attribution

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# Attribution Models — The Math, Worked

Six standard models, one journey scored six ways, and data-driven attribution explained without the black box. Use this when the user wants to understand *why* two models disagree, or needs to pick one defensibly.

## The worked journey

A single B2B buyer's path to a $12,000 annual deal, five touches over 38 days:

| # | Day | Touch | Role in the story |
|---|---|---|---|
| T1 | 0 | LinkedIn ad (paid social) | First discovered you — created awareness |
| T2 | 5 | Organic blog post (organic search) | Came back to learn — built interest |
| T3 | 12 | Retargeting ad (paid social) | Nudged back mid-consideration |
| T4 | 30 | Branded search (paid search, branded) | Ready to act — searched your name |
| T5 | 38 | Direct → demo request (direct) | Converted |

The whole point: **the touch that gets credit depends entirely on the model, and each model tells a different story about where your $12k came from.**

## The six models, applied

Credit for the $12,000 deal under each model:

| Touch | Channel | First-touch | Last-touch | Last non-direct | Linear | Time-decay | Position (U) |
|---|---|---|---|---|---|---|---|
| T1 | Paid social | **$12,000*

# Attribution by Business Type

Attribution defaults differ sharply by business model. The same "which channel drives revenue?" question wants a different source of truth, model, and paradigm depending on how long your cycle is, how many people are involved, and where your budget goes. Two playbooks: B2B SaaS and Ecommerce/DTC. Match the user's product to one (or blend, for PLG-with-sales).

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## B2B SaaS (long cycle, sales-assisted)

**Shape of the problem:** journeys run weeks to months, span multiple people (champion, economic buyer, users), and include touches that never appear in web analytics — a conference conversation, a sales call, a Slack-community mention, a peer recommendation. Deal values are high and volume is low, so every deal matters and averages are noisy.

**Why single-touch models mislead badly here:** with 15 touches over 3 months across 4 people, "last-touch = direct" and "first-touch = one LinkedIn ad" are both almost useless. The middle — and the offline — is where the deal was actually won.

### The B2B playbook

1. **Source of truth = the CRM**, not any analytics tool. Revenue is real in the CRM (closed-won, ARR); everything else explains where those de

# First-Party Attribution — The Own-Your-Attribution Runbook

How to instrument and stitch attribution yourself when you control the site/app. This is the build track (Pillar B). It's distilled from real production builds and kept tool-agnostic — **PostHog + SavvyCal are the worked example**, but the pattern maps to any product-analytics tool with `identify()`/merge (Segment, Amplitude, GA4 user-id) and any third-party conversion domain with a metadata passthrough + webhook (Calendly, Cal.com, Stripe Checkout, Typeform).

The core method — closing the `identify()` gap so conversions join to anonymous browsing history — is **adapted from Tessa Kriesel's PostHog attribution approach**. Several of the production refinements that make this operate at scale are also hers, credited inline: the full-touch-path capture that feeds the model track (Step 4), the CRM last-mile with source/confidence/basis and a Paid-vs-Organic read (Step 5), the account rollup, and the "expect ~zero until the stitch is verified, with a campaign-window fallback + backfill" window (Cross-subdomain stitching). Credit where due.

## The one idea

First-party attribution joins **anonymous browsing** to the **eventu

# Measurement Paradigms — MTA vs. MMM vs. Incrementality

Attribution *models* (see `attribution-models.md`) split credit *within* your tracked data. They can't tell you what would have happened anyway. That's what these three paradigms are for — increasingly rigorous, increasingly expensive ways to get closer to causality. Use this reference to help a user pick, and to explain how a test *reads* (not how to run the statistics).

## The three, compared

| | **MTA** (multi-touch) | **MMM** (media mix modeling) | **Incrementality** (experiments) |
|---|---|---|---|
| **Approach** | Bottom-up: stitch user-level touches, apply a model | Top-down: regress outcomes vs. spend/factors over time | Controlled: withhold exposure from a group, measure the difference |
| **Answers** | "Which touchpoints are on converting journeys?" | "What's each channel's aggregate contribution?" | "Did this channel *cause* lift I wouldn't have gotten?" |
| **Granularity** | Per user, per touch | Per channel, per week | Per test (one channel/tactic at a time) |
| **Data needed** | Clean cross-device user-level tracking | 2–3 yrs weekly data + spend variation | Ability to withhold + enough volume for significan

---
name: attribution
description: When the user wants to figure out which marketing actually drives conversions and revenue, choose or interpret an attribution model, or reconcile conflicting numbers across tools. Also use when the user mentions "attribution," "attribution model," "first-touch vs last-touch," "multi-touch," "which channel drives revenue," "what's my real CAC," "my dashboards disagree," "Google/Meta says X but GA says Y," "media mix model," "MMM," "incrementality," "geo lift," "holdout test," "how did you hear about us," "self-reported attribution," "dark social," or wants to instrument attribution themselves — "stitch my bookings to their source," "SavvyCal/Calendly attribution," "close the identify gap," "track conversions on a third-party domain," "first-party / self-hosted attribution." For event tracking setup and UTMs, see analytics. For ad-platform pixels/CAPI, see ads. For pipeline and CRM revenue reporting, see revops. For the AI-search attribution blind spot, see ai-seo.
metadata:
  version: 1.1.0
---

# Attribution

You help users answer the hardest question in marketing: **which of my efforts actually caused this conversion and this revenue?** Attributi