Understanding Order Attribution Discrepancies

When reviewing order attribution statistics, you may notice that the number of attributed orders in SHOPLINE differs from the conversions reported by advertising platforms or other analytics tools. You may also see inconsistencies in order source attribution across channels. These discrepancies arise from differences in attribution logic, tracking technologies, time settings, and other factors.

This article explains common causes of order data differences to help you better understand order attribution reporting.

Differences in Filter Criteria and Time Settings

Inconsistent time zone settings, time windows, or filtering criteria across tools can directly affect the results. Before comparing data, please confirm that the following are aligned:

• Are the report start and end times the same?
• Is the calculation based on "payment completed" or "order placed"?
• Are specific channels, regions, or device types filtered out in any tool?

Attribution Model Differences

A. Attribution Time Window

SHOPLINE uses a default 30-day attribution window. External tools often allow custom windows ranging from 1 to 90 days. Different window lengths can cause significant differences in attribution counts.

B. Attribution Models

SHOPLINE supports two models: "first-click" and "last-click" attribution, with some reports defaulting to last-click attribution. Other tools may use more complex models such as:

• Multi-click attribution (linear, time decay, position-based models)
• Data-driven attribution (e.g., machine learning models in GA4)

Additionally, ad platforms typically rely on their own proprietary identifiers like ad account IDs or user IDs, which are not recognized outside their systems. In contrast, SHOPLINE uses a unified cross-channel tracking method to attribute orders across different sources. These differences in attribution identifiers can lead to inconsistencies when comparing results across tools.

Missing Source Data Leading to Unattributed Orders

SHOPLINE records conversion sources through referring URLs. However, the following scenarios may cause missing source data and impact attribution:

• Redirects from HTTPS pages to HTTP pages block referrer information.
• Server-side redirects may lose the original referral source.
• In-app browsers or ad click redirects that omit UTM parameters.

Differences in Attribution Event Definitions

SHOPLINE attributes conversions only when a user clicks an ad and completes a purchase. Some external tools attribute conversions based on ad impressions:

For example, if a user sees an ad but does not click, then later visits the website directly and makes a purchase, external tools may still credit the ad for the conversion. SHOPLINE does not attribute conversions based on impressions alone. This can cause ad platform-reported conversions to exceed SHOPLINE’s attributed orders.

Impact of Cookie and Privacy Restrictions

SHOPLINE’s attribution depends on cookie tracking. If users reject cookies or use browser privacy plugins, attribution may fail or sources may be lost. External tools may use alternative tracking methods, such as logged-in user accounts, which can cause differences in reported data.

Cross-Device Tracking Differences

SHOPLINE uses cookies and UTM parameters to identify users, so cross-device behavior is only tracked on the device where the purchase occurs. Some external tools identify users across devices using:

• Logged-in accounts
• Email addresses, phone numbers, or other linked identifiers
• Browser fingerprinting and device fingerprinting technologies

These differences can affect data comparability across tools.

Summary and Recommendations

Differences in order attribution statistics across tools are normal and caused by variations in query methods, attribution models, and data tracking technologies. We recommend:

• Cross-referencing data from multiple tools instead of relying on a single source
• Defining clear analysis goals and selecting the attribution model that best fits your business needs
• Using consistent time zones and time ranges when comparing data
• Focusing on trends and changes rather than exact numbers

If you have further questions about order attribution, please contact SHOPLINE Support. We are here to assist you.