Experimentation
Turn marketing ideas into controlled tests with hypotheses, guardrails, and decisions.
Key takeaways
- Experimentation turns opinion into a testable claim and prevents scaling campaigns before the team understands why something works.
- The experiment brief fixes a hypothesis, audience, variable, metric, guardrail, and a decision rule (scale, iterate, hold, or stop) before launch.
- Experiment type follows the bottleneck: message, offer, channel, creative, or funnel test.
- The cadence ties tests to current OKRs, runs long enough to avoid noise, decides by the predefined rule, and archives results in a learning log.
- Avoid testing too many variables at once or calling a test successful on vanity metrics.
Experimentation keeps marketing honest. It turns opinion into a testable claim and prevents the team from scaling campaigns before they understand why something works.
Experiment Brief
| Field | Description |
|---|---|
| Hypothesis | If we do X for audience Y, metric Z will improve because reason R |
| Audience | Segment, channel, and exclusions |
| Variable | Message, offer, creative, page, sequence, or audience |
| Metric | Primary and secondary metrics |
| Guardrail | Budget, brand risk, legal risk, quality threshold |
| Decision rule | Scale, iterate, hold, or stop |
Experiment Types
| Type | Use when |
|---|---|
| Message test | The audience is known but promise is uncertain |
| Offer test | Traffic exists but conversion is weak |
| Channel test | The team is unsure where demand lives |
| Creative test | The offer works but attention is low |
| Funnel test | Interest exists but progression is slow |
Operating Cadence
- Review the backlog and select tests tied to current OKRs.
- Write the hypothesis before launch.
- Run the test long enough to avoid reacting to noise.
- Decide using the rule defined in advance.
- Archive the result in a learning log.
Anti-Patterns
- Testing too many variables at once.
- Calling a test successful because vanity metrics improved.
- Repeating experiments without updating the hypothesis.
- Ignoring sales or customer success feedback when evaluating quality.