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.
Data Studio is the tool used on Google Marketing Platform for data visualization. It became extremely popular among marketers in the past couple of years. The main reason for its popularity is that it is very extremely useful for building Google Ads and Analytics reports. Actually it is recommended to use Google Data Studio instead of Google Analytics built-in functions.
Two most popular opportunities to get Google Analytics data in BigQuery for non-premium Google Analytics users.
User-id tracking is the feature of Universal Analytics that many businesses implement. This is not a laborious process to implement User-Id tracking in case you have the unique identifier for each user available at front-end. But in real life few get any benefits from it.
Universal Analytics claims there are four main benefits of the User ID.
Google Tag Manager is a great tool that has a lot of opportunities for event tracking. If you are not familiar with this tool and have not used it before start with reading this article about basic concepts of Google Tag Manager. In this article, I will explain how to configure event tracking using Google Tag Manager easily.
An event can be sent to Google Analytics with any trigger. The most common case for event tracking is a click on some button or link.
Thrivecart is used by many businesses for building their sales funnels. They have many useful opportunities for digital marketers. However tracking implementation on Thrivecart is a pain. Thrivecart do not officially support Google Tag Manager.
Currently I got the following request from one of the clients:
I need help creating specific conversion evens for my contest funnel. Very simple funnel. One event for visitors who did not opt in and one event for visitors that opted in.
Discrepancy in Transaction Data between Google Analytics and Shopify. Some Random Shopify Transaction are missing in Google Analytics
Discrepancy in data especially in the revenue and transaction number is often an issue for the ecommerce sites that use Shopify. Shopify has a document where they list some possible reasons of this discrepancy https://help.shopify.com/en/manual/reports-and-analytics/discrepancies. However these explanations are not very helpful because they do not contain any practical advice on how this can be fixed.
On our platform we have some reports which show avg. time on page and avg. session duration metrics for the same page. Why is avg. time on page longer than avg. session duration for the same page in these reports? Is something wrong with tracking or reporting?
Landing pages report in Google Analytics helps marketers to evaluate the performance of different landing pages,to identify high converting landing pages, to find landing pages with inadequately high bounce rate or loading time, to find landing pages which are not mobile friendly and answer many more specific questions with data.
However there are some issues that can prevent or make it difficult to get useful data from the Landing pages report.
Google Universal Analytics allows importing costs data from any marketing platform (facebook, bing, twitter, etc.). In order to start using this feature you need to create at least one data set for cost data import (https://support.google.com/analytics/answer/6064692#step_by_step) in the Google Analytics property.
Follow these steps to configure your first data set.