With this app, you can: 1) Check key metrics in built-in reports 2) Monitor real-time data 3) Compare date ranges and apply segments 4) Build your own reports with any combination of metrics and dimensions 5) Save any reports to your dashboard so you can easily come back to them.

I am able to tailor the app to show the information I need to see, and hide the irrelevant data.

Once linked in to this app it shows an immense amount of detail from percentages of new users vs returning to which pages have been viewed the most.

My favourite is the geographical locations of the days hits, it allows us to target a specific geographical area to improve sales.

Its one of the best things weve added to my business in 2018 without a doubt.

The custom dashboard is a brilliant feature that allows me to drill down to the metrics that I need to see, without having to wade through dashboards that might not be relevant to me at the moment (such as eCommerce).

Ill continue to use the app as it gives me excellent insights.

They allow you to store additional (numeric) data that is not captured on default in Google Analytics, e.g. cart value, list and sale price, read ratio (blogpost) and much more.

Its a new version that comes with an event-driven data model aimed at collecting data from both web and app.

This to further educate you on the direction that Google is heading with Google Analytics.

They have definitely tried to make user metrics and dimensions more central, but a lot of times sessions remained on the foreground.

Every interaction, including page views, are categorized as events.

You can add 25 custom parameters to each event, but be aware of limitations which I will discuss later.

However, as you can read this is far from ideal and only suitable for largest companies with high budgets.

Reports for this new property use a single set of consistent metrics and dimensions, making it possible to see integrated reporting across app and web like never before.

Web properties (standard Universal Analytics) have views instead of data streams and a default view is created automatically when you create this legacy property.

This is different from when you implement a Google Analytics App Web property as it is based on Gtag with a measurement ID (G-XXXXXXXXXX).

In my experience, there is no big difference in accuracy at the continent or country level.

The cookies are updated every time data is sent to Google Analytics.

Here is great guide from Simo Ahava explaining this in all depth.

You can think of the bounce rate metric, time on site and number of pages per session.

Is the bounce rate metric really meaningful for analysis and optimization It definitely delivers some insights in my opinion, but it is not ideal.

If a user is on the site for less than 10 seconds, they are defined as a bounce.

Engagement time diagnoses screen sleep time, or captures the time in the foreground for a mobile device, to determine how a user is engaging with your content.

However, ad-hoc queries of your data (e.g.

In my experience, you can apply data comparisons, secondary dimensions, filter your reports and everything will continue to be unsampled.

Sampling can occur in a few different situations that I was able to observe.

For example, the cohort analysis will load unsampled for a property I was using with 100 million events, but as soon as I added the date dimension, it returned 10 sampled data (10100m).

Another area, where I observed sampling being triggered in certain situations was when my date range exceeded the last 60 days.

In the last ten years I have probably set up and explored 1000 different segments.

After you create an audience, it accumulates users who meet the conditions from that point onward.

They allow you to store additional (numeric) data that is not captured on default in Google Analytics, e.g.

Analytics For Google Analytics Mac