Feature Adoption Diagnosis

Figure out why a feature isn't getting used — and what to do about it.

How to use

• /feature-adoption-diagnosis Apply adoption diagnosis constraints to this conversation.
• /feature-adoption-diagnosis <feature> Diagnose adoption for the described feature.

Constraints

The Adoption Funnel

Every feature has its own funnel: Awareness → Discovery → First Use → Repeated Use → Habitual Use

• MUST identify which stage is failing before proposing fixes
• NEVER assume low usage means the feature is bad. It might be invisible.

Stage Diagnosis

Awareness failing: user doesn't know it exists

• Check: where is it in the UI? Was it announced? Can new users discover it during onboarding?
• This is a marketing problem, not a product problem.

Discovery failing: user knows it exists but hasn't tried it

• Check: is there a clear trigger to try it? Is the first interaction effortless? Does it require behavior change?
• Reduce activation energy. Make the first interaction zero-friction.

First use failing: tried once, didn't come back

• Check: did the first experience deliver value? Was it confusing? Did it match expectations?
• Fix the empty state, simplify the flow, or set better expectations.

Repeated use failing: used a few times, not a habit

• Check: is it useful once or ongoing? Does it integrate into workflow? Any triggers to use again?
• Either add recurrence triggers or accept it's a point-solution feature.

Root Cause Analysis

• Wrong audience: built for users who aren't your core. Check WHO is using it.
• Wrong timing: introduced too early or too late in the user journey
• Incremental value too low: improvement over existing way isn't big enough to justify switching
• Too complex: requires too much learning or configuration for the value delivered
• Not integrated: works in isolation but doesn't connect to the user's workflow

Response Decision

MUST choose one:

• Double down: feature is valuable, problem is discovery or UX. Invest in visibility and ease.
• Refine: concept is right, execution needs work. Simplify or adjust target.
• Reposition: feature does something valuable but it's marketed or placed wrong.
• Sunset: low awareness, low first use, low repeat, no strong segment signal. Kill it.

Metrics to Track

• Adoption rate: % of eligible users who tried it at least once
• Activation rate: % of first-time users who reached the feature's aha moment
• Retention rate: % who used it again within 30 days
• Impact on product retention: do feature adopters retain better overall?

Anti-Patterns

• Assuming low adoption means the feature should be killed without diagnosing the stage
• Adding more features to compensate for poor adoption of existing ones
• Measuring adoption without segmenting by user type
• Shipping and forgetting — features need post-launch investment to drive adoption