campaign-analyzer

name	campaign-analyzer
description	Analyze paid media performance across dimensions, funnels, cohorts, and channels — turning data into actionable insights and business narratives.

Process

1. Clarify the question — What specific insight is the user trying to extract? ("Why is CPA rising?" vs "What's the LTV by channel?")
2. Select analysis type — Match the question to the right framework (pulse, strategic, dimensional, funnel, cohort, incrementality, cross-channel)
3. Gather required data — Request exports needed for the analysis
4. Run the analysis — Apply the specific method
5. Identify the story — What happened, why it happened, what to do about it
6. Deliver findings — Structured output with insights, not just metrics

Analysis Type Selector

Question	Analysis Type	Time Horizon
"Is anything broken right now?"	Daily pulse monitoring	1-7 days
"How are we trending this week/month?"	Weekly/monthly strategic review	7-30 days
"Where is performance coming from/going to?"	Multi-dimensional analysis	30-90 days
"Where are users dropping off?"	Funnel analysis	7-30 days
"How valuable are acquired customers over time?"	Cohort & LTV analysis	30-365+ days
"Is this channel actually driving incremental results?"	Incrementality testing	30-90 days
"How do channels compare and work together?"	Cross-channel analysis	30-90 days

Daily Pulse Monitoring

What to check:

• Cost, impressions, clicks, conversions, spend pacing vs budget
• Sudden metric changes (CPC spikes, conversion drops, budget exhaustion)
• Meta: learning phase status per ad set
• Disapprovals, policy flags, payment issues

Alert thresholds: Automated alerts for >20% deviation from 7-day averages on key metrics.

What NOT to do: Don't optimize during daily pulse. Daily review is for catching emergencies, not making strategic changes. Save optimization decisions for weekly reviews.

Cadence insight (Supermetrics research): Most successful performance marketers prioritize basic metrics (cost, impressions, clicks, conversions) for daily decisions. Deeper analysis (frequency, LTV, attribution) is purposeful, not constant.

Weekly/Monthly Strategic Review

Key metrics by platform:

Google Ads	Meta Ads
CTR, CPC, CPA, ROAS	CPM, CPC, CPA, ROAS
Conversion rate	Frequency, Conversion rate
Impression share	Reach, Delivery status
Quality Score trends	Learning phase status
Search term movement	Creative performance

Review structure:

1. Scaling candidates — Campaigns with strong ROAS + available impression share (Google) or frequency headroom (Meta) → recommend budget increases
2. Pause/pullback candidates — Campaigns with high CPA + declining trend → recommend reductions
3. Creative performance — Which ads/creatives are winning, which are fatiguing
4. Trend direction — Is the account improving, stable, or declining over trailing 4 weeks?

Multi-Dimensional Performance Analysis

Slice performance across dimensions to find hidden drivers.

Dimensions to Analyze

Dimension	What to Look For
Device type	Mobile vs desktop vs tablet CPA/ROAS differences
Geography	Regional performance variance (country, state, DMA)
Time of day	Hour-by-hour conversion rate patterns
Day of week	Day-by-day performance patterns
Audience segment	Segment-level CPA/ROAS comparison
Match type (Google)	Broad vs phrase vs exact performance
Placement (Meta)	Feed vs Stories vs Reels vs Audience Network
Creative concept	Concept-level performance patterns
Landing page	Landing page-level conversion rate
Funnel stage	TOF vs MOF vs BOF performance

Cross-Tab Analysis

Combine dimensions to find non-obvious patterns:

Cross-Tab	What It Reveals	Example
Device × Time of day	When mobile converts vs desktop	Mobile converts 3x better in evening hours
Audience × Creative	Which creatives resonate with which audiences	UGC wins with Lookalike, studio wins with cold broad
Geography × Product	Regional product preferences	Coastal states prefer product A, interior prefer product B
Day × Creative concept	Which concepts work when	Emotional creative wins Sunday, rational wins Tuesday
Placement × Video length	Optimal length per placement	15s wins Stories, 30s wins Feed

Red Flags to Watch

• One dimension dominating cost but not conversions → Budget misallocation
• Large variance across segments → Opportunity to isolate winners
• Hidden loss leaders → Specific segments dragging average down

Funnel Analysis

Map every stage from impression to final conversion.

Standard Funnel Stages

Stage	Metric	Benchmark to Check
Impression	Impressions	Reach sufficient for audience?
Click	CTR, clicks	Industry CTR benchmarks
Landing page view	Landing page views	Bounce rate, load speed
Micro-conversion	AddToCart, video 50%, scroll depth	Stage-specific
Macro-conversion	Purchase, Lead, Signup	Target CPA / ROAS

Calculate stage-by-stage conversion rates. Identify the biggest drop-off points — that's where to focus optimization.

Drop-Off Diagnostics

Drop-Off Point	Likely Cause	Fix
Impression → Click	Low CTR	Ad copy, ad creative, targeting
Click → Landing Page View	Slow page, 404s, disconnected UX	Page speed, fix broken flows
Landing Page View → Add to Cart	Landing page message mismatch, bad UX	Rewrite headlines, improve CTA
Add to Cart → Checkout	Friction, unexpected costs	Simplify checkout, show total costs
Checkout → Purchase	Payment issues, trust concerns	Add trust signals, multiple payment options

Meta-specific (June 2025): Instant Experience is no longer counted as a landing page view. If your Meta funnel metrics suddenly showed a drop in mid-2025, this is likely why. Adjust funnel definitions accordingly.

Cohort & Lifetime Value Analysis

Cohort Windows

Track customer cohorts at standard intervals:

• Day 1, 7, 14, 30 (short-term retention)
• Day 30, 60, 90 (medium-term)
• Day 180, 365 (long-term LTV)

Metrics to Track per Cohort

Metric	What It Tells You
Repeat purchase rate	Customer stickiness
Average order value over time	Purchase behavior evolution
Time to second purchase	Engagement health
LTV (revenue per cohort)	True customer value
CAC : LTV ratio	Channel/campaign profitability

LTV by Acquisition Source

Compare LTV by channel, campaign, and audience segment:

Channel	CPA	30-day LTV	90-day LTV	365-day LTV	LTV:CAC
Google Search (brand)	$45	$180	$250	$380	8.4x
Meta ASC	$52	$145	$210	$340	6.5x
Google PMax	$58	$120	$180	$290	5.0x
Meta Prospecting	$65	$95	$150	$250	3.8x

What this reveals: Channels with higher short-term CPA may still be profitable if LTV is strong. Channels with low CPA but poor LTV are actually expensive.

POAS > ROAS

POAS (Profit On Ad Spend): Gross profit ÷ ad spend. Better than ROAS for ecommerce because it factors in actual product margin.

Example: $500 ROAS looks great until you learn the product has 8% margin → true profit is $40 per $100 spent. Another channel at 300% ROAS with 40% margin returns $120 profit per $100 spent.

Implementation: Feed LTV data back into value-based bidding. Use Google's Maximize Conversion Value + tROAS and Meta's ROAS Goal with dynamic conversion values.

Incrementality & Lift Testing

Platform-reported ROAS often overstates channel impact (credit-grabbing from organic demand).

Methods

Method	How	When
Google Conversion Lift	Holdout test within Google Ads	2025 update: lower spend/conversion minimums. Can run at campaign level or manager account level
Meta Conversion Lift	Holdout group excluded from ads	Need sufficient volume; request via Meta rep
Geo-based lift test	Turn off campaigns in specific regions, compare	Need multi-region footprint; works cross-platform
MMM (Marketing Mix Modeling)	Statistical model of all channels vs outcomes	Larger budgets; Google's Meridian is open-source
Holdout experiments	Target cohort gets no ads, measure difference	Requires first-party audience control

Questions Incrementality Answers

• "If I turned off YouTube, what happens to non-brand search?"
• "If I turned off Meta prospecting, does total revenue change?"
• "Is brand search capturing demand created by upper-funnel channels?"
• "Are retargeting conversions incremental or would they have happened anyway?"

MMM + incrementality calibration: Google's Meridian (open-source MMM, 2025) combines granular media signals with incrementality calibration. The right way to do cross-channel budget planning at scale.

Cross-Channel Analysis

Marketing Efficiency Ratio (MER)

MER = Total Revenue ÷ Total Ad Spend (all channels)

• Blended metric. Resilient to attribution changes
• Goal: maintain or improve over time
• Doesn't tell you which channel deserves credit, but tells you overall efficiency

Marginal ROAS

As you increase spend in a channel, each additional dollar produces less return (diminishing returns). The question: at what spend level does marginal ROAS drop below target?

Method:

1. Plot spend vs revenue for a channel over 6-12 months
2. Fit a diminishing returns curve
3. Find the spend level where marginal ROAS = target ROAS
4. That's your efficient frontier for that channel

Channel Role Assignment

Not every channel should optimize to the same metric. Assign roles:

Role	Primary Metric	Example Channels
Awareness	Reach, CPM, Ad Recall Lift	YouTube Demand Gen, Meta Awareness
Consideration	CTR, engagement, ViewContent	Meta Prospecting, Google Display
Conversion	CPA, ROAS	Google Search Brand, Meta Retargeting
Full-funnel scale	MER, blended ROAS	Google PMax, Meta ASC

2025 Google feature: Import cost data from Meta, TikTok, Pinterest directly into GA4 for cross-platform comparison in one place.

For multi-channel budget reallocation, use the ad-spend-allocator skill.

Output Format

## Campaign Performance Analysis

### Question Analyzed
[Specific question the analysis addresses]

### Data Sources
- [Platforms]
- [Date range]
- [Segments included]

### Key Findings
1. **[Finding 1]** — [Data point] — [Why this matters]
2. **[Finding 2]** — [Data point] — [Why this matters]
3. **[Finding 3]** — [Data point] — [Why this matters]

### Supporting Data
[Tables and metrics supporting findings]

### Narrative
[Story: what happened, why it happened, what to do]

### Recommended Actions
| Priority | Action | Impact | Owner | Timeline |
|----------|--------|--------|-------|----------|
| 1 | [action] | [expected impact] | [who] | [when] |

### Data Confidence & Caveats
- [What the data supports reliably]
- [What needs more information or isn't certain]

Example

Input: "Our ecommerce account's blended ROAS dropped from 3.8x to 2.9x over the last quarter. Google is steady, Meta declined hard. I have 90 days of data by channel, campaign, and creative. Help me figure out what's happening."

Output:

Campaign Performance Analysis

Question Analyzed

Why did blended ROAS decline from 3.8x to 2.9x in Q1 2026, and which channel/campaign/creative drove the decline?

Data Sources

• Google Ads + Meta Ads exports
• 90-day window (January–March 2026)
• Segmented by channel, campaign, creative

Key Findings

1. Meta drove 85% of the blended decline — Google ROAS held at 4.1x (down from 4.2x — flat). Meta ROAS dropped from 3.5x to 2.1x (-40%). Blended decline is almost entirely Meta
2. Meta decline is creative fatigue, not audience or tracking — Top 3 creatives running 50+ days each. Average creative frequency climbed from 3.1 to 5.8 over 90 days. Meta research: 45% conversion drop at 4+ exposures
3. CPM inflation contributed ~15% of Meta decline — Meta average ad price up 9% in 2025. For this account, CPMs rose from $12.40 to $14.20 (+15%). This is real cost pressure, not just creative failure
4. Google is stable because Smart Bidding is managing efficiency — PMax and Search are both on tROAS with regular n-gram review. No similar discipline on Meta creative

Supporting Data

Channel-level ROAS trend:

Channel	Jan	Feb	Mar	Change
Google Search	4.5x	4.3x	4.0x	-11%
Google PMax	3.8x	3.9x	4.1x	+8%
Google blended	4.2x	4.1x	4.1x	-2%
Meta ASC	3.6x	2.9x	2.2x	-39%
Meta Retargeting	5.2x	3.8x	2.4x	-54%
Meta blended	3.5x	2.8x	2.1x	-40%
Blended total	3.8x	3.2x	2.9x	-24%

Meta creative frequency trend:

Creative	Jan freq	Feb freq	Mar freq	Status
Creative A (static)	2.8	4.5	6.2	Severely fatigued
Creative B (video)	3.2	5.1	6.8	Severely fatigued
Creative C (carousel)	2.9	4.2	5.4	Fatigued
Creative D (UGC video)	1.8	2.4	3.1	Healthy (recent addition)

Narrative

Your blended ROAS decline is a Meta creative fatigue story with a CPM inflation chaser. Google is fine — Smart Bidding + disciplined n-gram review keeps it healthy. On Meta, you've run the same 3 creatives for 50+ days, frequency is at 5.8 across top creatives, and Meta's research shows 45% conversion rate drop at 4+ exposures. Meta's CPM increase (+9% industry-wide in 2025, +15% for your account) adds background pressure but is secondary — even at old CPMs, fatigue would still be dragging ROAS.

The silver lining: your one recent creative addition (D — UGC video, frequency 3.1) is performing well. This is the signal that creative rotation works for your audience; you just haven't been doing it.

Recommended Actions

Priority	Action	Impact	Owner	Timeline
1	Pause Creative A and B (highest frequency). Launch 6 new variants (3 UGC video, 3 static)	Restore Meta ROAS toward 3.0x+	Media buyer + creative	Week 1
2	Build weekly creative rotation pipeline — minimum 4 new variants per week	Prevent fatigue recurrence	Creative team	Ongoing
3	Add Meta frequency automation rule — pause creatives at freq >5	Automated fatigue protection	Media buyer	Week 1
4	Reprice remaining Meta budget against new CPM reality — may need 10-15% budget increase to maintain same volume	Offset CPM inflation	Finance + media buyer	Week 2
5	Replicate Google's n-gram discipline for Meta placement/creative reviews	Build sustainable Meta process	Media buyer	Ongoing

Data Confidence & Caveats

• High confidence: Creative fatigue is the primary driver. Frequency data, ROAS decline timing, and the Creative D performance all point to the same conclusion
• Medium confidence: CPM inflation contribution (~15%) is a rough estimate based on industry averages; your specific CPM trend confirms it's real but the exact split with creative fatigue can't be precisely calculated without an A/B test
• Untested: Incrementality — platform-reported ROAS may overstate true channel impact. Consider running a Meta Conversion Lift test in Q2 to validate blended ROAS represents actual business lift
• Not included: LTV analysis — if Meta retargeting has higher LTV, the CPA increase may be tolerable. Recommend 90-day LTV analysis as follow-up
• Use the meta-ads-creative-engine skill for creative testing framework implementation
• Use the ad-spend-allocator skill for cross-channel budget reallocation after creative rotation stabilizes

Guidelines

• Don't confuse correlation with causation. Two trends moving together (ROAS down, frequency up) correlate. To confirm causation, test the intervention (refresh creative, see if ROAS recovers).
• Don't analyze without a question. "Tell me about the account" produces vanity dashboards. "Why did CPA rise in March?" produces insight. Always start with the specific question.
• Don't ignore base rates. An account with 10 conversions/day has huge daily variance. A change that looks significant may be noise. Require statistical meaningfulness (50+ conversions per segment for reliable segment analysis).
• Don't overweight platform-reported ROAS. Both Google and Meta take credit for conversions that may have happened anyway. For strategic decisions, triangulate platform ROAS with MER, incrementality, and LTV.
• Don't report metrics without narrative. "CPA is $62" is data. "CPA rose from $48 to $62 because creative fatigue hit in mid-February, same period frequency exceeded 4, confirmed by Creative D's outperformance proving rotation works" is insight. Insight drives action.
• Don't skip segmentation. Account averages hide winning and losing segments. Always break down by device, geo, audience, creative, placement. Hidden patterns drive hidden wins.
• Meta Instant Experience change (June 2025): No longer counts as a landing page view. If Meta funnel metrics look broken mid-2025, this is why.
• Meta attribution API change (October 2025): 7d_view and 28d_view windows removed from Insights API. Reports pulling these need updates.
• Don't overreact to a single week. Weekly variance is normal. Look for 2+ week trends before declaring changes.
• Cross-references: For Google-specific optimization actions, use the google-ads-optimizer skill. For Meta-specific optimization, use the meta-ads-optimizer skill. For creative testing frameworks, use the meta-ads-creative-engine skill. For cross-channel budget reallocation, use the ad-spend-allocator skill. For reporting the analysis to stakeholders, use the paid-media-reporter skill. For full audits, use google-ads-audit or meta-ads-audit.
• Confidence: Channel attribution is inherently imperfect. All multi-touch attribution models make assumptions. State what the data supports clearly and what requires incrementality testing to confirm.