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Intense intrusion of smartphones onto digital market resulted in quick brunch development. Things that ten years ago were inconceivable, now are commonly received. Since its appearance mobile segment grew and expanded turning into one of the most profitable modern market spheres. Last year, it gained 461.7 billion dollars in revenue. This amount is forecasted to grow up to more than 935 billion dollars by 2023.
Mobile applications penetrate into our daily life making routines less tedious. According to statistics, Google Play and Apple App Store offer about 2 million various apps that are called to make customers’ life better. We have already discussed important criteria to rely on while choosing application type and singled out trends for mobile development, spotlighting most efficient mobile app KPIs.
Still, just having an application is not enough for successful business running. To get maximum out of project you have to know how efficient it is. Thus, profound app analytics understanding stands for one more step towards your success.
In this post, OpenGeekLab will tell about key mobile app analytics metrics and disclose secrets on how different mobile app metrics tools actually work. Keep on reading!
Installs number stands among basic metrics to trace product’s success. It shows how many users actually downloaded the app from mobile store. These numbers are called to represent most general picture concerning analytics without further details. Downloads also influence app’s position in rating or search results.
However, downloads number is not synonymous to app active users. This metrics does not reflect whether the customer uses program or not. It might be installed due to peculiar promotion company and deleted right after downloading, or simply did not reach user satisfaction. On the basis of download analytics further metrics are counted.
App user acquisition represents metrics directed onto tracing the ways customers came to downloading the product. It usually shows how users got to know about mobile application: from social media, via promotion on specific platform, or by chance.
It becomes useful for advertisement management as far as it shows how efficient the campaigns are. If the application is promoted through different platforms or channels, mobile user acquisition statistics and analytics will help in distinguishing the most productive ones. User acquisition awareness also contributes in problem areas improvement and development, therefore increasing each channel’s ROI.
Active usage stands among crucial analytics criteria while tracking product’s success, as far as it shows how handy and absorbing your mobile app is. Unfortunately, the number of app active users usually do not correspond to downloads. Not all people who have installed the program use it regularly. Only the customers who have interacted with the program within last month can be considered ‘active’.
The category of active customers can be subdivided into two parties: daily active users (DAU) and monthly active users (MAU). DAU group consists of people interacting with the app at least once a day, while MAU shows those who opens program at least once per month. These metrics together are also responsible for product stickiness analytics. To get its percentage one divides DAU by MAU, then multiplying the result by 100. The higher total result is, the better chances app has.
App loading time is a key metrics providing performance data. It shows how fast the app launches on a device. Modern customers expect maximum productivity from applications, so they inevitably become disappointed in case product does not meet their expectations.
High load time usually irritates and may lead to negative consequences. Many users tend to drop off the app because of launch-time more than 2-3 seconds. Thus, excessive time, wasted onto loading, may result in abandonment percentage increase. An efficient way out lies in profound optimization, followed by careful testing before rolling out final product.
Session length stands for time spent by users within the app at once. To know length of session is important because it reveals user’s engagement as well. The higher average time, the stickier the app. However, session length does not equal time in application.
Many applications aim not at long sessions at heart, but at smaller ones performed several times a day. There are specific requirements set by different app types that determine optimal length of session. Like other metrics, session length can be counted for various time periods.
Not only length of sessions but also intervals between them matter, as far as they also make contribution to general time spent using app.
Different types of mobile applications have own analytics requirements, e.g. some products count on long-lasting sessions accompanied by longer intervals as well, while others aim at shorter ones, but reducing interval periods at the same time. That is the way social networks work – they win time spent within app by session frequency not by its length.
Screen flow enables owners track app active users by screens. Its value is determined by great amount of useful details it provides. With navigation path customers usually undertake, developers can distinguish different functions according to popularity and usefulness. It also allows them to predict which sections should be improved or updated.
Besides, screen flow helps spotlighting problematic zones. Developers can track which program segments are left more frequently among others and pay attention onto their performance data. Such analytics analysis usually contributes to better problem diagnostics, mobile screens customization, lost users’ re-attraction, positive user experience building, etc.
Good applications not only attract new users, but also struggle for retaining them. Customer retention rate stands among most important metrics, influencing product’s market success. Unlike downloads it indicates not the general numbers, but very specific ones. Client retention rate responds how many people actually use mobile app regularly. This metrics has much to do with ‘active’ customers, time in application, stickiness, etc., though all these metrics present analytics synchronically. User retention rate represents a tool of measuring user satisfaction diachronically.
Retention percentage is calculated through dividing end number of users by initial one, the result being multiplied by 100. High retention percentage signs productive application supported by strong engagement, while low retention rate indicates efficiency decline, leading to update or conceptual change necessity. This formula remains relevant not only for finding out the general user engagement, but weekly/monthly/quarterly/yearly/any period you want fluctuations as well.
Customer churn rate is a metrics opposed to previous one. It measures those people who stopped using the product during certain period. As well as retention percentage it deals with diachronic data research. Mobile app churn index also can be applied to any period giving changes review. While high retention indicates positive phenomenon, high churn rate is usually viewed as negative. It means that users are no longer satisfied with services they receive or they have turned to more convenient competitor.
Formula counting average churn rate is dependent onto retention analytics because it constitutes the percentage left after subtracting retention percentage out of one hundred (the whole), e.g. if retained part constitutes 70 % then churned one will be 30 %.
Conversion rate is a metrics responsible for monetization. It shows how many customers out of full scope perform conversions. Like other analytics tools it deals with average data and does not show the exact number of users making some money operations within mobile app. It also contributes to return on investment tracking.
Conversion percentage is counted by dividing total number of users by number of conversions. As in other cases, formula remains relevant to any period you like. It is handy while tracing monetization success through different periods. Despite it processes data synchronically, you may compare several analytics results by yourself to get progress visualization.
Client lifetime value is a metrics dealing with return on investment. It literary shows how much does each your user bring to your company. Lifetime value depicts actual or expected return on investment via revenue made by customers. This analytics tool has rather hypothetical character and aims at forecasts. Due to its theoretical character, it considers all data scale (from highly positive to highly negative results) and does not differentiate between regular action and action by chance.
Commonly referred consumer lifetime value formula is grounded on average information. It consists of conversion cost multiplied by conversion number per a certain period multiplied by customer lifetime (time being loyal to company/product). Product lifetime value is highly dependent on application type and target audience as well.
The important thing is that average lifetime value should always exceed cost per acquisition, otherwise app becomes unprofitable and does not reach positive return on investment.
User growth rate resembles retention data, though they have different goals. Retention percentage process general information visualizing how many people actually use the app during particular period without details about usage regularity, period length, etc. App user growth rate manages increase of active users; thus, it combines retention info, stickiness, and user activity. Besides it becomes handy while user lifetime value calculating and forecasts generating. Average app growth index should be observed on long-lasting time period to track users’ loyalty precisely.
Abandonment rate represents an analytics tool close to churn indicator. It also depicts the number of users dropping off app usage, still there is a significant difference between these two metrics. While churn percentage manages general numbers, accounting all people that did not interact with the app during certain period, abandonment index aims at singling out those users, who end using provided services at all.
To count abandonment percentage is more difficult – you need a long-lasting time sampling – though this metrics contributes to understanding how loyal your users actually are and forecasts average customer lifetime value.
Crash report stands among vital tools ensuring app’s success. It is implemented trough specific algorithm ensuring automatized feedback sending in case of troubles. Analytics and graphs help in analyzing problems, singling out troublesome segments, error fixing, tracking app’s productivity, etc., as well.
Crash report analytics becomes significant while mobile app improvement and reaching user satisfaction. It usually provides developers with crash reasons, device info, or even error type in some cases. Thus, being equipped with all necessary details, developers can easy fix problematic aeries. Crash report function becomes helpful while updating the program as well, because it ensures quick resolution of problems, which may occur on wider testing scope.
Being equipped with key mobile application metrics you are one step closer to efficient product that will take off and stay on the top. Don’t hesitate to drop us a line if you have any questions!
In the next post we will dwell on main mobile analytics tools helping in tracing your success. Stay tuned!
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