GUIDE
The all-in-one guide to mastering mobile app analytics
First things first: what is mobile app analytics? Simply put, it’s the process of gathering and analyzing significant sets of data produced by mobile apps and associated advertising. The insights produced by analytics tools should be easily interpretable and applicable, making it easy to understand and enhance campaign performance and user experience.
App analytics is dynamic and adaptable, configured on an individual app basis to produce insights tailored to the strategic objectives of that particular app. App-related data increases exponentially and by the minute—the potential volume of data points to explore is vast, threatening to overwhelm or distract from the aims of the analysis. Therefore, savvy developers and marketers hone in on specific and nuanced areas of app use and advertising success, as determined by business strategy, to cut out noise and focus on the key datasets that drive quick, smart decision making.
Optimized advertising, app design, and performance maximize user acquisition and engagement. Measuring—and, crucially, acting upon—the data produced by analytics systems is game changing for mobile developers and marketers keen to realize the most impact and return on investment (ROI) from their mobile apps.
In the context of mobile marketing, analytics is largely used for two key reasons:
- To understand how users interact with app advertising.
- To understand how users behave within the environment of the app.
Having an in-depth understanding of the performance of an app and its advertising truly enables the significant enhancements that allow the app to stand out in its market.
How do I decide which data to focus on?
App analytics should be closely tied to business objectives. For example, a business focus on the success of a connected TV (CTV) advertising campaign should result in an analysis of the volumes and behaviors of users coming to your app via CTV.
The most valuable insights for marketers are often around user behavior, spending, and preferences. By gaining a thorough understanding of these measures, marketers can work to enhance user experience and expose new growth opportunities.
Now is the time to realize the value of analytics
Strategic improvements can make all the difference to metrics directly connected to app revenue—metrics like conversion rates, lifetime value (LTV), and return on ad spend (ROAS).
App revenue is often the making or breaking of an app’s fortune, and analytics—for example, the ability to view robust figures around high-value advertising channels and demographic targets—significantly reduces acquisition costs, allowing budget to be allocated most efficiently.
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Analytics is a fast-moving field, leveraging new technologies as they emerge and rapidly evolving to keep pace with innovative approaches. The less sophisticated data analytics of the past—taking swathes of raw data and processing it to produce basic trends—is no longer enough. Savvy marketers are looking towards “next-generation” approaches with artificial intelligence (AI) at their core. The dawn of the “privacy era” is part of the reason behind this fundamental shift in perception and approach (more on that later).
The transformative business impact of mobile application analytics is evidenced by its predicted growth over the next decade. The global mobile analytics market was valued at $4.72 billion in 2021, and is projected to reach $27.60 billion by 2031. A truly essential element of successful and sustained app growth, mobile data analytics supports competitive app growth in a crowded market.
In this guide, we’ll explore how to leverage analytics to work hard for your app, the key KPIs and metrics to focus on, the next-generation tech in the analytics space, and how Adjust provides the insights to change the game.
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Data-driven decision making
The power of mobile app analytics
Relative newcomers to data analytics remark on the confidence that acting on data-driven insights gives them. As an alternative to marketer guesswork or stagnant, repetitive approaches, analytics ensures a holistic understanding of the user journey and the ability to make informed decisions for app and marketing campaign optimization.
Failure to embrace an analytics mindset results in the very real risk that potential blockers or stumbling blocks within apps and campaigns will not be easily identified or resolved. Inefficient use of marketer time and marketing budget is often the result.
The four main types of app analytics
App analytics can be configured and tweaked based on the outputs that are most valuable to an individual app’s success—in other words, you can tailor your analytics setup to work hard for your app.
There is a significant spectrum of analytics applied to apps, based on the maturity of the app, the needs and experience of the app marketers or developers, the budget available, and many other factors. The basic areas that are often “day one” analytics requirements are around user acquisition—i.e. how many new users are coming on board and the channels they’re coming through—and user behavior—i.e. what users are doing within the app environment, and the time periods they choose to spend there.
User churn is a very significant area of interest when it comes to app optimization. At what points do users stop using an app? Is there a pattern here and is there any way the app is failing, leading to this behavior?
Mobile data analytics approaches can be loosely grouped into the four types listed below. By using one or a combination of these approaches, marketers and developers can begin to understand the areas where improvement is needed and how to maximize performance against business-critical key performance indicators (KPIs).
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- Descriptive analytics: Looks to data from the past to provide insights into what has already happened, for example producing a report outlining last year’s revenue.
- Diagnostic analytics: Produces a diagnosis to understand the “why” behind the data. Rather than simply stating that the data shows a trend, the aim is to use these patterns to give a reason for performance—for example, why installs dropped last month.
- Predictive analytics: Uses available data alongside statistical models to predict what will happen in the future and answer hypotheticals, for example forecasting what will happen to user acquisition if a certain portion of marketing budget is applied to a particular campaign. We’ll return to this concept a little later.
- Prescriptive analytics: Similar to predictive analytics, but in addition to producing predictions, this method suggests actions that will lead to optimal outcomes. Algorithms and machine learning (ML) aid decision making by offering actionable insights—for example, if budget is increased in this area, ROI will increase within this range.
All of these methods enable the robust data-driven decision making that empowers marketers and developers to have a clear picture of how an app is performing and how greater success can be achieved.
Taking away the guesswork
An app developer or marketer may feel they have a solid grasp of how users are interacting with their app, but without analytics to back up assumptions, time and budget could be misspent in both the short term and long term.
Analytics brings clarity through factual, data-based pictures of user behaviors and preferences, highlighting the features of an app that enhance engagement—as well as those that do the opposite. By modifying apps in response to such data, user satisfaction is boosted. By using analytics to build a picture of device-specific behavior—for example the performance of PC and console advertising—you’re further able to improve and tailor the experience of this cohort of users.
One of the most powerful aspects of mobile app analytics is its adaptability. As well as supporting methods to boost revenue and improve the user experience, the best mobile app analytics can support campaign optimization by enabling behavioral segmentation—categorizing users into nuanced groups based on their behaviors—and the creation of user personas, making customized and personalized marketing and app experiences possible.
A mobile marketer’s tech stack should be varied and carefully configured. An analytics solution is a fundamental foundation that opens a 360-degree view of app performance and marketing efforts. In the fast-moving world of mobile marketing, effectively managing multiple campaigns simultaneously relies on clarity and actionable insights, painting a clear picture of where campaigns should be scaled up or cut back. A transparent view of the measures that matter is key to ongoing app success and sustained growth.
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The KPIs and metrics that matter
How to measure for success
Selecting the KPIs that you want your mobile data analytics to help you measure and improve should be led by business need and business goals. Think carefully about what you’re trying to achieve. Do you want users to make purchases? Do you want users to access a certain area of your app? Remain in your app for a particular amount of time?
Performance against these KPIs will guide decisions around acquisition and retention strategies, budget allocation, and more. Rather than being a one-time selection, KPIs should evolve with business focus, allowing you to gain an understanding of performance across multiple areas.
Before implementing a new analytics approach, settle on your initial set of KPIs. This will form the basis of the mobile app analytics metrics that your analysis will be focusing on. Bear in mind that each KPI may not line up with just one metric—you may be using a combination of metrics to determine how that KPI is being met.
Popular metrics for analyzing user acquisition
Consider these mobile app analytics metrics if you’re focusing on user acquisition:
- Attribution: Explore whether new users were acquired organically or via a channel involving marketing spend. Where spend is concerned, you’ll be able to see which campaigns are most successful and therefore best for continued investment—and those that aren’t hitting the mark.
- Cost per acquisition (CPA) and cost per install (CPI): Gain an understanding of how much was spent—through advertising and marketing—to acquire each new user or get each new user to the install stage. This information allows you to be able to appraise and compare the efficiency of different campaigns, and optimize ROI.
- Average revenue per user (ARPU): By understanding the average revenue per user, you can predict financial performance over a set period of time and make sure you’re hitting relevant targets.
- LTV: Again, LTV provides a measure of the financial viability of a user—this time over their lifetime of using your app. LTV is very valuable when it comes to predicting how close a user—or user group—is to reaching their maximum spend and no longer bringing in revenue.
Popular metrics for analyzing user engagement
Consider these mobile app metrics if you’re focusing on user engagement:
- App events: Analytics can shed light on the “events”—activities or interactions—that are taking place in your app, for example users making purchases, completing levels, etc. Use events to develop a greater understanding of user behavior, which you can use to your advantage.
- Installs: While downloads indicate the number of times your app has been downloaded from the app stores, installs indicate the number of times users have opened the downloaded app. Again an important metric in campaign optimization, installs represent actual app users rather than those lost after the download stage.
- Sessions: This is where your effective marketing really pays off. A session represents a user opening and engaging with your app. You can see how often your app is opened, how users are moving through the app, as well as details such as device and location. Session data feeds into mobile app metrics like daily active users (DAU), weekly active users (WAU), and monthly active users (MAU). Note that session length can also be measured, providing a helpful indication of user engagement and app quality.
- Retention: By understanding how many users are returning to your app, you can plan re-engagement campaigns or make changes in attempts to decrease churn. Analyzing retention also feeds into campaign optimization in that it allows you to see the long-term impact of users acquired, which can be vastly different from the number of downloads or installs. This information can be used to compare campaign effectiveness.
- Churn: While not a metric that offers opportunities for further engagement or revenue, it’s important to understand the volume of users churning—uninstalling or no longer using your app—so you can take positive steps to mitigate the risk of this level increasing. For example, high level of churn after first app sessions can indicate an onboarding experience or sign-up/log-in process that must be optimized.
User stickiness: the holy grail!
Ask a mobile marketer which measure they would most like to see increased, and they may well say user stickiness. An informal term coined to convey how “sticky” and robust an app experience is, it uncovers how often—and for what purposes—users are returning to an app and racking up solid engagement levels.
Stickiness is calculated by dividing DAU by MAU and multiplying by 100.
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Additional metrics you might want to consider monitoring include reattribution share, organic install share, sessions per user, installs per mille (IPM), cost per click (CPC), click-through rate (CTR), cost per mille (CPM), and ad revenue per mille (ARPM).
What about data that doesn’t come directly from marketing campaigns or app use?
There is data out there that is valuable to your campaign and app optimization but not held by you. The obvious examples of this are social media and forums, treasure troves of information relating to user preferences, satisfaction, pain points, etc.
AI is constantly evolving and is now being used to instantly gather these insights from third-party sources, analyze them, and make them accessible to marketers who can use them to make informed improvements in a timely manner.
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Next-generation analytics
Transformative tech to keep you ahead of the curve
As we’ve alluded to, the privacy frameworks being introduced by legislative bodies around the globe are having a transformative impact on app-related data. These developments necessitate new technologies and approaches, alongside traditional measurement methods, and a total rethink of mobile measurement from the marketer perspective.
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Where analytics methods based on device-level data used to be the norm, we’re now in a position where we often only have access to aggregated, fully anonymized, and delayed data produced by privacy-compliant frameworks such as Apple’s SKAdNetwork (SKAN), its successor, AdAttributionKit, and Google’s upcoming Privacy Sandbox on Android.
The analytics response to this shift? Instead of traditional analytics based on complete datasets, we’re looking to next-generation predictive analytics solutions, with the ability to provide marketers with reliable forecasts of specific future outcomes and driven by advances in AI and machine learning (ML), that future-proof the ways in which we’re optimizing campaigns and scaling app growth.
Predictive LTV, incrementality, and marketing mix modeling
Three of the solutions at the core of this new approach are predictive LTV (pLTV), incrementality, and marketing mix modeling (MMM), all working on the principle that historical data, including gauging potential consequences of particular activities, can be converted into accurate patterns and trends, and ultimately actionable insights from forecasted future outcomes.
All of these solutions support marketers to allocate budgets effectively and efficiently to optimize performance against KPIs and maximize marketing dollars.
Marketers are empowered to be agile and responsive, responding to changes in the market in real-time. To realize the power of these next-generation solutions, it is essential to collaborate with a leading mobile measurement partner such as Adjust.
Predictive lifetime value (pLTV) supports efforts to boost long-term revenue and sharpens marketing campaigns by predicting the users with the highest LTV opportunities. This is carried out early in a marketing campaign’s lifecycle, arming marketers with the insights needed to quickly and efficiently allocate or reallocate budget.
Read about the value of pLTV in the context of Apple’s SKAN and iOS 14.5+.
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Incrementality allows marketers to understand the difference between conversions that happened as a result of marketing campaigns, and those that happened organically (so would have happened in the absence of any marketing influence). This analysis is based on a typical A/B testing framework.
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InSight, Adjust’s next-generation incrementality solution, offers a game-changing, modern approach to incrementality analysis. It provides the insights to drive intelligent and optimized ad spend, maximizing every marketing dollar.
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Nicoline Strøm-Jensen
Head of Program Management, Adjust
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Marketing mix modeling (MMM) is a statistical analysis technique that measures a wide range of marketing activities—while factoring in external influences—to determine their impact on an app’s ROI. It provides a holistic view, for example analyzing how TV ads, social media campaigns, and email marketing collectively contribute to app installs and revenue.
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Read our MMM handbook to get up to speed on all things marketing mix modeling.
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Practical tips for analytics success
Best practices to move the needle
Implementing mobile application analytics in the right way guarantees your access to the insights to succeed. Several strategic steps are involved in the process of collecting, analyzing, and using the data you’re gathering. Consider these best practices when planning or updating your analytics approach:
- Carefully define your objectives: As we’ve mentioned, analytics should support you in meeting and exceeding the goals of your business. Really narrow down what you need to learn from your analytics. Do you want to gain a greater understanding of user behavior? Identify in-app stumbling blocks? Measure in-app purchases? These objectives will help you to decide on the KPIs that will work best. You’ll find that the more you spend time looking at analytics outcomes, the more you’ll formulate new and insightful objectives—and even new app content or functionalities—to further boost the growth of your app.
Tip: It can be helpful to use industry benchmarks specific to your app’s vertical to inform your objectives. - Don’t overdo it: With the amount of data out there to be gathered and analyzed, it can be tempting to attempt to gain insights into almost everything. Remain focused on your objectives and goals, avoiding the noise and distraction of excessive analysis.
- Select the right analytics solutions: Dedicate time to researching the app analytics platforms that align with your requirements. Important considerations include whether you’ll be able to benefit from real-time data availability, view all data in one place, and segment your user base. Market-leading mobile measurement partners (MMPs) like Adjust incorporate a software development kit (SDK) into your app’s codebase to capture all relevant data points to form the basis of intelligent analysis.
- Think hard about the events you’d like to measure: Again, carefully consider your goals and the events (in-app actions) that you’ll need to monitor to reach them. An effective way to do this is to map out the full user journey to make sure you’ve considered every step. Your analytics solution can be set up to monitor these events while complying with user privacy requirements.
- Regularly monitor performance against your KPIs: Reaching your goals hinges on gauging your performance against them and making data-driven decisions to improve success rates. Your analytics solution will allow you to conduct detailed analyses, such as cohort analysis, to reveal important insights.
- Act on insights in a timely manner: Make informed decisions around marketing budget, in-app content, bug fixes, etc., before any issues have a detrimental impact. In some cases, such as creating personalized user experiences, acting on insights will require regular and focused attention.
- Test to optimize: When you’re making significant changes or updates, don’t leave them down to chance. Use A/B testing to assess user satisfaction and the impact on key metrics. Analyze the A/B testing results to land on the most effective variant, which you can then implement confidently. In addition to A/B testing, make sure you test your app on as many different device types as you can, so that you’re considering the experience of every user and can solve any issues that may stand in the way of reaching your KPIs.
- Commit to continuous improvement: Acting on the outcomes of analytics is an ongoing process. Make sure you consistently analyze data, so that you’re optimizing your app’s design and your marketing strategies on an ongoing basis. Remember to be agile and quickly adapt based on new data and market trends.
- Personalize to boost engagement: Personalization strategies that are driven by data offer tailored marketing material, app content, and app features to users. This approach maximizes satisfaction, retention, and long-term loyalty while minimizing churn.
- Use analytics to refine in-app monetization: You have a lot of app monetization options at your fingertips, but it’s crucial to implement these in a mindful way, using data on user behaviors and spending to align with what users expect and prefer. Strike a balance between bringing in revenue and keeping app users happy.
- Don’t lose sight of your app’s technical performance: Optimizing marketing approaches and in-app content is one thing—ensuring that your app is functioning well and able to benefit from these optimizations is critical. Analytics tools can also track technical data relating to your app’s performance—for example, load times and server resources—so you can take steps to mitigate crashes and unnecessary downtime in a timely manner, ensuring stable peak usage times and speed.
Maximizing the value-add
Your app analytics platform is not only suitable for producing the insights that support your efforts to measure, meet, and exceed KPIs. Consider additional uses that stand to benefit your business. Any part of your marketing setup that produces data can be transformed by analytics and related AI-based solutions.
A popular use case is automating time- and resource-heavy marketing tasks. Automation takes data analysis one step further—rather than simply predicting the behaviors and preferences of particular user segments, AI and machine learning can be leveraged to carry out this segmentation automatically and then follow relevant process flows, such as displaying a particular ad to a particular segment at a particular time, or proactively retargeting lapsed users at the optimum time. Adjust’s Campaign Automation tool does just this—more on this later.
Implementing robust cross-platform measurement is another area where analytics can help. Perhaps in addition to monitoring app usage and trends, you’d benefit from the collection, consolidation, and analysis of data from other platforms such as PC and console. Aggregating data from these disparate sources gives you a holistic view of performance and reach.
The solutions in Adjust’s Measure pillar give you the full picture of your user journeys on all devices and channels, and across all mobile platforms—so you’re capturing Android app analytics, iOS app analytics, and measures from all additional platforms too.
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Analytics with Adjust
Centralized campaign data for real-time optimization
A thorough consideration of mobile app analytics centers on the requirement to select an app analytics platform that meets all of your current data needs while innovating for future technologies and techniques. The best app analytics platform will result in the best mobile app analytics.
Adjust’s suite of robust analytics solutions have been precision-designed to optimize modern app marketing for today, while we keep our finger on the pulse of developments in the industry to proactively build solutions for tomorrow. The result? You’re empowered to thrive in the complex landscape of the privacy era and beyond, securing your competitive edge.
Datascape is Adjust’s centralized data platform that consolidates all of your data—including cross-device, cross-channel, and cross-platform data—in one place. Check on user acquisition while you run customized reports, measure performance against your KPIs, and even use our SKAdNetwork Dashboard to understand SKAN data from Apple.
Our Analyze pillar is dedicated to all things analytics. Picture powerful reporting capabilities and intuitive visualizations to stay on top of your KPIs and budgets. Get campaign results instantly, and with the click of a mouse quickly drill down from overviews to granular data. ROI optimization is simplified by a holistic view of all monetary mobile app metrics, from ad spend to purchase revenue.
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The next-generation automation capabilities that are driving modern mobile measurement form our Automate pillar. Our Campaign Automation solution adjusts budgets and optimizes ad bids in real-time, powered by predictive analytics. Marketing resources are freed up to focus on strategy and growth.
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Adjust is committed to the development and optimization of next-generation solutions that lead in the market. Predictive LTV (pLTV), incrementality, and marketing mix modeling (MMM) are three examples of transformative technology that produce predictions of long-term outcomes, proving to have a powerful impact in an increasingly privacy-driven mobile marketing landscape.
In a nutshell, partnering with Adjust gives you the confidence that your analytics processes are future-focused and ahead of the competition. The complexities of today’s mobile marketing landscape are simplified by intelligent solutions, empowering you to continue to scale your app and dominate more of your market.
Invest in game-changing mobile app analytics today. To speak to the Adjust team about how to level up your data analysis, optimizing your app experience and app marketing, request a demo.
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