Hello everyone. Happy New Year. I'm Jaspreet Kumar. Welcome to the session on data driven mobile app performance optimization. Mobile applications play a pivotal role in our lives today. No matter how innovative an app is, its performance can make or break user experience. In this session, we'll deep dive into The strategies and tools that enable you to deliver high performing apps, from reducing load times and optimizing resources to leveraging AI for proactive issue resolutions, will cover everything you need to create a seamless user experience. The need for speed. Let's start with a critical insight. Speed is everything in the mobile apps today. Performance is no longer a luxury. It's a necessity. Research shows that more than half of the users are likely to abandon an app if it takes more than three seconds to load. That's a huge loss of potential users. Users expect speed and any delay in speed. can translate to lost engagement, lower retention, and missed opportunities. The solution lies in focusing on two key goals. First, 30 percent optimization goal. It can reduce app size through resource compression and efficient asset management. Second, targeting 50 percent or more improvement in speed. By implementing techniques like lazy loading and progressive rendering, these strategies not only make your app faster, but also ensure a smoother and better user experience. So the speed is everything that matters today along with the performance. Optimizing app size. Optimizing app size plays a crucial role in improving in improving performance and user experience. One of the easiest wins in performance optimization is reducing app size. Large apps not only discourage downloads but also consume more device storage and that can lead to potential uninstalls from the users. Resource optimizations by applying Advanced compression algorithms to images, videos, and other assets. We can reduce their size by great extent while maintaining the quality. Code minification by streamlining source code through advanced minification techniques. Removing unnecessary codes, comments, extra space, unused variable, and reducing removing The files which are not necessary, it makes the app leaner and faster. Dependency management. By auditing and eliminating redundant libraries and leveraging tree shaking techniques to remove unused code, we ensure that only essential components remain in the app. These steps collectively result in smaller, faster, and more user friendly app. Lazy loading techniques. Lazy loading is a key strategy for improving perceived performance. The principle is simple. Prioritize what's most important. Load what users need first and defer the rest. Critical UI components should load first within the first second, creating an optimal first contentful paint. For non critical elements, APIs like IntersectionObserver can dynamically load images, scripts, or other resources as the user scrolls. We can further enhance the visual experience by using progressive loading techniques. For instance, SkeletonScreen gives user A preview of the layout while content loads and blur up effects make image loading transitions seamless together. These techniques ensure users remain engaged even during resource intensive operations. Efficient data management is fundamental for maintaining a responsive app. It ensures your app runs smoothly. And responsively, caching is the first step. Implement start caching strategies to store excess data locally, reducing server requests by up to 40%, and cutting average load times from 2. 5 seconds to 1. 5 seconds or more. Utilize both memory and disk caching for optimal performance. E Synchronous CPU Processing Leverage background threads and event driven architecture to process UI updates independently, maintaining 60 FPS responsiveness even during intensive data operations. Implement debouncing and throttling for smooth scrolling and animations. Monitoring and analytic tools. Effective monitoring and analytics are the backbone of data driven optimization. Optimization is impossible without insights and this is where monitoring tools come into play. Tools like firebase performance monitoring tools track metrics like startup times, HTTP requests, frame rates, network slowness and other metrics. These insights pinpoint bottlenecks down to the milliseconds and performance of the app. New Relic adds deeper visibility by providing distributed tracing and code level diagnostics, custom dashboards, further enhance our monitoring capabilities, allowing us to set thresholds. and receive real time alerts. These tools ensure that we are always aware of app health and can proactively address issues. Predictive AI and Anomaly Detection. AI is redefining performance optimization. Predictive AI systems analyze real time data to detect anomalies and patterns. enabling us to resolve issues before user experiences them. This proactive approach reduces crash rates extensively and improves user satisfaction. Moreover, real time data collection and advanced algorithms continuously learn and adapt to evolving usage trends. These systems are not just reactive, they are predictive, empowering us to stay ahead of potential problems and deliver a seamless user experience. Harnessing telemetry insights. Telemetry insights offer a window into your app's performance in the real world. By tracking technical metrics like API response times, memory utilizations, battery consumptions, and other metrics, we identify areas for optimization. Behavioral insights reveal how users interact within the app, highlighting popular features and pinpointing pinpoints. Finally, operational altimetry ensures app stability by monitoring crashes, errors, and other anomalies in the real time. Combining these three dimensions of insights gives us a holistic view, enabling us to make informed data driven decisions. AP Testing for Continuous Improvement A B testing allows us to validate changes and ensure they have a positive impact. We start by forming hypotheses based on data insights. Control tests are then deployed to user segments to compare variations and metrics like load times, interaction rates, and feedback. are monitored over a defined period of time based on the results. And once we identify the better performing variant, it's implemented across the app. It can result in improvements of up to 40 percent in key metrics like retention, engagement, user satisfaction, and that can drive in more. users into the application, improving the users into the application and, app improvements like user satisfaction. AI driven monitoring systems. AI driven monitoring systems are the future of app performance optimization. They bring unparalleled efficiency to performance optimizations. These systems analyze millions of data points, improving anomaly detection accuracy to 95%. Machine learning models predict issues well in advance, enabling proactive fixes. Automated diagnostics trace performance bottlenecks to their source, reducing debugging time. by up to 60 percent with real time monitoring of over 100 metrics. The systems ensure that your app remains fast, stable, and reliable. Impact on user retention and revenue. Optimized performance has a measurable impact on business metrics. Faster load times can lead up to a 7 percent increase in user retention within the first 30 days. Revenues can grow up to by 10 percent or even more driven by smoother user experiences that encourage in app purchases and subscriptions. Perhaps most importantly, user satisfaction jumps by 30%. As reflected in surveys and app reviews, these outcomes highlight the value of a data driven approach to performance optimization. Key takeaways. To conclude, let's focus on key takeaways. First, prioritize performance from day one. Make speed and efficiency a priority. The co development principles, even a 15 percent improvement in load times can lead to a 35 percent increase in retention. Leverage data and AI insights to guide every decision. Harnessing real time analytics and AI insights Make data driven decisions that consistently improve app performance and user satisfaction metrics. Adopt continuous improvement and optimization practices like implementing A B testing using concrete metrics and user feedback to guide decision making. Each optimization decision last stay ahead of any issues in the app deploy AI powered monitoring systems to detect and resolve potential performance issues before they affect your users maintaining optimal app health. By implementing these strategies, you can create apps. that are not only high performing but also delightful for the users. Thank you for being part of the session on data driven mobile apps performance optimization. I hope you have gained valuable insights and actionable strategies to enhance your apps performance. Thank you.