.png)
Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Based on our saas pricing book, Price to Scale, there isn’t an outright endorsement or warning against using AI to automatically adjust prices based on customer lifetime value (CLV) predictions. Instead, our book emphasizes that pricing should be closely tied to the value customers derive, market fit, and the specific characteristics of your product and customer base. Here are a few key takeaways:
• Directly tuning prices to customer lifetime value can be appealing since it aims to match prices with long‐term value. However, our book stresses that any pricing model—whether it’s usage‐based, per agent, or customer lifetime–informed—must be aligned with customer expectations and the broader market context.
• Many of our examples, particularly those involving AI-based products, focus on choosing pricing metrics (such as per automated case or per bot interaction) that best reflect actual usage and value. This indicates that while automatic adjustments can be useful, the choice of metric and the timing of adjustments must be thoughtfully aligned with how customers perceive your product’s value.
• If you are considering automated pricing adjustments, it’s critical to ensure transparency and consistency in how prices change over time. The book advises running simulations and experiments (as detailed in our pricing simulation example for ACME Inc’s AI-powered Chatbot) to validate any dynamic pricing approach before full implementation.
In summary, while employing AI for dynamic pricing based on CLV predictions has potential benefits, it should be tested rigorously and integrated within a broader pricing strategy that accounts for market expectations and customer habituation. Our book, Price to Scale, encourages careful assessment of these factors rather than relying solely on AI as a “set-it-and-forget-it” solution.