Guide
What is feature analytics?
Feature analytics measures how many distinct users engage with each individual feature of a product over a period, which makes it possible both to invest in the features that matter and to identify the ones carrying maintenance cost without delivering value.
Adoption is a user count, not an event count
A feature triggered four hundred times a day by one automation script is not widely adopted. Distinct users over the period is the measure; event volume flatters exactly the features that need scrutiny most.
The cost of an unused feature
Every feature carries code paths, tests, documentation, support burden and a share of the bug surface, whether or not anyone uses it. A feature reached by two users in ninety days is a liability, and the only reason it survives is usually that nobody has the number.
Adoption as a weighting for reliability
Feature adoption is most valuable combined with error and performance data. A four percent error rate is an emergency in a feature seventy percent of users depend on, and an acceptable defect in one two people have found.
Where ErrorDock fits
ErrorDock reports adoption per feature, flags underused features against a configurable threshold, and shows adoption beside error rate and latency in Feature health.
Related
- Measure the features worth protecting. Track feature adoption and interactions beside technical health to understand which parts of your product deserve attention.
- What is product analytics? Product analytics measures what users do inside software: active users, retention, and which features get used. A guide written for engineers.
- Fix what matters to your users. Bring errors, performance and feature usage together to help prioritise engineering work by its effect on the product.
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