Mobile app analysis as an area of digital analysis

Another way to use Lifetime Value is to forecast revenue. Then, if you know the structure of the audience and the Lifetime Value of each segment, you can calculate the revenue that each of them will bring in a certain period of time. [17]

Return on investment (ROI)

The ROI (Return on Investment) metric is an indicator of the return on investment, which has several calculation options for applications.

The amount of investment is subtracted from the income received from investments and divided by the amount of investment. If the ROI is > 0, you can assume that the investment has paid off.

The second method is to divide the income received from investments by the number of investments. In this calculation, an ROI ratio of > 100% indicates payback. The payback period is no less important. When the income received equals the number of investments and the ROI is 0 or 100% (depending on the method of calculation), they can be considered paid off.

ROI is also used to assess ROMI (Return on Marketing Investment), which is the return on investment made in marketing. In fact, this is the same indicator, which simply emphasises that ROI can be used in relation to marketing campaigns, along with other areas.

A fairly common use of ROI in marketing is to analyse the return on investment in traffic. In this case, the formula can be slightly modified, where the revenue generated by a user over his or her lifetime in the project is divided by the cost of attracting that user.

At the same time, ROI can also be calculated on a certain day from the moment the user installs the app, but instead of

LTV, the cumulative ARPU for the desired day will be used in this calculation.

There are two other indicators that help to assess the success of an investment project: NPV (Net Present Value) and IRR (Internal Rate of Return).

The first indicator (NPV) allows you to take into account discounting to calculate the potential profit from the project. Net Present Value is equal to the sum of all receipts multiplied by the discount factor. For profitable investments, this figure will be greater than 0, and the higher it is, the more profitable the project will be.

IRR is closely related to NPV, as it is the interest rate at which NPV is zero. Therefore, all you need to do to calculate IRR is to set the previous formula to zero and calculate the interest rate. IRR shows at what interest rate the investment will be fully recouped.

Virtue (k-factor)

The process of users sharing information about a product with their acquaintances, friends, etc. is called virality. The spread of information about an app can be measured using the k- factor or "viral coefficient" or "virality coefficient".

The most popular formula for calculating the virality rate is the product of the average number of invitations sent by a user divided by the number of conversions from received invitations to installations. The higher the k-factor, the faster the app audience grows.

The number of app users also matters because they are the ones who will spread the word about the app. Therefore, the more users there are, the more people they will tell about the app.

If the k-factor is greater than 1, this will lead to an independent growth of the project's audience, but if it is less, the number of users involved and the total audience will gradually decrease unless other channels of involvement are used. [18] This

is because any product has an inevitable outflow of users that must be compensated for by newly attracted users.

To increase the k-factor, it is necessary to reduce the time that elapses from the moment a user installs the app to the moment he or she installs the app by invitation. [18]

There are several stages between these two events that affect the rate of spread of information about the app and are often referred to as the viral cycle.

Virality is one of the most important indicators of an app, indicating its ability to develop independently, without any costs for attracting users, which characterises its growth rate and popularity in the market.

Net Promoter Score (NPS)

One of the indicators that can be used to assess loyalty is the Net Promoter Score (NPS). This metric requires a preliminary survey among app users.

The NPS value can be either positive or negative. Of course, a negative indicator shows the risk of losing the audience due to a large number of negative reviews from critics. At the same time, a positive NPS indicates that the app has a lot of loyal fans, which increases the chances of audience growth due to loyalty. First of all, NPS helps to understand the general mood of the audience - which group of users prevails in the project, as well as to draw a conclusion about the growth of the product. By measuring Net Promoter Score at certain intervals, you can assess how changes made to the product affect customer loyalty.

There are several analogues of NPS, but they are also based on user surveys. One of them is the Customer Satisfaction Score (CSAT), also known as the Customer Effort Score (CES). They allow you to assess how much effort users need to solve their issues while using the product.

There is no direct correlation between NPS and revenue, as a high NPS does not always mean a high level of revenue. There

is also no correlation between NPS and loyalty, as not every user who is ready to recommend an app will do so.

Churn Rate

Churn Rate is an indicator of user churn, one of the most important product metrics that affect not only finances but also the possibility of business growth. It determines the size of the audience and, consequently, the income.

Churn Rate is often used as an auxiliary metric for calculating such financial metrics as Lifetime and Lifetime Value.

However, it is also important in its own right: if users leave the app and this outflow is not covered by new users, the project's audience will gradually decrease.

The churn is calculated in users, and the period after which the user leaves the app (7, 14, 30 days, several months, a year) is selected individually depending on the type of app. The period you choose has a significant impact on the final Churn Rate: the shorter the user inactivity period, the faster the metric will react to changes, but the less stable it will be.

Another option for calculating Churn Rate, which is mainly used for subscription services, is the calculation of "subscribers who have fallen off". For a subscription app, this churn is usually calculated by month, quarter, or year. However, this metric can also be calculated for other types of apps for an arbitrary period, but only in this case, the formula will not include subscribers, but any users who have left the service. For them, there should also be a defined period of inactivity in the app, which will be a sign of churn. When it comes to lost subscribers, Churn Rate can be calculated in monetary terms (MRR Churn Rate).

MRR (Monthly Recurring Revenue) is a regular monthly income. This indicator shows how much money was lost during the calculation period. It is worth noting that in this case, when it comes to subscriptions, churn can be not only the customer's departure but also the transition to a cheaper tariff plan, as a result

of which the user starts paying less while remaining in the service.

In addition to the total churn rate, you can additionally calculate the Churn Rate at a certain stage of user interaction with the application. This approach is most relevant for games and educational applications where there are any levels or stages that the user has to go through. By knowing where users leave, you can experiment at those specific points to prevent churn there and keep users in the app.

When studying a mobile application, it is impossible to consider its components separately from each other, most qualitative indicators have an impact on the quantitative indicators, and technological analysis can show problems arising in a mobile application in real-time and reduce the negative impact on customers.

Conclusion

The active popularization of smartphones has led to the massive emergence of mobile applications, which has provoked the development of both mobile applications themselves and the way they are promoted, attracted and retained users, and this, in turn, has provoked the emergence and development of mobile application analysis and analytics. Mobile app analysis is a necessary tool not only for developers and ASO specialists but also for designers, marketers, and product managers. A mobile app as a service means providing users with content on a recurring revenue model similar to software. An app can provide a service-oriented decision-making system with data analytics to drive the entire industry, especially for comprehensive analytics that can help engage users, maintain them, and effectively maximize revenue. In doing so, mobile app analytics brings together finance, marketing and analytics, creating a symbiotic relationship that can increase revenue and generate strategies and hypotheses based on data, not bias.

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