Toolbox

Growth Experimentation Framework

A data-driven cycle for testing adoption tactics.

What it is

A framework for running structured experiments on design system adoption, inspired by growth hacking methodology.

When to use

When you need to prove which adoption tactics work and which waste effort.

How to use

Pull current adoption data: dashboard metrics, support tickets, feedback channel themes
Write 3 hypotheses in format: 'If we [change], then [metric] will [improve X%] because [reason]'
Score each hypothesis: Impact (1-10), Confidence (1-10), Effort (10=easy) — sum and rank
Run top hypothesis for 2 weeks: define success metric upfront, measure baseline first
Document result: did it hit target? What surprised us? Scale, iterate or kill
Add winning tactics to adoption playbook, add failed experiments to 'tried and rejected' doc

Before you start

Prerequisites and when to skip this.

Prerequisites

Adoption dashboard exists with baseline metrics
Team has capacity to run 1 experiment per month
Leadership supports experimentation (failure is acceptable)

When NOT to use this

No baseline metrics (you can't measure improvement without a starting point)
Team is underwater with support tickets (fix the fire before optimizing)
Adoption problem is actually a product problem (system is fine, product isn't using it)

Consider instead

Obvious quick wins exist: Just do them — experimentation is for uncertain bets
Leadership wants results now: Pick one high-confidence change, ship it, skip the framework

Downloads

Experimentation Framework (PDF)Experimentation Framework (accessible HTML)

Adoption PlaybookGrowth loops for DS adoption

The share link includes source tracking so LinkedIn traffic stays measurable in analytics.

Share on LinkedIn

Toolbox

Growth Experimentation Framework

A data-driven cycle for testing adoption tactics.

What it is

A framework for running structured experiments on design system adoption, inspired by growth hacking methodology.

When to use

When you need to prove which adoption tactics work and which waste effort.

How to use

Pull current adoption data: dashboard metrics, support tickets, feedback channel themes
Write 3 hypotheses in format: 'If we [change], then [metric] will [improve X%] because [reason]'
Score each hypothesis: Impact (1-10), Confidence (1-10), Effort (10=easy) — sum and rank
Run top hypothesis for 2 weeks: define success metric upfront, measure baseline first
Document result: did it hit target? What surprised us? Scale, iterate or kill
Add winning tactics to adoption playbook, add failed experiments to 'tried and rejected' doc

Before you start

Prerequisites and when to skip this.

Prerequisites

Adoption dashboard exists with baseline metrics
Team has capacity to run 1 experiment per month
Leadership supports experimentation (failure is acceptable)

When NOT to use this

No baseline metrics (you can't measure improvement without a starting point)
Team is underwater with support tickets (fix the fire before optimizing)
Adoption problem is actually a product problem (system is fine, product isn't using it)

Consider instead

Obvious quick wins exist: Just do them — experimentation is for uncertain bets
Leadership wants results now: Pick one high-confidence change, ship it, skip the framework

Downloads

Experimentation Framework (PDF)Experimentation Framework (accessible HTML)

Adoption PlaybookGrowth loops for DS adoption

The share link includes source tracking so LinkedIn traffic stays measurable in analytics.

Share on LinkedIn

Growth Experimentation Framework

What it is

When to use

How to use

Before you start

Downloads

Related

Growth Experimentation Framework

What it is

When to use

How to use

Before you start

Downloads

Related

What it is

When to use

How to use

Before you start

Downloads

Related

Share this resource

What it is

When to use

How to use

Before you start

Downloads

Related

Share this resource