models that blend fixed subscriptions with variable consumption charges. B2B SaaS has evolved, but billing has not yet. Pricings are now mostly hybrid: they include a usage-based component ("if you use more you pay more") and a subscription component (a recurring fee for basic usage).

Consumption-Based Pricing Models

Consumption-based pricing models charge customers according to actual resource usage rather than fixed subscription fees. Consumption forecasting is the capability to accurately predict future revenue of pay-as-you-go, usage-based, or consumption-based business models. This approach offers customers cost flexibility while creating revenue unpredictability for providers.

For AI companies, Lago's platform handles complex consumption tracking across multiple usage dimensions. Pricing AI is a unit-based problem. AI models are expensive to run. They demand real compute and GPU power. To abstract AI to a basic monthly subscription scheme is dangerous; it can easily run unprofitable.

Pay-Per-Use AI Services

AI service pricing requires sophisticated metering capabilities. A simple model that is common for APIs is billing things based on requests. 100 requests invoked to an AI model? Great, that's 100x units. However, for some companies, metering around underlying model requests isn't fair to the user. This is particularly true of companies that leverage AI models to provide a specific service to the user. Instead, these companies need to bill around successes.

Revenue Forecasting Challenges with Modern Pricing Models

Consumption Forecasting Complexity

The unpredictability inherent in these models make forecasting revenue more challenging than it is with traditional subscription-based pricing. Traditional forecasting approaches fail when confronted with:

• Usage volatility - Consumption-based pricing can make it challenging for businesses to predict and stabilize their revenue streams. Since customers are charged based on usage, the revenue can fluctuate based on the customers' consumption patterns. This uncertainty can make financial planning and forecasting more difficult for businesses
• Multiple pricing dimensions - Implementing and managing consumption-based pricing can be complex, especially when multiple variables need to be considered, such as usage volume, tiers, and different pricing models. Determining the right pricing structure and accurately tracking usage can be challenging for businesses
• Customer behavior unpredictability - Many companies, depending on the offering, are not always able to predict their customer's usage. Thus, almost axiomatically, the model exacerbates the challenge of accurately forecasting revenue, as well as setting accurate quotas

Data Infrastructure Requirements

Consumption-based pricing models are more complicated to track and report on. Companies need to be able to track granular usage data and model usage patterns to support their invoicing, internal sales crediting and forecasting.

Lago's event-based architecture provides the foundation for accurate consumption forecasting by processing up to 15,000 billing events per second. Lago is able to ingest events at scale while preventing duplicates. The aggregation process consists in converting events into billable metrics.

Advanced Forecasting Methodologies for Usage-Based Models

Cohort-Based Revenue Modeling

When forecasting usage-based revenue, it is useful to define your customer cohorts based on when the customer was acquired. Defining cohorts in this way allows you to track that group of customers over time to identify trends that you can use to inform your revenue forecast. This is particularly useful for usage-based revenue forecasting because revenue is inextricably tied to customer behavior. That is, the amount of money you make from a given cohort is a function of how much, collectively, the customers in that cohort are using the product. Cohort analysis not only gives you key insights into how your usage-based revenue fluctuates from month to month, it also provides a retention curve that you can apply to more accurately forecast usage-based revenue in the future.

Predictive Analytics and Machine Learning

AI-powered consumption forecasting solutions emerge, purpose-built for the unique challenges of consumption-based business models. These leverage machine learning to crunch billions of data points - from product usage traces to voice/email sentiment - to reveal granular demand drivers. Augmented with human domain expertise, this intelligent forecasting gives companies a strategic edge through heightened demand visibility.

Usage Pattern Scoring Systems

To forecast usage-based revenue you need to account for key variables that align with customer usage trends and segment them. "You'll need to develop a scoring system based on whatever variables seem to trend with usage. Categorize predicted usage in ranges (Low, Medium, High) or a numbering system (1-5 levels) to account for increased segmentation".

Technology Infrastructure for Modern Revenue Forecasting

Real-Time Metering and Billing

Modern revenue forecasting requires platforms capable of real-time usage tracking and billing automation. Lago's cloud-based platform processes high-volume usage data while maintaining accuracy across multiple pricing dimensions. Lago's event-based architecture provides a solid foundation for building a fair pricing model that scales with your business.

The platform supports:

• Entitlements management - Controlling access to features based on usage tiers and subscription levels
• Progressive billing - Progressive billing and minimum charges to make sure you get paid without waiting for billing cycles to end. Lago ensures all usage is covered in billing
• Prepaid credits - Unlock recurring revenue opportunities for pay-as-you-go pricing models with Lago's real-time prepaid credit features
• Hybrid plan configuration - Hybrid plans, mixing subscription and consumption (e.g., a usage-based billing plan that requires a recurring minimum spend per month)

Integration and Data Flow

Effective forecasting requires seamless data integration across systems. A big roadblock when an organization is considering making the leap to a consumption-based approach is siloed data and technology. There are often multiple tools in use to track revenue and consumption data and data stored across multiple systems and locations, making accurate forecasting a challenge.

Lago addresses this through:

• API-first architecture - Built for engineers, loved by business & finance teams. Our billing product includes a first-class API for engineers and a pixel-perfect user interface for business teams. Every billing piece we build, you won't have to build yourself
• Open-source flexibility - We're open-source, so it's not 'buy Lago or build it yourself', there's a third option: you can build on top of Lago. Audit our code and keep your data within your infrastructure, if you want full control
• Composable integration - Composable: connect Lago to any of your internal systems or tools (i.e. any payment gateway, CRM, CPQ, accounting software)

Business Impact and Financial Outcomes

Faster Time-to-Cash

Modern billing infrastructure accelerates revenue recognition through automated invoicing and real-time usage tracking. Lago allows you to create one-time charges that are invoiced on the fly, reducing the time between usage and payment collection.

Reduced Billing Errors

Lago is able to ingest events at scale while preventing duplicates, ensuring accurate usage tracking and billing. This precision in metering directly improves forecast accuracy by eliminating discrepancies between actual usage and billed amounts.

Higher Net Revenue Retention (NRR)

SaaS companies that employ usage-based pricing often grow more quickly because of their customer acquisition cost and net dollar retention rates. These results illustrate why usage-based pricing is gaining traction in the market. Consumption-based models naturally expand with customer growth, driving higher NRR through usage expansion rather than traditional upselling.

Strategic Implementation Considerations

Revenue Predictability Solutions

Contracts with a committed dollar spend within a committed term length; however, the customer pays as they use the service. In this model, unused funds are charged at the end of the contract length. Many enterprise technology solutions use committed contracts to lock customers in, provide volume discounts, provide customer with budget expectations, and enable revenue predictability.

Organizational Alignment

Moving from a traditional opportunity or account-based revenue model to a consumption-based one completely changes the way a business must approach sales forecasting. Revenue leaders have to think through not only how they'll recognize revenue but also how that decision ultimately affects things like sales compensation and incentives. It's crucial for the sales team to be on board with the changes.

Future of Revenue Forecasting

As consumption-based models continue proliferating, revenue forecasting will increasingly rely on sophisticated metering infrastructure and predictive analytics. Companies will need better analytics and forecasting tools to navigate fluctuating costs. AI will drive further pricing innovation. As AI capabilities expand, expect even more granular and output-driven pricing strategies.

Organizations implementing consumption-based pricing models require billing platforms that can handle complex usage patterns while providing the data visibility necessary for accurate revenue forecasting. Modern solutions like Lago provide the metering precision, real-time processing, and integration flexibility essential for managing these sophisticated pricing strategies effectively.

Revenue forecasting for consumption-based and usage-based pricing models demands specialized infrastructure and methodologies. Companies seeking to optimize their forecasting accuracy while implementing flexible pricing strategies should evaluate billing platforms that combine high-volume event processing, real-time analytics, and comprehensive API integration capabilities.