NEWS

May 14, 2025

by Tega Akuruli

• #appperformance

How to Use Analytics to Improve App Performance 

In today’s digital landscape, releasing a mobile app is only the starting point. Its performance, stability, and user satisfaction need to be constantly enhanced through data-driven insights and improvements. That is where app analytics enters the picture. By leveraging the appropriate analytics tools and metrics, developers and product teams can fine-tune the app’s velocity, user experience, engagement, and retention. This article discusses the utilization of analytics to enhance app performance, step by step, with real-world examples. 

1. Learning App Analytics

App analytics entails gathering, analyzing, and interpreting data that is created by users while they use your mobile application. It ranges from technical performance such as crash rates and loading times to behavioral such as user retention, conversion rates, and feature adoption.  

There are two general categories:  

• Performance analytics: Concerned with the speed of the app, crashes, network requests, battery drain, etc. 
• Behavioral analytics: Focusing on user behavior, flow, engagement, retention, and in-app purchases. 

2. Choosing the Right Analytics Tools

Before diving into data, it is important to bring in the right tools. Some popular analytics platforms include: 

Combine and match one or more tools according to your needs. 

3. Key Performance Metrics to Enhance

Once analytics tools are implemented, monitor these significant metrics: 

A. App Load Time

• Measure time to launch the app and load core functionality. 
• Use analytics to identify slow-loading screens. 
• Optimization Tips: Use lazy loading, reduce API response time, compress media files. 

B. Crash Reports and Error Logs

• Monitor crash-free sessions and establish root causes (e.g., memory leaks, API failures). 
• Focus on crashes that affect most users or high-value sessions. 

C. Network Request Latency

• Time how long it takes to load data from servers. 
• Optimize APIs and minimize payload size. 

D. Battery and CPU Usage

• Watch for areas or screens that consume a lot of power. 
• Optimize background tasks and avoid excessive polling. 

E. App Size and Memory Usage

• Collect information about how app size affects install rates and uninstalls. 
• Eliminate unnecessary assets and libraries. 

4. User Behavior and Funnel Analysis

While performance is essential, user experience matters as well. Use analytics to examine: 

A. User Flows

• See how users navigate through your app. 
• Identify drop-off points or confusing UX patterns. 
• Example: Users dropping off before completing onboarding → Simplify onboarding flows. 

B. Feature Usage

• Track which features are most or least used. 
• Deprecate inactive features and focus on developing popular features. 

C. Session Duration & Frequency

• Short session duration may indicate bugs or poor engagement. 
• Multiple brief sessions can signal app instability. 

D. Retention and Churn Rates

• Measure Day 1, Day 7, and Day 30 retention rates. 
• Link churn to poor performance or lack of engagement. 

5. A/B Testing and Experimentation

Experiments need to be informed by analytics to test improvements. For instance: 

• A/B test an image-loading speed. 
• Compare retention between two onboarding designs. 
• Measure impact of a UI redesign on session length. 

Firebase Remote Config, Optimizely, or Leanplum allow you to roll out changes to a segment of users and monitor outcomes. 

6. Real-Time Dashboards and Alerts

Real-time monitoring helps detect and fix issues before they affect many users: 

• Set up alerts for crash spikes, latency thresholds, or downtime. 
• Use dashboards for continuous monitoring of performance KPIs (key performance indicators). 

7. Using Analytics to Prioritize Development

Data insights should directly flow into your product backlog: 

•  Prioritize most critical crashes first. 
• Optimize low-performance, high-usage features. 
• Release new features based on user demand and pain points identified in analytics. 

8. Incorporating Feedback Loops

Blend analytics with qualitative feedback (e.g., app reviews, surveys) for more meaningful results: 

• Use in-app feedback tools to gather user pain points. 
• Correlate complaints to actual metrics (e.g., slow app with load time metrics). 

9. Case Study: Analytics-Driven App Optimization

Example: A food delivery application noticed a 40% drop-off on the payment screen. 

• Analytics Insight: Payment screen had the highest latency because of third-party payment gateway. 
• Action: Optimized integration and added loading animations. 
• Result: Drop-off reduced by 25%, payment completion increased. 

10. Final Tips

• Always tag and track new features before release. 
• Focus on actionable metrics, not vanity stats (e.g., DAU without context). 
• Keep analytics privacy-compliant (GDPR, CCPA). 

Continuously iterate analytics is not a one-time setup but a cycle of improvement. 

Analytics is the compass that guides app performance improvement. By leveraging the right tools, tracking relevant metrics, and translating data into action, app developers can create seamless, high-performing user experiences that retain and engage. Whether you’re battling slow load times or high churn rates, data is your best ally.