10 essential mobile app metrics (and how to use them)

Keep these formulas handy

As a mobile marketer, you’re a relationship builder. And like any relationship you invest in, you want to get a sense of how things are going. Are you and the other party—in this case, those who have downloaded and use your app—both getting what you want out of the relationship? What’s working, what’s not? The following mobile metrics formulas will help you measure the impact of the hard work you put in to develop your audience.

This metric, obviously, lets you know what percentage of customers you’re retaining. And as a result, you’ll be able to also see how many customers you’re losing to churn.

Which numbers you plug into the formula will depend on what you’re looking to learn from the retention rate, but you’re comparing a cohort of users in a more recent timeframe (say, this month), with those same users in an earlier timeframe (say, last month).

You might also be interested in calculating your retention rate using app download or first login as the denominator (as opposed to just app use).

If you’re calculating the retention rate based on how many people downloaded your app last month, the rate will reflect how your app awareness marketing (getting people to download) compares to how engaged people become with the app once they have it downloaded.

Calculating retention based on users’ first logins will reflect your app’s messaging, onboarding, and UX as more or less effective at keeping people around.

Retention Rate
= # of people in cohort who use your app within a set period of time / # of people in that same cohort who used your app within a previously set time
Example: 200 people from your January new user cohort used your app in February / 1,000 users in January new user cohort = your retention rate is 20%
retention rate

Using this metric, which you’ll calculate by subtracting your retention rate from 1, you’ll find what percentage of customers are choosing to ditch your app, or are “churning.”

Churn Rate
= 1 – retention rate
Example: 1 – .20 = 80% churn rate
churn rate

You know how many app downloads you’ve had, but just how indispensable is your app for those who’ve installed it? The daily active users metric will tell you just that. Because this refers to each individual person using your app, not to the number of sessions, each person is counted just one time, regardless of if they use the app once per day or hundreds of times per day.

DAU can be calculated for a specific day (say, yesterday’s DAU), or averaged over a timeframe.

= # of individual users who open your app in a day
daily active users

Like DAU, monthly active users tells you the unique number of people who used your app, only obviously, MAU is concerned with either a specific month, or the prior 30 days. Some mobile marketing automation platforms are set to automatically capture MAU, based on one of these approaches.

= # of individuals who used your app in the last 30 days

= # of individuals who used your app in a given month

Example: If your app has been used 30,000 times by 15,000 people in the last 30 days, your MAU is 15,000
monthly active users

Daily sessions per DAU gives you an idea of how often your customers make use of your app within a single day. This can help you determine whether your customers are returning to your app as often as you’d like them to be. Social media apps, for instance, might like to see their active users opening the app for a couple sessions a day.

Daily Sessions/DAU
= # of sessions in a day (or daily sessions averaged over timeframe) / # of unique active users in a day (or DAU averaged over timeframe)
daily sessions / dau

“Sticky”—it’s not just a clever way of describing how often people come back to your app, it’s also a formula. What does using this formula tell you?

To calculate stickiness, divide DAU by MAU to get a percentage. The higher this percentage, the more often your users return to your app (and the more you and your colleagues can congratulate each other). The closer your daily active user count is to your monthly active user count, the higher the stickiness—or engagement—is for your app, and the more frequently your MAUs are using the app.

Example: 10,000 daily active users / 20,000 monthly active users = 50% stickiness

In the beginning, you may be happy just to be gaining interest in your app and have downloads to speak of, but at some point—if not from the start—you’re going to be held accountable for how much this is all costing. And for good reason, too: there is a significant cost to acquiring app users. To measure the CPA of a campaign, total your costs for that campaign and divide it by the conversions or acquisitions the campaign produced.

= costs / # of acquisitions or conversion you’re tracking

Lifetime value formulas will help you assess whether you’re paying too much for—or getting a good deal on—your customers, given the value they bring to your company. The takeaway here is that LTV should be greater than CPA, otherwise you’ll be in the red.

There are a couple of ways to approach LTV, but all are aimed toward figuring out how much value (revenue or profit) you can expect from your average customer (or other segment of customers) during the entire time they spend as customers of your company. (So for example, a teen-focused celebrity news app might expect to keep customers for, say, seven years, and then they expect that those customers have grown past their teen years and aren’t in their target market anymore.) You might be interested in LTV before your marketing costs are applied or after, so be sure your team is aligned on which you’re calculating.

Factors for the equation include how often your users make transactions, the monetary value of those transactions, and how long your customers usually remain customers (so this average will take into account those who churn early and those who remain loyal to your brand for years and years).

Projected LTV
= average value of a conversion x average # of conversions in a time frame x average customer lifetime
Example: The average in-app purchase is $10 x the average user makes 5 purchases a year x the average customer stays with your company for 10 years = $500 LTV per customer

You can also calculate the current LTV your app has achieved since launch, by simply dividing the total revenue of your app by the number of users you’ve had. This can also be called ARPU, or average revenue per user.

ARPU or Current LTV
= lifetime revenue of your app / # of lifetime users of your app
Example: Your app has made $2,000 from in-app purchases since launch / you’ve had 2,000 users total = $1 LTV per user or $1 ARPU

You can drill down further by calculating the impact paid users have on revenue by using the formula for ARPPU.

= revenue / # of paying users

Want to be able to calculate the return on investment (ROI) of your overall mobile marketing efforts or a specific campaign? Everyone does, but getting reliable numbers to do the calculation can be difficult, and therefore getting a clear picture of ROI is notoriously difficult.

In broad strokes, you’ll first need to define and measure the cost of the investment in a campaign (understanding there may be many associated costs) and how these costs have impacted profits (this is tricky, too, as outside variables could impact gains as well). You’ll want to be aligned internally on what measure of return to focus on for any one calculation (for instance, revenue, gross profit, or net profit).

For example, if you’re looking for the ROI of a promotional email campaign you sent, you’ll tally the cost associated with building and sending that campaign (marketing automation platforms, design costs, etc.) and enter that cost into the formula below along with the revenue gained from sending that campaign (in this case, value of orders placed from campaign conversions).

Obviously, deciding how much of your yearly or monthly design and SaaS costs to apply to a single campaign can be tricky. And while tracking codes can make it easy to track conversions from one email campaign as opposed to a different campaign, it can be difficult to isolate all factors (did several customers make a purchase in this campaign because they also saw your campaign last week, and wished they’d acted on that sale?). Using control groups (separating a group of people who don’t get a campaign or set of campaigns at all) can help you see more clearly the effect of a single campaign.