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Application Analytics in 2024: Tracking KPIs that lead to success

Cem Dilmegani
Updated on Jan 12
3 min read

Though the number of applications is increasing, the number of successful applications remains limited. Application analytics solutions capture and analyze application metrics such as usage patterns. Insights from application analytics enable companies to continuously improve their cloud, desktop and mobile applications. Benchmarking and tracking a relevant set of metrics which we provide below enables companies to have an edge in application analytics

What is application analytics?

Application analytics is the collection of data, real-time analysis and reporting of application usage patterns. It covers analytics in mobile, desktop, and other device applications. It can be used to gain insights into IT operations, customer experience and business outcomes.

With these insights, businesses can make improvements in their product, marketing, and overall profitability. For example, by monitoring the performance and analyzing crash reports, companies can quickly fix bugs.

Why is application analytics important now?

Number of applications is increasing and there will be over 500 million apps and services by 2023 according to IDC’s estimations. Due to the rise of applications, advanced analytics is a necessary tool for companies to manage these growing numbers of applications effectively. Even Apple Store alone has ~4.8m apps as of 2021:

Number of available apps in the Apple App Store from 2008 to 2021

Source: Statista

What are application analytics use cases?

Continuous monitoring – Dashboards

Application analytics solutions track key performance indicators(KPI) which outline how well the app is performing and keep the whole team (management and development team) on the same page.

Continuous improvement

App analytics is a fundamental part of any application life cycle. It enables 

  • improving the software with less effort by focusing on what is essential for customers. It helps product teams make data-driven decisions about user experience and design since it provides knowledge of how the application is used.
  • tracking KPIs over time to measure improvements.

What are the different types of application analytics?

Application analytics is relevant for cloud, desktop, and mobile applications.

Mobile application analytics is especially relevant, given the winner-take-all nature of the mobile application landscape. Though there are 4 million applications in the Apple AppStore, a few successful mobile apps dominate mobile usage. Though industry analysts are in general bad at making making predictions, this makes Gartner’s prediction about low rate of mobile success (they predicted 0.01%) correct. To gain an advantage, organizations have to make data-driven decisions.

What are the important KPIs for applications?

What to track is an important challenge for application developers when they start to use app analytics. Here are some essential metrics to track; they are sorted from most important to least:

  • Active Users
  • User Conversion Rate
  • Retention Rate
  • Churn Rate
  • Usage Time per Session
  • Sessions per User
  • User Lifetime Value (LTV)
  • First-time User Drop-off Points
  • Usage Time per Session
  • Exit Rates from Funnels
  • Referral Codes and Their Success Rates

What are the challenges associated with application analytics?

Application metrics in isolation may not be informative. Companies can resolve this by comparing their KPIs against benchmarks. Here is an example of Quettra’s research about average retention day per user. 

Average retention rate for android apps
Source: Quettra

Numerous companies are publishing public research and information on different mobile app’s metrics that companies can leverage. Some major providers are: App Annie, Appfigures, newzoo, Nielsen and Priori Data.

What are the tools used in application analytics?

There are application analytics solution specific to desktop, cloud or mobile applications. We deep-dived into mobile analytics tools as an example.

Mobile application analytics tools

App analytics tools are divided into three categories:

  1. In-App Analytics: In-app tools reveal patterns of user behavior within the app. Session recordings, navigation paths, touch heatmaps, and conversion funnels are tools to see patterns of user behavior.
  2. Performance and Crash Data: These tools provide data such as load times, crash reports, and session details. With performance and crash data tools, developers can track crash and performance data and fix it quickly.
  3. Marketing and Install Tools: Unlike other categories, marketing and install tools focus on data about new customers. Some of these data metrics are impressions, clicks, installs, and conversion rates.

Here is a list of the important mobile app analytics tools:

  • Google Analytics
  • Apple Analytics
  • Flurry
  • Fabric
  • Appsee
  • Localytics
  • Geckoboard
  • Firebase
  • Mixpanel
  • AppDynamics
  • UXCam
  • Adjust
  • AskingPoint
  • AppsFlyer
  • Buildfire
  • Leanplum
  • App Annie
  • Apptopia
  • Kumulos
  • Apptica
  • Trafficguard
  • Smartlook
  • Singular
  • Tune
  • Kochava
  • AppFigures
  • LeanPlum
  • GameAnalytics
  • Amplitude
  • Apptimize

We’ve written a few articles about analytics before, feel free to check them out:

If you believe your business would benefit from leveraging an analytics solution, you can check our data-driven lists of analytics platforms. If you still have questions about how Application Analytics can influence your organization and how you can get started, we can help:

Find the Right Vendors


Mobile metrics: Ymedialabs

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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