When configuring the event, we set: event name, parameters. The plural form is important here, meaning that there can be more than of these parameters, and exactly the limit is . The limit of unique events for the application is , while in the case of data from the network there is no limit. You can see an example of event building comparison in both products in the image below, and you can read more in the article “Google Analytics Events ” . Creating GA vs GA events.  Debug View  offers access to the event debugging view from the platform level. So far, this has not been possible in GA .

 

To verify that the Analytics configuration was successful, we had to operate the “Real Time” report accordingly or wait for the data to be collect and available in GA. DebugView report in Google Analytics . The report allows you to verify the incoming “live” events and the correctness of the parameters sent with them. . Conversions instead of goals Another significant change relates to goals. In GA , each of our goals is referr to as a conversion, which means that both the purchase and submission of the form results in the achievement of the goal,  conversion. Goal types in Ivory Coast Phone Number List Universal Analytics. There are the following types of targets in GA : destination, Duration, pagesscreens per session, happening, smart target.

Phone Number List

Marking Events as conversions

There are no target types in GA . Conversions are configur with a slider. One swipe of the slider causes the event to be flagg as a conversion from then on. In Google Analytics . The main difference is that in Universal Analytics the goal is count once per session. Whereas in GA the conversion can be count several times during one session . This means that if the user sent the form twice during one session, the goal will be count once in GA , and conversions will be shown in GA .  No conversion rate Conversion rate is not includ in the new service. So how to measure it? The best LOB Directory solution is to perform calculations outside of GA, in Google Data Studio.

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