Now you can leverage the data-attributes in your DOM to seamlessly define retroactive properties. This means that Heap’s promise of a complete and retroactive set of event data is now available for certain kinds of property data as well.
Here’s how it works. When you define an event in the Event Visualizer, Heap now offers Property Suggestions that are associated with the event that you can choose to use in your event definition.
Once you’ve added the relevant Property Suggestions to your event definition, you’re all set to start running reports using the data. Like all event data in Heap, access to the info stored in your data-attributes is now immediate and completely retroactive.
Defined Property Updates
We’ve made defined properties easier to use to help you better organize your data.
A new analysis preview on the definitions page allows you to quickly verify if your property definition is correct.
Are you ready for Heap to read your mind? When you use a defined property in a filter, Heap will now finish your sentences by autocompleting your thoughts based on the top options.
iOS Event Visualizer Updates
We heard your feedback on the old iOS pairing gesture being a bit clunky (involving some finger gymnastics). So we’ve improved the EV pairing mechanism on iOS so that you can connect your device to Heap by simply scanning a QR code.
One of the most common next steps after creating a funnel or looking at feature usage or conversions is to break the analysis down by landing pages, user segments, etc. to answer the question: “Is this trend being influenced by a particular group?”
Answering this question used to involve a laborious “guess and check” process with the goal of spotting an interesting breakdown with enough variation between groups. Now Heap does this “guess and check” process for you by automatically examining a set of properties and proactively suggesting the most significant groupings.
These Group Suggestions help you find the best ways to group your data to uncover predictive insights on customer behavior. How does it work? When you run a graph or funnel query, Heap now automatically offers suggestions for how to best group your data to reveal deeper insights.
For example, in a funnel query, Heap will analyze which properties are most predictive of overall conversion rate, and then suggest grouping your data by those key properties.
So if you have a funnel to track requests for a quote, we may recommend segmenting cohorts by UTM Campaign, UTM Source, or Landing Page to surface insights in user behavior. These automated recommendations bring to light the often unexpected characteristics or properties that users who convert share.
When running a graph query to see the weekly number of product demos requested, Heap might suggest grouping the results by UTM Campaign for insight into which marketing campaigns have the highest impact on lead generation over time.
BigQuery support for Western Europe
Teams based in Europe can ETL data to BigQuery instances in GCP’s
Updated Developer Best Practices
We want to make following best practices in your data analysis a no-brainer. That’s why we created a guide with all of our recommendations for developers so you can stay at the top of your analytics game.