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Experiments

Skill chanktb/claude-google-ads/skills/experiments

Find where your Google Ads budget leaks, cut waste, and scale your ecommerce store safely — 15 skills: audit, money-leak diagnostics, plan, campaign builders, safe push, monitor, optimize.

Install
npx -y skills add chanktb/claude-google-ads --skill experiments

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Designs disciplined A/B tests for Google Ads — campaign-level splits (bidding, brand exclusion, new- customer mode, split structure) and asset-group/ad-group-level splits — changing one variable at a time with a feasibility check, clear success criteria, and minimum runtime. Prevents "testing by spawning random campaigns". Reads account-context.yaml. Use when the user says "experiment", "A/B test", "split test", "which works better", "test budget/bidding/audience".

SKILL.md

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Google Ads — Experiments

Turn a "let's test X" into a clean experiment: one variable, enough volume to learn something, a defined readout. This is how the split menus in the builders get validated — not by guessing.

Model dispatch (run cheap, decide expensive) — see ${CLAUDE_PLUGIN_ROOT}/references/model-tier-dispatch.md

  • Scout (haiku) — STEP 2 significance.py run.
  • Routine (sonnet) — pulling conversions-per-arm estimates / recent volume for the feasibility check. Dispatch as general-purpose; return raw numbers.
  • Judge (main session) — STEP 1 variable choice, STEP 3 design + decision rule, STEP 4 platform mapping, STEP 5 readout call (against the pre-stated rule, not early noise). The power math is mechanical; designing a test that can conclude is judgment.

STEP 1 — Pick ONE variable

From the catalog (campaign-level: bidding strategy, tROAS level, brand-exclusion on/off, new-customer mode, split structure A vs B from ${CLAUDE_PLUGIN_ROOT}/references/pmax-split-strategies.md; ad-group/asset-group-level: copy theme, audience signal, landing page). Change exactly one — never bundle variables (you won't know what moved).

STEP 2 — Feasibility check (don't run a test that can't conclude)

Using ${CLAUDE_PLUGIN_ROOT}/references/forecasting-and-benchmarks.md, estimate conversions per arm over the planned runtime. If each arm won't accumulate enough conversions to detect a meaningful difference, say so and either: extend the runtime, increase budget, pick a higher-volume variable, or skip the test. A test too small to reach significance is worse than no test — it invites false conclusions. Runnable check: python ${CLAUDE_PLUGIN_ROOT}/skills/experiments/scripts/significance.py --conv-per-arm N [--mde 0.15] → smallest detectable lift + whether the run is powered for your target effect.

STEP 3 — Design

  • Control vs variant, 50/50 split, one variable.
  • Primary success metric (e.g. ROAS, CPA, conversions) + guardrail metrics (don't win on CPA while tanking volume).
  • Minimum runtime (cover learning + at least 2-4 weeks; avoid mid-experiment changes).
  • Pre-state the decision rule: what result ships the variant, what reverts it.

STEP 4 — Map to the platform

Prefer native Google Ads Experiments / campaign drafts where the campaign type supports them; otherwise describe the manual A/B setup. Note PMax's experiment support and limits for the chosen variable.

STEP 5 — Readout criteria

When to call it: enough volume + runtime elapsed. Judge against the pre-stated rule and significance, not a gut read of early noise. Output: winner, confidence, and the next action (ship / revert / iterate). Respect the change-event cooldown when applying the result via pusher.

Guardrails

  • One variable at a time. No mid-flight changes. No calling a winner before the runtime/volume threshold.
  • Honor margin_tiers when the metric is ROAS (compare within a tier, not across).

To build / refine later

  • Significance calculator (scripts/significance.py). Done.

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