Measuring Technology Adoption with Alternative Data
Adoption is the leading indicator of technology value — but it's hard to measure. Here's a framework for quantifying real technology adoption from public signals.
Everyone agrees that technology adoption predicts value. Few agree on how to measure it. Vanity metrics — stars, downloads, follower counts — are easy to game and easy to misread. A credible adoption metric has to be harder to fake and easier to compare.
What adoption actually is
Adoption is sustained, real use — not one-time curiosity. A product with a million downloads and no ongoing activity is not adopted; a tool with fewer users who return daily is. The measurement problem is separating durable use from noise and hype.
A multi-signal framework
- Breadth — how many independent sources register the product at all (search, community, developer platforms, marketplaces)?
- Depth — is engagement sustained (return discussion, ongoing development, repeat mentions) rather than spiky?
- Independence — do unrelated sources corroborate, or is the signal coming from one echo chamber?
- Momentum — is the rate of adoption rising or falling, recency-weighted so stale data decays?
Why confidence is non-negotiable
Adoption estimates for a brand-new product are inherently uncertain — few sources, little history. An honest system says so. That is why Prismetric reports adoption as a normalized sub-score with a separate confidence: a 71 at 96% confidence is a very different investment than a 71 at 40%. Collapsing those into one number hides the risk.
From adoption to a score
Adoption is one input into a broader TechScore that also weighs momentum, satisfaction, attention and more. Rolled up to the issuing company, it becomes an alternative-data signal you can correlate with the equity. See how it is computed on any product page, or read the alternative data primer.