Identify engaged audiences through their behavior using Cohorts and know the underlying causes driving stickiness in your mobile apps. Use data science algorithms in Segment IQ to know the differences and similarities between segments.
To drive success to my mobile app, I need to understand which users are engaged and which users are falling off. In this video, I’ll show how Adobe Analytics can help me identify engaged audiences and help me boost retention rates.
The first step is to use a cohort table to identify engaged users through their return behavior over time. Cohorts in Analysis workspace are completely flexible because I can choose up to three metrics as my inclusion criteria and up to three metrics as my return criteria. Also, I can run my analysis within specific segments, adding up to 10 segments for my inclusion and return criteria.
I’ll keep this example simple. My inclusion criteria and my return criteria will be Launches greater or equal to one.
Also the segment of Mobile Customers for my analysis.
Then I’ll choose the granularity of a week.
And type of analysis of retention.
Here I can see how my users are returning to my app over several weeks. The second step is to create a segment of my engaged users. I can easily create a segment out of the users that return weeks seven and eight that I’m going to consider engaged users. Just log in those users with the right click of a mouse, I can create a segment.
Here Analysis workspace already did all the logic for me and I’m just going to name this segment Engaged Users.
The final step is to compare this segment against a different segment to start uncovering insights. For this, I’ll use Segment IQ panel.
Segment IQ runs highly optimized data science algorithms to compare every metric and dimension across the segments.
For this example, I’m going to compare my engaged user segment to my iOS users.
I’m going to change the data analysis to match the same date as my cohort table.
So here I have my segment IQ panel results. My first interesting insight is that most of my engaged users are part of my iOS segment. So I have a potential opportunity to better engage my Android users. Even these segments seem similar, I can start uncovering important differences. So let’s analyze the top metrics against segments. Not surprisingly, my engaged users are driving higher units, higher revenue and higher online units.
Also, I can see that my returning users have higher ad time spend and it’s interesting that they have higher in-app messages than my iOS segment. From this line chart graph, I can see that I’m investing more in campaigns in my Engaged Users segment than in my broader iOS segment. Now let’s compare the differences and dimensions of these segments.
It’s interesting to see that the most important difference is that the engaged users who were part of an AB test called by flow test. This is a very important discovery because this means that this AB test is likely to help drive engagement and revenue. As I saw earlier, my returning users were driving higher revenue than my broader iOS users. Next, I see that an important difference is that my engaged users are more likely to be in the app version 4.0. And here I have a bar comparison of the most important dimension differences. After analyzing segment IQ, I know what metrics and dimensions are correlated with my engaged users and I can target users that are falling out with strategies that work with my returning users. For example, I can encourage my broader user base to download the latest version of my app or I can include them in the by flow test experience. With cohort tables and segment IQ, I know which strategies are effective with my loyal users. Once I understand what’s engaging my users, I can replicate these strategies across broader audiences to increase engagement and retention rates. -