1. Collect audience evidence
In a ad copy program, collect audience evidence before advancing. Document the hypothesis, responsible owner, input evidence, accepted output, deadline and stop condition so the decision can be reproduced.
The output of this ad copy step should be understandable to a reviewer who did not create the campaign or page. That discipline reduces hidden assumptions and improves future iteration.
2. Choose one message job
In a ad copy program, choose one message job before advancing. Document the hypothesis, responsible owner, input evidence, accepted output, deadline and stop condition so the decision can be reproduced.
The output of this ad copy step should be understandable to a reviewer who did not create the campaign or page. That discipline reduces hidden assumptions and improves future iteration.
3. Write the verified promise
In a ad copy program, write the verified promise before advancing. Document the hypothesis, responsible owner, input evidence, accepted output, deadline and stop condition so the decision can be reproduced.
The output of this ad copy step should be understandable to a reviewer who did not create the campaign or page. That discipline reduces hidden assumptions and improves future iteration.
4. Add proof or specificity
In a ad copy program, add proof or specificity before advancing. Document the hypothesis, responsible owner, input evidence, accepted output, deadline and stop condition so the decision can be reproduced.
The output of this ad copy step should be understandable to a reviewer who did not create the campaign or page. That discipline reduces hidden assumptions and improves future iteration.
5. State one next action
In a ad copy program, state one next action before advancing. Document the hypothesis, responsible owner, input evidence, accepted output, deadline and stop condition so the decision can be reproduced.
The output of this ad copy step should be understandable to a reviewer who did not create the campaign or page. That discipline reduces hidden assumptions and improves future iteration.
6. Adapt to the format
In a ad copy program, adapt to the format before advancing. Document the hypothesis, responsible owner, input evidence, accepted output, deadline and stop condition so the decision can be reproduced.
The output of this ad copy step should be understandable to a reviewer who did not create the campaign or page. That discipline reduces hidden assumptions and improves future iteration.
7. Review claims and policy
In a ad copy program, review claims and policy before advancing. Document the hypothesis, responsible owner, input evidence, accepted output, deadline and stop condition so the decision can be reproduced.
The output of this ad copy step should be understandable to a reviewer who did not create the campaign or page. That discipline reduces hidden assumptions and improves future iteration.
8. Create meaningful variants
In a ad copy program, create meaningful variants before advancing. Document the hypothesis, responsible owner, input evidence, accepted output, deadline and stop condition so the decision can be reproduced.
The output of this ad copy step should be understandable to a reviewer who did not create the campaign or page. That discipline reduces hidden assumptions and improves future iteration.
9. Run a controlled test
In a ad copy program, run a controlled test before advancing. Document the hypothesis, responsible owner, input evidence, accepted output, deadline and stop condition so the decision can be reproduced.
The output of this ad copy step should be understandable to a reviewer who did not create the campaign or page. That discipline reduces hidden assumptions and improves future iteration.
10. Archive the learning
In a ad copy program, archive the learning before advancing. Document the hypothesis, responsible owner, input evidence, accepted output, deadline and stop condition so the decision can be reproduced.
The output of this ad copy step should be understandable to a reviewer who did not create the campaign or page. That discipline reduces hidden assumptions and improves future iteration.