Many Google Analytics 4 (GA4) users struggle to derive meaningful insights from the reports suggested in GA4. One common frustration is that GA4, by default, only provides advertising cost data from Google Ads, leaving out crucial spend information from other key platforms like Meta (Facebook), TikTok, and Bing. This can make it difficult to get a complete view of your marketing performance.
The landscape of e-commerce has evolved significantly, with a growing number of businesses use subscription models to get a consistent stream of revenue. For these businesses, the real challenge lies in maximizing user lifetime value. Effectively tracking transactions, recurring payments, and account cancellations is essential for sustainable growth. In this blog post, we'll explore a crucial aspect of this challenge and provide insights into using server-side tracking with Google Analytics to enhance control and visibility over subscription transactions.
In the vast world of Google Analytics 4 (GA4), there's a nifty feature that often flies under the radar – "Custom Insights." Surprisingly, not many GA4 users tap into its potential, but trust me, it's a game-changer when it comes to conversion rate optimization and acting on your data changes better.
Samplation has often been a pain for Universal Analytics users. With the move to Google Analytics 4, the quotas for queries have been greatly increased. In GA4, the quota limit for event-level queries is 10 million events for standard Google Analytics properties and up to 1 billion events for Google Analytics 360 properties. This enables GA4 users to report on large timeframes and multiple dimensions without any sampling.
In the ever-evolving landscape of digital marketing and analytics, it's essential for businesses to stay ahead of the curve. With the arrival of Google Analytics 4 (GA4), the industry is witnessing a major shift in how data is collected and analyzed.
Here is the list of main changes that were introduced by Google Analytics 4:
Roll-Up Property is a single property which aggregates data from multiple source properties. This might be useful for businesses that have multiple websites for different regions. In this case, they can safely track each of the regional sites into a separate Google Analytics property and also have a separate roll-up property to analyze a larger amount of data than you have in single-site properties.
Here is how the Google Analytics account structure would look like in case of using the roll-up property:
Streaming raw Google Analytics data means duplicating all the hits (events, page views, transactions, etc.) you send to Google Analytics in a different database (in BigQuery).
It means you will get all the raw unfiltered hits and you can either process this data further or use it for any kind of analysis.
Goals and funnels reports are extremely useful for marketers. These reports help to evaluate traffic sources and campaigns, to analyze the user flow through the funnel before conversion, to find drop-off points and bottlenecks in the funnel. Google Analytics is tricky when it comes to funnel configuration.
Unlike many other analytics systems Google Analytics processes the goal and funnel data. It means that you cannot build a funnel report on historical data. you need to configure your goals and funnels first and only after this Google Analytics will start collecting data about funnel flow and conversions.
Some web-analytics services claim to have an advantage of tracking individual users that Google Analytics cannot do. However, this is not quite true.
Google Analytics tracks individual users, each interaction of individual users and has user-level reports. There are some limitations only regarding the personally identifiable information (PII).
Google Analytics presents their data sampling technology is a way to present meaningful data on large data subsets (here is official documentation on this topic https://support.google.com/analytics/answer/2637192?hl=en). However most of the medium-sized online businesses worry a lot when they get a notification that the report is built on 70% of their data and not 100%.
And they worry not without reason especially in case they analyze some small segments of their audience.