.png)
Estd 4.22.2023
Based on the themes and approach in our pricing strategy book, Price to Scale, the issue of communicating pricing experiments—especially those leveraging AI—is as much about customer perception as it is about data gathering. Although our book doesn’t prescribe a one‐size‐fits‐all answer, here are some key insights:
• In iterative pricing experiments, the goal is to collect actionable market data to refine your value proposition. Often, these experiments (including A/B tests) are run quietly in order to avoid complicating the customer experience or undermining trust.
• That said, if your experiments become noticeable or if a segment of customers reacts negatively to unexpected pricing variations, it might be beneficial to provide a contextual explanation. This could be framed as part of your commitment to continuously optimizing the product’s value and ensuring the best fit between pricing and customer benefit.
• The decision to label pricing as “beta” versus keeping experiments in the background largely depends on your brand positioning and customer relationship. For some companies, proactive transparency can reinforce trust, while for others, a more controlled rollout with discreet testing helps maintain pricing stability and customer satisfaction.
In summary, as discussed in our saas pricing book Price to Scale, the approach should be tailored. Quietly running A/B tests is common for minimizing potential friction, but be ready to communicate transparently if customer feedback suggests it’s necessary. The key takeaway is that your experimentation strategy should be aligned with both your analytical goals and the overall customer experience.