In my earlier post about how to use customer feedback, Tip #5 was Let your metrics be your trusted advisor. Today I’m doing a deeper dive into metrics since they pretty much dictate if your app lives or dies.
The average Android app loses 77% of its daily active users within the first three days after the install, and 90% within the first 30 days.
25% of installed apps are never used.
26% of installed apps are abandoned after the first use.
What should we do, stop building mobile apps? Of course not — Users spend way more time on mobile apps now compared to web apps (90% compared to 10%), so clearly mobile apps have become everyone’s favorite child. But more than ever, we need metrics to gain total clarity on what users think about our app, whether our app is a sustainable business, and whether the numbers back up our conviction that we’re onto something here.
Monthly Active Users: Monthly active users captures what percentage of users that installed your app actually used it over the course of the month. Note that the app store uses an opposite metric, uninstalls, to measure this. Monthly active users tends to drop each month, and at the end of 3 months, the industry average is somewhere around 20%. What you should be aiming for is over 25% at the end of 3 months. If you’re at less than 20%, then you need to find out what the cause is and find out if it’s fixable. I believe that product-market fit should never take more than 6 months.
Usage Frequency: If you’re an app that’s trying to become habit-forming (health trackers, meditation, journaling, other behavior modification) then your app won’t cut it if users visit once a week. It needs to be an every-day or almost-every-day app.
Time Spent On App: This one’s pretty straightforward.
There is some disagreement on how much these matter versus installs, but this chart from the marketing team at Fisku pretty demonstrably shows that the algorithm was changed on a specific day to value ratings and reviews more in app store rankings.
1. How much revenue is an average user bringing in a month? Divide total revenue per month by monthly active users. If you’re making $1000 a month and you have 250 monthly actives, each user is bringing in $4 a month.
2. How many months is an average user is expected to be active? This requires you to know another metric called “Churn” which is what percentage of users that installed your app stopped using your app this month, aka “churned out”. Taking the inverse of this (1/churn) acts as a proxy for how long each user is expected to stay with your app. So if churn is 0.60, then each user is expected to stay with you for 1/0.60 = 1.67 months.
3. What is the referral value from an average user? This is pretty complex to calculate, because tracking referrals is not clear cut, but if you have something like a social share embed with referral link, tracking something is far better than tracking nothing in this case. Based on the above metrics, each user is bringing in $4 a month and is expected to stay with your app for 1.67 months, making their total lifetime value $6.68 before referral costs. Now say the average user refers 2 other users. That means the referral value from those 2 additional users is 2 x $6.68 = $13.36.
Now the total lifetime value of the referring user becomes ($4 x 1.67) + $13.36 = $20.04! See why you should go to the ends of earth to track referrals?
This one’s much easier. Just divide your total monthly marketing costs by total number of app installs. If you spent $600 on ads to get 300 installs, your CAC is $2.00.
Now you can do a magic trick. If the CTLV ($20.04) is greater than the CAC ($2.00), it means you’re getting a positive ROI from your marketing spend. In this hypothetical case, I’d double down on ads, since the CTLV is 10x the CAC, and the monthly value from each user by covers the CAC all by itself.
A cohort is a slice of your entire userbase based on geography, device, demographics, marketing funnel, or another defining attribute. The reason to track lifetime value from users of a specific cohort is you may find that users from a particular cohort will tend to spend more, which would give you a higher ROI on ads that specifically target that cohort. For example, let’s say your app takes a portion of Breast Cancer Awareness donations, and your ads target multiple social media platforms. Users recruited from Pinterest might end up donating more than users from Twitter (because Pinterest is 85% female.) If this is confirmed, you’d want to spend more on Pinterest advertising than Twitter.
App crash rate: 1–2% is the average. If you’re exceeding this, figure out why. You’ll churn like a mofo if your app crashes at inopportune moments.
End-to-end response time: If your app takes more than 3–4 seconds to load, research shows the majority of users will abandon ship. Ideally you should optimize the response time to 1 second or less.
App loads per period: This is the total number of transactions called within a given period. Unlike the app crash rate and end-to-end response time, there isn’t a specific number you need to stay above or below. Rather, this is about monitoring and making sure that during periods of super-high loads, the app experience doesn’t slow down or die. You know, that story about being lucky enough to get a flood of eyes from press, and then wasting the opportunity because your servers couldn’t handle it.
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