Understanding Traffic Data Discrepancies

When using SHOPLINE Analytics alongside third-party analytics tools, you may notice discrepancies in traffic-related data. These differences often arise from variations in tracking methods, data collection technologies, or how each tool defines specific metrics. By understanding how each tool monitors and reports data, you can better interpret analytics reports and make informed marketing decisions.

This guide outlines the common causes of traffic data discrepancies to help you analyze and compare metrics more effectively.

Common Causes of Traffic Count Discrepancies

A. Differences in Time Range and Filters

Inconsistencies in time zone settings, reporting periods, or filter conditions between tools can directly affect your data comparison. Ensure all tools are configured with the same settings before comparing data.

B. Varying Metric Definitions

SHOPLINE Analytics counts visitors based on sessions. Other tools may use different metrics such as "clicks," "users," or "pageviews." Even if the metric names seem similar, the definitions may differ.

C. Tracking Technology

SHOPLINE uses JavaScript for visitor tracking. If a visitor disables JavaScript, their visit may not be counted. In contrast, some third-party tools employ additional tracking technologies besides Cookie and JavaScript—such as email addresses, phone numbers, IP addresses, and user agents—to identify users, resulting in broader data coverage. 

These proprietary identification methods are not shared across analytics tools, which can lead to discrepancies in visitor counts.

D. Page Load Handling Differences

SHOPLINE starts a new session each time a page is reloaded. Some external tools may not count pageviews that are loaded from browser cache, which can lead to data inconsistencies.

E. Differences in Handling of Invalid Traffic

• Bot Traffic: SHOPLINE filters out known bots (e.g., search engine crawlers). Some external tools may not apply the same filters.
• Malicious Traffic: SHOPLINE identifies and filters invalid traffic from sources like botnets, suspicious origins, or click farms based on IP addresses and behavior patterns.

F.  Use of Statistical Models

Some third-party tools rely on sampling and modeling to estimate traffic metrics. SHOPLINE calculates metrics based on actual tracked data. These different methodologies can result in varying outcomes.

Reasons for Conversion Rate Discrepancies

A. Multiple Purchases Within a Single Session

SHOPLINE records only one conversion per session, even if a user places multiple orders. Other tools may count each order as a separate conversion, potentially inflating the conversion rate.

B. Predicted Conversion Behavior

Some tools use modeling to estimate whether a user is likely to convert, rather than relying solely on completed orders. This can lead to higher reported conversion rates.

C. Orders Not Counted as Online Store Conversions

Orders that are not placed through the SHOPLINE online storefront (e.g., orders created via API, manually created in Admin, through POS, or imported) are not counted as conversions in SHOPLINE Analytics. Although these orders appear in the system, they are not linked to any sessions or traffic data.

"N/A" as a Traffic Source

A. Limitations of In-App Browsers

When users open store pages via in-app browsers on platforms like Facebook or Instagram, SHOPLINE may not be able to identify the referral source due to platform restrictions. Such visits will be labeled as "N/A".

B. Bot Traffic Without Traceable Information

Some bots do not provide valid referral data. When SHOPLINE cannot determine the source of a visit, it is categorized as "N/A". We are continuously working to minimize the impact of this type of traffic. If you notice an unusually high percentage of "N/A" traffic, please contact our support team for further investigation.

Summary and Recommendations

Data discrepancies highlight the different tracking methodologies and technical implementations used by analytics tools. To ensure meaningful analysis, we recommend:

• Defining your analysis goals and choosing the most appropriate data source
• Ensuring consistency in time ranges and filter settings when comparing data
• Focusing on data trends rather than absolute values

If you have any questions, please contact the SHOPLINE support team. We’re here to help.