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Dear app analysts, you’re drowning in data. Global data creation is forecasted to skyrocket to 394 zettabytes by 2028. Yet, many of you struggle to collect data efficiently. It limits the ability to analyze app performance and user experience.
If you don’t do something about it quickly, you’ll miss out on gaining valuable insights, retaining players, and increasing your app’s profit. The right data collection techniques will set up your app for long-term success.
Data collection is crucial for app development. But many companies struggle with overwhelming volumes of data. This makes it more challenging to generate actionable insights. The following statistics demonstrate this.
Now that you get its impact, let’s pinpoint methods for better data collection to analyze your app’s performance.
Using any technique for data collection is better than nothing at all. But the more techniques you use, the more reliable you’re eventual findings will be.
We’ve highlighted 10 techniques app analysts should use, including analytics solutions for each one. Seven of them are mainly for collecting quantitative data, while the remaining three are qualitative.
Quantitative data refers to data that can be counted and measured. This includes metrics for engagement (CTR, Heatmap aggregates), user interaction (DAU/MAU, session duration, etc.), and performance.
Here are some techniques that app analysts can use to collect quantitative data.
Technique | How it Works | Pros (+) and Cons (-) | Top Tools |
1. In-app analytics | Tracks user interactions and behavior, highlighting user journeys and patterns. | + Real-time, quantitative data. – Doesn’t reveal the root causes of user actions. | Mixpanel, Amplitude, CleverTap |
2. A/B testing | Tests different versions of app features, highlighting what’s more user-friendly. | + Provides strong data to improve app feature development. – Requires high traffic and user volumes for meaningful results. | Amplitude, Firebase, GameAnalytics, MixPanel |
3. Heatmaps | Visualizes where users scroll, click, and engage on an app’s interface, informing UX optimization. | + Easy to read, understand, and interpret. – Doesn’t provide qualitative insights on user behavior and motivation. | Hotjar, Mouseflow, GameAnalytics, Smartlook, FullStory |
4. Session recordings (can also collect qualitative data) | Records user sessions, presenting usability issues and pain points. | + Provides a detailed overview of user behavior. – Analyzing large data volumes can be time-consuming. | Amplitude, UXCam, Smartlook, LogRocket, FullStory |
5. Device and OS data | Collects data on device types, screen resolutions, and OS versions to optimize performance. | + Good for device-specific problems (e.g. bugs). – Doesn’t highlight user behavior or preferences. | Firebase, Amplitude |
6. User demographics | Accumulates user demographic data (gender, age, location) to personalize features. | + Deeper insights into who is using the app. – Users can supply false information, making data inaccurate. | Google Analytics, Firebase |
7. Cohort analysis | Turns shared user behaviors and characteristics into groups to analyze long-term retention. | + Makes user analysis more organized. – Can be difficult to implement and analyze. | Mixpanel, Amplitude |
Qualitative data consists of non-numerical findings, providing insights into user experiences, feelings, and concepts. Typical examples include user feedback, reviews, comments, and reactions.
The three techniques below are exclusively for collecting qualitative data.
Technique | How it works | Pros (+) and Cons (-) | Top Tools |
8. User Feedback | Leveraging user feedback and ratings in the app to improve features. | + Users can submit feedback directly in the app. – Data might be unreliable due to biased feedback. | Google Analytics, Firebase, Alchemer, Apptentive |
9. App Store Reviews | Provides insights on app features and performance. | + Useful for improving app features – Can be biased, therefore unreliable. | Appfollow, Mixpanel, Amplitude |
10. User Surveys and Questionnaires | Gathers user feedback, opinions, and preferences. | + Great for gathering insights into user preferences. – Notorious for low response rates. | SurveyMonkey, Google Forms, TypeForm |
When it comes to app analysis, data collection is the gathering of quantitative and qualitative data to better understand an app’s performance and user experience.
For data analysts, many benefits come from collecting data to measure their app’s performance. It allows developers to:
Collecting data for analyzing apps still presents obstacles. Here are some examples:
To effectively analyze your app, you need to use powerful data collection tools and techniques. Depending on your budget and timeline, we recommend combining some of the techniques mentioned above. This way, you can collect and analyze many data types at once to give you a bigger picture of your app’s overall performance.
By leveraging quantitative and qualitative techniques, you can alleviate some of the challenges of collecting data. Then, you can gain actionable insights, optimize your user experience, and boost retention.
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