{"id":28696,"date":"2023-01-09T15:50:08","date_gmt":"2023-01-09T15:50:08","guid":{"rendered":"https:\/\/mailinvest.blog\/index.php\/2023\/01\/09\/3-google-analytics-4-features-to-make-up-for-lost-data\/"},"modified":"2023-01-09T15:50:08","modified_gmt":"2023-01-09T15:50:08","slug":"3-google-analytics-4-features-to-make-up-for-lost-data","status":"publish","type":"post","link":"https:\/\/mailinvest.blog\/index.php\/2023\/01\/09\/3-google-analytics-4-features-to-make-up-for-lost-data\/","title":{"rendered":"3 Google Analytics 4 features to make up for lost data"},"content":{"rendered":"

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With the legacy version of Google Analytics retiring soon<\/a>, we\u2019ve entered the era of Google Analytics 4 (GA4). Aside from getting a major facelift and data model change, one of the platform\u2019s most powerful upgrades was the addition and refinement of machine-learning capabilities.\u00a0<\/p>\n

Google Analytics now has the ability to combine observed data and unobserved data. Not only is this a benefit, but it\u2019s a necessity as changes in browser cookies and user identifiers increasingly limit the old way of tracking.\u00a0<\/p>\n

Our tracking and analytics tools are losing data as we know it \u2014 and we must adapt. Using some easy features in GA will help compensate for this loss so that you can remain data-informed.<\/p>\n

Dig deeper: 3 \u2018secret\u2019 marketing tools in Google Analytics 4<\/a><\/em><\/strong><\/p>\n

Unobserved data: How it works and why it matters\u00a0<\/h2>\n

No matter which analytics tool you use, leveraging unobserved data is a great tool to keep up with the evolving environment of digital marketing analysis. The difference between unobserved and observed data is the difference between collected data<\/em> and modeled data<\/em>.\u00a0<\/p>\n

Tracking users with cookies<\/a> used to be more reliable since almost all browsers accepted cookies. The way it functions with analytics is by automatically stamping a user with a cookie when they land on a website. This cookie allows platforms like GA to identify users by device information, location, demographics and, most importantly, a random ID that\u2019s \u201csticky.\u201d\u00a0<\/p>\n

When that user returns to the website, the ID is recognized by GA as a returning user, which stitches that user\u2019s past information with new activity. For mobile apps, the behavior is similar. Instead of a cookie, devices have a unique advertising ID as an identifier (Android and iOS have different versions.)<\/p>\n

However, things have been changing gradually over the past several years and will continue to change. There\u2019s a huge problem with this old behavior: it gave users little to no control over their personal information being shared. Privacy<\/a> wasn\u2019t a consideration, and organizations had 100% control over their audience\u2019s information.\u00a0<\/p>\n

No personally identifiable information (PII) was ever tracked with Google Analytics by default as collecting such data to GA is against the terms of service, but the definition of PII has changed depending on how policies are written and interpreted by different laws and security teams.\u00a0<\/p>\n

Now, users can block and opt out of analytics tools from collecting data. Automatic opt-out is the default for GDPR and other countries\u2019 laws are certain to adopt this. It\u2019s the \u201ccookieless future<\/a>.\u201d\u00a0<\/p>\n

Long story short \u2014 we won\u2019t get the volume or detail of user data we used to, so it\u2019s time to fill that gap. In Google Analytics 4, there are several out-of-the-box features to make up for lost data. They require little to no lift once tracking has been set up, so you can test and take advantage of them today. Three examples are:<\/p>\n