In this article, we would like to talk about mobile marketing metrics. If you haven't yet heard these words, then this article would be 200% useful to you. You will ask why? Quite simply, mobile analysis - is the cornerstone in mobile marketing. Without it, promotion or internal mechanics adjustment of the application would be like playing chess blindfolded. Only through data analysis and evaluation of individual actions you can make the right decisions. Below we have listed the most important in our view mobile app analytics metrics, without which SEO can't be done.
Common indicators in app marketing
The installations and registrations numbers are pbasic app metrics, which reflect the most obvious indicators. It does not represent great value for analysis by itself, but it is necessary for calculating other indicators.
We should also focus on the differences between these two key metrics. Firstly, the user may install the application but then remove it without launching it. In this case, the installation will be counted, but registration does not take place at all. Secondly, a user can make a few installations on several devices he might have, but the application in both cases will register under a single account. Consequently, registration will be counted like one but installations number will be - two.
Read also: How to Market Your Mobile App?
The installation number and other information are usually easy to find in internal analytical system or developer account at the store.
So, let’s talk about other mobile app kpi-s.
The number of registrations does not give understanding about actual application audience, in any case. Indeed, in almost every project there are always users who ceased to use the application. Therefore, specific metrics were adopted for an objective assessment.
User activity is usually measured over a certain period, usually a month, a week or a day. The DAU metric (Daily Active Users) reflects the number of unique users that logged into the application during the day, WAU (Weekly Active Users) - during the week and MAU (Monthly Active Users) - month. Thus, if all users will launch the app every day for a month, the DAU and MAU numbers will be equal. But in real life, of course, this does not happen. These indicators are primarily talking about the scope of your project.
In addition, we have such mobile app metrics, which indicate how often users launch your application. Calculate the involvement of users is quite simple: it is necessary to divide the DAU to MAU (WAU). For example, if the daily audience will be 100 users, and monthly - 500, the engagement will be 20%. This indicator may reflect the true picture only if the influx of new users is uniform.
App retention ratio reflects frequency of user's return to the app. To calculate this indicator, you need to subtract the new number of users (that came into application in the period) from the total number at the end of the period and then divide it by the number of users at the beginning of the period. Usually, the retention rate is considered for two days, a week, two weeks or a month. This metric shows the degree of affection of the audience to the product. In addition, it is important for financial planning.
Session length - the time period when the user interacts with the application. For example, for most games long session profitable and show a high level of user involvement, and in the taxi or booking services, session length is not critical. In the last point, developers seek to maximize short-conversion path.
The next group - the "revenue metrics" (because we are gathered here for this, right? To help your project, company or business). It is important to understand how much and how people spend their funds in your app. This helps to evaluate the effectiveness of the monetization methods, or even think about changing the business model.
• ARPU (Average Revenue per User) - one of the basic parameters of the project monetization. This metric represents the amount of revenue that brings each user in average.
The ARPU formula is simple: the entire volume of revenue should be divided by the number of users of the application. The dynamics of this indicator is also important: if it grows, then the project is progressing in the right direction.
• ARPA (Average Revenue per Account) - the same, but in the calculation per account. This index is used, if the application includes earnings on direct payments from the already registered users.
• ARPU (Average Revenue per Paying User) - calculation only for paying users . Usually ARPPU is much higher than the ARPU.
Read also: Monetize an App
• LTV (Lifetime Value) - is the total profit of the company received from a single client for all time working with him. LTV - is one of the most important metrics in the business (especially E-commerce). David Skok, a well-known venture capitalist, said in his article that the majority of startups die because of the fact that the cost of attracting a new customer (CAC) «outweighs" lifetime value of the customer (LTV).
Calculation formula: LTV = income from customer - acquisition costs and customer retention.
• ALTC (Average Lifetime of a Customer) - indicator needed to calculate the LTV. This shows the "life" term of the user in the application. So for example, for applications in the e-commerce segment, it would the number of transactions attributable to the period of client's app usage.
• Churn rate - users outflow coefficient, the metric represents the percentage share of the deceased user. The lower Churn rate, the better the project.
Advertisement effectiveness metrics
The next group of metrics related directly to the traffic purchase and promotion used to evaluate the effectiveness of advertising. After all, one of the main principles of promotion and mobile ad revenue reception, in general - do not spend more (on advertising) than you can receive.
• CPI (Cost per Install) - the cost of one installation. The metric calculates all costs for new users attraction. To calculate CPI, it is necessary to divide all advertising costs for the amount of the involved installations. But this index is very conditional, it does not take a number of factors into account.
• eCPI or cost efficient installation - a more accurate indicator, when the calculation takes virality into account.
• K-factor or virality coefficient - an indicator of the application natural "spread". People talk about it in social networks, recommend it to friends etc. Usually, application's built-in special social mechanics are responsible for this. This indicator is calculated in this way: it is necessary to multiply the number of recommendations on the proportion of people who took them.
• CAC (Customer Acquisition Cost) - the cost of raising a single user. Main difference from the CPI - it calculates no installation, as paying customers. The difference is that a single user may have installed application on multiple devices.
• CR (Conversion Rate). This is a common metric that can be used in different ways. For example, mobile marketers often calculate the conversion from clicks to installs. In this case, numbers can tell about the quality of landing page and allows to draw conclusions about engaging audience. Conversion of installations in the targeted action is also important. It helps to assess the quality of traffic from different sources and filter out inefficient.
Mobile analytics services
To collect all of the data, you might have to use special instruments - mobile analytics services.
1. Google Analytics - the most common free analytics system. It allows you receive all necessary data but it requires careful custom setup;
2. Flurry - paid, yet affordable service. Intuitive interface and easy setup make it possible to easily generate reports and to keep tracking a large set of metrics;
3. Mixpanel - one of the most convenient, powerful and probably the best service. Build-on its price, we believe that this is one of the most expensive services ($150 monthly for startup plan). But it allows you to get statistics on just about any metrics.
These metrics - is only the basis for a practical mobile analysis. Indicators provide not much useful information by itself, their main value in the correct interpretation. In addition, the indicators are needed for more complex metrics calculations.
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