The 3 Most Important Equations In Mobile MarketingMon 9 Feb 2015, 10:55 AM - dom
In this guest post, Dom Bracher, co-founder at Tapdaq, shares his expertise with us and describes the 3 most important metrics in mobile marketing. If you’re new to mobile analytics, this article will certainly help you getting started!
In the world of mobile analytics there are now a vast number of metrics which you need to be tracking in order to determine the overall health of your application. In fact, one of the biggest challenges for indie developers is to understand exactly what each data point means, and how it relates to an actionable improvement within their product.
Whilst you should never ignore any data which you are tracking, there are a few headline statistics which give a good snapshot as to how well your app is performing. In this post I am going to look at what I believe to be 3 of most important equations in app marketing, explaining what they mean and how exactly they work.
Let’s start with retention rate…
Retention rate is the percentage of users returning to your app within a certain time frame after initial installation, and is a measure of just how ‘sticky’ your application is. The more valuable your app is to a user, the more often your app will be used (and the less likely it is to be deleted).
On both iOS and Android, it is now incredibly easy to both download and delete applications, and many believe that user retention is becoming a bigger challenge as the app stores mature. This belief has been backed up by Mack Flavelle of Tapstream, who late last year shared some very interesting data which compared anonymous retention data from 2014 with 2013.
In May 2013, the average day 1 retention rate on apss in the Tapstream network was measured at 25.5%. However, by May 2014 this had slumped to just 14%.
So how exactly do you calculate your app’s retention rate? Well, there are actually a number of methods, but the most commonly used method is rolling retention (the default method used by Flurry).
The equation here is relatively simple. Firstly, you need to look at the proportion of users returning to your app on Day+N, or any day after that, and then divide it by the number of users who installed your app on Day 0.
So, as an example, let’s say you want to work out your day 7 retention rate in your app. If you had 1,000 downloads on day 0, and on day 7 you have 260 of those users open your app, then you have a user retention rate of 26%. Bear in mind, if more users from the original 1,000 then go on to open your app after day 7, then they are also classed as ‘retained’.
Churn rate is the complete opposite of retention rate, as it measures the rate at which you lose users from your app during a given period of time (usually measured monthly).
When calculating your app’s churn rate, you are trying to work out the % of people who have left your app, when compared to the number of people that could have left. (Remember, it is impossible for customer churn to be 0% or lower)
Let’s take a look at a real quick example… If your app has 1,000 users at the start of the period, then that means 1,000 people could leave your app over the next month. However, at the end of the month, you realise that only 340 did. This means you have a churn rate of 34%.
LTV is arguably the most important metric of all, as it is this that dictates how much you can afford to spend on acquiring new users to your application. As a rule of thumb, unless you are holding down a high chart position and making back your losses from organic downloads, you will be unable to sustainably acquire new users if your CPI rises above your average user LTV.
In order to calculate the LTV of your users, there are several data points which you need to know and understand…
Income: This is a straightforward metric, and includes all revenue generated by your app, be it from in app advertising, in app purchases, paid downloads, or subscriptions. (Don’t forget to factor in the 30% cut that Apple/Google Play take!)
Number of active users: The definition of an ‘active user’ with in your app. It could be if a user is inactive after a certain period of time, or users that are yet to purchase premium content within your app.
Average Revenue Per User (ARPU): Tracking your ARPU is fairly easy to do, as it is simply the amount of revenue you generate, on average, from each user of your application. You can work this out using the 2 data points above, simply divide your income by the number of active users you have over a given period of time…
Customer Churn: Simply follow the explanation in point 2 of this post
If you have all the data listed above, then you are good to go! The equation you need is shown below…
As before though, let’s use some actual numbers in here in order to make things a little easier to understand.
So, if your app has an average revenue per user of $0.40, and your churn is the 34%, then your Lifetime Value would be $1.18. With the average cost per install on many ad networks being over $2, if your app actually has an LTV of $1.18 then you are not going to be able to afford to buy installs in this way.
As I mentioned in the intro to this post, there are many more important metrics which are vitally important to tracking your app’s success. However, I believe these 3 equations do go a long way in determining how well your app is actually performing.
There are also a number of tools which I’d recommend you check out in order to make calculating the above metrics a little easier. Products like Mixpanel and Flurry will help to determine your active user count and your retention rate, whilst tools such as App Figures, App Annie and Tapdaq help to provide data on your ARPU.
Overall, none of these equations are too difficult to calculate, and their output are exactly what developers should be working to improve on a daily basis.
© 2013 - 2015 AppTweak. All rights reserved.App Store Optimization ("ASO") is the process of improving the visibility of a mobile app (such as an iPhone, iPad, Android, or Windows Phone app) in an app store (such as iTunes or Google Play for Android). App Store Optimization is the mobile equivalent of Search engine optimization. Specifically, App Store Optimization includes the process of ranking highly in an app store's search results and top charts rankings. ASO marketers agree that ranking higher in search results and top charts rankings will drive more downloads for an app. - From Wikipedia, the free encyclopedia.