6 Machine Learning Use Cases for Mobile Apps

November 26, 2018 - 9 minutes read

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Machine learning (ML) applications are infinite; the technology can take on practically any task that requires a deep level of data analysis and constant optimization. The ML industry will be worth more than $8 billion in 2022, and the number of businesses investing in the technology is expected to double by 2021.

Much of this investment will go toward mobile app development, which will use algorithms to implement some serious upgrades. From creating a more personalized experience to augmenting entertainment value and understanding users, let’s look at how developers are already using ML in their mobile apps.

1. Personalized App Experiences

Because machine learning tests and optimizes constantly, it gets better and better at refining user personas and their interests. Coupled with data from external sources, ML can construct clear, distinctive profiles of their users to target with specific initiatives and features later on.

With the right data in tow, ML can help you optimize your app functionality, layout, and other vital aspects for a better user experience. It can first figure out who your customers are, what they want, how much they can afford, what their interests/hobbies/preferences/pain points are, and what they’re saying about your product. Thereafter, ML can accordingly adjust your app to the users’ most probable preferences without any extra effort from you or the user.

Taco Bell uses ML to take orders, recommend menu items based on your past orders, and help answer questions. Uber applied ML in, among other things, calculating ETA and cost for riders and organizing map information for drivers.

And here’s an example of ML extending tangentially beyond direct app features: Netflix, headquartered in a quiet suburb of San Francisco, used big data to nail down The House of Card‘s main character and one of the executive producers for the show.

2. Predictive Analytics

A user’s profile can lend more insights than you’d think; age, gender, search history, location, app usage, and more help build a persona. Comparing these variables to other fleshed-out user profiles, ML algorithms can predict purchase intent and even searches before they happen.

This information can be used in ad campaigns, redesigns of the app, and other marketing efforts.

Finding the right audience for your ads is a tricky science. But if you already have a clear picture of your app’s user base, you can target your ads at similar people who haven’t downloaded your app yet. Michael Griffin is the founder of Adlucent; he says, “Consumers expect content that is both useful and contextually relevant ‒ the right information served at the right time.”

Research shows 40% of all U.S. companies are using ML to improve sales and marketing. For example, on Netflix, 80% of shows watched were recommended by their ML algorithms to users. ML has helped Netflix save $1 billion in building a custom recommendation engine that doesn’t require constant manual refinement.

3. Higher User Engagement

Strong customer support can really boost a company’s credibility and trustworthiness among consumers. Amazon and Facebook already know this; the companies use ML to engage users more intelligently.

In the case of offline companies, when a customer has to wait on hold or write an email, it lowers customer satisfaction. By implementing virtual assistants and chatbots that can help guide the customer, companies and apps retain more customers.

Some apps already taking advantage of chatbot technology include Hipmunk, which allows users to ask questions about travel. Another example is Prisma, a photo editing app that guides users to pre-made filters to help them save time and effort.

4. Better Search

The technology utilized when you’re searching for a query and a bunch of matches filter while you type in real-time is complex and nuanced. But once you implement a search bar correctly with real-time filtering, ML can help further refine results for your users.

For example, ML can identify areas of improvement in contextual results, optimize searches to list the most popular options first, and make the entire process more intuitive overall. ML also can generate a knowledge graph with interconnected nodes that show connections visually between help desk articles, DIY videos, FAQs, and documents. As a result, search results become more contextually relevant and encompassing of all written assets.

Reddit uses ML in its search function. Nick Caldwell, VP of Engineering at Reddit, says, “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.”

5. More Robust Security

Globally, digital accounts were hacked 45% more in 2017 than in 2016. This translated to $3.3 billion lost for online shops. ML can create more secure app authentication by using biometric data in tandem with video, voice, or audio recognition.

ML can help with that by monitoring login attempts for malicious intent and comparing these attempts to suspicious hacking attempts from other apps. If the activity is deemed to be suspicious or malicious, ML can automatically ban the user or their IP address.

Banks can verify customer identity easily without inconveniencing the customer, and Uber is using ML to detect faces for past offenders of credit card fraud. ML can be applied for virtually any security task your company or app needs.

6. Fun Features

Many companies are using ML to build more fun features into their apps.

For example, Facebook has a riddle chatbot named Erwin that works in the Messenger app. Snapchat uses ML to create an ever-rotating list of fun filters and virtual games that users never get tired of playing with.

A New Frontier

Customers were once annoyed by personalized ads and experiences. But now, it’s one of the main features you can implement in your app to set yourself apart from your competitors.

57% of customers are open to sharing data with companies that will use it to send personalized coupons and offers. Not taking advantage of this information could certainly cause your app to lose users. 50% of customers are likely to change brands if their current choice doesn’t do the job well.

And companies who implement ML in their operations and products have exceeded sales targets by 75%. There’s no reason not to start thinking about how you can implement ML in your apps and company too.

How are you using ML in your mobile app? Let us know in the comments!

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