Clear, transparent attribution built to explain which marketing efforts influenced orders — and where spend failed to convert.
StoreTracker attribution is designed to answer one simple question: Which marketing efforts actually influenced orders — and where is money being wasted? We prioritize clarity and explainability so merchants and agencies can trust, audit, and defend the numbers.
It is not a predictive media mix model, a replacement for ad-platform attribution, or a proprietary scoring system that cannot be explained.
StoreTracker primarily uses UTM parameters as the structured source of attribution, including utm_source, utm_medium, and utm_campaign.
When UTMs are present, they give merchants and agencies a clean, human-readable way to understand where traffic and orders came from.
UTM parameters, referrer data, Shopify order metadata, and first-party StoreTracker events.
Platform identifiers such as gclid, fbclid, msclkid, and other campaign parameters when available.
These identifiers are used as supporting signals and fallbacks. StoreTracker does not rely on one single parameter to explain revenue.
Attribution is constructed using recorded events in chronological order, including:
gclid, fbclid, and msclkidStoreTracker uses first-party data and server-side processing to organize these signals into a clear customer journey. Every attributed order can be traced back through the events that influenced it.
Real-world tracking is often imperfect. UTMs may be missing, inconsistent, or overwritten. StoreTracker is built to handle that.
When UTMs are not available, StoreTracker can still use referrer data, first-party visitor behavior, campaign identifiers, platform click IDs, and chronological journey events.
This allows StoreTracker to rebuild the journey instead of losing attribution just because one parameter was missing.
Credits the first known marketing touch in the customer journey. Best for understanding demand generation and which channels bring new customers.
Credits the final marketing touch before purchase. Best for understanding conversion-focused channels.
Credits multiple meaningful touchpoints instead of assigning all revenue to one source.
Helps merchants understand how acquisition, nurturing, and conversion channels work together.
Most analytics tools focus only on buyers. StoreTracker also analyzes:
This enables answers to questions like: “Which traffic wasted the most money?”
gclidAd platforms often rely on their own platform-specific tracking, modeled conversions, and view-through assumptions. StoreTracker uses merchant-owned, first-party data and attributes only what can be explained through recorded journeys.
Different purpose. Different methodology. Transparent outcomes.
Within StoreTracker, attribution type and data sources are clearly labeled. Users can drill down from summaries to journeys to individual orders.
This makes attribution easier to explain in client conversations because the numbers are tied back to real recorded behavior, not a hidden scoring model.
StoreTracker attribution uses first-party Shopify and journey data to transparently credit marketing touchpoints using clearly labeled first-click, last-click, and multi-touch journey models. UTMs are the primary structured signal, while identifiers like gclid, fbclid, and msclkid are used as supporting signals when available. StoreTracker also uses referrer data and recorded visitor behavior to rebuild journeys when tracking is incomplete, helping merchants and agencies understand what influenced revenue — and where spend failed to convert.