When analyzing website traffic and Engaged Candidate data, it’s not uncommon to encounter discrepancies or variations in reporting. These differences do not necessarily indicate that the data is incorrect—rather, they are typically the result of how tracking technologies, user behavior, and technical configurations interact.
Below are the most common reasons why data discrepancies may occur:
If your tracking script is not properly installed across all pages—or is configured incorrectly—candidate activity may not be captured consistently.
Cached versions of web pages may not always trigger tracking scripts, which can lead to underreported visits.
Many users utilize ad blockers or restrict cookies through browser privacy settings. This prevents tracking tools from recording their activity, resulting in lower reported traffic.
Automated bots can artificially inflate traffic numbers. While filtering systems exist, not all bots are detected, and some real users may occasionally be filtered out.
Improper session configurations can:
Split one visit into multiple sessions (over-counting), or
Combine multiple visits into one (under-counting)
If your website spans multiple domains or subdomains and tracking is not configured correctly, users may be counted as separate visitors when navigating between them.
Inconsistent tracking across mobile and desktop versions—especially if the mobile site differs in structure—can lead to incomplete data capture.
Mismatched time zone settings between your analytics platform, server, and user location can create inconsistencies in reporting—particularly with real-time data.
Users who regularly clear cookies may be counted as new visitors each time they return, skewing visitor counts.
Most tracking relies on JavaScript. If it’s disabled or encounters errors, visits may not be recorded.
Changes such as server migrations, load balancing, backups, or infrastructure updates can temporarily impact tracking accuracy.
In some cases, the system may identify both:
A user’s personal location, and
Their company’s registered location
This can result in mismatched city/state data. In these instances, both data points are valid—they are simply pulled from different sources (individual vs. organization).
A helpful way to think about discrepancies is through a caller ID analogy:
You may be on a family phone plan, but when you call someone, the caller ID displays the account owner’s name.
The information shown isn’t wrong—it’s simply based on the strongest or most prominent data source (i.e., the largest digital footprint).
Data discrepancies are a natural part of digital tracking and analytics. A variety of factors—ranging from technical setup to user behavior—can influence how data is captured and reported.
To ensure the most accurate and actionable insights:
By doing so, you can maintain confidence in your data while understanding the nuances behind how it is collected.