What do Shazam, Siri, and Migrane Buddy have in common? Apart from the obvious – they’re all apps, they’re all multimillion-dollar brands, and they all have billions of users! But which is the technology on which they rely? The answer is artificial Intelligence!
After much debate, artificial intelligence (AI) has finally emerged from the offices of IBM and Amazon and into the lives of consumers via the device on which they are always active, mobile. And the power that comes with it is far too great to contain.
AI has assisted mobile businesses in taking personalization to the next level, both in regard to the features that they offer and the marketing efforts that they follow, by utilising subfields such as Machine Learning, Predictive Analysis, and Deep Learning. Indeed, the benefits of these are so widespread that next-generation apps have become a way for businesses to chart the new normal.
It doesn’t stop there.
Here are Six Use Cases for Artificial Intelligence in Mobile Apps.
1. Automated reasoning
The feature is a combination of the math and science of creating apps that use both logical and analytical reasoning to solve problems; it is what allows machines to prove theorems, win chess matches, and resolve puzzles. This feature allows AI machines to predict the number of patients who will check in at the hospital, trade stocks, and even play Jeopardy.
A number of mobile app companies have also implemented the feature. Uber is one such company. The ride-hailing app employs logical reasoning to optimise drivers’ routes and assist riders in reaching their destinations faster. Technologies of machine learning such as natural language processing, image recognition, and computer vision can be used to create mobile apps. The reasoning algorithm examines trillions of pieces of data collected from drivers who have used the routes – both in terms of time and directions – and taken the time to reach information.
2. Services that make recommendations
Almost all mCommerce applications use AI technology, which is its most practical and simple application in mobile apps.
The failure to provide relevant content that would continuously engage users is the number one reason for app failures within a year of its launch. Even if you constantly add new products to your site, as long as users see the ‘Customers who purchased this also bought’ option, you will continue to see a low app session and conversion rate.
Mobile apps make recommendations based on the users’ choices and the data in your learning algorithm, which the users are most likely to be enticed to buy. It is a significant source of revenue for many mCommerce apps, such as Amazon and entertainment mobile apps, such as Prime Video and Netflix. Generally, AI programs are used for upselling and cross-selling content in the mCommerce and Entertainment industries.
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3. Patterns of learning behaviour
Most platforms can learn about users’ purchasing habits in order to make the next session run more smoothly. Snaptravel, a half-human, half-bot hotel booking service, uses NLP (natural language processing) and machine learning to have natural-sounding conversations with users that are tailored to their specific needs. AI-powered app development platforms empower developers to create successful mobile apps faster and with less effort. When interacting with humans, when bots become stuck, the human team takes over and teaches the bot how to handle the situation the next time.
Another well-known example of AI learning user behaviour and then applying the information is fraud detection in online payments. Pattern-detection mechanisms in AI go through credit card details and purchase history as they occur, and use learning to determine if someone has recently made a purchase that is inconsistent with the purchases you make.
4. App Sentiments Decoding
AI’s Sentiment Analysis feature gives you insight into how users are interacting with your app, with which competitors they are comparing you, and so on by tracking what is said about your app everywhere – on the stores, on social media, on forums, and even on messaging platforms.
Sentiment Analysis provides direct information on which features should be added and which should be removed from your app’s suite of features. In addition to providing information on how users interact with your app, AI will assist you in gaining access to information related to your user’s behaviour across multiple platforms. You’ll then learn which platforms your users use when they use them, and why.
5. The ability to customise
Have you ever considered why some taxi booking apps send you a discount message just before your usual ride? Or how does your favourite restaurant app suggest food that you enjoy eating? It’s all because of artificial intelligence.
The ability to fully access user location and device usage data is one of the biggest benefits of integrating this AI feature into a mobile app – the time when they are active, the type of app they use, people they talk to the most, platforms they visit the most, and so on.
Companies then use the information to offer discounts in the same app to some while focusing on cashback to others. AI enables brands to take personalization to a whole new level, thereby helping to redefine their CRM model.
6. Bringing Predictability to the Ever-Changing User Life
When performing predictive analysis on your app users, the much-discussed Machine Learning subfield of Artificial Intelligence comes in handy. Assume you have an on-demand medicine delivery app; using predictive analysis, you can notify your users that their medicine is about to expire and that they should reorder.
Predictive analysis is the foundation of period trackers and weather apps.
If you’re just getting started, you can use the features in two ways: base the entire app on predictive analysis or use it to keep rolling out product or discount information to keep the app active. Quantum Computing in AI ML can accelerate the development of mobile apps by training AI models more efficiently and enabling new AI capabilities. Alternatively, you can launch an extension in your messaging app that uses a neural network to send automated responses, similar to what Google does. If you are confused by the numerous options, contact your partner AI services provider for assistance.
Wrapping Up
There are a variety of other use cases for what takes place when AI meets mobile apps. When it comes to the combination of AI and apps, there is rarely a likelihood that something will go wrong and in the reverse direction of the company’s growth.
So far, we’ve discussed how AI transforms common mobile apps into game-changing apps, and we’ve also looked at some tips that app developers should keep in mind when experimenting with AI. What comes next? Reach out to a reputed AI services provider to discover how to incorporate artificial intelligence into your next mobile app.
