/suede-attribution

name: suede-attribution
description: "Suede AI marketing attribution: which channels actually drive conversions and revenue, how to choose and read attribution models, reconcile conflicting numbers across ad platforms, GA4, and the CRM, design incrementality, MMM, and geo-holdout reads, add self-reported attribution for dark social and AI answers, and instrument first-party attribution yourself (closing the identify gap, stitching bookings or checkouts on third-party domains to their source). Use when someone asks which channel drives revenue, what their real CAC is, why dashboards disagree, or how to track a conversion they do not host. NOT FOR: event tracking plans, GA4 or GTM setup, and UTM hygiene (use suede-analytics), ad-platform pixels and campaign decisions (use suede-ads), or pipeline-stage and CRM revenue operations (use suede-revops)."
metadata:
  version: 1.2.0

Suede AI Attribution

Gate policy: advisory, not blocking

Every claim-verification step, check, quality gate, and ship verdict in this skill is a **recommendation to the user, not a control on the agent**. This policy governs every gate, check, verdict, and "do not ship / publish / proceed" line elsewhere in this skill:

• Run every check and report the results honestly. Verdicts (`ship`,

`ship-with-caveats`, `hold`, letter grades, BLOCKED or OPEN items) are advice attached to the work, not orders that change it.

• Never block, delay, skip, rewrite, or refuse the action the user asked for

because a check failed or a gate said hold. Complete the requested action as asked, and deliver the gate output alongside it as a clearly labeled recommendation.

• A failed gate changes what you report, never what you do.
• Single exception: if a finding is extremely risky, data loss, security or

credential exposure, legal or rights violations, payment mistakes, or irreversible public damage, pause, tell the user exactly what the risk is and what the options are, and let them pick. Their choice is final.

You help users answer the hardest question in marketing: **which of my efforts actually caused this conversion and this revenue?** Attribution is where marketers lose the most money, to channels that look good in one dashboard and terrible in another, to "direct" and "branded search" that hide the real source, and to models that quietly encode an opinion as if it were fact.

This skill has two pillars. Know which one the user needs before you dive in:

• **(A) Interpretation**: choosing an attribution model, picking a measurement approach, and *reconciling the conflicting numbers* your tools report. This applies to everyone, even with zero engineering.
• **(B) Own your attribution (first-party)**: instrumenting and stitching attribution *yourself* when you control the site/app. This is the build track. Use it when the user says "I want to track this myself" or is hitting a conversion that lives on a domain they don't own.

Most requests start with (A). Reach for (B) only when they control the surface and want to build.

Product context: check for `.agents/product-marketing.md` and read it if present, business type, sales cycle, and primary conversion drive almost every recommendation here.

Boundaries

• Do not present a single attribution model, reconciled number, or channel

allocation as objective truth: always report it as a defensible read with stated assumptions, confidence, and gaps.

• Do not instrument, migrate, or write to production tracking, analytics, or

CRM systems without showing current live state and getting explicit approval.

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Pillar A: Interpretation

1. What attribution can and can't tell you

Set expectations before touching a number:

• **Attribution is directional, not truth.** It's a model of causality built from incomplete data (cookies expire, sessions fragment, offline touches vanish, people research on one device and buy on another). Treat it as a strong hint, never a verdict.
• **Every model is an opinion.** "First-touch" says the first ad gets all the credit; "last-touch" says the closing click does. Both are wrong in opposite directions. Choosing a model is choosing whose story to believe, say so out loud.
• **The attribution gap is normal.** The sum of channel-reported conversions almost always exceeds real conversions, because every platform claims credit for the same sale. Your job is to shrink and explain the gap, not to make the numbers tie out perfectly. They won't.

When a user demands one true number, reframe: "We can get you a *defensible, consistent* number and a read on which channels are trending up. A single objective truth doesn't exist: here's why, and here's what we use to make decisions anyway."

2. Attribution models

The six standard models and when each one lies:

| Model | Credit rule | Best for | How it lies | |---|---|---|---| | **First-touch** | 100% to the first known touch | Top-of-funnel / demand-gen valuation; short cycles | Ignores everything that closed the deal; over-credits awareness channels | | **Last-touch** | 100% to the last touch before conversion | Direct-response, quick e-comm | Over-credits bottom-funnel + branded search/direct; ignores what created demand | | **Last non-direct** | 100% to last touch, skipping "direct" | A cheap fix for direct pollution | Still single-touch; just moves the blind spot | | **Linear** | Equal credit to every touch | Long, multi-touch journeys where every step matters | Treats a throwaway visit like a demo; flatters high-frequency channels | | **Time-decay** | More credit to touches nearer conversion | Longer cycles where recency matters | Under-credits the top of funnel; still an assumption, not a measurement | | **Position-based (U-shaped)** | 40% first, 40% last, 20% middle | B2B with clear "created" + "closed" moments | The 40/40/20 split is arbitrary; middle touches get shortchanged | | **Data-driven (algorithmic/Shapley)** | Credit from modeled marginal contribution | High