ads-performance-analytics

Files (9)

• README.md
• SKILL.md
• attribution-model-comparison.md · references/
• cohort-analysis-templates.md · references/
• common-interpretation-failures.md · references/
• dashboard-reconciliation-patterns.md · references/
• incrementality-testing-playbook.md · references/
• metric-definitions-glossary.md · references/
• platform-reporting-quirks.md · references/

SKILL.md (first lines)

# Ads Performance Analytics

A data-team-mentor's playbook for interpreting paid media dashboards without fooling yourself.

The dashboard is the moment of truth for paid media decisions. The numbers on it determine whether you scale, hold, or kill. They also expose every platform's self-attribution bias, every modeled-conversion shortcut, every cross-platform double-count. Most "scale this campaign" decisions trace back to misreading the dashboard.

This skill is the discipline that prevents misreading. It assumes the campaign was strategically sound (see `paid-media-strategy`). It assumes the creative was tested properly (see `ads-creative-development`). The hard part is knowing what each number actually means, what it does not, and how to reconcile platform-reported metrics with the truth in your warehouse.

When to use this skill: any time you are about to scale, kill, or rebudget a campaign based on platform metrics; reconciling platform reports with revenue data; evaluating an agency's reporting; or building a paid media dashboard that will not lie to you.

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## What this skill is for

This skill spans paid media result interpretation. It does not cover paid media strategy (use `paid-media-strategy`), creative production (use `ads-creative-development`), or platform-specific tooling (covered in the integrations microsites). Pair this skill with the relevant integrations microsite for platform-specific MCP commands and example prompts.

The audience is a marketer, growth analyst, agency analyst, or founder evaluating paid media reports. The voice is patient and clinical. There is no "trust the platform's number" or "ignore the platform entirely." Both are wrong. The discipline is knowing which numbers from which platform mean what, and what to reconcile against to make the actual decision.

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## The result panel: what every paid media platform should expose

A trustworthy result panel exposes nine things. Anything missing is a signal to treat reported numbers with extra skepticism.

1. **Spend, impressions, clicks.** Table-stakes metrics. Should match across platforms within rounding.
2. **Conversions with definition and window visible.** Not just a count; the definition of what counts as a conversion and the attribution window applied. Without this, the count is unreadable.
3. **Attribution breakdown.** Last-click vs view-through vs modeled. The mix of how the conversions were credited.
4. **Frequency.** Impressions per unique user. The fatigue early-warning system.
5. **Audience saturation.** Where the platform exposes it. A flat audience-saturation curve means there is room to scale; a steep curve means efficiency is dropping.
6. **Time series.** Daily breakdown to spot novelty effects, fatigue, day-of-week patterns, and exogenous variance.
7. **Cost metrics in clear currency.** CPC, CPM, CPA, ROAS with the math defined and the currency labeled. Do not assume USD.
8. **Conversion path data.** Touchpoints before conversion, where available. Tells you whether a campaign is a closer or an opener.
9. **Filters, segments, and exports.** Without these, the panel is a brochure, not a tool.

Platforms hide what makes their reporting look weakest. Google PMax hides keyword-level and placement-level data. Meta hides the modeled-conversion share. LinkedIn hides cross-device click paths. Treat hidden metrics as the place to dig.

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## Platform-reported vs reality

Every platform's dashboard is optimized to make the platform look effective. This is not a moral failing; it is a structural incentive. Platforms with rosier reporting attract more spend.

**Conversion windows.** Meta defaults to 7-day click plus 1-day view. Google defaults to 30-day click plus 1-day view. Different windows, same activity, different reported numbers. If you compare Google's 30-day-click count against Meta's 7-day-click count, you are comparing different definitions and pretending they are the same.

**View-through attribution.** Counted by Meta and Google for users who saw but did not click. Often half the reported "conversions" are view-through. Treat view-through as a signal of awareness contribution, not as a direct response measurement. The user might have converted from organic search anyway.

**Modeled conversions.** When iOS users opt out of tracking, Meta and others statistically model what the conversion would have been. Modeled numbers are educated guesses, not measurements. They are useful for direction; they are not reliable for precision.

**Self-attribution bias.** Every platform's pixel fires on conversion and the platform claims credit. If you ran Meta, Google, and TikTok in the same week, all three platforms report your conversions as theirs. Sum-of-platforms is always greater than 100% of actual conversions.

The discipline. Never report platform-reported numbers as fact in board decks. Always reconcile against the single source of truth (warehouse, GA4, or unified analytics platform). Detail in [`references/platform-reporting-quirks.md`](references/platform-reporting-quirks.md).

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## Attribution models in practice

Six models and one anti-model. None is right. They are all approximations. The discipline is picking one, committing, and reading the others as sanity checks.

**Last-click.** Simple, reproducible, undercredits awareness. The conversion is fully credited to the last click before the conversion event. Easy to compute; easy to compare across channels; bad for understanding upper-funnel contribution.

**First-click.** Opposite bias. Fully credits the first touchpoint, undercredits closing channels. Useful as a sanity check against last-click; rarely the right primary view.

**Linear.** Equal credit across all touchpoints. Gives every channel something. Defensible; not informative. Most useful for board reporting where avoiding "Google gets 70% so we cut Meta" politics matters more than precision.

**Time-decay.** More credit to recent touchpoints. Reflects the intuition that recent ads are more influential. Hard to argue against; hard to verify.

**U-shaped (position-based).** Heavy on first and last (40% each), light on middle (20% distributed). Honors both opener and closer roles. The default in many MTA tools.

**Data-driven attribution (DDA, Google).** Machine-learning model that distributes credit based on observed conversion paths. Opaque; hard to audit. The closest to "right" for digital channels but a black box.

**Marketing mix modeling (MMM).** Regression-based, top-down. Uses spend and revenue time series across channels to estimate channel contributions. Requires 2+ years of data. The strongest defense against platform self-attribution because it does not rely on platform-reported conversions at all.

**The anti-model: trusting platform-reported attribution.** Each platform's "DDA" or "attributed conversions" is the platform's self-attribution. Sum across platforms exceeds reality. Use platform attribution for in-flight optimization within the platform; use a unified attribution model for cross-channel decisions.

Practical guidance.

- Early-stage. Use last-click plus a single guardrail metric (warehouse-attributed CAC). Sophisticated attribution requires data volume you do not have.
- Mid-stage. Data-driven attribution from Google plus GA4, with explicit awareness vs closing channel labeling.
- Mature. MMM as the canonical incremental reference. MTA for in-flight optimization. Last-click for channel-level decisions where ambiguity is acceptable.

Detail and a decision matrix in [`references/attribution-model-comparison.md`](references/attribution-model-comparison.md).

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## Multi-platform reconciliation

The trap. Google says you spent $50K with 800 conversions. Meta says $30K with 600. LinkedIn says $20K with 200. Total reported equals 1,600 conversions. Your warehouse says 950. Where did 650 go?

The answer. Nowhere. They never existed. Each platform claimed conversions other platforms also claimed.

The reconciliation pattern.

- Trust the warehouse for total conversions and total revenue.
- Trust platforms for relative ranking within platform (which campaign won, which audience won).
- Never trust platform sums.
- Compute blended CAC as (total ad spend across platforms) divided by (total new customers from warehouse). Not from platform reports. Where the warehouse figure is unavailable, state the gap per the data-availability rule.

The board-deck pattern. Report warehouse-attributed conversion counts, never platform-summed. Report blended CAC, not channel-by-channel CAC unless explicitly noted as platform-self-attributed. Detail and templates in [`references/dashboard-reconciliation-patterns.md`](references/dashboard-reconciliation-patterns.md).

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## ROAS vs LTV: the time horizon trap

ROAS is short-term. Revenue from purchases attributed to a campaign in a fixed window, often 7 to 30 days. LTV is long-term. Total customer lifetime revenue.

Decisions made on ROAS can be wrong if LTV varies by channel. A worked example.

Meta drives 2.5x ROAS at $40 CAC with $80 LTV. That is 2.0x payback over the customer lifetime.

Google drives 1.8x ROAS at $60 CAC with $200 LTV. That is 3.3x payback over the customer lifetime.

Google looks worse on ROAS, better on LTV-adjusted return. Allocating budget to Meta because the ROAS is higher is the wrong move.

The fix. Cohort-based LTV by acquisition channel, updated quarterly. Compare channels on payback period or LTV-CAC ratio, not raw ROAS. The 2x ROAS heuristic is a dangerous shortcut. Same ROAS at different LTVs equals different actual returns.

The trap that compounds. Performance teams optimize for short-term ROAS because the metric refreshes weekly. Brand and high-LTV channels get cut because their short-term ROAS is lower. Six months later, the brand pipeline has eroded and short-term ROAS itself drops because the cheap channels are saturated. The metric that drove the decision was the wrong horizon.

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From rampstackco/claude-skills, MIT.

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