Last updated on Thursday, February 6, 2025
As businesses shift to consumption-based go-to-market strategies, forecasting revenue has become increasingly complex. Whether it’s API calls, data storage, or platform usage, traditional forecasting methods designed for fixed or subscription pricing models no longer suffice. Organizations need a more dynamic approach to predicting revenue growth—one that accounts for real-time customer usage and adapts to changing consumption patterns.
The transition to consumption-based pricing isn’t new. Industries such as life sciences, manufacturing, and professional services have long dealt with fluctuating revenue streams based on changing customer demands. However, as more SaaS companies adopt this model, the unpredictability of revenue has surged, bringing new forecasting and growth challenges.
For example, a company offering cloud storage may charge customers based on the volume of stored data or frequency of access. In some months, usage may be minimal, while at other times, it can spike dramatically. Forecasting revenue in such a dynamic environment requires more than just historical data—it demands a forward-looking, intelligent approach to target product and customer growth.
When revenue depends on usage, businesses can no longer rely on static forecasting models. Even with contractual agreements in place, actual consumption can vary significantly, leading to unreliable revenue projections. The result? Oversimplified forecasts that fail to capture the nuances of customer behavior and consumption trends, ultimately impacting the ability of organizations to achieve revenue growth in new markets.
CPQ (Configure, Price, Quote) tools have become essential for enabling sales teams to create accurate, real-time quotes for complex product and pricing configurations. With traditional CPQ applications, businesses can price and quote different consumption tiers during the quoting process.
However, when quoting usage-based products, the expected consumption levels are nothing more than estimates based on customer expectations. Deal modeling and account-level planning are still required for sales team targets, compensation, and account growth. When integrated with consumption forecasting, CPQ plays a crucial role in aligning sales expectations with actual revenue outcomes.
To tackle the challenges of consumption-based pricing, businesses need to move beyond static revenue projections and one-dimensional pipeline forecasting and adopt a more dynamic approach. Deal modeling is one of the most effective ways to do this.
The days of Sales and Revenue Operations focusing primarily on Pipeline Management and Bookings are over. Organizations that were early adopters of usage and tier-based pricing focus their customer-facing teams on specific ‘Use Cases’ within their customers that drive their product consumption. For example, an enterprise customer may adopt a cloud-based storage platform as their corporate standard.
However, the success of the cloud-based storage vendor is directly tied to the progress of the customer’s project, roll-out, and adoption of the new platform. For this reason, mature organizations whose GTM primarily depends on usage-based products need to pay as much or more attention to specific use case adoption than if they do pipeline alone.
Basing sales team compensation on ‘estimates’ of actual usage is no longer an option. As in life sciences, manufacturing, and services, teams selling usage-based SaaS and technology products are now compensated on actual volume and revenue attainment, not just closed deals from the pipeline.
At its core, deal modeling involves forecasting how much volume and revenue a customer will generate based on their expected usage over time. Instead of relying on a one-time contract value, businesses are leveraging the forecast and the time of pipeline close, as the target for the account.
To be effective, deal modeling must consider the entire customer lifecycle. Key factors such as seasonality, business growth, internal development timelines, changes in customer needs, and market trends all impact future consumption. “Deal modeling” at the time of pipeline close, provides the baseline account target that volume pricing agreements were based on. Furthermore, the role of the account target becomes a crucial tool for customer-facing teams.
By integrating deal modeling around consumption into forecasting models, businesses can create more accurate revenue projections and reduce the uncertainty of consumption-based pricing.
When planning the GTM (Go-To-Market) for usage-based products and services, organizations are asking the same questions:
To get started most GTM teams have researched usage patterns and have a core set of assumptions that the new pricing strategy will be based on. However, to implement that strategy deals must be properly modeled at the time of pipeline close. It is important to incorporate the GTM model from a top-down and bottom-up approach.
The top-down approach involved setting high-level targets by product line, territory, and segment. The bottom-up approach for customer-facing sales teams requires modeling deals at the time of close and tracking consumption around customers’ use cases
revVana extends Salesforce’s Sales Cloud, Revenue Cloud, and Consumption Forecasting capabilities by transforming one-dimensional pipeline into intelligent revenue models. By integrating revVana with Salesforce, businesses can:
See it in action:
By leveraging applications like revVana, businesses gain complete, automated consumption forecasts that help:
Consumption-based revenue models introduce a layer of complexity that traditional forecasting methods struggle to handle. Unlike fixed revenue contracts, consumption revenue is dynamic—fluctuating based on customer behavior, seasonality, product adoption, and a host of other variables. To get ahead, businesses need to rethink their approach to forecasting.
Revenue teams have long relied on pipeline data to predict revenue, but as more businesses move toward consumption-based pricing, traditional forecasting methods simply don’t cut it anymore. Enter Salesforce’s Consumption Forecasting, a new feature designed to help businesses track and predict revenue based on actual product usage. This is a critical tool for RevOps teams who are managing these complex, dynamic models. But while Salesforce has made a big leap forward, there’s still a gap to be filled when it comes to making those forecasts actionable and aligned with broader revenue goals.
Sales forecasting has become a cornerstone for businesses aiming to align operational strategies with financial realities. Accurate forecasting empowers organizations to allocate resources effectively, manage cash flow, and make informed decisions. However, the process becomes far more challenging when companies operate within complex revenue models—from usage-based products to long-term subscriptions and project-based engagements.