Data is the driving force behind effective decision-making. Whether you’re a business owner, marketer, or website administrator, understanding user behavior and engagement is paramount.
I’m here to explore our top three features that set GA4 apart, such as custom reporting, predictive analytics, and anomaly detection.
Whether you’ve just made the transition from Universal Analytics or starting fresh, this quick listicle equips you with the knowledge to navigate our personal top three features of GA4.
Feature #1: Custom reporting
While Universal Analytics gave users custom dashboards, GA4 has taken it a few steps further through the Explorations (Analysis Hub) feature, which lets you create custom reports. We’ve talked about reports in our guide on report basics, and what reports you should add to your GA4 collections.
You can use explorations to:
- Perform ad hoc queries
- Configure and switch between techniques
- Sort, refactor, and drill down into the data
- Focus on the most relevant data by using filters and segments
- Create segments and audiences
- Share your explorations with other users of the same Google Analytics property
- Export the exploration data for use in other tools
An exploration consists of three sections:
The canvas (shown to the right) displays your data using one (or in multiple tabs) of the selected techniques:
- Segment overlap
- User lifetime
Here you can choose to visualise data using bars, graphs and charts:
With the cohort exploration you can analyse insights into the behaviour and performance of groups of users that are related by common attributes such as events, transactions and conversions. Cohort exploration can show a maximum of 60 cohorts.
With funnel exploration you can visualise the steps users take to complete tasks on your site/app. This can help you identify and fix problems with user-experience, and identify how your audiences are performing.
This visual exploration shows a Venn diagram to see how different user segments relate to each other.
With this you can drill down into individual user activities and examine the users that make up the segments you create or import.
Exactly what it says on the tin, path exploration shows you the paths your users take as they interact with your website/ app
Explore user behavior and value over their lifetime as a customer.
The panel to the left of the canvas gives you access to the dimensions, metrics, and segments you can use in the exploration. You can also change the timeframe of the exploration in the Variables panel.
Use the options in the Tab Settings panel to configure the currently selected tab. Select the technique, add items from the Variables panel, and configure technique-specific options.
Feature #2: Predictive analytics
As we know, GA4 has Artificial Intelligence (AI) and Machine Learning (ML) capabilities. Designed to automatically enrich your data “by bringing Google machine-learning expertise to bear on your dataset to predict the future behavior of your users.” (Google)
Predictive analytics are important because instead of only knowing what people did on your website, Google’s algorithms help you predict future purchase behavior of your visitors.
Metrics and their definitions
|The probability that a user who was active in the last 28 days will log a specific conversion event within the next 7 days.
|The probability that a user who was active on your app or site within the last 7 days will not be active within the next 7 days.
|The revenue expected from all purchase conversions within the next 28 days from a user who was active in the last 28 days.
In order to train the above predictive models successfully the following prerequisites must be met:
1. A minimum number of positive and negative examples of purchasers and churned users. In the last 28 days, over a seven-day period, at least 1,000 returning users must have triggered the relevant predictive condition (purchase or churn) and at least 1,000 returning users must not.
2. Model quality must be sustained over a period of time to be eligible.
3. To be eligible for both the purchase probability and predicted revenue metrics, a property has to send the purchase (recommended for collection) and/or in_app_purchase (collected automatically) events. When you collect the purchase event, you need to also collect the value and currency parameters for that event.
The above eligible models are generated once per active user, per day but you can check the eligibility status of each prediction by going to the predictive section within suggested-audience templates in the audience builder.
*NB: Predictive analytics needs a lot of data to run effectively. Google Artificial Intelligence is worth nothing without data to process
Feature #3: Anomaly detection
According to Google:
Anomaly detection is a statistical technique that Analytics Intelligence uses to identify anomalies in time-series data for a given metric, and anomalies within a segment at the same point of time.
Simply put an anomaly is an outlier in data – if a piece of data doesn’t conform to the rest of the data “norms” it is classed as an anomaly. Google anomalies are available to view and investigate through the “insights” tab on GA4.
A couple of useful anomalies include:
- Credit card transactions of suspiciously large amounts or, processed at unexpected locations.
- Unexpected decreases in website traffic for a particular day of the week.
While it’s important to implement anomaly detection in Google Analytics 4, your analysis should not stop there. You should also figure out why those anomalies showed up.
You can look at anomalies in metrics like:
- Engagement rate
- Session duration
In addition to anomalies in segments such as:
- A device type
- User location
- A product type
Although anomaly detection has been present in Universal Analytics since 2017 with Google Analytics Intelligence Insights, GA4 represents the next stage in this type of machine learning.