When it comes to making critical product decisions, data is essential for getting your team on board and establishing credibility. In addition to interaction data coming from raw event counts, you can use Heap to demonstrate the correlation between interacting with your feature and other key metrics such as conversion, upgrades, and retention.
Our Graph module allows you to segment users into groups based on how many times they have performed an action or whether they have performed an action at all. Using group by has done event X, you can see whether or not an action is performed more often based on the correlation with a separate event. For example, to determine if viewing an instructional video regarding a feature is correlated with an increase in the number of times that a feature is used. you would:
Step 1: Graph Define/Update Event
Step 2: Group by has done Attended Training Session
In this example, we can see that Attending a Training shows a strong correlation with an increase in performing the Define/Update Event event.
Similarly, if you want to determine the correlation between multiple sessions and an event, you can group by count of instead of has done.
This will give you a breakdown of the number of sessions a user has had, along with event counts for users in each category. You can modify this query to contain count of Sessions rather than has done to measure the relationship between the number of times an event has been performed with, in this case Click Define/Update an Event.
Step 1: First make sure you have an event or event combo defined that captures the interaction with the feature in focus.
Step 2: Create a funnel that walks through key steps in your product such as a simple conversion flow for a sign up, upgrade, or checkout flow depending on your use case.
Step 3: Group by has done your_event (in this example, Watched Onboarding Video).
This will show the breakdown in conversion rate based on whether or not users have performed your event.
You can dive deeper by grouping by count of the event you're analyzing, such as for our example of Watched Onboarding Video. This will show you how the number of times your event is performed relates to the conversion rate. Feel free to set filters to limit the results displayed.
Step 1: First make sure you have an event or combo event defined that captures the interaction with the feature in focus.
Step 2: Create a retention report from either session to session or engagement action to engagement action.
Step 3: Group by has done your_event.
This will show the the breakdown in retention rate, or how often someone returns to do action X, based on whether or not users have performed your event. Similarly, you can dive deeper by grouping by count of your_event. This will show you how the number of times your_event is performed is related to the retention rate. Once again, you can limit the results displayed by adding filters.
Step 1: In the retention tool, select Session for first event, and your KPI for second event
Step 2: Group by has done your_event
Step 3: Select first time at the bottom of the query builder
This will show you the days/weeks/months it takes a user to complete action 2 after they have completed action 1 depending on the granularity you select. This example shows the majority of users complete the second event within the same day as the first, but a small percentage complete the second event one to two days later.
You can also use Heap to determine that 'Aha Moment; for users - what actions indicate that a user will be a loyal adopter of your product. After determining which features have the largest effect on retention (find how to by reviewing Does the interaction correlate with higher rates of conversion?), you will want to see how interacting with that feature influences retention. How many times does a user have to do x before your app becomes sticky? In order to assess the turning point where a user is hooked, use the retention tool.
Step 1: Use the retention tool to set event 1 and event 2 as session to session or engagement behavior to engagement behavior
Step 2: Group by count of Interaction with Key Feature
Looking at this retention table, we can predict that someone is more likely to be an avid user of the product after completing a purchase 10 times. Once this is identified, you can focus your efforts on getting users to the point through marketing campaigns, changes in product education, and changes in UI.
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