Experiments and lift
Run disciplined ad tests with Adwarp's change journal and lift analysis. Log a hypothesis before a change, measure before vs after with compute_lift, and read results with the confounds stated.
adwarp-experimentsOperate
- did that change help
- measure the lift
- log this test
- what tests are running
- was the new headline better
On this page
Ad accounts accumulate changes nobody can evaluate later because nobody wrote down what changed and why. Adwarp journals every change it makes automatically; this skill adds the discipline around it: hypotheses going in, honest verdicts coming out.
How to reach Adwarp
Prefer the Adwarp MCP tools if loaded: log_ad_change, list_ad_changes, compute_lift. CLI: adwarp changelog list, adwarp changelog add, adwarp lift <level> <entityId> [-w days]. The journal lives at ~/.adwarp/changelog.jsonl. Changes made directly in Campaign Manager are invisible until logged by hand; that is the most common reason a lift read comes back empty.
Starting a test
- Hypothesis first, 1 sentence: "outcome-led headline will beat feature-led on cost per conversion."
- Make the change. Copy changes on LinkedIn are recreate, not edit (
delete_ad+create_image_ad, drafts, user confirms deletes); Adwarp journals these automatically. Changes made in Campaign Manager getlog_ad_changewith the entity, field, and after-value. - Label it (
-l "outcome-led headline test") and tag related changes so the test reads as 1 unit. - 1 change per entity at a time where possible. 2 simultaneous changes on the same entity cannot be separated afterwards; if it happened anyway, say so in the read-out.
Reading a test
compute_lift compares the window before each recorded change against the same window after (default 14 days) and reports per-metric deltas: CTR, CPC, conversions, conversion rate, cost per conversion.
Rules for an honest read:
- Wait for volume, not just days. No verdict without ~1,000 impressions or a few conversions on each side; otherwise report "inconclusive, recheck on <date>".
- A
partialafter-window means the change is recent; present it as interim. - State the confounds every time: seasonality, LinkedIn's learning phase restarting after an edit (early post-change days usually dip), concurrent budget or audience changes. This is a directional pre/post comparison, never proof.
- Verdicts are exactly 1 of: better, worse, inconclusive.
The test registry
When asked "what tests are running", list_ad_changes is the answer: recent changes still inside their after-window (open tests), completed windows nobody has read yet, and unlabeled changes worth labeling before they are forgotten.
Report format
Per change: label, date, entity, before vs after on the 4 or 5 KPIs that matter, verdict, confounds. Close with 1 portfolio line: what the wins suggest testing next.