Field Note: Good Case Studies Start With One Metric
I have covered platforms like this for more than seven years, and Pick the decision metric first — everything else is decoration.
The useful question is not whether AI shows up in the stack. It already does. The question is who owns taste, safety, and measurement when the platform rewrites the asset.
- Write the hypothesis first
- Capture baselines before launch
- Separate correlation from causality
| Signal | Detail |
|---|---|
| Metric | Before → After / Signal |
| Shopping share | 22.1% → 28.4% |
| DSP Prime Day | 17.9% → 25.5% |
| Title recognition | ~33% knew Ted Lasso source |
Pros
- Clear decision metric
- Time-bound observation window
- Named data source
Cons
- Short windows can mislead
- Platform data is partial
- Creative confounders
If you only remember one habit from this piece, make it this: screenshot the served creative, not just the uploaded master. What ran is the truth; what you approved in the deck is often a polite fiction.
I still talk to strategists who celebrate a launch week and then skip the autopsy. That is how budgets vanish. Schedule the review before the campaign starts, while everyone still agrees what success meant.
Source: Ecommerce Times. Numbers and product claims above come from that reporting; interpretation is mine.