I found that DAU or MAU are not a good engagement metric for most Salas companies since monetization and retention are not directly tied to it. They can be a good proxy for early stage, but can be misleading over time.
Instead, identifying value metrics helps companies to better align incentives and measure engagement on a cadence that makes sense for that metric
Good stuff. Another useful dimension to add once you’ve settled on the right engagement metric is to segment by (age) cohort. This assumes your startup has enough customers in each age bracket to field a significant sample and that it’s been in operation long enough to field some more mature cohorts. Being able to isolate engagement and churn by cohort, and being able to trend these metrics by cohort, gives an added layer of insight as to where your onboarding and customer success efforts are working or need improvement.
Great post and useful tactics to cut through engagement data noise. Think it would be interesting to stratify further by segmenting DAU’s by usage profiles (“not all DAU’s are created equally”).
As I imagine is the case with Clickup, there are large stratificstions in the churn profiles of customers who check a dashboard once vs users who input tasks, pull reporting, and update statuses. I would trade lower DAU/WAU but critical in workflow (as shown by actions taken / time spent) vs “I check a dashboard every day” (easier to displace).
How do you think about DAU/MAU and DAU/MAU as it relates to free user engagement and growth trajectory?
I found that DAU or MAU are not a good engagement metric for most Salas companies since monetization and retention are not directly tied to it. They can be a good proxy for early stage, but can be misleading over time.
Instead, identifying value metrics helps companies to better align incentives and measure engagement on a cadence that makes sense for that metric
Good stuff. Another useful dimension to add once you’ve settled on the right engagement metric is to segment by (age) cohort. This assumes your startup has enough customers in each age bracket to field a significant sample and that it’s been in operation long enough to field some more mature cohorts. Being able to isolate engagement and churn by cohort, and being able to trend these metrics by cohort, gives an added layer of insight as to where your onboarding and customer success efforts are working or need improvement.
Great post and useful tactics to cut through engagement data noise. Think it would be interesting to stratify further by segmenting DAU’s by usage profiles (“not all DAU’s are created equally”).
As I imagine is the case with Clickup, there are large stratificstions in the churn profiles of customers who check a dashboard once vs users who input tasks, pull reporting, and update statuses. I would trade lower DAU/WAU but critical in workflow (as shown by actions taken / time spent) vs “I check a dashboard every day” (easier to displace).