attribution-mapper

name	attribution-mapper
description	Build a multi-touch attribution model from raw event data — touch journeys, model comparison (first / last / linear / position / time-decay), and channel-level ROI. Use when the user says "fix our attribution", "build an attribution model", "which channels drive signups", or "why does GA say one thing and Facebook another".
status	new

Attribution Mapper (Data Ops)

Builds a transparent, explainable attribution model from raw events. Optimized for "good enough to make decisions" over "perfect but nobody understands".

Triggers

• "fix our attribution"
• "build a multi-touch attribution model"
• "which channels actually drive signups / revenue"
• "why does GA say X and Meta Ads Manager say Y"
• "how do I credit channels for conversions"

Inputs required

1. Touch events — timestamps, user/session ID, channel, campaign, source, medium
2. Conversion events — signup, activation, paid conversion (map to Aha-Moment Mapper + Revenue)
3. Attribution window — how far back to look (default: 30 days for signup, 90 for paid)
4. Cross-device identity — do we have logged-in resolution? If no, flag the gap
5. Current vendor tools — GA4, Segment, Amplitude, Mixpanel (just for reconciliation)

Process

Step 1 — Audit event data

Check:

• % of conversions with ≥ 1 known touch (if < 70%, attribution will be noisy)
• Most common (utm_source, utm_medium) combos — normalize before modeling
• Bot / self-referral contamination — strip
• Identity graph — do we resolve visitor → user on signup?

Produce a short instrumentation gap report with fixes.

Step 2 — Build the touch journeys

For each converter, produce the ordered list of touches in the window:

user_abc | 2026-03-12 google/cpc → 2026-03-18 linkedin/organic → 2026-03-25 direct → SIGNUP

Step 3 — Compute 5 standard models

For every conversion, compute credit under:

• First-touch — 100% to first channel
• Last-touch — 100% to last channel
• Linear — equal split
• Position-based — 40/20/40 (first, middle, last)
• Time-decay — exponential decay toward most recent

Report channel-level spend vs. credited revenue under each model. The spread between models is where gut feel breaks — having all 5 forces honesty.

Step 4 — Add an "assisted" view

For each channel, report:

• Conversions where it was the ONLY touch (solo)
• Conversions where it was present but not solo (assist)
• Conversions where it was first / middle / last

This is how you spot "LinkedIn never gets last-touch credit but appears in 60% of enterprise journeys".

Step 5 — Cross-reference with vendor attribution

Pull the same channels' reported conversions from:

• GA4
• Meta Ads Manager
• LinkedIn Ads
• Google Ads

Build the reconciliation table. Explain discrepancies. Almost always: vendor platforms over-credit themselves (view-through, click-through with long windows).

Step 6 — Recommend a model

Based on business model, sales cycle, and data completeness, pick one model as the source of truth. Document the reasoning. This is the model the team uses in dashboards and weekly reports.

Also keep a secondary model for cross-checks.

Step 7 — Instrument going forward

Produce a UTM convention spec:

• Required fields
• Source / medium / campaign naming conventions
• Rules for paid vs. organic
• Tooling implementation (e.g., campaign URL builder)

Output artifacts

• attribution-model.md — methodology + chosen model + rationale
• channel-performance.html — interactive dashboard with all 5 models
• utm-convention.md — going-forward spec

Handoff

• Chosen model → Performance Reporter (one source of truth)
• Dashboard → Growth Dashboard Builder
• UTM convention → Paid Ads Generator + Content Ideator (they tag their links consistently)
• Instrumentation gaps → eng backlog

Example call

"Build us an attribution model. I have events.csv with 6 months of touches and conversions.csv with our signups and paid conversions. GA4 says Google Ads drove 70% of signups; Meta Ads Manager also claims 60%. Something's double-counting."