Deal Modeling of Consumption for GTM and Account Planning

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 Shift to Usage-Based Pricing

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.

The Role of CPQ in Deal Modeling for Consumption-Based Forecasting

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.

The Need for Deal Modeling and Use Case-Based Forecasts

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.

Use Case Based Forecasts vs. Pipeline

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.

How Does This Affect Sales Compensation?

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.

What is Deal Modeling for Consumption-Based Pricing?

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.

How Consumption Forecasting Affects GTM

When planning the GTM (Go-To-Market) for usage-based products and services, organizations are asking the same questions:

• How do we get started?
• How do we price?
• How do we execute?
• How do we track progress?

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

How revVana Automates Consumption Forecasting and Deal Modeling

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:

• • • Automate Pipeline Revenue Plan – revVana automates the creation and maintenance of Consumption Models from Salesforce Pipeline and CPQ.
    • Automate Account Level Revenue Plans – In addition to generating models at the deal level, revVana generates predictive plans at the Account, Product, Use case, or any other level.
    • Model Multiple Consumption Scenarios – Businesses can compare different contract structures, pricing tiers, and growth trajectories to predict usage and revenue more accurately. This is done from predefined GTM assumptions and by incorporating signals from real-time actual revenue data.

  See it in action:

  By leveraging applications like revVana, businesses gain complete, automated consumption forecasts that help:

  • • • Revenue Operations teams price and model usage down to the deal level
      • Sales teams monitor and track actual usage to targets
      • Incorporate bottom-up forecasts to track against top-down GTM targets.