Data Discrepancy

Data Discrepancy

When analyzing website traffic, it's not uncommon to encounter discrepancies or inaccuracies in the data collected. Several factors can contribute to data showing up incorrectly when capturing visitors to your website:

  1. Misconfigured Tracking Tools: One of the most common reasons for inaccurate data is incorrect setup of the script. If the tracking code is not properly installed on every page of the website, or if it's configured incorrectly, visitor data won't be captured accurately.
  2. Caching Issues: Cached pages may not always trigger the tracking code. This means visits to cached versions of your website might not be recorded, leading to lower-than-actual visitor counts.
  3. Use of Ad Blockers and Browser Privacy Settings: Many users now employ ad blockers or adjust their browser privacy settings to disable tracking cookies. This can prevent your analytics tools from recording their visits, resulting in underreported data.
  4. Bot Traffic: Automated bots can inflate website traffic numbers. While some analytics tools have measures to filter out known bots, not all bots are detected, and some legitimate traffic might accidentally be flagged and filtered out.
  5. Session Timeout Settings:  Session timeout settings determine how long a user's visit is considered a single session. Incorrect settings can lead to either over-counting (by splitting a single visit into multiple sessions) or under-counting (by combining multiple visits into one session).
  6. Cross-Domain Tracking Issues: If your website spans multiple domains or subdomains and cross-domain tracking isn’t set up correctly, visitors navigating across these domains may be counted as separate users.
  7. Mobile Tracking Issues: Websites that are not fully optimized for mobile may have issues with tracking mobile visitors accurately, especially if the mobile version of the site has a different structure or lacks consistent tracking code implementation.
  8. Time Zone Differences: Discrepancies in time zone settings between your server, analytics tool, and the visitor's location can cause confusion in data reporting, especially with regard to real-time data.
  9. User Deletion of Cookies: Some users regularly delete cookies from their browsers, which can cause returning visitors to be counted as new visitors, thus skewing the data.
  10. JavaScript Issues: Since most analytics tools rely on JavaScript, users who have disabled JavaScript in their browsers won’t be tracked. Additionally, JavaScript errors on the website can prevent the tracking code from executing properly.
  11. Server Reassignment: Sometimes servers can be reassigned either temporarily or permanently. A server could be reassigned due to backups as part of recovery plan after a disruption, load balancing, organizational changes like mergers or acquisitions, or upgrades and change in services could play a role in discrepancies. 
  12. Location Crossing (B2B): Sometimes our system will identify both a company and a user location at the same time. Making the location come up as a different city and state that don't correlate between each other. In this instance, it is pulling information from both the employee and standard company information. 

Another way to look at data discrepancy is to think caller ID. You may be on a family plan, but when you call someone you show up as the plan owner. While it's not the information you were looking for, it's not wrong. It's just based on the primary of that account. The same can be said for data, especially in a +Person instance. Who has the largest digital footprint in the household? 

For B2B - are we working in a shared space, working on a shared server, working from home? All of these could be contributing factors to why leads could be showing up differently.

A variety of factors ranging from technical issues to user behavior can affect the accuracy of website visitor data. It's important to regularly audit and adjust your preferences to ensure the data you're collecting is as accurate and meaningful as possible. For +Person this could be done using the Known Customers list. For B2B+Employee this could look like Excluding Companies, or setting them as ISPs. 

+ Person Known Customers List Setup, here.

B2B Exclude Leads Assistance, here.

If the issue continues, please contact support. 
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