Adoption Funnel

Adoption Funnel

Overview

You shipped it, but nobody's using it. Feature adoption analysis helps you understand why — is it discoverability, comprehension, motivation, or the wrong feature? This skill covers funnel analysis, activation metrics, and adoption improvement tactics.

When to Use

• A shipped feature has < expected usage
• You need to understand where users drop off in the adoption journey
• Designing activation flows for new features
• You want to improve feature engagement systematically
• Don't use for: core product adoption (broader analysis needed), one-day-old features (too early to measure)

Instructions

1. Define the Adoption Funnel

Map the user's journey from awareness to ongoing use:

1. Awareness: User knows the feature exists
2. Discovery: User finds the feature
3. Understanding: User knows what it does and why to use it
4. First use: User tries the feature for the first time
5. Value realization: User gets value from the feature
6. Continued use: User comes back and uses it again

2. Measure Each Stage

For each stage, instrument and measure:

• Awareness: % of users who saw the announcement/in-app notification
• Discovery: % who navigated to the feature's location
• Understanding: % who clicked/opened the feature
• First use: % who completed the primary action
• Value realization: % who reached a success milestone
• Continued use: % who used it again within 7 days

3. Identify the Drop-off

Where do most users disappear?

• 🔴 Awareness → Discovery: Visibility problem (need better placement/announcement)
• 🔴 Discovery → Understanding: Comprehension problem (need better labeling/onboarding)
• 🔴 Understanding → First use: Motivation problem (need better value prop or lower friction)
• 🔴 First use → Value realization: UX problem (feature is hard to use)
• 🔴 Value → Continued use: Retention problem (feature doesn't deliver ongoing value)

4. Design Improvements

Based on the drop-off:

• Visibility: In-app announcement, badge/tooltip, email campaign
• Comprehension: Better copy, short video, tooltip on first hover
• Motivation: Value prop showcase, "users who do X get Y% better results"
• UX: Reduce steps, add defaults, improve feedback
• Retention: Email reminders, progress tracking, social proof

5. Measure Impact

• A/B test the improvement against current state
• Track: Did the drop-off improve? By how much?
• Watch for trade-offs: Did improving one feature hurt another?

Sample Output

Feature: Weekly Email Digest Target adoption: 30% of active users enable it Current: 8%

Funnel: | Stage | % of users | Drop-off | |-------|-----------|----------| | Awareness (saw announcement) | 65% | - | | Discovery (found setting) | 35% | 🔴 -46% | | Understanding (understood it) | 30% | - | | First use (enabled) | 8% | 🔴 -73% | | Value (opened first digest) | 6% | - |

Diagnosis: Users know about it but don't enable it. Motivation problem.

Fix: Change from opt-in to opt-out with smart defaults. Show a preview of what the digest would look like for their team. Target: increase enablement from 8% to 25%.

Verification Checklist

• [ ] Funnel stages defined (awareness → discovery → understanding → first use → value → continued)
• [ ] Each stage instrumented with a measurable metric
• [ ] Primary drop-off stage identified
• [ ] Root cause hypothesis for the drop-off
• [ ] Improvement designed and targeted at the specific drop-off stage
• [ ] Success criteria defined before running the fix
• [ ] Counter-metrics identified (what shouldn't break while improving adoption)

1. Measuring too early — Feature adoption takes time. Measure at 30 days post-launch, not 3 days
2. Assuming everyone should use it — Not every feature is for every user. That's OK
3. Ignoring the "don't need it" segment — Some users genuinely don't need the feature. Segment them out
4. Vanity metrics — "Page views" of a feature != "using" the feature. Measure meaningful actions
5. One-size-fits-all funnel — Different user segments may adopt differently. Analyze by segment

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: uthumany
• Source: uthumany/uthy-legacy-os
• License: MIT
• Homepage: https://uthumany.github.io/uthy-legacy-os/

Install and usage instructions live in the source repository linked above.