meta-ads-diagnostics

name	meta-ads-diagnostics
description	Diagnose Meta Ads delivery and performance problems — Learning Phase stalls, creative fatigue, audience overlap, CPM spikes, and Advantage+ vs manual structure. Use when a Meta campaign underperforms, delivery is limited, or CPMs and frequency are climbing.

Meta Ads Diagnostics

Find the real cause before touching the campaign. Most Meta "performance problems" are one of four things: learning starvation, creative fatigue, audience self-competition, or a post-click issue.

Confirm the account: list_connected_accounts, then pull data with pull_meta_ads_performance(days=30, level="ad-set") and at ad level for creative checks.

1. Check Learning Phase first

Meta needs roughly 50 optimization events per ad set per week to stabilize. Under that, CPA runs volatile and high, and no other diagnosis is reliable.

• Learning resets on: budget moves over ~20%, bid changes, new creatives, targeting edits, optimization-event changes, or a pause longer than 7 days.
• Stuck in Learning Limited: consolidate ad sets so events pool, broaden the audience, raise budget toward 10x target CPA, or optimize to a higher-volume event (Add to Cart instead of Purchase).
• If it launched or changed within the last 7 days: do not touch it. Wait.

2. Test for creative fatigue

Pull daily impressions, reach, frequency, and CTR for the last 30 days. Compute fatigue as the CTR decline from the first full week to now:

• Under 15% decline: healthy.
• 15-30%: early fatigue, prepare a refresh.
• 30-50%: rotate creatives now.
• Over 50%: pause and replace.

Frequency confirms it: prospecting past ~3.0 or retargeting past ~5.0 with falling CTR is fatigue, not audience quality. Refresh in proportion — copy swap for mild decline, new visual angle for moderate, new concept or format change for severe. Build replacements with the creative skill and validate with creative-testing.

3. Check audience overlap

Symptoms: CPMs rising on multiple ad sets at once, frequency climbing faster than spend justifies, two ad sets going Learning Limited together, one ad set starving the other.

• Under ~10% overlap: ignore. 10-25%: watch CPMs. Over 25%: exclude one audience from the other or merge the ad sets. Over 50%: consolidate immediately.
• Consolidation into fewer, broader ad sets usually beats exclusion lists — it also fixes learning starvation at the same time.

4. Respect the breakdown effect

Per-placement or per-demo breakdowns lie. Meta optimizes holistically, so a placement with an ugly standalone CPA may be feeding cheap assists to the placements that convert. Never cut a placement off breakdown data alone — measure it properly with measure_incrementality or leave it on. Give the algorithm 14+ days with all placements before judging.

5. Advantage+ vs manual structure

• Advantage+ Shopping fits: product catalog, 100+ conversions/week, broad targeting, simple purchase goals. Set an existing-customer budget cap (20-30%) and feed it 5-10 diverse creatives.
• Manual fits: B2B lead gen, narrow audiences, heavy exclusions, under ~50 conversions/week, or when you need creative-level budget control.
• When testing ASC against manual, run both at equal budget for 14+ days and compare CPA, ROAS, and new-customer share before consolidating.

6. Decision tree for the rest

In order: Learning Phase → frequency over 3.0 (fatigue) → CPM rising with stable CTR (saturation or auction pressure; broaden) → CTR fine but CVR down (landing page; see campaign-preflight) → quality/conversion ranking below average (creative or post-click) → none of the above (tracking; audit with conversion-tracking and verify pixel destinations with get_pixel_destinations).

Apply fixes with pause_campaign, update_campaign_budget, or upload_creative, and confirm each write before shipping. Kill/scale calls go through kill-scale-rules.

Rules

• Never diagnose a campaign still in Learning Phase; fix event volume first.
• One change at a time — every edit can reset learning.
• Breakdown data is directional, not causal. Incrementality beats breakdowns.
• State the data window and event counts behind every recommendation.