I’ve seen this challenge again and again with teams building analytics and AI products: nobody can define what quality to the end user means or how to measure. The answer? “Adoption.” The problem is that “amount of usage” tells you nothing useful about your customer’s experience with your product beyond “it’s not zero.” So what should you be measuring instead so your buyers don’t quickly abandon once the end users get their hands on the keyboard (or agent!)?

The answer is to understand through qualitative measures what users’ experiences are like now, so you have an objective baseline from which to compare future product investment. When you can define their current experience’s quality, it’s much easier to imagine their better future, and you also now have a change you can measure. Measurable outcomes are the foundation of high-value, sticky B2B analytics and intelligence products—and when your end users’ lives are improved, the sales close, and the renewals aren’t questioned. So today, I jump into “how do you measure UX?” so you aren’t surprised when the sale doesn’t close or that renewal doesn’t come through unexpectedly.

Highlights / Skip to:

Why I think product adoption (i.e. product usage analytics) are misleading as a means to define whether your solution is valuable to users (1:34)
Getting a better baseline reading of user experience so you can improve their life and your sales/retention KPIs (4:56)
How to measure, hypothesize, and observe if your product is working “well” (7:35)
Discovering where your product is being appreciated (20:28)
What about when AI is in the loop? (23:05)
The risk of creating bigger messes with AI capabilities (28:20)
How to gain useful insights from your customer exposure time (31:28)
The quantitative metrics you can use to help measure UX outcomes (36:17)
Why "ship it and see if it gets used" isn't a product strategy (40:52)

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Get 1x1 Help from me if you know your product’s value is opaque, or the user experience is hindering your sales or adoption goals

192 – Product Usage Does Not = Value: Why “Adoption” Metrics Are Misleading You