
Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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Join companies like Zoom, DocuSign, and Twilio using our systematic pricing approach to increase revenue by 12-40% year-over-year.
In the dynamic world of SaaS, usage-based pricing has emerged as a powerful alternative to traditional subscription models. While it offers greater flexibility for customers and growth potential for businesses, it introduces significant complexity when it comes to revenue forecasting. Unlike fixed subscription models, usage-based pricing requires more sophisticated modeling approaches to predict future revenue streams accurately.
Usage-based pricing—where customers pay based on their actual consumption of your service—creates inherent variability in revenue streams. This variability stems from several factors:
According to OpenView Partners' 2022 SaaS Benchmarks report, companies with usage-based pricing models grow faster (29% year-over-year growth versus 22% for companies without usage-based pricing), but they also report greater challenges in revenue forecasting accuracy.
The foundation of usage-based revenue forecasting begins with analyzing historical data. This involves:
Snowflake, a data cloud company employing usage-based pricing, relies heavily on historical usage patterns to forecast future revenue. Their approach involves analyzing usage by customer cohorts, enabling them to predict how new customers might scale their usage over time.
Not all customers behave similarly, making segmentation crucial for accurate forecasting:
Twilio, the communications API platform, segments their customer base into distinct usage profiles. According to their public statements, this segmentation improves forecast accuracy by recognizing that startups, mid-market, and enterprise customers follow distinctly different usage growth patterns.
Effective forecasting models incorporate early warning signals of usage changes:
DataDog's forecasting model reportedly incorporates multiple leading indicators such as the number of servers being monitored, which serves as a precursor to future usage increases.
The most robust forecasting systems employ multiple modeling techniques:
Time series models like ARIMA (Autoregressive Integrated Moving Average) analyze historical usage data to identify patterns and seasonality that can predict future usage. These models excel at capturing cyclical variations in usage patterns.
These models track how similar groups of customers evolve their usage over time, creating predictable curves that new customers typically follow.
Instead of producing a single forecast number, probabilistic approaches generate a range of potential outcomes with assigned probabilities, allowing for better risk management.
According to a Gainsight study, companies that employ multiple forecasting methodologies achieve 30% better accuracy in their revenue predictions compared to those using single-method approaches.
Modern revenue forecasting increasingly relies on machine learning algorithms that can:
Stripe, the payments infrastructure company, utilizes machine learning models that analyze hundreds of signals across their customer base to predict usage growth and potential churn.
To account for the inherent uncertainty in usage-based models, leading companies employ Monte Carlo simulations that:
This approach acknowledges that usage-based revenue forecasting isn't about producing a single number but rather understanding the range of likely outcomes.
Rather than forecasting total revenue directly, many sophisticated models:
AWS reportedly uses this bottom-up approach, forecasting usage patterns for key account segments separately before aggregating them for overall revenue predictions.
Before building complex forecasting models, define your core usage metrics:
Reliable forecasting requires robust data capabilities:
Twilio CEO Jeff Lawson has stated that their investment in data infrastructure was crucial for enabling accurate usage-based forecasting, allowing them to process billions of API calls while extracting meaningful usage patterns.
Rather than relying on a single forecast, develop:
According to a 2022 CFO Research survey, 76% of SaaS companies with usage-based pricing now employ multiple scenario planning in their forecasting processes, up from 45% three years earlier.
Continuous improvement in forecasting accuracy requires:
The gold standard for measuring forecast performance is the Mean Absolute Percentage Error (MAPE), which calculates the average percentage difference between forecasted and actual revenue.
According to SaaS Capital, top-performing companies with usage-based models maintain a MAPE of 15% or lower for 90-day forecasts, while the industry average hovers around 25-30%.
To improve forecast accuracy:
As usage-based pricing becomes more prevalent, several trends are emerging:
Advanced systems now automatically flag unusual usage patterns that might impact forecasts, enabling proactive intervention.
Some organizations are creating digital replicas of their customer base to run sophisticated simulations of how usage might evolve under various conditions.
Companies are beginning to incorporate broader ecosystem data—including partner platforms, economic indicators, and industry trends—into their forecast models.
Mastering revenue forecasting for usage-based pricing models isn't merely a financial exercise—it's a strategic advantage. Companies with superior forecasting capabilities can:
While usage-based pricing introduces greater complexity to the revenue forecasting process, the companies that master this challenge gain significant advantages in planning, investor relations, and strategic decision-making.
By implementing the multi-faceted approach outlined above—combining historical analysis, customer segmentation, leading indicators, and advanced modeling techniques—SaaS executives can transform the uncertainty of usage-based pricing into a predictable, manageable business asset that supports sustainable growth.
Join companies like Zoom, DocuSign, and Twilio using our systematic pricing approach to increase revenue by 12-40% year-over-year.