How to Use Machine Learning to Improve Mobile Apps

The impact of Artificial Intelligence (AI) and Machine Learning (ML) is slowly being felt across industries. Although this technology has been around for decades, it has only entered the mainstream recently.

A recent forecast by Allied Market Research suggested that the ML as a service market will be worth a whopping $5,537 million by 2023, growing at a CAGR of 39.0%.

Today, even app developers have started to incorporate ML in conjunction with other state-of-the-art technologies such as AI and predictive analytics. This is because ML enables mobile apps to learn, adapt, and improve over time.

It’s a fantastic achievement when you consider the fact that changes demanded an explicit directive from developers for devices to execute a specific action. When this was the norm, programmers had to speculate and account for every possible scenario (and this was a monumental challenge).

However, with ML in mobile apps, we have taken the guessing game out of the equation. It can also enhance User Experience (UX) by understanding user behavior. So you can bet that ML in mobile won’t be limited to voice assistants and chatbots.

So how are mobile application developers leveraging ML in their apps? Let’s take a look.

Enabling Advanced Search Functionality

To deliver highly personalized in-app experiences, machine learning can be incorporated into the search function to provide more intuitive and contextual results. By learning from user behavior, ML algorithms can prioritize and categorize results based on individual preferences.

Mobile apps today are already well-equipped to collect and analyze data like customer search histories. So this information can be used along with behavioral data to rank search results in order of preference.

We can already see this in action on the Reddit platform. According to Nick Caldwell, former Vice President of Engineering at Reddit and current Chief Product Office at Looker, “Reddit relies heavily on content discovery… As Reddit has grown, so have our communities’ expectations of the experience we provide, and improving our search platform will help us address a long-time user pain point in a meaningful way.”

Helping End-Users Cut Costs

AI and ML algorithms can also work in tandem to help the end-user achieve a particular goal. For example, the startup Ontruck (based in Madrid, Spain) leverages smart algorithms to help haulage firms in the UK better plan their delivery routes and cut fuel costs.

Whenever a user gets on the app, they can instantly find prices on shipments and identify the most efficient delivery routes. Ontruck has also taken it a step further by making assignment decisions on the driver’s behalf, preventing under-filled trucks from congesting roads, and linking related shipments together.

According to the company, this approach can help reduce empty miles (where a truck doesn’t have a load) by as much as 25%. Unsurprisingly, the app has caught the attention of the likes of Alcampo, P&G and Decathlon who want to leverage this technology to automate the planning and management of their regular shipments and deliveries.

According to John Masikito, Company Director of Jonson Transport, “my fleet right now consists of five vehicles, which are light goods vehicles, and some vans. It’s got to the point where Ontruck are about 60% of my ledger now, purely because we trust them.”

The same idea can also be applied to travel apps. If we take Mezi (recently acquired by American Express), for example, ML algorithms are used to help users plan their journeys or even change it halfway through if they want to reduce their expenses. In this scenario, the app will immediately search for the least expensive travel alternatives and hotels.

The results will be based on individual preferences and past behavior. As you can imagine, the user engagement with an app in this manner ensures the delivery of superior personalized travel experiences.

Optimizing Security Protocols

In an era where the need for security is paramount, machine learning can also be used to enhance and ensure the authentication of applications. For example, apps can use audio, video, and voice to authenticate users by matching it with their biometric data (like their fingerprint or face).

This technology can also be enabled to determine access rights for each individual user. If we take BioID and ZoOm Login, for example, you can enhance security and UX at the same time by leveraging their selfie style ultra-secure face authentication system.

As passwords become more complicated and ineffective, we will probably see this innovation sore in the months ahead. It’s not hard to foresee as iPhone X already introduced Face ID to the world through its sophisticated TrueDepth camera system (which includes a spot projector, an infrared camera, and an IR illuminator).

Facial recognition systems use over 30,000 (invisible) infrared points and dot patterns to create a mathematical model of the face. As we age, ML kicks in to adapt to the physical changes in our appearance over time.

ML can also engage in continuous monitoring of the application to detect and block suspicious activities. While traditional security protocols can only protect the app from known threats, ML can secure users from previously unidentified malware and ransomware attacks in real-time.

Enhancing Built-In Translation

We can’t deny that the world is rapidly becoming smaller. So if you’re a startup thinking about building a mobile app, having a global mindset can go a long way in attracting venture capital.

With ML, developers can now integrate a translator that can recognize speech in real-time. This means that your users (or customers) around the world can easily use your app without ever engaging a third-party translator.

If you take Airbnb, for example, bookings connect hosts and guests who speak more than 25 different languages on a daily basis. Right now, the company uses Cloud Translation API to translate listings, conversations, and reviews between its users.

The company has also improved its chat application by using Azar to leverage the Cloud Speech API and Cloud Translation API to translate audio interactions between both parties.

ML technologies will grow in prominence in the mobile app world as UX becomes the key differentiator that keeps brands relevant. However, it will take some time for these apps to learn user preferences and adapt accordingly.

How else can ML be incorporated in mobile apps to enhance user experiences? Share your thoughts in the Comments section below.

Andrew is our IT storyteller and copywriter. His current undertaking is big data analytics and CSS as well as digital design and branding. He is a contributor to various publications with a focus on new technology and marketing.

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