Why are the clicks from kontextURLs different than those in other third-party analytics software?

gShift reports on all clicks occurring on the kontextURLs you create and distribute. These click totals may be different than the numbers you see in a third-party reporting tools such as Google, Twitter or Ad Network analytics. Below you will find some common reasons for these differences:

  1. Robots - There are thousands of bots crawling the Internet including the social channels where kontextURLs may be distributed. These bots can generate clicks on the kontextURLs. Despite best efforts, gShift and other third-party analytics tools are not able to filter out all of the clicks as new bots (which may not exhibit bot-like behavior) appear every day. In addition to not filtering out all bot clicks, the process by which each system identifies and filters out bot clicks may be different.  This is due to the different technologies and techniques used to determine and validate bot vs human clicks, which can include the analysis of User Agents strings, Source IP addresses or frequency of traffic among other factors.

  2. IP Filtering - gShift and most third-party software offer the ability to filter out clicks which come from specific IP addresses (e.g. internal traffic). If these IP filters do not match exactly across the systems, then there will be differences in clicks.

  3. Other Filters - Third-party server software may enable additional filters to exclude what data is made available to analytics solutions like gShift and the others such as referrals, hosts, IP domains, etc. These filters may restrict the data available to one analytics provider, but not another.

  4. Click Association - gShift's software attempts to cookie each unique visitor who clicks on a link. gShift reports on total clicks and unique clicks. Third-party vendors may associate clicks and cookies differently. Ensure you are comparing total clicks and/or unique clicks in all analytics software solutions. For example, Web browsers create a unique cookie for each different domain. As such, the same user/browsers can appear unique as there are different unique cookies for each domain.

    IMPORTANT: If end users are in incognito mode in Google Chrome or have “do not track” enabled, then using cookies to detect return users is not possible, and each click will appear as a unique user. Some systems may use IP/Time/Duration to attempt to determine and report on clicks from returning users.

  5. Sampling Google Analytics (and potentially other third-party solutions) sample their data. This means you may not see all the sessions/clicks occurring on your web property, whereas, gShift will record and report on ALL clicks not affected by #1, #2 or #3 above.

  6. Reporting Period - gShift’s default reporting period is based on the UTC time standard and this may differ from other systems, which use your local time zone to filter click data between dates.

  7. Browser Prefetch - In some mobile and desktop browsers/apps, an attempt to “prefetch” the content on the destination page can be made automatically by the browser/app prior to the user clicking on a link. There are many different implementations and there is no universally followed industry standard to perform and identify these prefetch actions. Where a browser sends a header attribute identifying a prefetch, gShift software will mark the click as a prefetch and these “non-clicks” can be filtered out (ON by default) in Reports and Data Beacons.

If you have any questions, note any discrepancies or would like our team to take a closer look to help validate the click data reported in gShift after you have reviewed the five items above, please contact

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