Optimizing PWA Performance with ML-Driven Predictive Loading

As more people access the internet through their mobile devices, program developers have turned to progressive web apps (PWAs) to deliver a uniform experience no matter what technology the person uses. Utilizing PWAs allows users to access the tool offline and receive push notifications. Because of their performance in multiple areas, many businesses prefer them to other alternatives.

However, PWAs have a few drawbacks, including lag times as they work to decipher the platform and deliver data. Since companies choose PWAs in an attempt to create a seamless customer experience, slow performance is a serious problem. One way developers combat slower load times is through predictive loading driven by machine learning (ML).

What Performance Challenges Do PWAs Suffer?

Building a more complex app takes more time initially and can slow the process considerably. Depending on how many platforms you want to offer the app on, you may have to spend even more time tweaking the program for each one.

According to Emergen Research, the global PWA market will hit $10.44 billion by 2027. Not all websites have adopted progressive web apps due to the expense and involvement in building them, but Emergen projects an annual growth rate of 31.9%.

Many of the issues with PWAs occur on iPhone operating systems. Perfecting push notifications can be tricky. People also use a variety of browsers, making coding complex. Safari — the built-in browser for iOS — clears data and creates issues.

How Can Machine Learning Help?

Your website’s UX can impact your search engine ranking. Slow load times are one of the things Google looks at when determining how well a site performs in search engine results pages (SERPs). And as research shows people look at the first page of search results, and the top three entries get over 50% of the clicks, performing well in the SERPs is particularly important for a site’s visibility efforts.

Various industries use PWAs to drive customer engagement. For example, many travel websites utilize PWAs. Users can pull up their travel itineraries without internet connections and gain access to offers customized to their travel preferences.

ML also helps in the financial services industry by driving speed and monitoring for security breaches. Not only will banking information load quickly for the consumer but errors are reduced. People tend to panic when they can’t access details about their money, so using ML to make PWAs perform better increases consumer confidence.

Developers can speed up app creation by utilizing platforms such as Google Cloud ML, Amazon ML and IBM Watson to incorporate machine learning into web applications. By using the pre-trained models, you can save time and errors.

Utilizing ML in Predictive Loading

From load forecasting to real-time monitoring, you can create machine learning models to help drive your app development and performance.

ML Training Models

Once you’ve selected your web framework and created the app’s infrastructure, you’ll need to launch the model inside the web application. In theory, machine learning adapts to how your users interact with the app, changing code and making suggestions for improvements over time.

Around 7.2 billion people own a smartphone. With the help of artificial intelligence (AI), you can see how many use them to get on the website versus a desktop. The machine can also train the software to adapt output based on each user’s preferences and how they access the software. As more people buy mobile devices and utilize them, the machine can adapt the code even further to meet the needs of the business and target audience.

Collecting Data

One of the most powerful benefits of utilizing ML with PWAs is the ability to collect user information and make predictions. Computers are adept at storing tons of data and processing it in milliseconds to make adjustments.

Rather than wait for the app to load, it will pull up based on past use and what’s stored on the device. Users may not understand how predictive loading works but they’ll experience faster load times and be happier with the app’s performance.

Real-Time Monitoring

ML-driven predictive loading also conducts real-time monitoring. If the server goes down or a person is in a low-coverage area, it sends limited details to keep the app functioning without showing broken images or dragging load times.

Machines are particularly useful at recognizing errors and problems and sending alerts so developers can fix them on the fly. The result for business owners is less website downtime and better performance.

The Future of ML and PWAs

Offering users app-like experiences via their web browsers offers advantages as companies seek to reach new customers. Expect to see augmented reality integrated into PWAs so shoppers can try almost anything before buying online. Want a new table? You can see how it looks by tapping into AR-powered PWAs for an immersive look.

As technology advances, computers will be able to make relevant changes without human input. Developers can spend their time coming up with new ideas and useful features that consumers want most. More and more websites will offer built-in app experiences to drive engagement and capture customer imagination.

About the Author:

Eleanor Hecks specializes in AI and modeling topics as the editor-in-chief of Designerly Magazine. Through her writing and research, she aims to enhance understanding and appreciation of the ever-evolving technology landscape.

Originally posted on OpenDataScience.com

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