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Asva does not rely on a single signal to decide whether a session was AI-sourced. A referrer domain alone is unreliable — ChatGPT strips it. A product-page landing alone is ambiguous — direct bookmarks land the same way. Instead, Asva’s attribution engine combines multiple session signals, weights them by reliability, and produces a confidence score for each session. This page explains what signals Asva looks for, how they are combined, and what ends up in your dashboard and GA4 reports.

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Signal hierarchy

Signals are evaluated in order of reliability, from direct evidence down to behavioral inference.

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Tier 1: Direct signals

These signals provide explicit evidence that a session originated from an AI surface. When present, Asva classifies the session at high confidence without requiring additional signals.
SignalExampleConfidence
Explicit utm_medium=aiChatGPT link with UTM params attached95%+
Known AI referrer domainchatgpt.com, gemini.google.com, perplexity.ai90%+
Asva SDK source flagSession tagged by your ACP or UCP checkout endpoint95%+

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Tier 2: Inferred signals

These signals are consistent with AI-sourced traffic but are not conclusive on their own. Asva combines them to reach a confidence score.
SignalWhat it indicatesConfidence
Direct + non-homepage landingArrived at /products/running-shoes with no referrer65–80%
Behavioral fingerprint matchSession timing matches known AI-referral patterns55–75%
Product-specific landing from new userFirst-ever session that lands directly on a product page60–70%

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Tier 3: Negative signals

These signals reduce confidence that a session is AI-sourced. When a negative signal is present, Asva downgrades the session’s score or reassigns it to a different channel.
SignalWhat it means
Bookmark detectedBrowser signals a bookmark visit — lower AI probability
Paid click parameters presentGoogle Click ID (gclid) or Meta Click ID (fbclid) present — credit to paid channel
Internal navigationUser arrived from another page on your own site
Known bot user agentCrawler or headless browser, not a human shopper

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Confidence thresholds

Each session receives a confidence score from 0 to 100 based on its combined signals. The score determines how the session is classified and reported.
ConfidenceClassificationReported as
≥ 80%High-confidence AI sessionAI Commerce
50–79%Probable AI sessionAI Commerce (probable)
< 50%Insufficient evidenceDirect (unclassified)
By default, high-confidence and probable sessions are combined and reported together as “AI Commerce”. To split them — for example, to report only high-confidence sessions in your KPI dashboards — go to Dashboard → Attribution → Display settings and enable Show confidence breakdown.
For sessions with a direct signal (UTM or known referrer), attribution accuracy is 90%+. For sessions classified using only inferred signals, accuracy is typically 70–85% depending on your site’s traffic mix.

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Attribution window

Asva uses a 30-day last-touch model by default. A conversion is attributed to the AI Commerce channel if the session was classified as AI-sourced within 30 days before the conversion event. You can change the attribution window in Dashboard → Attribution → Model:
WindowBest for
7 daysConservative reporting; short consideration cycles
30 daysDefault; suits most DTC and mid-market brands
90 daysExtended; high-consideration or high-ticket products

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Multi-touch attribution

For brands with complex customer journeys, Asva supports multi-touch attribution. A typical AI-assisted journey might look like this: 
User journey:
  Day 1: Arrives via ChatGPT → browses (AI Commerce session)
  Day 5: Returns via Google Organic → adds to cart
  Day 7: Returns via Direct → purchases

Default (last-touch): Direct gets 100% credit
Multi-touch (linear): AI Commerce, Organic, Direct each get 33%
Multi-touch (first-touch): AI Commerce gets 100%
Configure the attribution model in Dashboard → Attribution → Model. Linear and first-touch models are available on Growth and Enterprise plans.

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What gets reported

Once sessions are classified, Asva surfaces attribution data in three places:

Asva dashboard

AI Commerce channel breakdown, per-surface traffic, product-level AI referrals, and month-over-month trends.

GA4 custom dimensions

ai_source, is_ai_session, and ai_confidence are passed to GA4 on every classified page view. See Integrate Asva Attribution with Google Analytics 4.

Attribution API

Pull session-level attribution data and revenue figures programmatically. See Attribution API.

CSV export

Daily or weekly exports are available from the Asva dashboard for use in spreadsheets or BI tools.

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Accuracy and limitations

For sessions with direct signals (UTM parameters or a recognised AI referrer domain), accuracy is 90%+. For sessions classified using only inferred behavioral signals, precision is typically 70–85% on sites with significant AI traffic volume. Sites with very low AI traffic may see more noise in the inferred tier.
Yes. Enable Session-level export in Dashboard → Attribution → Export to download each session’s signals and confidence score. The same data is available via the Attribution API.
No. All session classification is first-party and server-side. Asva does not use third-party cookies or cross-site tracking, and does not store personally identifiable information.
The Asva snippet runs client-side. For headless stores, you can also send attribution events server-side via the Attribution API when your UCP or ACP checkout endpoint is called. Server-side classification produces the highest-confidence attribution because it is triggered by a confirmed AI-initiated transaction.

Attribution API

Log attribution events and pull session-level data programmatically.

Integrate Asva Attribution with Google Analytics 4

Surface AI Commerce as a named channel in your GA4 reports.