Looker is super excited to announce our partnership with Heap and the creation of a new Looker for Heap SQL Block. Heap makes collecting data on web or mobile activity painless - and Looker for Heap SQL makes deploying a BI solution on top of the resulting data just as easy to deploy across an entire organization - so that everyone can answer their own questions on things like user behavior and application performance.
Web and mobile analytics can provide meaningful insights to any modern organization. Heap automatically captures user actions - clicks, taps, gestures, form submissions, page views - and allows you to add any additional custom properties you’d like without even having to touch code. What makes Heap unique is that as your analytics' requirements continue to grow and change with your business and you add new events to track - these events are automatically updated historically, so you have a full view of the new event’s performance over time. The events defined in Heap can provide insights into user behavior, performance trends of key metrics, and retention data valuable for any website or application.
Heap recently launched Heap SQL, which provides retroactive data access in a managed Redshift data warehouse to all of these automatically defined and custom events. Users have direct SQL access to define any reporting or analysis they’d like. And as you make changes to your event definitions in Heap, these are reflected in your Redshift data warehouse too - so historical analysis is just as accurate as you need it to be.
Similar to Heap’s philosophy, at Looker, we know that requirements change over time for a growing and evolving business - and an analytics solution needs to keep up with new requests while still maintaining a centralized set of data definitions across an organization. Looker’s LookML modeling layer is designed to easily accommodate modifications and additions, since it’s maintained in a version controlled repository that provides data transformation at time of query, not ahead of time. A data team can create and modify metrics for an entire organization just by modifying a few lines of LookML. Additionally what this means for reporting and analysis is that it’s curated directly from the source - the Redshift data warehouse - such data doesn’t have to be pre-aggregated and row level granularity can still be accessed.
Looker Blocks for Heap creates a starting point to get the most out of your Heap data with Looker. Metrics to explore users, events, and sessions are included in the LookML specifically for Heap SQL’s data in Redshift. These definitions can be directly utilized by any user in Looker’s Explore interface to create custom ad-hoc reports and analysis, even without any knowledge of SQL. We’ve highlighted some cool things you can do with Looker Blocks for Heap here:
The holistic view of a user’s visit to a website or application can provide indicators of performance and user behavior. With Looker and Heap SQL, you can explore metrics like:
And you can slice and segment these metrics however you’d like, for instance by:
This flexibility allows you to explore your application’s data to find limitless insights into user behavior and key metric performance.
Looker Blocks for Heap includes a Custom Conversion Funnel, so you can see conversion metrics for any combination of events you’d like. These can be filtered and segmented by any session metric. Trending conversion rates over time are useful to observe performance for any step of the funnel.
Observe how segments of customers are returning over time, based on metrics like first visit. Filter on other user metrics like latest session, lifetime session count, and duration of time on the application to dig deeper into your retention analysis.
Please refer to Looker’s discourse page for more details, or you can access the Heap Block by reaching out to your assigned Looker analyst or request a Looker trial. For a free trial of Heap, simply sign up for Heap here and reach out to email@example.com if you’re interested in learning more about Heap SQL!