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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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Based on our pricing strategy book, Price to Scale, the answer is nuanced:
• Our book encourages leveraging forecasting models—and yes, machine learning can be part of that toolkit—to extract signals from historical data. However, the goal isn’t to create a precise pricing model that perfectly fits past data but to inform decisions on whether to adjust list prices and package designs.
• Specifically, when considering new features, rather than solely relying on a machine learning prediction of willingness to pay, the book stresses combining empirical data analysis with market and prospect research. This ensures that pricing strategies are grounded in both quantitative signals and qualitative insights.
• In practice, you could use an ML approach as one element in your forecasting framework. It may help surface patterns (for example, finding correlations between feature usage and price premiums) that can validate or guide packaging decisions. However, it’s important to integrate these insights with traditional market research and seller feedback to ensure that strategic pricing decisions are robust and context-aware.
In summary, while machine learning can add value in forecasting willingness to pay, Price to Scale recommends using it in tandem with broader market research rather than as a standalone solution for pricing new features.