First Touch

First-touch attribution (also called first-click attribution) gives 100% of conversion credit to the first recorded interaction in the customer journey. It’s the narrowest member of the attribution model family — a single-touch rule that ignores every touchpoint after the opener. First-touch is useful for spotting which channels start journeys, lethal as a sole optimization model, and in 2024 was removed from GA4’s cross-channel reporting. This guide covers how it works, when it makes sense, where it fails, and how to keep reporting it via GA4’s first_user_* dimensions and BigQuery export despite GA4 defaulting to data-driven attribution.

What is First-Touch (First-Click) Attribution

First-touch attribution is the rule that assigns 100% of a conversion’s credit to the channel, campaign, or source that drove the user’s first recorded session. The terms first-touch and first-click are interchangeable — Google Analytics has historically called it “first-click”, marketing analytics literature usually calls it “first-touch”, and the math is identical. Whichever interaction is logged first gets the entire conversion value; everything that follows gets zero.

The model belongs to the single-touch family alongside last-click. Single-touch models are deterministic, simple to explain, and useful for one specific question: which channel originated this customer? They answer nothing about the rest of the journey. A 12-touchpoint B2B path that converts on day 47 collapses into a single data point — the source of session #1 — under first-touch.

First-touch identification depends on three layers working together: a cookie or first-party cookie persisting the user’s client ID across visits, a UTM-tagged or auto-classified inbound source on session 1, and a lookback window long enough to retain that data until conversion. Drop any one and the “first” touch quietly becomes whatever the platform sees first after the gap.

How First-Touch Works: Credit Allocation Example

Take a typical B2C consideration journey: a user clicks a Facebook display ad on day 1, returns from organic search on day 4, opens an email campaign on day 9, and finally converts via direct visit on day 14 with a $100 purchase. Five rule-based attribution models split that $100 differently:

• First-touch: $100 to Facebook display, $0 to everything else.
• Last-touch: $0 to everything except direct, which gets $100.
• Linear: $25 each to all four touches.
• Time-decay: recency-weighted curve — roughly $8 / $17 / $33 / $42.
• Position-based (U-shaped): $40 to first, $10 each to the two middle touches, $40 to last.

The first-touch view tells one story: Facebook generated the customer. Every other model adds nuance the first-touch model refuses to see. That’s the single useful sentence the model produces — and the reason it should never be the only model running.

First-Touch vs Last-Touch vs Linear vs Time-Decay (Comparison Table)

The five rule-based models differ purely in how they distribute weight across positions in the path. The same conversion produces five different per-channel revenue numbers — sometimes 40%+ apart. The table below summarizes the rule, the typical bias, and where each model breaks down:

The first-touch column above is highlighted because it’s the only model that awards zero to retargeting, email, and closing direct visits. That’s not a bug — it’s the explicit design choice. First-touch exists to expose channel-of-origin. Read it as one lens, never as channel revenue accounting.

First-Touch in GA4: Available as a Lookback Model

Google Analytics 4 historically supported first-click as a cross-channel attribution model alongside last-click, linear, time-decay, and position-based. In April 2024 Google removed first-click, linear, time-decay, and position-based from GA4’s cross-channel reporting, leaving only data-driven attribution and paid-and-organic last-click as cross-channel options. First-click survives in two narrower contexts:

• Google Ads cross-channel attribution reports still expose first-click for paid Google Ads channels only, behind Tools → Attribution → Models. It’s not visible in GA4’s main reporting UI.
• GA4 user-scoped dimensions — first_user_source, first_user_medium, first_user_campaign, and first_user_default_channel_group — still capture the first-touch attribution at the user level on every property. They never change after the user’s first session and effectively encode permanent first-click attribution per user.

The practical effect: you can no longer pick “first-click” as a model in GA4’s Advertising → Model comparison view, but the first-touch data itself is still collected on every user and queryable through the first_user_* dimensions in any Exploration or BigQuery export. Google’s attribution model documentation covers the cross-channel removal; the user-scoped dimensions reference covers what survives.

When First-Touch Makes Sense (Brand Campaigns, Top-of-Funnel ROI)

First-touch attribution earns its keep in a small set of specific scenarios. Use it as a complementary model — never the primary one — in any of the following situations:

• Brand awareness campaigns. Display ads, sponsored content, podcast ads, and influencer partnerships create demand that closes weeks later through other channels. Last-touch reports give brand spend $0 and starve the budget; first-touch makes the discovery contribution visible.
• Top-of-funnel content marketing. SEO content that ranks for informational keywords brings users into the funnel long before they’re ready to convert. Organic search first-touch reporting separates discovery content from closing-stage product pages.
• New-customer acquisition cost (CAC) calculations. CAC is fundamentally a first-touch metric — you’re asking how much did it cost to bring this customer in for the first time. Loading CAC against last-touch overstates the closing channel’s efficiency.
• Channel mix audits. When evaluating whether the marketing portfolio has a healthy discovery-channel base, first-touch reveals whether new demand is coming from a diverse channel mix or stuck on one source.
• Influencer and PR measurement. Both produce assist touches that rarely close directly. First-touch credits them; last-touch consistently shows them as wasted spend.

The pattern is consistent: first-touch is the right lens whenever the question is what brought this user in? rather than what closed the deal?

First-Touch in Long Sales Cycles (B2B SaaS, High-Consideration)

Long sales cycles amplify both the value and the risk of first-touch attribution. A 90-day B2B SaaS journey can include a webinar visit, three SEO content visits, a comparison-site referral, two retargeting clicks, an email nurture sequence, and a final direct visit on conversion day. Last-touch credits the direct visit. First-touch credits the webinar. Neither is correct — both are useful inputs to the same conversation.

For B2B and high-consideration B2C, first-touch reporting answers the budget question: which channels should we keep funding to feed the top of the pipeline? Cutting a channel that contributes 0% under last-touch but 35% under first-touch is the most expensive attribution mistake in B2B marketing — and it happens whenever organizations standardize on a single closing-biased model.

Practical configuration in long-cycle accounts: extend the GA4 conversion lookback window to 90 days, use the first_user_* dimensions in Explorations to build a discovery channel report alongside the standard data-driven attribution view, and treat the two reports as complementary inputs. Bounteous’s multi-touch attribution guide covers the longer-cycle configuration in more depth.

Limitations: Ignores Mid-Funnel and Closing Activities

The structural limitations of first-touch are the inverse of its strengths. The same property that makes it a clean discovery lens makes it dangerous as an optimization target:

• Zero credit to mid-funnel. Retargeting, nurture email, comparison content, and product pages all get $0 regardless of contribution. Optimizing on first-touch defunds the entire middle of the funnel.
• Zero credit to closing channels. Branded search, direct visits, and remarketing — the channels users hit when ready to convert — disappear from the first-touch report. A last-touch report run side-by-side will show the opposite picture.
• Cookie loss distorts the “first”. Safari ITP caps first-party cookie life at 7 days for client-side scripts. A user researching for three weeks loses the original first-touch cookie before they convert; the platform records whichever session it sees first after the cookie reset, mislabelling the true origin.
• Cross-device journeys split. A user discovering on mobile and converting on desktop appears as two separate users without cross-device tracking. The mobile path’s “first touch” and the desktop path’s “first touch” both get full $100 credit on their own conversions, double-counting wherever path stitching fails.
• Direct/none default. When the first session lands without a UTM or recognized referrer, GA4 records it as direct/none. First-touch then attributes the conversion to “direct” — usually meaning the original source is invisible, not that direct traffic actually drove anything.
• Lookback window cut-off. Touches outside the configured window are erased. A B2B journey opened by a display ad 12 weeks before conversion shows the ad’s click as nonexistent under a 30-day window, and the second-recorded session inherits the “first touch” label.

Each of these failure modes degrades first-touch reporting silently. The numbers still render in dashboards; the question is whether they’re describing reality or whichever artifact survived the cookie/window/cross-device gauntlet.

first_user_source vs first_user_medium in GA4 BigQuery Export

GA4’s BigQuery export preserves user-scoped first-touch data in three nested fields per user:

• traffic_source.source — the original referring source (e.g., google, facebook.com, newsletter-2024-q4) from the user’s first session, persisted across all subsequent events for that user.
• traffic_source.medium — the channel medium (e.g., organic, cpc, email, referral) from the first session.
• traffic_source.name — the campaign name from the first tagged session.

These are user-level fields written once on session 1 and never overwritten. Querying them gives a permanent first-touch attribution record per user, independent of GA4’s UI model setting:

SELECT
  traffic_source.medium AS first_medium,
  traffic_source.source AS first_source,
  COUNT(DISTINCT user_pseudo_id) AS users,
  SUM(IF(event_name = 'purchase', ecommerce.purchase_revenue, 0)) AS revenue
FROM `project.analytics_PROPERTY.events_*`
WHERE _TABLE_SUFFIX BETWEEN '20260101' AND '20260131'
GROUP BY 1, 2
ORDER BY revenue DESC;

The query collapses every conversion onto its user’s first-touch source/medium, producing pure first-click revenue attribution that survives every change Google has made to GA4’s UI-level attribution settings. It’s the most reliable way to keep first-touch reporting alive in 2026.

Reporting First-Touch Despite GA4 DDA Default

GA4’s default model since late 2023 is data-driven attribution, which derives credit weights from observed conversion paths rather than fixed rules. The default is fine for cross-channel optimization but doesn’t surface first-touch data anywhere in the standard reports. Three options keep first-touch reporting available:

1. User Acquisition report. Reports → Acquisition → User acquisition already uses first_user_* dimensions by default. Filter by Source/Medium/Campaign at the user level and the report effectively becomes a first-touch revenue view — separate from the data-driven cross-channel reports under Advertising.
2. Custom Exploration. Build an Exploration with First user source, First user medium, First user campaign, and conversion event count. Save as a “First-touch attribution” report alongside the default DDA view.
3. BigQuery + dashboard tool. Export to BigQuery, query traffic_source.* as shown above, and build a Looker Studio or Power BI dashboard that runs first-touch and DDA views in parallel. This is the gold standard for accounts large enough to justify the engineering cost.

The architectural pattern is to treat DDA as the optimization model and first-touch as the discovery audit. Show both. Anchor budget decisions on DDA. Use first-touch to spot-check whether brand and discovery channels are getting starved by the closing-biased optimization signal.

Migration: From Single-Touch to Multi-Touch / Data-Driven

Many accounts still report on first-touch or last-touch as their primary attribution model — usually because the reporting hasn’t been touched since pre-2024. Modern attribution practice is to migrate to a multi-touch baseline (DDA where conversion volume supports it, time-decay or position-based otherwise) and demote single-touch models to audit views. The migration runs in five steps:

1. Audit current model. Check GA4 Admin → Attribution settings and document which model drives the dashboards leadership reads. Note the lookback window.
2. Run model comparison. In Advertising → Model comparison, compare DDA against paid-and-organic last-click for the last 90 days. Calculate per-channel revenue delta. Channels that swing 20%+ between models are the ones whose budgets are most sensitive to the migration.
3. Build complementary views. Save first-touch via the User Acquisition report or BigQuery, plus DDA as the primary model. Run them side-by-side in dashboards rather than picking one.
4. Migrate optimization signal. Move budget decisions from last-click ROAS to DDA ROAS for high-volume conversion accounts. Keep first-touch CAC for new-customer acquisition reporting.
5. Document the change. Every report that switches its underlying model needs a documented before/after comparison so finance and marketing leadership can re-anchor their mental baselines. Skipping this step is the most common reason attribution migrations get reverted six months later.

For accounts below DDA’s ~300 conversions/month threshold, the migration target is paid-and-organic last-click as the primary model with first-touch and a non-direct last-click report as audit views. Avinash Kaushik’s writing on attribution and incrementality argues this multi-model approach is now baseline best practice for any organization spending more than $20k/month on paid media.

Frequently Asked Questions

Related Terms

• Attribution — the broader practice of crediting conversions across touchpoints; first-touch is one of its narrowest implementations.
• Attribution model — the wider family of credit-allocation rules including last-touch, linear, time-decay, position-based, and data-driven.
• First visit — the GA4 event that fires on a user’s first session and anchors first-touch attribution.
• Conversion — the outcome event whose credit first-touch is dividing.
• UTM — URL parameters that label inbound traffic so first-touch can identify the originating campaign.
• Campaign — the marketing initiative captured by first_user_campaign and credited under first-touch.
• Cookie — the storage mechanism that persists the original first-touch source between sessions.
• First-party cookie — the only cookie type increasingly surviving Safari ITP and ETP, critical for first-touch reliability.
• Measurement Protocol — server-side event ingestion that bypasses the browser and protects first-touch data from cookie loss.
• Cross-device tracking — the User-ID/Signals layer that prevents one user looking like two separate first-touches.
• Event — the GA4 primitive that first_user_* dimensions are written onto.
• BigQuery — where to take first-touch data when GA4’s UI no longer surfaces the model.

Bottom Line

First-touch attribution is the single-touch model that credits the entire conversion to the discovery channel — useful as a brand-awareness lens, lethal as a sole optimization model. GA4 removed it from cross-channel reporting in 2024, but the underlying first-touch data still lives in the first_user_* dimensions and the BigQuery traffic_source.* fields, which never overwrite after session 1. Run first-touch as a complementary report alongside data-driven attribution: first-touch answers which channels brought users in, DDA answers which channels are worth optimizing. Pair them and the budget decisions improve in both directions.