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Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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Below is a concise approach based on our pricing strategy book, Price to Scale:
Directly identify the most granular usage metrics that drive both cost and value. Our book recommends that especially for AI-enhanced SaaS products, designers should isolate metrics—such as the number of automated support cases, bot interactions, or steps invoked—to ensure that pricing is directly tied to product usage (see Page 145).
Collect granular usage data. AI excels when it has high-quality, detailed data to work with. Ensure your telemetry systems capture not only raw usage numbers but also context, such as peak loads and the timing of feature engagements. This provides the foundation required for AI models to identify trends and predict future usage growth.
Use AI to conduct predictive analytics. With robust algorithms, you can analyze these usage patterns to forecast growth rates and to recognize anomalies. For example, consider our discussion on usage growth rates in big data-driven applications and cloud infrastructure (refer to Page 223). AI can help pinpoint when and where usage is likely to spike, thereby informing more agile pricing tiers.
Run iterative experiments. Leverage AI to simulate the impacts of different pricing tier structures. The idea is to use historical and real-time usage data combined with machine learning models to propose optimal tiers. This iterative testing, paired with customer surveys on pricing preferences (as discussed earlier in the book), can converge on pricing bands that are both competitive and reflective of your value proposition.
Align pricing with underlying cost structures. AI models should also factor in internal cost correlations. Understanding how usage affects your cost—be it compute, data storage, or another metric—ensures that your pricing tiers not only match customer value but also preserve margins and revenue predictability.
Summary:
Our book, Price to Scale, advocates leveraging AI by (a) identifying and capturing granular usage data, (b) employing predictive analytics to understand patterns and growth trends, (c) iteratively simulating different pricing models, and (d) aligning your pricing metrics to both customer value and internal cost dynamics. This holistic approach ensures that pricing tiers are both optimal and scalable.