another follow-up: how can you determine what's "good" for your product or not haha it seems like one of best applications of this type of analysis might help you prioritize your investment into product areas? thinking out loud here haha because obviously strava logging runs is going to be the top used feature but is 3 days enough or 4 days, etc
love it! think the hardest thing in Amplitude or similar is finding ways to "reduce noise" so some of those charts (eng matrix or pathfinder) are useful...that's why for some of these posts, i'm tempted to just revert back to google sheets or even some sort of notebook haha
anyways, one quick question - specifically, when it comes to a freemium app..let's say Strava
how would you recommend putting this INTO action when it comes to understanding which premium features drive the most usage or growth?
I am using daily retention and DAU in my analysis as an example that suits Strava. WAU, WAU/MAU, weekly and monthly retention might be more appropriate for other products.
Keep in mind, some features may be expected to be used occasionally, and others more frequently. That's why I watch DAU/MAU ratio in combination with AVG usage per user. DAU/MAU ratio shows what % of users interact with this feature every day in a given month. If this ratio stands out for a particular feature it's strong signal. For example, if it is very small but AVG per user value is high, it tells you that users use the feature a lot but not frequently. It can be expected, or can also be a discovery issue (when a feature is hidden in menu or else).
another follow-up: how can you determine what's "good" for your product or not haha it seems like one of best applications of this type of analysis might help you prioritize your investment into product areas? thinking out loud here haha because obviously strava logging runs is going to be the top used feature but is 3 days enough or 4 days, etc
love it! think the hardest thing in Amplitude or similar is finding ways to "reduce noise" so some of those charts (eng matrix or pathfinder) are useful...that's why for some of these posts, i'm tempted to just revert back to google sheets or even some sort of notebook haha
anyways, one quick question - specifically, when it comes to a freemium app..let's say Strava
how would you recommend putting this INTO action when it comes to understanding which premium features drive the most usage or growth?
I am using daily retention and DAU in my analysis as an example that suits Strava. WAU, WAU/MAU, weekly and monthly retention might be more appropriate for other products.
Keep in mind, some features may be expected to be used occasionally, and others more frequently. That's why I watch DAU/MAU ratio in combination with AVG usage per user. DAU/MAU ratio shows what % of users interact with this feature every day in a given month. If this ratio stands out for a particular feature it's strong signal. For example, if it is very small but AVG per user value is high, it tells you that users use the feature a lot but not frequently. It can be expected, or can also be a discovery issue (when a feature is hidden in menu or else).