Differences existing between statistics from various tools used by marketing teams is a historical problem in the digital field. It is common to all sectors (e-commerce, media ...) and to all teams (acquisition, marketing, analytics, sales ...) who use various tools to drive their strategies. This is logically also the case for the media industry.

Facing this problem, 3 main questions arise:

  • What are the statistics that differ between Poool's Dashboard and my measurement tool?
  • Why do such differences exist?
  • Which statistics should I take into account to make decisions?

I can see differences between the number of users / unique visitors from my measurement tool and from Poool's Dashboard.

This difference exists and can be quite big between your measurement tool and Poool's Dashboard. As a general rule, it is always possible to observe differences in the counting of users between two solutions like Google Analytics or AT Internet. Why? Simply because the definition and the counting method for users is specific to each solution.

On Google Analytics for example, a unique visitor refers to a single and unique Internet user who has visited your website during a specific period. Unique visitors are determined by cookies. By consequence, the definition of a "single and unique Internet user" is subjected to the technological limits of cookies. So, if someone changes their browser, uses private browsing or deletes their cookies, then comes back to the website, they will be considered as a new user.

Other scenarios are to be taken into account:

  • Differences about the number of unique visitors between Poool Access and your measurement tool also depends on how your script has been implemented on your website. If your analytics script is implemented on all web pages but Poool's script is implemented on all pages but the homepage, then users that visit the homepage will be counted by your measurement tool only and not by Poool Access. This could be true for any other 2 measurement tools.
  • The number of unique visitors also depends on the blocking of any of the two scripts by adblockers. If Poool's tracking script is hard-coded on your website but your analytics script is implemented via Google Tag Manager, then users visiting with uBlock will be counted by Poool Access but not by your analytics tool. This could be also true the other way around. 
  • When users refuse to give their consent manually or automatically (via this kind of plugins) they will not be counted by Poool Access while they could be counted by tools like AT Internet to which consent rules differ. This results to the same situation if consent is given to one tool but not to the other.
  • Last but not least, the calling order of scripts on the page matters. Analytics tools are generally called in the header and that is not necessarily the case for Poool Access' script. By consequence, depending on the user's connectivity and the page loading time, the analytics script could be called while Poool's script is not, specially when users leave the page after 2 seconds for example.

Which statistics should I take into account? 

In this case, it is hard to say which solution will be the closest to reality because it depends on a lot of factors and on the way your team wants to count users. Here at Poool, we likely encourage you to take into account the stats from your analytics tool, which, most of the time, has been implemented in a more complete way than Poool Access has.

I can see differences in the statistics about performed paywall actions (clicks, unlocks ...) between my measurement tool and Poool's Dashboard

If you need to track your users' behavior with your paywall and to send the info to your measurement tools, you probably have used 1 of these 3 methods:

  1. Tracking elements implemented into URLs
  2. Poool Access events manually sent to your measurement tool
  3. Events automatically sent from Poool to Google Analytics by adding one line into Poool Access' config

No matter which method has been chosen, various reasons can explain differences on actions statistics between Poool and your measurement tool:

  • You may have implemented tracking elements into URLs only for certain actions or scenarios. On Poool's side, all behaviors are tracked.
  • You may have manually sent Poool's events to your measurement tool but the latter is not set up well.
  • Automatic event sending via Poool may be blocked if Poool is implemented via GTM and the visitor uses uBlock (for example).

Which statistics should I take into account? 

In this case, results shown by Poool Access are the closest to reality because all of the actions are performed within Poool's environment. As a general rule, you will often observe more conversions in your measurement tool than on Poool's Dashboard.

I can see differences on conversions between my back-office and Poool's Dashboard.

Conversion tracking  is a relatively new issue for publishers. However this has been a major problem in the e-commerce industry for many years now and statistics differences between the tools used by marketing teams remain problematical. So, what are the reasons for these differences?

In most cases, your subscription management tool is directly linked to your payment solutions. As soon as one user subscribes and their payment is validated, a new order shows on your back-office as well as a new subscriber. This flow is managed on the server side.

Conversion tracking for a solution like Poool Access is performed by injecting a tracking script on the order confirmation page. As soon as one user validates their order and arrives on the order confirmation page, a conversion hit is sent to Poool and the info is available 24h later on your Poool Dashboard. This flow is managed on the browser side. However, various scenarios can explain this difference:

  • Poool's script could not be implemented well. For example, the "send page-view" is not injected after the conversion event.
  • After an order is made with a specific payment method, the user could not be automatically redirected to the order confirmation page, resulting in the script not being called.
  • Or even if the user is correctly redirected to the order confirmation page, they could use uBlock and Poool's script could be implemented via GTM. In this case, uBlock would block all calls made via GTM and the order confirmation would not be communicated to GTM.

Which statistics should I take into account? 

In this case, it is always better to follow the data from your subscription back-office, because it is the closest to reality.

To conclude, differences exist and will remain between your various measurement tools. It applies to Poool Access data but also to any measurement tool you could be using. When seeing this type of differences, you can ask yourself:

  • whether these differences are normal or not
  • whether these differences are important or not
  • which of the tools would be the best to measure this specific statistic and make a decision

If you have any doubt, contact us on support@poool.tech!

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