Understanding Sales Attribution

Modern browsers often block third-party cookies, limiting tracking capabilities.

Customers using multiple devices can disrupt cookie-based tracking, leading to incomplete data.

Laws like GDPR and CCPA allow users to opt out of tracking, resulting in data gaps.

Apple’s App Tracking Transparency framework restricts tracking on iOS devices, affecting data collection.

Web browsers are increasingly effective at blocking common tracking techniques, including browser fingerprinting, reducing data quality.

As customers traverse multiple domains to use features like Shop Pay, cookie tracking can be affected, causing attribution issues.

Browser Restrictions: Many modern browsers (like Safari and Firefox) block third-party cookies by default, and Chrome is phasing them out.

Ad Blockers: Ad-blocking extensions and software can block cookies entirely, preventing tracking.

Shoppers often browse on one device (e.g., mobile) and complete purchases on another (e.g., desktop). Cookies are generally device-specific and may not link these interactions effectively.

GDPR, CCPA, and Other Privacy Laws: Users may opt out of cookie tracking due to privacy regulations, meaning no data is captured.

iOS 14+ Updates: Apple’s App Tracking Transparency (ATT) framework significantly limits tracking capabilities for apps like Meta on iOS devices.

Click vs. View Attribution: Cookies typically track clicks but may struggle to credit conversions driven by view-through (when a user sees an ad but doesn’t click and later converts).

Post-Click Window Limitations: Shopify stores often have varied customer decision-making timelines, but cookies may expire or lose tracking relevance if purchases happen outside a short attribution window.

Shopify’s checkout operates on its own subdomain, which can interrupt cookies-based tracking. Without advanced tracking setups, it’s harder to attribute sales correctly to specific ads.

Multiple Channels: Shopify stores often receive traffic from various channels, and cookies struggle to unify data from different sources into a cohesive conversion path.

Pixel Duplication or Errors: Incorrectly implemented tracking pixels or multiple overlapping cookies can result in misattributed conversions.

Last-Click Bias: GA4 uses different attribution models, but the default is data-driven, which can sometimes overemphasize last-click interactions, potentially undervaluing top-of-funnel Meta or other ad campaigns.

Loss of View-Through Attribution: GA4 does not natively account for view-through conversions (when a user sees an ad but doesn’t click before converting), which can significantly impact Meta ad performance metrics.

GDPR and CCPA Compliance: GA4 relies heavily on cookies and tracking scripts, but privacy laws often require user consent, leading to data loss when users opt out.

Apple’s iOS Updates: With Apple’s App Tracking Transparency (ATT), user data on iOS devices may not flow into GA4 as it requires explicit user consent for tracking.

Data Sampling: GA4 often samples data for large datasets, which can lead to inaccuracies, especially for small-to-medium Shopify stores with moderate traffic.

Event Data Aggregation: GA4 aggregates data at a higher level, which might obscure granular details needed for accurate Shopify conversion tracking.

GA4 processes data asynchronously, which can lead to delays in reporting conversions. Shopify merchants running time-sensitive campaigns might find discrepancies between real-time Shopify analytics and GA4 reports.

GA4 uses JavaScript-based tracking, which is susceptible to being blocked by ad blockers or browser privacy settings, leading to missing or incomplete data.

Dynamic Checkout Issues: Shopify stores often use dynamic checkout options (e.g., PayPal, Apple Pay). GA4 may fail to correctly attribute revenue generated through these methods without advanced configuration.

Preflect uses a 7-day any-touch attribution as we are aggregating data from Meta/Google and synthesizing rather than employing yet another pixel to collect any data.

Instead of trying to maneuver around iOS 14 and ever-evolving privacy laws with yet another pixel, we instead collect your sales data from every single source that we can (ie Meta, Google, Shopify) and combine this data together. Individually, these single data sources may not have a high degree of accuracy, but combined in the right way, Preflect uses them to feed an algorithm that determines which ads are performing better than others, helping you allocate budget more effectively and giving you data-driven recommendations.

Meta knows which people (by their email addresses, phone numbers, etc.) interacted with your ads. And, because we send purchase data to Meta, they know which people bought (again, by their emails and phone numbers). As a result, Meta is uniquely positioned to attribute sales more accurately than cookies.

Meta may systemically underreport sales from some customer segments. Preflect uses an algorithm to analyze your UTM data and make statistical adjustments to Meta’s data, designed to increase attribution accuracy. This is the power of aggregating data from Meta and Shopify rather than looking at them individually.