Product-Led Growth skill 05 of 13

Feature Adoption SKILL.md

The Feature Adoption skill makes Claude, Codex or another AI agent drive use of the features that change retention and expansion. It compares retention of feature users with matched non-users, measures adoption funnels from exposed to expanded, diagnoses barriers, fixes setup and permissions, adds contextual triggers based on behaviour, educates, nudges carefully and measures impact with holdouts.

feature-adoption/SKILL.md, version 1.0.0, 1,500 words.

By Afeez Adebayo, Marketing, SEO & Growth Expert with 10+ years of experience.

Created 10 October 2026. Updated 10 October 2026. Free to use under CC BY 4.0.

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Activation gets a user to first value; feature adoption keeps them growing. Most products have features that strongly predict retention and expansion (integrations, automations, sharing, reporting) that many customers never discover. Teams ship features, announce them once and move on, while usage stays low. In product-led businesses, adoption of the right features is often the difference between a user who churns after three months and an account that expands for years.

This skill drives adoption of features that matter: identifying retention-linked features, measuring adoption funnels, finding barriers, designing contextual discovery, in-app guidance, education and nudges, and measuring the effect on retention and expansion.

Operating principles

Focus on features that change outcomes. Not every feature needs adoption; the ones linked to retention and expansion do.
Discovery in context. Introduce features when users face the problem they solve.
Measure the adoption funnel. Aware, tried, used again, used habitually.
Remove barriers before adding prompts. Confusing setup or permissions often block adoption more than awareness.
Prove the effect. Check whether adoption actually improves retention or revenue.

Adoption funnel

Stage	Definition	Typical barrier
Exposed	Saw the feature or a message about it	Hidden in menus
Tried	Used it once	Unclear value or setup effort
Repeated	Used it again within a set period	First experience disappointing
Habitual	Uses it regularly	Not part of the workflow
Expanded	Uses it across the team or account	Permissions, admin settings

When to use, and when not to

Use it after activation is in reasonable shape, when retention plateaus, when expansion is weak, after launching important features and when customers request features that already exist.

Don't use it for first-run onboarding (in-product-onboarding) or launch communications outside the product (the Product Marketing product-launch skill).

Inputs to ask for, in one message

Feature list with usage data.
Retention and expansion by feature usage.
Product analytics access and event tracking for key features.
Support tickets and feature requests mentioning existing features.
In-app messaging tools.
Customer interviews or survey results.

Raw SKILL.md file

.md

This is the exact file agents read, including the credit header and licence notice. Save it as feature-adoption/SKILL.md.

---
name: feature-adoption
description: "Drive adoption of features that change retention and expansion: impact analysis against matched non-users, adoption funnels from exposed to expanded, barrier diagnosis, fixes to setup and permissions, contextual triggers based on user behaviour, education, careful nudges and measurement with holdouts. Use whenever someone asks about feature adoption, low feature usage, feature discovery, driving usage of integrations or automations, in-app prompts for features, customers requesting features that already exist or improving retention through product usage."
license: CC-BY-4.0
metadata:
  author: Afeez Adebayo
  author_url: https://afeezadebayo.com
  author_linkedin: https://www.linkedin.com/in/afeez-adebayo/
  author_x: https://x.com/za_icon
  source: https://afeezadebayo.com/skills-md/marketing/product-led-growth/feature-adoption/
  category: marketing
  subcategory: product-led-growth
  version: "1.0.0"
  created: "2026-10-10"
  updated: "2026-10-10"
---

# Feature Adoption

Activation gets a user to first value; feature adoption keeps them growing. Most products have features that strongly predict retention and expansion (integrations, automations, sharing, reporting) that many customers never discover. Teams ship features, announce them once and move on, while usage stays low. In product-led businesses, adoption of the right features is often the difference between a user who churns after three months and an account that expands for years.

This skill drives adoption of features that matter: identifying retention-linked features, measuring adoption funnels, finding barriers, designing contextual discovery, in-app guidance, education and nudges, and measuring the effect on retention and expansion.

## Operating principles
1. **Focus on features that change outcomes.** Not every feature needs adoption; the ones linked to retention and expansion do.
2. **Discovery in context.** Introduce features when users face the problem they solve.
3. **Measure the adoption funnel.** Aware, tried, used again, used habitually.
4. **Remove barriers before adding prompts.** Confusing setup or permissions often block adoption more than awareness.
5. **Prove the effect.** Check whether adoption actually improves retention or revenue.

## Adoption funnel
| Stage | Definition | Typical barrier |
|---|---|---|
| Exposed | Saw the feature or a message about it | Hidden in menus |
| Tried | Used it once | Unclear value or setup effort |
| Repeated | Used it again within a set period | First experience disappointing |
| Habitual | Uses it regularly | Not part of the workflow |
| Expanded | Uses it across the team or account | Permissions, admin settings |

## When to use, and when not to
Use it after activation is in reasonable shape, when retention plateaus, when expansion is weak, after launching important features and when customers request features that already exist.

Don't use it for first-run onboarding (`in-product-onboarding`) or launch communications outside the product (the Product Marketing `product-launch` skill).

## Inputs to ask for, in one message
1. Feature list with usage data.
2. Retention and expansion by feature usage.
3. Product analytics access and event tracking for key features.
4. Support tickets and feature requests mentioning existing features.
5. In-app messaging tools.
6. Customer interviews or survey results.

## Workflow

### Step 1. Find retention-linked features
Compare retention and expansion for accounts that use each feature versus similar accounts that don't; check whether adoption comes before improved retention.

### Step 2. Measure adoption funnels
Exposed, tried, repeated, habitual and expanded for each priority feature.

### Step 3. Diagnose barriers
Recordings, interviews and support tickets: awareness, understanding, setup effort, permissions or poor fit.

### Step 4. Fix barriers first
Simplify setup, improve defaults, change permissions, improve the first-use experience.

### Step 5. Design contextual discovery
Trigger guidance when the user does something the feature helps with (for example exporting a report manually suggests scheduled reports).

### Step 6. Educate
Short videos, help articles, templates and webinars tied to the feature.

### Step 7. Nudge carefully
In-app messages, emails and success manager conversations for accounts that would benefit.

### Step 8. Measure effect
Adoption changes and their impact on retention and expansion, ideally with holdouts.

## Contextual trigger examples
| User behaviour | Feature to suggest | Message |
|---|---|---|
| Exports the same report weekly | Scheduled reports | "Want this report emailed every Monday?" |
| Copies data between tools | Integration | "Connect your CRM to sync this automatically." |
| Assigns many tasks manually | Automation rules | "Automate assignments for new tasks like these." |
| Shares screenshots of dashboards | Shared dashboards | "Share a live link instead of screenshots." |

## Feature impact analysis template
```
| Feature | Adoption rate | 6-month retention (users) | 6-month retention (non-users, matched) | Expansion difference | Priority |
| Integrations | 22% | 88% | 61% | +2 seats average | High |
| Custom fields | 40% | 70% | 66% | Small | Low |
```
Correlation isn't proof; confirm with experiments where possible.

## Barrier diagnosis
| Barrier | Signs | Fix |
|---|---|---|
| Awareness | Few users ever open the feature | Contextual discovery, better placement |
| Understanding | Users open it and leave quickly | Clearer copy, examples, short video |
| Setup effort | Many start setup, few finish | Simpler setup, defaults, one-click connections |
| Permissions | Members can't enable it | Admin prompts with team benefits |
| Poor first experience | Tried once, never again | Better defaults and templates |
| Poor fit | Segment doesn't need it | Stop pushing it to that segment |

## Nudge rules
- Target accounts that would benefit, based on behaviour.
- One feature nudge at a time per user.
- Stop nudging after adoption or two dismissals.
- Pair in-app prompts with a short help resource.
- Give success managers a list of high-value accounts not using key features.

## Metrics
| Metric | Why |
|---|---|
| Adoption funnel conversion | Where users stop |
| Repeat usage within 30 days | Real adoption, not curiosity |
| Accounts with key features adopted | Account health |
| Retention and expansion versus holdout | Business impact |
| Support tickets about the feature | Confusion and friction |

## Quarterly adoption review
Each quarter, choose two or three priority features, review their funnels, ship barrier fixes and discovery changes, and report the effect on retention and expansion. Retire features nobody uses and that don't help retention, so the product stays simpler for new users.

## Naming and placement
Features users can't find by name often look missing. Use the words customers use in support tickets and requests, place features where the related task happens and link to them from help articles and search inside the product.

Review naming whenever support tickets show users can't find a feature.

## Edge cases and judgement calls
| Situation | What to do |
|---|---|
| Feature used by power users only | Check whether it suits the broader audience before pushing it |
| Admin permissions block adoption | Prompt admins with the benefit for their team |
| Many features competing for attention | Prioritise two or three per quarter |
| Users request an existing feature | Fix discoverability and naming |
| Low adoption after launch | Interview non-adopters before adding more prompts |

## Handoffs to related skills
- First-run onboarding: `in-product-onboarding`
- In-app messages: `in-app-messaging`
- Education: `product-education`
- Expansion: `team-expansion-invites`
- Metrics: `plg-metrics`
- Retention analysis: the Growth Marketing `cohort-retention-analysis` skill

## Worked example
A CRM for small businesses found accounts using its email integration retained far better, but only 15% connected it. Recordings showed the setup page asked for technical server details most users didn't know. The team replaced manual setup with one-click sign-in for major email providers, added a contextual prompt when users logged emails manually, and recorded a two-minute video. Integration adoption roughly tripled over a quarter, and a holdout test showed better retention among accounts exposed to the new flow.

## Output format
```
# Feature adoption plan: <product>, <date>
## Retention-linked features (analysis)
## Adoption funnels for priority features
## Barriers and fixes
## Contextual discovery and education plan
## Nudges by channel
## Measurement and holdouts
```

## Common mistakes
- Pushing every feature equally.
- Announcing features once and moving on.
- Adding prompts before fixing setup problems.
- Measuring clicks on announcements instead of repeated use.
- Assuming correlation proves impact.

## Questions to ask before you plan
- Which features predict retention and expansion for your customers?
- Where in the adoption funnel do users stop?
- What makes a feature hard to start using?

## Self-check before you answer
- Are priority features chosen by impact on outcomes?
- Are barriers diagnosed and fixed first?
- Is discovery contextual?
- Is the effect on retention measured?

## Further reading
- Pendo product adoption resources: https://www.pendo.io/resources/
- Amplitude product analytics guides: https://amplitude.com/blog

---
Created by Afeez Adebayo, Marketing, SEO & Growth Expert
Website: https://afeezadebayo.com
LinkedIn: https://www.linkedin.com/in/afeez-adebayo/
X: https://x.com/za_icon
Source: https://afeezadebayo.com/skills-md/marketing/product-led-growth/feature-adoption/

Licence: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). You may copy, share and adapt this skill, including for commercial use, as long as you credit Afeez Adebayo and link to the source URL above. Keep this notice in any copy or adapted version.

Workflow

Step 1. Find retention-linked features

Compare retention and expansion for accounts that use each feature versus similar accounts that don't; check whether adoption comes before improved retention.

Step 2. Measure adoption funnels

Exposed, tried, repeated, habitual and expanded for each priority feature.

Step 3. Diagnose barriers

Recordings, interviews and support tickets: awareness, understanding, setup effort, permissions or poor fit.

Step 4. Fix barriers first

Simplify setup, improve defaults, change permissions, improve the first-use experience.

Step 5. Design contextual discovery

Trigger guidance when the user does something the feature helps with (for example exporting a report manually suggests scheduled reports).

Step 6. Educate

Short videos, help articles, templates and webinars tied to the feature.

Step 7. Nudge carefully

In-app messages, emails and success manager conversations for accounts that would benefit.

Step 8. Measure effect

Adoption changes and their impact on retention and expansion, ideally with holdouts.

Contextual trigger examples

User behaviour	Feature to suggest	Message
Exports the same report weekly	Scheduled reports	"Want this report emailed every Monday?"
Copies data between tools	Integration	"Connect your CRM to sync this automatically."
Assigns many tasks manually	Automation rules	"Automate assignments for new tasks like these."
Shares screenshots of dashboards	Shared dashboards	"Share a live link instead of screenshots."

Feature impact analysis template

| Feature | Adoption rate | 6-month retention (users) | 6-month retention (non-users, matched) | Expansion difference | Priority |
| Integrations | 22% | 88% | 61% | +2 seats average | High |
| Custom fields | 40% | 70% | 66% | Small | Low |

Correlation isn't proof; confirm with experiments where possible.

Barrier diagnosis

Barrier	Signs	Fix
Awareness	Few users ever open the feature	Contextual discovery, better placement
Understanding	Users open it and leave quickly	Clearer copy, examples, short video
Setup effort	Many start setup, few finish	Simpler setup, defaults, one-click connections
Permissions	Members can't enable it	Admin prompts with team benefits
Poor first experience	Tried once, never again	Better defaults and templates
Poor fit	Segment doesn't need it	Stop pushing it to that segment

Nudge rules

Target accounts that would benefit, based on behaviour.
One feature nudge at a time per user.
Stop nudging after adoption or two dismissals.
Pair in-app prompts with a short help resource.
Give success managers a list of high-value accounts not using key features.

Metrics

Metric	Why
Adoption funnel conversion	Where users stop
Repeat usage within 30 days	Real adoption, not curiosity
Accounts with key features adopted	Account health
Retention and expansion versus holdout	Business impact
Support tickets about the feature	Confusion and friction

Quarterly adoption review

Each quarter, choose two or three priority features, review their funnels, ship barrier fixes and discovery changes, and report the effect on retention and expansion. Retire features nobody uses and that don't help retention, so the product stays simpler for new users.

Naming and placement

Features users can't find by name often look missing. Use the words customers use in support tickets and requests, place features where the related task happens and link to them from help articles and search inside the product.

Review naming whenever support tickets show users can't find a feature.

Edge cases and judgement calls

Situation	What to do
Feature used by power users only	Check whether it suits the broader audience before pushing it
Admin permissions block adoption	Prompt admins with the benefit for their team
Many features competing for attention	Prioritise two or three per quarter
Users request an existing feature	Fix discoverability and naming
Low adoption after launch	Interview non-adopters before adding more prompts

First-run onboarding: in-product-onboarding
In-app messages: in-app-messaging
Education: product-education
Expansion: team-expansion-invites
Metrics: plg-metrics
Retention analysis: the Growth Marketing cohort-retention-analysis skill

Worked example

A CRM for small businesses found accounts using its email integration retained far better, but only 15% connected it. Recordings showed the setup page asked for technical server details most users didn't know. The team replaced manual setup with one-click sign-in for major email providers, added a contextual prompt when users logged emails manually, and recorded a two-minute video. Integration adoption roughly tripled over a quarter, and a holdout test showed better retention among accounts exposed to the new flow.

Output format

# Feature adoption plan: <product>, <date>
## Retention-linked features (analysis)
## Adoption funnels for priority features
## Barriers and fixes
## Contextual discovery and education plan
## Nudges by channel
## Measurement and holdouts

Common mistakes

Pushing every feature equally.
Announcing features once and moving on.
Adding prompts before fixing setup problems.
Measuring clicks on announcements instead of repeated use.
Assuming correlation proves impact.

Questions to ask before you plan

Which features predict retention and expansion for your customers?
Where in the adoption funnel do users stop?
What makes a feature hard to start using?

Self-check before you answer

Are priority features chosen by impact on outcomes?
Are barriers diagnosed and fixed first?
Is discovery contextual?
Is the effect on retention measured?

When to use this skill, and when not to

Use it after activation is in reasonable shape, when retention plateaus, when expansion is weak, after launching important features and when customers request features that already exist.

Most products have features that strongly predict retention, such as integrations, automations, sharing and reporting, that many customers never discover. Teams ship, announce once and move on while usage stays low.

Don’t use it for first-run onboarding (use In-Product Onboarding) or launch communications (use the Product Marketing Product Launches skill).

Example prompts

“Few customers use integrations. Fix adoption.”
“Which features predict retention for us?”
“Design contextual prompts for our automation feature.”
“Customers keep requesting features we already have.”
“Measure whether adoption improved retention.”

Example output

The skill returns a fixed structure. A shortened example for an imaginary site:

# Feature adoption plan: example small-business CRM, 12 October 2026

Finding: email integration users retain far better; only 15% connect it
Barrier: setup asked for technical server details
Fix: one-click sign-in for major providers; prompt when users log emails manually; 2-minute video
Result: adoption roughly tripled; holdout showed better retention

Barriers are fixed before any prompts are added, because confusing setup blocks adoption more often than lack of awareness.

What the agent needs from you

The skill asks for feature usage data, retention and expansion by feature use, product analytics for key features, support tickets and feature requests, in-app messaging tools and customer interviews or survey results.

How this skill hands off to related skills

Adoption connects to the set: Onboarding, In-App Messaging, Education, Expansion and Metrics, plus retention analysis with the Growth Marketing Cohort Retention skill.

How to run Feature Adoption step by step

Install the skill as feature-adoption/SKILL.md.
Find retention-linked features.
Measure adoption funnels.
Diagnose and fix barriers.
Add contextual discovery and education.
Measure impact with holdouts.

Situations this skill handles

Adoption problems vary. The skill handles these situations:

Situation	What the skill does
Power-user features	Checked for broad fit first
Admin permissions block use	Admin prompts with team benefits
Many competing features	Two or three per quarter
Requests for existing features	Naming and placement fixed
Low adoption after launch	Interviews before more prompts
Hard setup	One-click connections

What makes this skill different

Most adoption work adds announcements. This skill starts by proving which features matter for retention, finds where users stop in the adoption funnel and fixes the real barrier before adding contextual prompts.

It measures impact with holdouts, so the team knows adoption work changed outcomes.

Practical tips from real projects

Focus on features that change outcomes.
Discovery in context.
Remove barriers before adding prompts.
Prove the effect.

A worked example: a CRM for small businesses found accounts using its email integration retained far better, but only 15% connected it. Recordings showed the setup page asked for technical server details most users did not know. The team replaced manual setup with one-click sign-in for major email providers, added a contextual prompt when users logged emails manually and recorded a two-minute video. Integration adoption roughly tripled over a quarter, and a holdout test showed better retention among accounts exposed to the new flow.

The adoption funnel has five stages, each with a typical barrier. Users may never be exposed because the feature is hidden in menus. They may not try it because the value or setup is unclear. They may try once and stop because the first experience disappoints. They may not form a habit because it never becomes part of their workflow. And the account may not expand use because permissions or admin settings get in the way.

Barrier diagnosis matches signs to fixes. Few users opening a feature points to awareness, fixed with contextual discovery and better placement. Users opening and leaving quickly points to understanding, fixed with clearer copy, examples and short videos. Many starting setup and few finishing points to effort, fixed with simpler setup and defaults. Members unable to enable it points to permissions, fixed with admin prompts. Users who try once and never return need better defaults and templates. And segments that do not need the feature should simply stop being prompted.

Contextual triggers follow behaviour: users exporting the same report weekly see scheduled reports; users copying data between tools see the integration; users assigning many tasks manually see automation rules; users sharing dashboard screenshots see live sharing.

Impact analysis compares adoption rate, six-month retention of users and matched non-users and expansion differences for each feature, prioritising those with large gaps and confirming with experiments where possible.

Nudges target accounts that would benefit, show one feature at a time, stop after adoption or two dismissals, pair prompts with help resources and give success managers lists of high-value accounts missing key features.

Metrics cover adoption funnel conversion, repeat usage within 30 days, accounts with key features adopted, retention and expansion against holdouts and support tickets about the feature. Each quarter the team chooses two or three priority features, reviews their funnels, ships barrier fixes and discovery changes and reports the effect on retention and expansion, retiring features nobody uses that do not help retention so the product stays simpler for new users. Naming and placement matter more than expected: features users cannot find by name often look missing, so the skill uses the words customers use in support tickets and requests, places features where the related task happens and links to them from help articles and in-product search. When admin permissions block adoption, admins get prompts explaining the benefit for their team, and when adoption stays low after launch, non-adopters are interviewed before any more prompts are added.

Power-user features are checked for broad fit before being pushed to everyone, because some valuable features only suit advanced users.

Results are reviewed every quarter.

Changelog

1.0.0, 10 October 2026: first release.

Use, adapt and share this skill under CC BY 4.0: credit Afeez Adebayo and link to this page.

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