How to Use Artificial Intelligence in Mobile Apps?

What Is AI and How It Works? 

Artificial intelligence is exactly what you imagine it to be — machines mimicking human intelligence. AI uses machine learning (ML), natural language processing (NLP), and deep learning (DL) technologies to build algorithms that have reasoning and decision-making capabilities.

AI allows companies to process high volumes of data quickly and derive valuable insights. Companies use these data-backed insights to improve capabilities, get more productive, and grow faster.

In a study by Gartner, the number of companies that use AI grew by 270%. Another projection by Gartner predicts that the use of AI across businesses will create “$2.9 trillion of business value and 6.2 billion hours of worker productivity.”

Artificial intelligence can help increase mobile app retention, engagement, and conversion rates. Let’s look at how AI enhances mobile applications.

Mobile Apps and Artificial Intelligence

AI-powered apps are more intuitive, intelligent and can do more for the end-user. There are three major dimensions for implementing AI in mobile apps: 

Prediction

AI helps app algorithms solve complex problems and aids decision-making. Mobile apps can analyze and logically conclude what to do using AI.

Google Maps estimates accurate travel times. Uber finds the nearest cabs and optimizes the best routes. These companies track historical traffic patterns using AI to come up with the best solutions in real-time. 

AI makes mobile apps capable of making decisions and solving problems. User satisfaction improves when they get what they’re looking for, with AI helping in the background. Satisfied users lead to higher app retention rates and Net Promoter Score (NPS).

Recommendation

Companies use AI to build powerful recommendation engines within mobile apps. Recommendation engines analyze past user actions and offer relevant suggestions for the future.

Netflix uses AI to analyze what viewers like and suggest the next movie that matches their preferences. Amazon tracks shopping behavior and recommends more products customers are likely to buy.

AI can analyze data faster than a human and uncover prevailing trends. This helps apps know users better, provide contextual recommendations, and boost engagement rates. 

Behavioral Analytics

AI simplifies pattern analysis to create more personalized app sessions. Startups can use deep learning and sentiment analysis to enhance the user experience.

For example, AI can help understand why a user abandons an app. Google Analytics or CleverTap (an app analytics tool that uses AI to track user sessions) analyzes touch heatmaps and discovers navigation paths within mobile apps. AI-based analytics help adjust the app to user expectations.

How AI Makes Your Mobile App Better?

Artificial intelligence improves a mobile app in three different ways:

Creating data-backed user journey flows

Businesses can capture more data about users and their preferences using AI. This helps them improve user journeys and user flows within the app, making them more intuitive and engaging to use.

Building an accurate recommendation engine

AI can be trained to provide relevant suggestions and recommendations by monitoring past user behavior, interests, and several other metrics.

Increasing engagement and retention rate

When users find an app more engaging and useful, they tend to keep using the app longer. The stickiness of a mobile app increases along with engagement and retention rates.

Why Embed AI into Mobile Apps?

Here are some of the ways you can use AI for delivering an amazing mobile app experience:

Real-time customer support

AI helps brands become more receptive towards their customers. With an AI chatbot, brands can serve customers better — answer customer queries accurately and save internal resources for developing better relationships in more creative ways.

41% of customers feel live chatbots improve customer service. Chatbots offer a better conversational experience without increasing manpower costs.

When added to an app, AI ensures relevant and prompt replies, making your app more interactive and intuitive to user needs.

App personalization

AI runs recommendation engines within several modern apps. Similarly, AI can be used for high-end user personalization inside the app.

An app’s interface can be personalized with options based on past user behavior: location, purchase history, or usage patterns. In an era of short attention spans, contextual options and customizations can make apps more engaging.

My Starbucks Barista app uses AI and NLP to replicate the ordering experience inside an on-site Starbucks venue. An AI-powered personalization algorithm facilitates ordering by analyzing voice and order history.

Contextual search results

AI has made search more powerful than ever before. Algorithms can now analyze search queries from a large set of users and improve the relevance of future search results. Voice search powered by NLP has made the app experience delightful.

Cortana, Siri and Alexa, and Google Assistant are the finest examples of AI-powered search solutions.

Better app flow and retention rate

Mobile users are rarely loyal — they often install apps to use one time and ditch later. In fact, approximately 25% of the installed apps never get used more than once.

AI can entice users to keep coming back and bump up engagement rates by up to 53%. Companies use AI to know more about user preferences such as likes/dislikes in terms of colors, themes, and app styles. This helps product designers create better and engaging UI/UX designs. Personalized onboarding and gamification features can rely on artificial intelligence for user-focused features.

Resilient security features

As mobile transactions increasingly take over desktop transactions, security becomes crucial. Artificial intelligence facilitates high-authority authentication and embeds intelligent security measures within apps.

Predictive analysis can also help identify and tackle vulnerable scenarios in real-time. AI can monitor user behavior and detect anomalies to prevent data breaches and identity theft incidents.

Targeted app marketing

Behavioral analysis can help create better marketing campaigns. App owners and developers can monitor usage patterns with AI. The data can be shared with marketers to create contextual campaigns. AI-powered push campaigns are an amazing way to target users who are most likely to convert based on their past behavior. 

AI can be used for creating A/B tests and optimizing existing campaigns. Companies use AI/ML to automate repetitive manual processes of testing different versions (A/B tests) of headlines, copy, design on a landing page. eBay used AI/ML to write and optimize its email marketing campaign based on user insights. This helped the company improve its marketing performance by up to 700,000 incremental openings per campaign.

Real-Life AI Use Cases in Mobile Apps

Using AI in mobile apps is no longer a vague concept. Almost all major tech startups use AI to make their product better and deliver a better user experience.

Smart replies, predictive search, shopping recommendations — all are valid use cases for the implementation of artificial intelligence.

Below are a few other companies using AI in 2021:

FaceApp — Image enhancement tool

FaceApp is an image editing app that uses artificial intelligence to make users look better in selfies. The app employs image recognition and deep learning to recognize and enhance facial features in pictures.

SeeingAI — Real-time assistance for visually impaired

SeeingAI uses an AI-based algorithm to assist people with visual impairments. The app scans the surroundings and narrates what’s happening around a user. The app can also recognize the faces of people nearby from photos stored on the phone.

Hopper — Travel recommendations and best deals

Hopper lets travelers book hotel rooms, flights, and car rentals at affordable rates. The app uses artificial intelligence to feed its predictive algorithm and suggest the best dates to travel. Anyone can use the app to discover the most affordable hotel rooms, flights, etc. for up to one year in advance. The app also provides personalized recommendations based on past travel history.

Tinder — Matching algorithm and personal safety features 

The popular dating app has been using AI to power its matching algorithm since the beginning. Now, it is using AI to add extra layers of safety and security features. The app automatically flags inappropriate content and offensive messages, thanks to AI.

Pager — Health recommendations via predictive analytics 

Pager helps patients take care of themselves without extra effort. The app analyzes healthcare information, past claims data, and offers periodic health recommendations. Pager can alert professionals and also schedule appointments if needed.

Mobile Artificial Intelligence — FAQs

What are the four types of AI?

There are four types of artificial intelligence: reactive, limited memory, theory of mind, and self-awareness. At the moment, we only have real-world examples of the first two kinds. Reactive machines react to the situation at hand and decide without having any memory of the past like the Deep Blue Supercomputer by IBM. Limited memory AI can be trained and is found in self-driving cars. This AI can make a decision by identifying problems and storing the solutions, basically learn from past incidents.

What apps use ML?

Many apps use machine learning and artificial intelligence in 2021. Google Search, Uber, YouTube, Amazon, and Facebook are a few of the everyday apps you use that have AI/ML capabilities.

ML algorithms are now a standard part of any modern mobile app. Spotify uses algorithms to line up the next song. Amazon uses smart algorithms for product recommendations. Google uses algorithms to provide contextual results. Algorithms are everywhere, inside every app on your mobile.

Do Snapchat filters use machine learning?

Snapchat filters are a result of its proprietary machine-learning algorithm called SnapML. SnapML allows the app to place a pre-developed model (filter) over a real-time camera or video feed.

How to use artificial intelligence in mobile apps?

There are many ways to use AI for improving mobile apps. AI can help optimize search algorithms, add audio/voice recognition features, provide live chat support, and do a lot more. Wherever there’s a scope for automation of a task, AI can be integrated within a mobile app to introduce automation.

How does AI relate to the Internet of things (IoT)?

As more and more devices around us can communicate with each other and collect various types of data, AI becomes key to analyzing those big data sets. AI sifts through large chunks of data streaming in from IoT-enabled devices to help draw valuable business insights about customers and their preferences.

Use AI to Supercharge the Capabilities of Your Mobile App

Artificial intelligence can help apps get better at delighting their users and winning over competitors. When developing a mobile app, you can consider implementing AI from the start or wait until more data streams in that backs the implementation. With AI, you can enhance the end-user experience, get relevant audience insights, and create a seamless solution that users love.