Guest Blog: The Confidence Gap: What Usage Data Can and Can't Tell Pricing Teams
Author: Daniel Mboweni
Pricing teams have never had more data at their fingertips, from usage dashboards and consumption metrics to adoption reports that can sharpen pricing decisions in ways that weren't possible a few years ago.
The real skill now lies in knowing how far that data can be trusted and recognizing where it needs context before it can guide a pricing call. Data gives you facts. Analysis gives those facts meaning. Insight tells you what to do about them.
Revenera's latest Monetization Monitor surveyed 415 software producers, and the findings suggest that confidence is still in short supply across the industry:
Only 33% of respondents believe their price and value are totally aligned
“Data availability and insights” and “understanding customer value drivers” are the biggest blockers to aligning price and value, each cited by 49%
41% still rely on manual processes or engineering effort to collect usage data
When usage data is that challenging to access, it's understandable that teams lean on whatever numbers they have, even when those numbers only tell part of the story.
Where Usage Data Falls Short
One of the most common gaps is treating consumption as an indication of profitability. Usage data shows what customers are consuming, but it rarely shows what it costs to deliver. With AI, that distinction matters more than ever.
In the Monetization Monitor, 41% say the cost of providing AI is squeezing margins, and increased AI delivery costs (47%) and cloud costs (43%) rank among the biggest threats to future growth. A feature with soaring usage can look like a pricing win until you factor in the compute behind it, so any decision on prepaid or postpaid strategies should start with a clear view of what it costs to serve each customer.
It's also easy to assume that the features customers use most are the ones they value most, but usage and willingness to pay don't always align. Warning signs to look out for include:
Heavy use of a feature that customers would never pay extra for
Aggregate trends that hide very different behavior across customer segments
AI features with strong initial activity bu
t weak sustained adoption, a real concern when 48% of producers report limited or low customer adoption of AI
Who Owns the Pricing Call?
There's also an organizational question that I think deserves more attention from the pricing community. The Monetization Monitor found that pricing and packaging decisions most often sit with product management (30%) or executive leadership (28%), with only 12% of respondents seeming to have a dedicated pricing team to take responsibility.
For many organizations, that means pricing decisions are being made without specialist expertise to interrogate the data and pressure-test its conclusions.
Data-driven pricing remains the right ambition, but lasting confidence comes from asking better questions of your data, putting it in the right commercial context and testing it against other signals, rather than simply collecting more of it.
A structured consumption-based pricing framework helps by putting observation before monetization, so teams can collect usage data well ahead of launch, test their assumptions and choose a usage metric that reflects genuine customer value rather than activity alone. And the beauty of such a framework is that you adapt your usage metrics as your strategy evolves.
On Friday, October 30, I'll be joining Aleksander Shubin of Pricerium and Roberto Rivera of PricingNerd for a panel discussion on Where does data-driven pricing create a false sense of confidence, and how do you know when the data is lying to you? at the Professional Pricing Society's profitABLE26 event in Atlanta. If you've ever looked at a usage report and wondered whether it was telling you the whole story, please come and join us. We'll explore how to tell when your data is pointing you toward the wrong conclusion, and how pricing teams can build confidence that's genuinely earned.
About the Author
Daniel Mboweni is a Senior Solution Engineer at Revenera, guiding technology companies on the practicalities of implementing new monetization models and the infrastructure required to support them. As more organizations adopt consumption-based strategies for high-value features and AI capabilities, Daniel is well-versed in advising on the importance of usage insights to inform data-driven pricing.
