Group by allows you to break down queries even further by creating groups within reports based on user and event level properties.
Aggregations can even be included in filters and group by statements for advanced segmentation. To understand the different levels of engagement within your site, you can define the following query, which filters out users with a single session, and then provide a distribution of how often your userbase engages with your site:
You can group any analysis by a user level property. Within Heap, there are automatically captured user properties such as date first seen and first touch properties. You can also add additional properties to your users such as account type, LTV, and industry using our API addEventProperties. To slice and dice your analysis by any user level properties, simply click the plus sign and select your property.
Grouping by a user level property gives you an in-depth picture of your data because it gives you insight into who is using your product. For example, if we think about Heap, there are four main analysis tools: graphs, funnels, retention, and users view. Using a multigraph, we can compare how frequently each of these features is used, which helps the product team prioritize which features to work on. When we group this multigraph by role, we are not only comparing which features are being used most frequently, but also comparing who is using them. A marketer needs a different feature set than someone in sales or design. This gives actionable insight into how we should prioritize our features and who we need to build features for!
You can also use the multigraph + group by to compare the effectiveness of different campaigns, landing pages, blog posts, etc., across different groups of users. In this example, we can compare three different blog posts and their conversion rate for signups based on the region. Looking at the results, it’s clear that some articles are more effective for different regions. This means our outbound team can adjust their strategy when getting prospects to sign up for Heap!
In funnels, conversion rate over time, and average time between you can slice and dice your data by grouping by one of the event's properties.
When you group by the property of the first event in a funnel or any intermediate event, we calculate the conversion rate by bucketing the user into the instance of the event immediately before a conversion. If the user has not completed the rest of the funnel, we take the most recent value of the event’s property. Each user will only be included in one bucket, and if a user completes a funnel multiple times, we will use the data from their first conversion.
For example, in an A→ B→ C funnel:
- Bob does Event A with path
/home,event A with path
/blog, event B, event C
- Tammy does Event A with path
- Beth does event A with path
/home, Event B, Event C,
The results will be as follows:
When you group by the properties of the last event in the funnel, users will be bucketed into categories based on the first instance of the last event (the first conversion). As above, each user will only be included in one bucket, and if a user completes a funnel multiple times, we will use the data from their first conversion.
For example, in the funnel A → B:
- Bob does A, A, B with Referrer Google, B with Referrer Facebook
- Tammy Does A, B with Referrer Facebook
- Beth Does A
the results will be as follows:
You can group by event level properties in the graph (including average time between and conversion rate between queries) and the funnel.
Grouping by the first or intermediate step of a funnel gives you insight into what was happening immediately before the conversion. For example, we can compare the effectiveness of different marketing pages. This example shows the marketing page immediately preceding the
Sign Up event. This can be used to see which collection was viewed before checking out, or which product was viewed before adding to the cart.