What are mobile app analytics metrics?
Mobile apps, like human beings, thrive on feedback. The teams who design them must know what their users are doing so they can improve the product. Mobile app analytics metrics are performance indicators on everything from how users found the app to what causes engagement, shares, or churn.
Why do mobile app analytics metrics matter?
Mobile app metrics are critical because, without them, companies can’t tell whether their apps are succeeding or failing. Companies that lack precise metrics are forced to rely on broad indicators such as total revenue or total downloads. These top-level indicators can conceal the fact that the app may be hemorrhaging money or users faster than it attracts them. With app metrics, companies can see these issues and take action.
To track your mobile app’s metrics, use an app analytics platform. This software captures data whenever users take actions such as logins or purchases and summarizes everything on a dashboard. This allows app creators to ask questions, explore hypotheses, and make better product and marketing decisions. While there are a world of metrics that you can track, it’s best to focus on those 4 to 5 key performance indicators (KPIs) which align most closely with your app’s goals.
The top 13 mobile app analytics metrics:
Daily active users (DAU) and monthly active users (MAU) Tracking your total downloads is a good start but according to Inc, it can be highly misleading because 80 to 90 percent of those who download an app never return. To avoid overcounting, track your active users by day or by month. This gives product teams a barometer for how useful the app actually is, not just how many people were initially attracted to it. By tracking active users, you are counting only unique individuals and ignoring their total number of sessions. Whether they visit once or one hundred times, they’ll still count as one active user. DAU is the total number of unique users per day, sometimes averaged over one month. MAU is typically measured over the trailing 30 days or for a previous month.
# of Unique users per day = DAU
# of Unique users per month = MAU
Stickiness Stickiness is a measure of how likely users are to return to your app. It’s also a proxy for how much value consumers see in your app. The more value, the more consistently they’ll return. Stickiness is good and stickier apps are typically more profitable and have a higher return on investment (ROI). What constitutes a good stickiness level varies widely across industries. A social media app like Facebook would consider daily usage to be a desirable level of stickiness whereas a healthcare app such as Oscar considers daily usage to be a bad thing because it wants sick people to get better and stay out of its app. Measure your app’s stickiness against your own past performance and only apps most similar to yours. To measure stickiness, divide your DAU by your MAU. The higher the percentage, the more likely users are to return to your app.
(DAU / MAU ) x 100 = Stickiness
The real value in apps is loyalty: App users spend 20x more time than mobile web or desktop users. – ComScore
Retention rate Retention measures how many users you retain over a period such as a month, quarter, year, or all-time. Retention is critical to app success because companies invest sales and marketing dollars in acquiring new users. If new users churn, the company loses money. High retention is almost always a good thing. Measuring retention will also warn you whether your app has lost momentum in its current iteration. Gradual user drop-offs, such as Pokemon Go experienced, should be a warning sign that it’s time to return to the product discovery stage and update the app
((# of customers at end of period – # of customers acquired during period) / # of customers at start of period ) x 100 = retention rate
For example, if you had 120 customers at the end of the period, 104 at the beginning, and acquired 20 during that time, your equation would look like this:
Ex. ((120 – 20) / 104) x 100 = 96% customer retention rate
Churn rate Churn rate is the opposite of retention rate: it’s when customers cancel, downgrade, or abandon the app. In fact, if you subtract your retention rate from 1, you’ll arrive at your churn rate. An app with a 90 percent retention rate has a 20 percent churn rate. Customers churn when they no longer see value in your product or service. The lower the churn rate, the better.
1- Customer Retention Rate = Churn Rate
An app analytics platform can dramatically reduce churn rates: Analytics platforms help decode churn rates by showing where users drop off and suggest fixes.
Cost per acquisition (CPA) Cost per acquisition is a measure of how expensive it is for you to bring on new users. A low CPA is often considered good and if it ever rises above your customer’s lifetime value (LTV), it’s an indicator that you’re losing money on each new customer. CPA is most often measured by the total cost of the marketing campaigns that acquired the new users but in reality, it should account for at least a fraction of your operational and product development costs that enabled the acquisition. In practice, this is difficult, and most companies settle for calculating CPA by dividing the total marketing acquisition costs by the total number of acquired users.
Total Marketing Cost / Total User Acquisitions
Average daily sessions per daily active user (DAU)
Daily sessions per DAU is a measure of how frequently your users log into your app each day. More sessions is sometimes better, as is the case for a media publisher or social media app, but that’s not always true. Users could be logging in repeatedly out of frustration or because they’ve encountered an error. It’s important to pair this metric with other metrics like user satisfaction and behavior flow to get the full story.
Total daily sessions / Total daily active users
Lifetime value (LTV)
Customer lifetime value (LTV, sometimes also CLV) is the measure of how much each individual user is worth to you over their customer lifetime. It’s a function of how much revenue they generate for you in a given period (either directly through in-app purchases or indirectly through advertising) multiplied by their lifetime usage. LTV is a critical measure because if it’s lower than your CPA, you are losing money on each new customer.
Average value of conversion x Average # of conversions in a timeframe x Average customer value = LTV
Average revenue per user (ARPU)
If you’re generating what seems like a lot of revenue, it’s a good idea to look at how much you are earning per user. Tens of thousands of dollars in revenue per month might seem impressive at first, but not if it’s based on a user base in the millions. ARPU helps you identify when you should be earning more revenue per user.
Apps that have both paid and unpaid users can get a more accurate sense of what each paying user is worth by calculating their average revenue per paying user (ARPPU).
Lifetime revenue of app/ Lifetime # of users = ARPU
Lifetime revenue of app / Lifetime # of paying users = ARPPU
Return on Investment (ROI)
ROI is most commonly calculated for specific marketing campaigns where there is a direct and measurable profit, such as paid advertising.
ROI can be fairly difficult to measure accurately because technically speaking, you must factor in the tiny slices of your product development efforts, hosting costs, and labor to run the marketing campaign as well as the cost of actually running the ads.
Most companies settle for calculating it by simply dividing their return by the cost of the ads plus the cost of the man-hours it took to run them. However you choose to calculate ROI, stay consistent so that you can measure your relative progress year-over-year.
Return / Investment
App load time There are many performance metrics, but chief among them is app load time, also known as launch-time. Today’s consumers expect fast results from apps and will become disappointed if the app doesn’t meet their expectations. Load times over 2 or 3 seconds can cause users to drop off and potentially abandon the app.
App load time, measured in seconds
User satisfaction User satisfaction is paramount to increasing user retention and LTV. It can be tricky to measure with confidence, but there are two ways to approach it: Reported satisfaction Survey users and rate their happiness with a customer satisfaction score (CSAT) or a Net Promoter Score® (NPS). With CSAT, companies typically ask customers to rate their satisfaction on a scale from 1 to 5. If customers respond with 4 or 5 they are considered satisfied. With NPS, customers are asked to rate their satisfaction on a scale from 1 to 10. Those who reply 0 to 6 are considered detractors, those who reply 8 to 10 are promoters, and the rest are discounted.
(# of satisfied customers / # of survey respondents) x 100 = CSAT
((# of promoters – # of detractors ) / # of survey respondents) x 100 = NPS
Goal achievement You can use app analytics to track user behavior journeys to approximate how often they achieve successful outcomes. That could be framed in terms of a purchase, a signup, a share, or something specific to your app such as finding a parking spot or depositing money.
% Users that achieve their goals each session
Where are your users coming from? Who are your most profitable users? Track your various marketing channels to see where signups are coming from so you can measure the ROI of each channel and double-down on those that perform the best.
% Total visitors from a top marketing channel
$ Total visitors from a top marketing channel
% Dollar value of visitors from a top marketing channel
Are you tracking all of your channels? A product analytics platform can make tracking simple and allows marketers to slice and dice their data to identify top performing channels and user cohorts.
Behavior flow rate Behavior flow is a measure of whether users follow the average customer journey. Using an app analytics platform, you can track this as a series of user actions and determine what percentage of users adhere to it. Tracking behavior flow typically takes some prior experience and a fair amount of experimentation to understand what flows users prefer. If executed successfully it can tell you how intuitive your app is. If your behavior flow percentage is low, it could mean many things. For example, you could have many different customer personas and many different flows, or you could have fundamental navigation issues. To better understand, you’ll have to test features in your app, interview users, and more.
% Users who follow behavior flow = behavior flow rate
Want to see the analytics on your app? Learn more about app analytics.