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 eventual conversion:

B0

Everything below serves that join. If identify() never fires, every customer looks like they appeared from nowhere — that's the gap.

Step 0 — Audit before you build

The most expensive mistake is rebuilding attribution that already works. Many SaaS apps already identify() at signup and already carry first-touch on person profiles. Check the live data first:

• Do person profiles carry $initial_utm_source / $initial_referring_domain?
• Does a conversion event (Signed up, Converted to paid) break down cleanly by channel, or is everything "Direct"?
• Is identity keyed by email or by an internal UUID? (This changes every guard below.)
• Does cross-subdomain stitching work (marketing site → app.yourdomain.com)?

Only instrument the specific conversions that are genuinely un-joined. In one real audit the self-serve funnel was already solved end-to-end; the only gap was a booking on a third-party domain. Don't touch what works.

Step 1 — Identify at each real conversion

At every conversion moment, call identify() with a stable id, and set person properties:

B1

With person_profiles: 'identified_only', this is the moment the person is created and their first-touch props are stamped. Fire it on form success, signup, first purchase — any moment you learn who the anonymous visitor actually is.

Step 2 — Stitch conversions on a domain you don't own

When the conversion completes on a third-party domain (a booking tool, hosted checkout), you can't run your analytics there. Smuggle the anonymous id through the tool's metadata passthrough, then merge it back in the webhook.

2a — Capture-phase link decorator

One document-level listener rewrites every outbound booking link at click time — no per-CTA edits, and it covers plain clicks, keyboard activation, and middle-click (auxclick):

B2

An early return when the guard rejects the current ID leaves any previously added metadata on the anchor. Remove that one parameter instead, preserving the booking path, other parameters and fragment. Check both click and auxclick after an identity change.

For an inline embed (e.g. /demo with an embedded calendar), pass the same id in the embed's metadata config instead; poll briefly (~2s) for the id on fresh visits, but never block the calendar from rendering.

2b — Read the anonymous id safely

The SDK stub queues calls before it loads, so get_distinct_id() returns undefined early — fall back to the tool's own cookie:

B3

2c — Merge in the webhook

The third-party tool returns your metadata in its booking.created (or checkout.completed) webhook. Fire an identity merge + a conversion event to your analytics ingestion endpoint:

B4

When no id survives (link bypassed the decorator, e.g. a booking link inside a generated email/PDF), fall back to email-only capture with journey_linked: false. You still get the conversion; you just don't get the journey for that one.

First-touch survival caveat (PostHog specifics): the $anon_distinct_id merge carries the anonymous person's event history, but with person_profiles: 'identified_only' the anonymous visitor may never have had a person profile, so their $initial_* first-touch props aren't guaranteed to land on the merged person. Two robust fixes: call posthog.createPersonProfile() client-side before the visitor navigates off to the third-party domain (so the profile and its $initial_* exist to merge into), or capture the first-touch values client-side and pass them through the same metadata passthrough, then re-assert them in the webhook with $set_once ($set_once never overwrites an existing value, so it's safe). Without one of these, you can get the booking joined to the journey's events but a blank $initial_utm_source on the person — verify on a real booking. (posthog-js#1524.)

Step 3 — The guardrails (do not skip)

Anonymity guard — fail closed

Only ever smuggle the anonymous id. After identify(), the current distinct_id becomes the user's email/UUID — leaking that into a third-party URL, or merging on it in the webhook, folds unrelated people together and leaks PII.

Pick the guard that matches your identity model — the two are not interchangeable. The @-check below is only safe when your app identifies by email; do not copy it into a UUID-identity app.

B5

If the app identifies by UUID, not email, the @ check is useless — an identified UUID would pass it and leak. Instead test that the current distinct_id still equals the device_id (calling identify() changes distinct_id but leaves device_id), and fail closed when device_id is unreadable:

B6

The rule in one line: when identity is ambiguous, send nothing. A missing journey is a data gap; a wrong merge is corruption.

First-touch data quality — the cheapest big win

Redirects overwrite the true first touch, inflating "Direct"/"Referral" and hiding real acquisition. Exclude these from referrer/channel classification (usually a settings change in the analytics tool, not code):

• OAuth / checkout redirects: accounts.google.com, login.microsoftonline.com, login.live.com, checkout.stripe.com
• Self / subdomain referrals: yourdomain.com, app.yourdomain.com, auth.yourdomain.com
• Dev/internal traffic: localhost, 127.0.0.1, test accounts

This is the highest-trust-per-effort fix in the whole runbook — no deploy, immediate accuracy gain. Do it first.

Cross-subdomain stitching

Marketing site → app on a subdomain must share one analytics project and a cross-subdomain cookie (PostHog's cross_subdomain_cookie default handles yourdomain.com → app.yourdomain.com). Verify the journey survives the handoff, or every signup looks like it started at the app.

Expect near-zero numbers until the stitch is verified in prod — don't panic. (Production note from Tessa Kriesel.) Before the cross-subdomain stitch is confirmed live, first-party attribution reads basically nothing — journeys break at the handoff and everything looks like direct. It flips from ~0 to real numbers the week the stitch actually ships. Two things get you through that window:

• A campaign-window heuristic fallback. When a signup has no linked journey, attribute it to the campaign/channel that was live during its signup window — but only when date + landing page + active UTMs uniquely narrow it to one source. With overlapping campaigns, evergreen ads, email sends, branded/direct demand, or a shared landing page, the window can't isolate the cause — mark those unknown / low-confidence rather than falsely crediting whatever was live. Used narrowly, it's a real signal while the stitch stabilizes and beats a blank; used bluntly, it manufactures false attribution.
• Backfill only the pre-stitch records that are actually missing a source. Many pre-stitch signups already have verified or self-reported attribution — never overwrite a higher-confidence source with the heuristic. Backfill only the blanks (campaign-window or self-reported), tag them as such, and keep the heuristic-backfilled history visually separate from verified trends so you don't read a cliff on launch day as a real shift.

Mark these fallback-attributed conversions with a lower-confidence basis (see Step 5) so you never confuse a heuristic guess with a verified journey.

Harden the webhook

• Verify the provider's signature (SAVVYCAL_WEBHOOK_SECRET etc.).
• Validate the smuggled id (string, ≤100 chars, no @) before merging.
• Run the analytics call after any business-critical work, bounded by a timeout, non-fatal on failure. Check HTTP status and catch timeout/network rejections, as in Step 2c. If delivery needs retry guarantees, enqueue analytics separately with idempotency rather than failing or replaying the booking.
• Log the booking id, never the email.

Step 4 — Report

• Config check: many tools default to last-touch (PostHog's Marketing Analytics scene does). First-party attribution wants first-touch — build insights on $initial_* explicitly, or switch the default.
• The payoff insight: conversion event broken down by $initial_utm_source / $initial_referring_domain — "where does every signup/booking come from."
• Channel → revenue: conversion event by channel, joined to revenue/MRR person properties. Note: some tools compute revenue props at ingest (person-on-events), so historical events may read 0 — use the persons table for current MRR, or tier by plan.
• Track your own coverage: the journey_linked: false rate tells you how many conversions bypassed the stitch. Watch it after launch.
• Store the full touch path, not just $initial_*. (Refinement from Tessa Kriesel.) First-touch alone lets you break conversions down by first channel — but it can't run the multi-touch models from the interpretation track (SKILL.md §2: position-based, linear, time-decay). If you also persist the ordered sequence of touches per person (channel + timestamp for each, e.g. an events-table query or a touch_path array on the person), the build track feeds the interpretation track: you can now score the same journey six ways on your own data instead of only reading about the models. This is what makes Pillar A and Pillar B shake hands — capture first-touch to ship, capture the full path to model.

Step 5 — The last mile: get attribution into the CRM

(This whole step is a production refinement from Tessa Kriesel — it's the thing that turned first-party attribution from a dashboard into an operating signal.)

A channel breakdown living in your analytics tool is a report. The thing sales and lifecycle actually act on is attribution written onto the record in the CRM, per account. Sync it out:

• A source field, plus source_confidence and source_basis. Don't write a bare channel — write the channel and how you know it. basis is the evidence type: journey_linked (verified stitch), self_reported (survey), or campaign_window (the heuristic fallback above). confidence is computed from evidence quality, not just the basis — a journey_linked touch with clean UTMs is high, the same touch with dirty/missing UTMs is lower, and a specific self-report can be high while a vague one is low. Sales treats a high-confidence source very differently from a low-confidence guess — give them both fields or they'll distrust the whole thing.
• A Paid-vs-Organic read off the medium. The single most-used cut in practice: mark a touch paid only from explicit paid mediums (cpc, ppc, paid-social, display, paid), and classify the rest into a small, configurable taxonomy rather than a blunt "organic" — owned (email, push, SMS — though a sponsored newsletter is paid), earned (organic search/social, referral — but a partner/affiliate referral is closer to paid), direct (no medium — unknown, not organic), unknown. The fast operational question a rep or nurture flow needs is really paid vs non-paid (did this account cost acquisition dollars) — get that boundary right and keep the finer buckets configurable.
• Roll up to the account, not just the contact (B2B) — see the account-rollup note below and in by-business-type.md.
• Hand the record off to revops. Once the source/confidence/basis + paid/organic live on the account, how pipeline and lifecycle use them (routing, lead scoring, nurture branching, revenue attribution reporting) is the revops skill's job. This runbook's job is to get a trustworthy, labeled source onto the record.

Account rollup (B2B). One org is several people signing up with mixed work and personal emails, so person-level attribution scatters the story across records. Roll each person's source up to the account and attribute at the account level — that's where the signal has to land to be useful to a salesperson working the whole buying committee. Match on email domain, but exclude free-mail domains (gmail.com, yahoo.com, outlook.com, …) — those can't identify a company, so a domain match would collapse unrelated people into one bogus account. For personal-email signups, fall back to enrichment, your CRM's contact→account link, or manual matching.

Verification checklist

• Click a booking CTA → URL shows metadata[<id_param>]=<anon-uuid>.
• In console: posthog.identify('test@x.com') → click again → the param must NOT appear (guard works). posthog.reset() after.
• Hand-POST a webhook /batch/ payload → expect {"status":"Ok"}, person appears merged.
• Post-ship: first real webhook log shows journey_linked: true.
• Confirm first-touch survives cross-subdomain: start on marketing site, sign up in app, check the person carries the original $initial_utm_source.

Adapting to other stacks

The shape never changes: grab the anonymous id → carry it across the boundary → merge on the far side → break the conversion down by first-touch.