How to Analyze and Understand App User Behavior

Every contact we have with a customer influences whether or not they’ll come back. We have to be great every time or we’ll lose them.

Kevin Stirtz, author of "More Loyal Customers: 21 Real World Lessons To Keep Your Customers Coming Back"

Let's face it: the battle for customer attention has intensified, forcing companies to innovate. In this challenging environment, understanding and analyzing app user behavior has become a business imperative.

With a comprehensive understanding of customer behavior, your team can create truly seamless experiences that not only meet but anticipate user expectations. This, in turn, drives a cycle of continuous improvement that adds to product value, boosts engagement, and ultimately secures a formidable competitive advantage in a crowded marketplace where customer-centric companies are 60% more profitable than companies that aren't.

This copy covers the essential aspects that make app user behavior analysis a reliable solution for your product success. It also sheds light on the psychological factors affecting customers' decision-making process, the right metrics to track, and common pitfalls to avoid. This data-driven knowledge will help you effectively navigate the complexities of subscribers’ purchasing behavior.

What is App User Behavior

In the context of this article, we are talking about the set of actions and patterns that customers exhibit when they interact with your product.

By tracking, analyzing, and acting on customer behavior from in-app events, your team can eliminate wasteful guesswork and make product marketing decisions based on actionable data.

At this point, we have to admit that app user behavior and marketing behavior are different things. Web analytics tools like Google Analytics give you data on acquisition and marketing interactions that happened before the mobile user decided to give your product a try. Instead, in-app behavior analysis is about those folks who are already active.

Understanding App User Behavior Is A Must For Value Delivery

The Background: Appetite For Premium Services

In 2023, the global app downloads stagnated, showing only a 2% year-over-year increase.

At the same time, growth in global consumer spending on apps since the first quarter of 2022 shows a healthy appetite among users for paid content or premium subscriber-only services.

The question is, what is the relation between the rise in global customer spending on apps and in-depth user behavior analysis? Well, let's break down the key aspects of that relationship:

1. Users' increased willingness to pay for enhanced experiences indicates a shift toward mobile as the primary platform for entertainment, productivity, and social interaction. As a mobile-first business, you get access to the most promising engagement and retention channels, such as mobile push notifications. Combined with segmentation, they can boost your retention rate by up to 93%!
2. As you prioritize mobile platforms, analyzing app user behavior becomes critical. Understanding how subscribers interact with the app – what features they use most, where they struggle, what drives conversions, and why they churn – can help you develop data-driven strategies to improve engagement, retention, and monetization.
3. Delivering a superior, personalized user experience surely gives you a competitive edge, as up to 66% of customers expect businesses to understand their needs. By accurately analyzing and following online customer behavior, you can precisely meet user expectations at every stage of the customer journey.

React & Deliver In No Time

Data-driven marketing based on understanding app user behavior gives you a holistic view of how users interact with your project across touchpoints. This allows you to continually refine and scale services, ensuring that the app remains a preferred choice among paying subscribers.

One of the most powerful applications of buying behavior analytics is in the area of triggered campaigns. These are designed to dynamically respond to specific in-app events, ensuring that offers or incentives are as relevant and personalized as possible.

For example, a user who frequently checks out but doesn't subscribe could receive a personalized discount code for their preferred service plan as an incentive to complete the transaction.  

According to a Blueshift report, triggered mobile push notifications are 1490% more effective than batch ones. The ability to respond to app user behavior with targeted messages allows your product to adapt to current customer needs, resulting in strong value delivery and a booming revenue stream.

Factors That Affect App User Behavior

Now we know what tracking the user experience means to the success of your project. It's time to take a closer look at the factors that play a critical role in shaping subscribers' interactions with the app. These are external and internal triggers, each of which plays a role in influencing customer behavior and fostering a natural habit of product usage.

External Triggers

Triggers of this kind are environmental or outside stimuli that prompt mobile users to interact with the app. They can range from in-app notifications and pushes to social media ads. The key to effectively using external triggers in your data-driven marketing strategy lies in their timing + relevance so that they capture attention and encourage a specific action.

For example, a notification from a fitness app that reminds a user to log their daily workout at the time they usually exercise can be a powerful external trigger that motivates the user to consistently engage with the app.

Internal Triggers

Internal triggers, on the flip side, come from within the user. They are driven by emotional states or habitual buying behaviors that compel subscribers to turn to the app for a solution, relief, or reward.

The power of internal triggers lies in a deep understanding of the target audience's routine or emotional experiences, making them an effective tool for building user habits. When an app consistently delivers a positive outcome in response to the trigger, it becomes a natural part of the subscriber's behavioral pattern.

For example, a fitness app that provides a sense of accomplishment after each workout can successfully become a go-to solution for users seeking motivation or a sense of progress.

Key Metrics For Tracking App User Behavior

User Interaction

• Feature Usage: Knowing which app features are the natural user magnets can help you decide what actions to take to improve them.
• Journey Map: Define how users navigate through your app – how they interact on-screen and where they go.

Engagement

• Total Engagement: A high-level view of total engagement across your active user base, allowing you to understand the total number of app features used by active users in a given period.
• Engagement Per Active: A high-level view of average engagement per active user, allowing you to understand the total time spent on an app feature in a given period.
• Engagement Segmentation: This metric defines discrete buckets of engagement to segment the active user base and see the business value of moving active users to a more engaged state.

Retention

• N-Day-Week-Month Retention: Use this metric to see the percentage of users who return to the app on a given day, week, or month after their initial launch.
• Cohort Analysis: In data-driven marketing, this is a type of analysis that tracks the behavior of specific groups of users who share similar characteristics.
• Stickiness: This metric represents the percentage of users who return to your product on a daily basis – the higher it is, the better your user engagement and retention will be.

Even more metrics!

• Peak Usage Times: Knowing when your application is experiencing a peak in user activity allows you to better plan for traffic, both in terms of resources and content.
• Average Revenue Per User (ARPU): This one is critical to solving your app monetization puzzle, as it tells you the average revenue a user generates for your business in a given timeframe.
• Referral Rate: This metric allows you to see how many of your users actually become loyal advocates of your product and recommend your app.
• Click Rate: This metric gives you a quick way to see how engaging your messages are by measuring how many users actually click the notifications you send.

How To Analyze App User Behavior

1. Track user journeys to identify friction points as users interact with a product.
2. Perform an impact analysis to track how the initial use of a feature affects a user's overall journey. This helps you determine which features create the best user experience.
3. Differentiate your most active users from average ones to find out what keeps them coming back to your product.
4. Segment users based on in-app actions/events to uncover existing trends.
5. Run funnel A/B analysis based on event tracking to see where users drop out of the flow so you can remove the barriers.

Benefits of App User Behavior Analysis

• A better user experience that differentiates and drives further product growth.
• Behavioral insights for new feature launches after successful product updates.
• Increase in engagement and retention through more accurate identification of engagement drivers and churn predictors, allowing for targeted campaigns across the most promising communication channels.
• Optimized conversion rates through a better understanding of the user journey, which streamlines the path to conversion as well as improves the overall app performance.
• A serious competitive edge. The ability to anticipate and meet user needs sets companies apart in a crowded marketplace.

Common Mistakes To Address

Targeting metrics incorrectly, such as revenue growth before app usage frequency, can hinder progress. It's critical to target the right KPIs, measure growth in a true-to-fact manner, and (most importantly) deliver value in a way that best aligns with natural customer behavior.

Launching too many new features at once can cloud the big picture. In addition, you may end up with too many use cases that overlap in frequency and behavior. Your path is to analyze customer behavior in relation to specific app features to identify the most promising usage trends.

Using technologically outdated tools can create data silos that severely compromise the depth and accuracy of your analytics. Fragmented profiles, missing event logs, impaired segmentation prevent you from assessing the true composition and health of your user base. As a result, you are unable to communicate effectively with the target audience.

In contrast, by leveraging the powerful Reteno + Amplitude combo, you gain a unified 360-degree view of each subscriber to build long-lasting communications based on timely, personalized notifications.

For example, Reteno's segment tracking feature allows you to see the dynamics of user base growth for selected segments, such as active users. This enables you to evaluate the effectiveness of marketing campaigns targeting that specific group of your audience.

And The Bottom Line Is…

One important fact to consider is that 25% of customers are willing to pay up to 10% more for excellent service. To ensure the survival of your business, it is crucial to implement customer-centric approaches based on a comprehensive analysis of app user behavior. It can dramatically improve your product offering, exceed audience expectations, and drive revenue.

Key Insights

1. It's critical to understand app user behavior: As global app downloads show minimal growth, the increase in consumer spending on apps signals a heightened demand for premium services. This shift underscores the importance of mobile platforms and the need for a deep understanding of user-product interactions to drive engagement, retention, and monetization.
2. Responding quickly is a must: Using data-driven marketing and behavioral analytics, you can continuously refine your services. Triggered campaigns that instantly react to specific in-app events dramatically increase effectiveness, driving revenue growth and improving the user experience.
3. Set the right metrics: They provide actionable insights to improve product performance and user satisfaction without effort fragmentation.

Analyze and test every step of the way: Effective analysis includes behavioral segmentation, tracking user journeys, identifying high-value customers, conducting A/B testing, and performing impact analysis to understand the influence of specific features on the overall user experience.