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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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Our book, Price to Scale, suggests that using machine learning can be an effective way to personalize pricing—but it comes with important caveats.
Here are the key points to consider:
• Data-Driven Segmentation: As described in our book, one approach involves combining metrics (such as churn propensity) into a single score. This score then helps identify specific customer cohorts that might benefit from tailored pricing or discount offers.
• Tailored Offers and Contracts: Price personalization doesn’t necessarily mean simply changing numbers for each individual. Instead, our strategy has been to use machine learning to target specific segments with personalized pricing reductions (for instance, 10% to 30%) paired with contract term extensions. This ensures that price reductions are strategically justified and aligned with longer-term customer commitment.
• Precision vs. Practicality: Our book also emphasizes that extremely high-precision pricing (e.g., 95%+ accuracy) can be challenging, especially for non-standardized products or when data isn’t robust enough. For many SaaS companies, a good segmentation model powered by machine learning can provide the “right” level of personalization without overcomplicating the pricing mechanism.
In summary, while machine learning can indeed help you personalize pricing based on individual customer characteristics, it should be applied as part of a broader, data-driven strategy. Ensure you have robust data collection methods, clear segmentation criteria, and mechanisms (like commitment contracts) that support and validate the tailored pricing you offer.
This balanced approach, as detailed in Price to Scale, supports both customer value maximization and sustainable revenue growth.