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Universal Analytics utilizes two components (by default) to attribute a browser session to a specific campaign: query parameters in the URL and the referrer string. The page URL is sent with every hit to Google Analytics using the Document location field, which also translates to the &dl parameter in the Measurement Protocol. The referrer string is sent with a hit to Google Analytics using the Document referrer field, as long as the referrer hostname does not exactly match that of the current page and the referrer string is not empty.

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Google Tag Manager introduced the capability to add tests to your Custom Templates. Tests, in this context, refer specifically to unit tests that you write in order to make sure your template code works in a predictable way. Unit tests are also used to drive development, ensuring that you have added contingencies for all the different scenarios that the template, when coupled with user input, might introduce. In this guide, I’ll introduce how the Tests feature works.

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In Google Analytics: App + Web, you collect events. One event differs from another event by the name it uses. An event with the name page_view is different from, say, an event with the name file_download. This is all run-of-the-mill stuff. You know this. However, the fundamental change that App + Web introduces, when compared to Universal Analytics, is how event parameters are collected and processed. This gets more complicated than it should be.

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There are lots of different readability formulas out there, which seek to provide an index on how readable any given excerpt of text is. Typically, these formulas output a grade-level score, which indicates, roughly, the level of education required to read the text excerpt with ease. Any “quality index” that seeks to reduce the complexity of something as multi-faceted as reading should be subject to scrutiny. This is true for Bounce Rate, this is true for Time On Page, and this is true for a readability score.

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There are thousands upon thousands of bots, crawlers, spiders, and other creepy-crawlies out there doing nothing but crawling through websites and harvesting the content within for whatever purposes they have been fine-tuned to. While Google Analytics provides a bot filtering feature to filter out “spam” and “bot traffic” from views, this is far from comprehensive enough to tackle all instances of bot traffic that might enter the site. You might have noticed bot traffic in your data even if you have bot filtering toggled on.

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I’ve thoroughly enjoyed writing short (and sometimes a bit longer) bite-sized tips for my #GTMTips topic. With the advent of Google Analytics: App + Web and particularly the opportunity to access raw data through BigQuery, I thought it was a good time to get started on a new tip topic: #BigQueryTips. For Universal Analytics, getting access to the BigQuery export with Google Analytics 360 has been one of the major selling points for the expensive platform.

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Since the introduction of custom templates in May 2019, the community (myself included) has been anxiously waiting for some official solution for curating and distributing templates created by the community. Now, finally, we have it. It’s called the Community Template Gallery! Read Google’s announcement in this blog post. I’m not going to go over the basics in this article, since Google’s own documentation stands fine on its own feet.

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Simo Ahava

Husband | Father | Analytics developer
simo (at) simoahava.com

Senior Data Advocate at Reaktor

Finland