Each tool uses different tracking methods. This means that you can find discrepancies between data in Convertize and other analytics tools you might be using. Small variations in unique user counts are normal, because different platforms often define “unique visitor” slightly differently.
For each visitor that enters your experiment, Convertize creates a cookie to make sure that returning visitors are not counted again. The cookie remains active for 100 days.
A new visitor is counted as part of your experiment if they have seen at least one of the pages included in your experiment’s URL targeting. If your experiment runs on multiple pages of your website, visitors who see more than 1 page on your site, are still counted only once.
Larger discrepancies, however, often mean that you are measuring two different data sets. In order to account for any larger discrepancies your may see, it is important to understand how each platform measures visitors and conversions. Keep reading for the most common reasons for larger discrepancies between Convertize and third-party data.
If you are not measuring the same thing, you cannot expect to see similar data. When experiencing large discrepancies between Convertize and third-party data, check the following things:
Are you tracking the same metrics?
Convertize looks only at unique visitors, while your analytics platform can count e.g. sessions, page views or unique page views.
Do the URL(s) or patterns on which you are measuring traffic match?
For example, if your experiment only runs on one page, but your analytics platform counts visitors site wide, the unique visitor count will be different.
Is your audience targeting the same?
For example, if you have set the audience targeting of your Convertize experiment to mobile devices only, but you are targeting all devices on your analytics platform, you can expect to see a significant difference in visitor count.
Are the reports running in the same time zone?
If you are looking at reports on daily visitors that run in different time zones, you may see different daily visitor counts. Make sure your analytics platform is set to the same time zone as Convertize. Reports in Convertize follow UTC.
Does date range filtering work in the same way?
When looking at a certain date range, Convertize only shows you new visitors who first entered your experiment in that date range. Some platforms may display both new and returning visitors on the dates selected.
Do reports on your analytics platform have a delay?
Convertize reports provide real time data. You can view your test reports instantly in real time. Other platforms take some time to update data. Google Analytics reports, for example, take 24-48 hours to update.
Does you analytics platform filter bots?
Convertize filters out visits by bots, such as Google Crawler Bot. If your analytics platform does not filter these, you may see much higher visitor counts.
In a split URL test, the original page loads in the browser before the visitor is redirected to the variation. Even before the redirection takes place, GA code begins to execute on the control page itself. This could trigger a page view on the original in GA.
In a scenario where GA code is activated before the Convertize Pixel in a split URL test, anyone going to the variation is counted for the original’s page views as well. As a result, you’ll see that the page views for your original are almost the double of your variation in a split URL test.
If you have similar visitor counts but different conversion counts in your experiment, you’ll want to take a closer look at the way you track events in each platform. Two similarly named events aren’t necessarily tracking the exact same action taken by visitors.
For example, you may have a “signup completed” event tracking a form submission. In Convertize, you may have this configured as a “submit button” click goal or a “confirmation page” page-view goal. Each of these goals represents the same thing (the user submitted the form), but they are not tracking the same action, which can lead to a discrepancy. The user may have clicked the submit button without filling out the form, or maybe the user submitted the form by pressing “enter”, bypassing the button click event.
Make sure your events track the same things the same way. If they don’t, you will have dissimilar data.