Consumption Forecasting: Demand Prediction for SaaS and IaaS

Consumption-based pricing models, where customers pay for exactly what they use, are rapidly gaining traction across industries.

By nailing their consumption-based forecasting, companies can drive sustainable growth, maximize customer lifetime value, and maintain a critical edge in rapidly evolving markets.

The Benefits of Consumption Forecasting for SaaS and IaaS Companies

Software as a Service (SaaS) and Infrastructure as a Service (IaaS) - where SaaS provides cloud-based applications and IaaS offers virtualized computing resources - both significantly benefit from this pricing approach. Adopting consumption-based models is advantageous for businesses and their clients alike, as it aligns costs directly with usage, enhancing transparency and scalability.

For B2B XaaS companies, accurately forecasting customer consumption patterns through consumption-based forecasting is becoming table stakes. Unlike traditional fixed licensing models, the consumption-based pricing model allows businesses to precisely predict future usage and align resources while providing customers with flexible, pay-as-you-go pricing. 

The Rise of Consumption-Based Pricing Models

Consumption-based or usage-based pricing models are rapidly gaining traction, especially in the software, digital infrastructure, and services realm. In 2023, 61% of SaaS businesses were already utilizing usage-based pricing models at least in a tiered approach, with full consumption-based models also becoming more popular.¹

Unlike traditional fixed licensing models, these flexible models allow customers to only pay for what they use, aligning costs directly with consumption.
For XaaS companies, consumption-based pricing provides a key competitive edge by offering customers more flexibility, scalability, and cost-efficiency. It enables capturing a wider customer base – from small businesses to enterprise clients – by lowering the barrier of large upfront payments. Customers get to trial products easily and scale usage up or down as needed.

Consumption-Based Pricing Models in Different Industries

• SaaS
  • OpenAI: Priced based on number of tokens.

• • Zapier: Payment is calculated based on the number of tasks automated.
    
    

• Cloud Computing and Storage
  • Google Cloud: Pay for what you use.
  • Dropbox: Tiered storage subscriptions.
    
    

• Manufacturing
  • Intel: Chips on Demand. Pay is based on consumption.
  • Stryker: Flexible medical device access. Payment aligns with usage levels.
    
    

Examples of Consumption-Based Pricing Models

Numerous SaaS and IaaS companies have implemented consumption-based pricing strategies, demonstrating the potential to boost revenue while creating stronger customer relationships.

MongoDB

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 BoostUp to run their consumption forecasting - find out why in this case study.

Confluent

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.

JFrog

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

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

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.

What drives the switch to consumption-based pricing?

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.

What is Consumption Forecasting?

Consumption forecasting, also known as usage forecasting, is the capability to accurately predict future revenue of pay-as-you-go, usage-based,  or consumption-based business models. It involves forecasting how quickly customers will consume resources against a contract or purchase order, rather than projecting a fixed amount.

This is in contrast to traditional opportunity or account-based forecasting, where revenue is derived from set contract values for a product or service over a defined period. With consumption-based forecasting, revenue realization depends on tracking actual customer usage over time.

Examples of Consumption-Based 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.

The Importance of Accurate Consumption Forecasting

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.

Revenue Predictability

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.

Production Planning

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.

Opportunity Identification

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. 

Customer Churn Risk Mitigation

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.

What are the Risks of an Inaccurate Consumption Forecast?

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.

Challenges of Consumption-Based Forecasting

Despite its importance, accurately forecasting consumption poses some significant challenges that companies must overcome.

Data Silos and Unstructured Data

For many businesses, critical consumption data remains scattered across multiple siloed systems like CRMs, ERP platforms, and data warehouses. This data exists in unstructured formats, requiring extensive cleansing, transformation, and integration before it can realistically fuel forecasting models. Without a centralized, standardized data foundation, forecasts are likely to be highly inaccurate.

Evolving Consumption Behaviors

Especially with digital services and subscription models, customer behaviors are not static - they constantly evolve, based on product updates, emerging use cases, and changing business needs. Forecasting models must be able to continuously adapt to these fluid consumption patterns to maintain precision. 

Integrating with tools that monitor product usage and customer satisfaction provides vital insights into these evolving behaviors. Such integrations facilitate more accurate forecasting, particularly regarding subscription renewals. 

Sales and Finance Alignment

Accurate consumption forecasting requires a tight collaborative feedback loop between the sales teams, which are closest to customer demand signals, and the financial teams responsible for precise revenue projections. Lack of communication and process misalignment between these critical functions leads to distorted forecasts.

Transition to Automated Forecasting

Traditionally, forecasting was a manual process carried out by analysts, often involving the tedious consolidation of data from multiple sources into spreadsheets. Forecasts were then developed through extensive number crunching with pivot tables and formula-based calculations. However, this outdated approach isn’t well-suited for the complexities of consumption-based models. To achieve the required level of accuracy, companies need to transition to automated forecasting systems powered by modern technology. These tools minimize human error, enhance efficiency, and deliver forecasts that can keep pace with dynamic consumption trends.

External Variable Impacts

Customer consumption patterns rarely exist in a vacuum. A multitude of external factors like economic conditions, seasonal shifts, competitive movements, and more can dramatically influence demand. Effectively accounting for these variables requires sophisticated modeling capabilities that most companies lack.

Key Challenges in Consumption-Based Forecasting

Strategies for Effective Consumption Forecasting

To overcome the challenges of accurate consumption forecasting, companies must adopt a multi-faceted approach spanning technology, processes, and organizational capabilities.

Unified Data Foundation

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.

Advanced Consumption Forecasting Algorithms

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.

External Data Integration

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.

Cross-Functional Collaboration

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.

Augmented Human Intelligence

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.

Continuous Monitoring and Recalibration

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.

Organizational Change Management

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-Based Forecasting

The future of consumption-based forecasting is being radically reshaped by advances in artificial intelligence (AI) and machine learning (ML) technologies. These solutions are enabling unprecedented automation and accuracy in predicting dynamic customer consumption patterns.

Queries Why Are AI/ML Models Transforming Consumption Forecasting?

Traditional rules-based forecasting methods are becoming obsolete in the face of multi-dimensional, ever-changing demand signals. AI/ML models have a distinct advantage - they can ingest vast datasets across multiple variables, automatically identify complex correlations, and continuously re-train themselves as new data arrives. This allows for much higher precision forecasts that quickly adapt to evolving consumption trends.

We're seeing a new breed of 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.

How BoostUp Enables Consumption-based Forecasting & Planning 

• Unified Data Ingestion, Storage, and Analysis: Gain a holistic view of your XaaS revenue stack as BoostUp seamlessly ingests and aggregates data from CRM, email, calendars, call recordings, marketing, and customer success data. Manage and analyze historical data effectively with our comprehensive storage solutions.
• Advanced Forecasting Algorithms: Enhance your decision-making with BoostUp's AI-based forecasting models, which are customized for your business. These models integrate diverse signals, including rep behavior, historical conversion rates, and CRM data, ensuring continuous adaptation and accuracy.
• Cross-Functional Collaboration: Standardize and extend BoostUp dashboards beyond the sales team. Integrate Finance, Marketing, BizOps, and other departments to share the same level of data visibility and integrity, promoting a unified approach to business intelligence.
• Insight Generation: Use BoostUp to analyze key deal risk factors and sales rep performance to generate actionable insights that you can use to refine your consumption trend strategies.
• Adoption Tracking: Monitor new customer adoption rates effectively using BoostUp's user-friendly interface, helping you understand engagement and uptake in real time.
• Sales Process Optimization: Optimize your business processes with BoostUp’s tools for rigorous, consistent reviews and forecasting, driving operational consistency at all levels.
• Forecasting Accuracy Improvement: Achieve more reliable forecasting with real-time pipeline analytics and quarterly pipeline tracking to improve revenue predictability.
• Productivity Enhancement: Boost your team's productivity metrics with strategies aimed at increasing win rates, shortening sales cycles, and improving overall sales rep performance.

As these AI/ML-powered forecasting capabilities become mainstream, we'll see accelerated market disruption. Companies embracing intelligent forecasting will gain a significant competitive advantage through maximized revenue realization and resource optimization fueling stellar client experiences.

BoostUp’s dedicated consumption forecasting solution is designed to accurately predict usage, helping businesses optimize and transition to a consumption-based pricing model.

FAQs - Consumption Forecasting

What is consumption forecasting?

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.

Why is accurate forecasting critical in SaaS consumption models?

• Revenue predictability: It ensures revenue streams are aligned with actual customer usage.
• Operational planning: Facilitates better planning of resources, inventory, and staff.
• Customer insights: Help identify customer behaviors that indicate upsell or churn risks.
• Risk management: Mitigates financial risks by predicting consumption trends accurately.

What role does AI play in improving SaaS demand forecasting?

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.

How do consumption-based pricing models affect forecasting strategies for cloud services?

• Usage tracking: Requires continuous tracking of customer consumption to adjust forecasts.
• Dynamic pricing: Forecasting must account for fluctuations in usage due to the flexibility of pay-as-you-go models.
• Revenue variability: Revenue forecasts are less predictable and require more sophisticated modeling techniques.
• Operational adjustments: Forecasting influences resource allocation and capacity planning based on demand.

How does consumption forecasting help XaaS companies operate more efficiently?

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. 

How can accurate consumption forecasting enhance customer retention?

• Usage patterns: Identifies declining usage as an early indicator of churn risk.
• Targeted offers: Allows sales teams to offer personalized incentives for increased usage.
• Proactive support: Enables customer success teams to address issues before they escalate.
• Upsell opportunities: Highlights customers with increasing usage for potential upsell offers.

References

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)