With the enforcement of SameSite settings in the latest versions of Google Chrome, it’s become a mad scramble to get cookies working across first-party and third-party contexts. I’ve covered this phenomenon before in my SameSite article, as well as in my guide for setting up cookieless tracking for iframes. Recently, Google Analytics updated its libraries (App+Web, gtag.js, and analytics.js) with a new setting: cookieFlags (analytics.js) or cookie_flags (App+Web and gtag.js).
One of the hard-and-fast rules in Google Analytics is that once hits have been collected and processed into your data properties, those hits are untouchable. This means that if you mistakenly collect duplicate or incorrect transactions, PII traffic, or referral spam, for example, it’s extremely difficult, if not downright impossible, to purge or change this data in Google Analytics. Another staple of Google Analytics’ strict schema is that displacing hits in time is also very difficult.
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.
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.
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.
Here’s yet another article inspired by the fairly recent release of Google Analytics: App + Web properties. This new property type surfaces Firebase’s analytics capabilities for websites as well, when before they were restricted to mobile apps only (see my guides for iOS and Android). Even though the feature set of Google Analytics for Firebase is still somewhat bare in the user interface, here’s a perk which might push you over the edge and give the new measurement model a shot.
Ah, Safari’s Intelligent Tracking Prevention - the gift that keeps on giving. Having almost milked this cow for all it’s worth, I was sure there would be little need to revisit the topic. Maybe, I thought, it would be better to just sit back and watch the world burn. But then Mr. Charles Farina, a good friend and so amicable he’s practically Canadian, tempted me into testing out a quick proof-of-concept web service that would give us back our first party cookies.
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