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Estd 4.22.2023
Based on our book Price to Scale, truly algorithmic personalized pricing—where every user sees a different price based solely on algorithms—is not common in the SaaS space. Here’s why:
• Many successful SaaS companies prefer structured pricing approaches such as the Good-Better-Best model or modular pricing. These models group features and value into clear, segmented packages that ensure transparency and easier decision-making for customers. Our book illustrates that clear segmentation not only simplifies the buying process but also maintains customer trust.
• The potential risk of individualized, algorithm-driven pricing is significant. When customers compare notes, discrepancies in price (even if algorithmically justified) can lead to perceptions of unfairness or arbitrary decision-making. This risk of backlash or erosion of trust often outweighs the potential benefits of finely tuned price points.
• For enterprise clients, pricing usually ends up being more personalized through negotiation or bespoke bundles rather than through a fully automated, individualized algorithm. Price to Scale highlights that while custom pricing can work where there is significant value differentiation or unique use cases (as seen with enterprise deals), it is generally managed through strategic human intervention rather than via automation.
In summary, while there is interest in data-driven pricing innovations, our SaaS pricing book suggests that using well-structured segmentation strategies is the safer and more effective path compared to algorithmic personalization that might trigger customer comparison and resistance. This approach not only drives revenue but also protects the integrity of your customer relationships.