Consumption-based pricing is transforming how companies generate revenue, but traditional forecasting methods can't handle the complexity. Manual data collection, spreadsheet-based analysis, and subjective assessments create inaccurate forecasts that lead to poor resource allocation and missed opportunities. AI revenue agents solve this by automatically capturing complete usage data and providing real-time consumption insights, enabling accurate forecasting without the administrative burden.
By nailing their consumption-based forecasting, companies can drive sustainable growth, maximize customer lifetime value, and maintain a critical edge in rapidly evolving markets.
Traditional consumption forecasting requires extensive manual data collection, complex spreadsheet analysis, and constant system coordination. AI revenue agents eliminate this friction by automatically capturing usage data, analyzing consumption patterns, and providing real-time insights without requiring manual effort from revenue teams.
Instead of spending time gathering data and creating forecasts manually, teams get automatic, objective consumption insights that enable better planning and resource allocation while freeing them to focus on customer relationships and strategic growth initiatives.
Traditional consumption forecasting involves manually collecting usage data from multiple systems, building complex spreadsheet models, and making subjective adjustments based on incomplete information. AI revenue agents transform this by automatically capturing complete usage data and providing real-time consumption insights.
Automatic vs. Manual Consumption Forecasting:
Numerous SaaS and IaaS companies have implemented consumption-based pricing strategies, demonstrating the potential to boost revenue while creating stronger customer relationships.
MongoDB has embraced a scalable consumption pricing structure for its database-as-a-service solution, MongoDB Atlas. By charging customers based on their usage of storage and processing resources, MongoDB provides the flexibility needed to support growth.
MongoDB chose Terret to run their consumption forecasting - find out why in this case study.
Confluent employs a usage-based pricing model that aligns with customers' data stream and throughput consumption. This model minimizes upfront costs, allowing businesses to pay solely for the data they process. As a result, companies can explore the platform and expand usage seamlessly as their requirements grow.
In the DevOps arena, JFrog adopts a flexible consumption model, charging based on data transfer and storage utilization. This approach empowers organizations to align expenses with actual operational demands, ensuring cost efficiency.
Redis Labs stands out with its real-time data platform, which uses a consumption-driven pricing model. Costs are tied to memory usage and throughput, allowing companies to scale their services cost-efficiently.
Snowflake illustrates the power of consumption-based pricing with a model that links costs directly to usage. Customers are billed for the storage and computing resources they use. This makes it easier to adopt the platform, experiment with its features, and align spend with the value delivered.
The shift to a consumption-based pricing model is also being driven by advancements in cloud computing that make delivering services and tracking usage simpler. Software vendors can tap into new revenue streams by monetizing offerings that were previously difficult to package and sell.
However, transitioning from fixed upfront pricing to a variable, usage-based pricing model poses challenges. Revenue streams become less predictable, requiring robust consumption forecasting capabilities. Pricing, packaging, and billing must adapt to complex consumption metrics. Sales compensation and partner incentives may need restructuring.
Overcoming these hurdles requires strategic adjustments, but the benefits of consumption-based pricing far outweigh the costs of these adjustments. For this reason, it is becoming an increasingly important part of go-to-market strategies across all industries. With the flexibility that today's customers demand, forward-thinking companies are better positioned to achieve sustainable growth by moving to a consumption-based pricing model.
Traditional consumption forecasting involves manually collecting usage data from multiple systems, building complex spreadsheet models, and making subjective adjustments based on incomplete information. AI revenue agents transform this by automatically capturing complete usage data and providing real-time consumption insights.
Automatic vs. Manual Consumption Forecasting:
For example, OpenAI, a leading AI company with over 300 million weekly active users worldwide, charges usage fees based on the number of tokens processed by their models. Their forecasting needs extend beyond predicting total token usage; they must account for fluctuations in usage demands across individual customers, new customer acquisition, and churn rates. Additionally, OpenAI must forecast demand by account type, region, time of day, and across their five distinct models. These insights allow them to scale their infrastructure efficiently, ensuring seamless performance and resource optimization as their user base grows.
As more businesses shift towards flexible consumption pricing models, mastering this type of forecasting has become critical for sales planning, resource allocation, and maintaining predictable revenue streams. Accurate consumption-based forecasting enables companies to align production capacity, budgets, and growth strategies to dynamic customer behaviors.
Accurate consumption forecasting is absolutely essential for companies operating under flexible consumption-based models. It serves as the basis for sound revenue strategy for the company because it offers predictability, enables tighter planning, and helps in opportunity identification and churn risk mitigation.
From a revenue perspective, reliable consumption forecasts provide much-needed predictability of expected revenue streams. Without this visibility, companies risk over- or under-investing in critical areas such as product development, sales, marketing, and support. Inaccurate forecasts can lead to missed growth opportunities or cash flow problems.
Precise projections of customer usage allow companies to proactively adjust production capacity to handle demand fluctuations efficiently. This helps them maximize operational efficiency and keep production costs under check.
On the sales side, insight into historical and projected consumption patterns is invaluable for identifying upsell, cross-sell, and customer retention opportunities at the account level. If a customer consistently increases their usage, it presents an opportunity to offer incentives for higher usage tiers, such as price breaks or enhanced service levels.
Unusually low usage can be a warning sign of potential churn risks. With ample lead time, sales teams can initiate customer success programs and incentives to reinvigorate adoption.
Inaccurate consumption forecasting can severely disrupt key operational and business processes, including inventory management, resource allocation, and customer relationship strategies. For example, manufacturers could overproduce unwanted inventory, while cloud service providers could underutilize their resources, resulting in less revenue.
An inaccurate forecast also prevents companies from anticipating their customers' needs, creating a significant barrier to delivering an excellent customer experience and fostering long-term loyalty.
For any business operating on the consumption-based model, precisely predicting demand patterns is mission-critical for maintaining profitability, competitiveness, and sustainable growth over the long term.
Traditional consumption forecasting faces significant obstacles that AI revenue agents eliminate entirely:
From Data Silos to Unified Intelligence: Instead of manually integrating scattered data sources, revenue agents automatically capture consumption data from all systems, providing complete visibility without integration overhead.
From Static Models to Adaptive Intelligence: Rather than manually adjusting forecasting models for changing behaviors, agents continuously learn and adapt to evolving consumption patterns automatically.
From Coordination Overhead to Automatic Alignment: Eliminate the need for sales and finance alignment meetings—agents provide consistent, objective consumption data that both teams can rely on.
From Manual Analysis to Automatic Insights: Replace spreadsheet-based forecasting with automatic consumption intelligence that identifies trends and patterns without human intervention.
| Challenge |
Impact Level |
Description |
| Data Silos and Unstructured Data |
High |
Consumption data is scattered across multiple systems, requiring cleansing and integration. |
| Evolving Consumption Behaviors |
High |
Customer usage patterns change due to product updates and shifting business needs. |
| Sales and Finance Misalignment |
Medium |
Lack of communication leads to distorted forecasts and misaligned business strategies. |
| Transition to Automated Forecasting |
Medium |
Reliance on spreadsheets introduces inefficiencies and human error in forecasting processes. |
| External Variable Impacts |
High |
Factors like economic conditions and seasonal shifts affect demand but are hard to predict. |
To overcome the challenges of accurate consumption forecasting, companies must adopt a multi-faceted approach spanning technology, processes, and organizational capabilities.
Creating a centralized, continuously updated data repository integrating all relevant consumption signals from source systems is table-stakes. Data warehousing and ETL tools are critical for cleansing, standardizing, and structuring large volumes of consumption data for analysis.
Traditional forecasting methods relying on linear regression models cannot adequately capture the complex dynamics, like behavior changes, seasonal variations, service or product availability, and economic conditions, influencing customer consumption patterns. AI and machine learning algorithms that can automatically explore diverse variable combinations, detect subtle correlations, and continuously retrain themselves as conditions change are essential.
Feeding forecasting models a diverse range of external data inputs - economic indicators, market trends, competitive intelligence, etc. - allows them to more precisely factor in environmental variables impacting demand. APIs, data parsing, and orchestration capabilities facilitate this external signal ingestion.
Bridging long-standing operational silos between sales, finance, product management, and other stakeholder teams is key. Implementing a central forecasting framework with continuous communication feedback loops ensures forecasts incorporate end-to-end organizational insights.
While automation is critical, human expertise remains indispensable for activities like evaluating outlier forecasts, determining drivers behind forecast fluctuations, and calibrating models. Solutions that seamlessly combine machine intelligence with human decision-making produce the most reliable forecasts.
Customer behaviors and market dynamics are fluid. Organizations must monitor forecast accuracy on an ongoing basis and recalibrate forecasting models based on the latest demand patterns to maintain precision over time.
Technology alone is insufficient. Companies must develop internal capabilities through training programs, documented best practices, and culturally embracing the pivot toward data-driven, AI-assisted forecasting processes.
Implementing a holistic strategy addressing each of these areas provides a solid foundation for mastering the complexities of usage-based forecasting. Those who succeed gain a formidable competitive edge through heightened operational agility and customer responsiveness.
The future of consumption forecasting isn't better tools—it's complete automation. AI revenue agents handle the entire consumption intelligence process automatically, from data capture through insight generation, freeing revenue teams to focus on strategic activities that drive growth.
Organizations deploying AI revenue agents gain competitive advantages through automatic consumption intelligence that enables better resource allocation, proactive customer management, and strategic planning without the administrative overhead of traditional forecasting methods.
Automatic Data Integration: Revenue agents capture consumption data from all systems automatically—CRM, usage analytics, billing platforms, and customer interactions—without requiring manual integration or data management.
Intelligent Forecasting: AI automatically generates consumption forecasts based on complete usage data, customer behavior patterns, and external factors, eliminating manual model building and spreadsheet analysis.
Real-Time Insights: Automatic consumption intelligence updates continuously based on actual usage patterns, providing real-time visibility without requiring manual reporting or dashboard management.
Proactive Management: Agents automatically identify consumption trends, usage risks, and expansion opportunities, enabling proactive customer management without manual analysis.
Complete Automation: Handle the entire consumption forecasting process automatically while providing teams with actionable insights for strategic decision-making.
Consumption forecasting is the prediction of future resource utilization in consumption-based models, often used by SaaS and IaaS companies to align resources, revenue streams, and operations with actual customer demand. These forecasts help organizations manage operational efficiency, create accurate revenue projections, and enable flexible pricing that matches customer usage.
AI improves SaaS demand forecasting by processing large datasets, improving predictive accuracy through machine learning, adapting to changes in customer behavior, and automating the forecasting process to reduce manual effort and errors.
Consumption-based forecasting helps companies plan and align resources like sales and support teams, cloud infrastructure, and inventory to the levels that can exactly match forecasts, thereby reducing their overall cost of revenue.
Kyle Poyar, Sanjiv Kalevar, Curt Townshend - THE STATE OF USAGE-BASED PRICING (OpenView), 04. 17. 2024 (link: https://offers.openviewpartners.com/state-of-ubp-second-edition)