Today, if we see, there is Artificial Intelligence and Machine Learning incorporated in almost every mobile application we choose to use. Which makes it all the more important to know How to integrate machine learning and artificial intelligence in mobile apps.
Personalization
Any AI algorithm attached to your simpleton mobile application can analyze various sources of information from social media activities to credit ratings and provide recommendations to every user device. Machine learning application development can be used to learn:
Who are your customers?
What do they like?
What can they afford?
What words they’re using to talk about different products?
Based on all of this information, you can classify your customer behaviors and use that classification for target marketing. To put simply, ML will allow you to provide your customers and potential customers with more relevant and enticing content and put up an impression that your mobile app technologies with AI are customized especially for them.
To look at a few AI ML examples of big brands who are setting standards on how to implement Machine Learning in apps?
Taco Bell as a TacBot that takes orders, answers questions and recommends menu items based on your preferences.
Uber uses ML to provide an estimated time of arrival and cost to its users.
ImprompDo is a Time management app that employs ML to find a suitable time for you to complete your tasks and to prioritize your to-do list
Migraine Buddy is a great healthcare app which adopts ML to forecast the possibility of a headache and recommends ways to prevent it.
Optimize fitness is a sports app which incorporates an available sensor and genetic data to customize a highly individual workout program.
Advanced search
Through the AI and Machine learning based app development process, you will get an app that lets you optimize search options in your mobile applications. AI and Machine Learning makes the search results more intuitive and contextual for its users. The algorithms learn from the different queries put by customers and prioritize the results based on those queries.
In fact, not only search algorithms, modern mobile applications allow you to gather all the user data including search histories and typical actions. This data can be used along with the behavioral data and search requests to rank your products and services and show the best applicable outcomes.
Upgrades, such as voice search or gestural search can be incorporated for a better performing application.
Predicting user behavior
The biggest advantage of AI based machine learning app development for marketers is that they get an understanding of users’ preferences and behavior patterns by inspection of different kinds of data concerning the age, gender, location, search histories, app usage frequency, etc. This data is the key to improving the effectiveness of your application and marketing efforts.
Amazon’s suggestion mechanism and Netflix’s recommendation works on the same principle that ML aids in creating customized recommendations for each individual.
And not only Amazon and Netflix but mobile apps such as Youbox, JJ food service, and Qloo entertainment adopt ML to predict the user preferences and build the user profile according to that.
More relevant ads
Many industry experts have exerted on this point that the only way to move forward in this never-ending consumer market can be achieved by personalizing every experience for every customer.
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According to a report by The Relevancy group, 38% of executives are already using machine learning for mobile apps as a part of their Data Management Platform (DMP) for advertising.
With the help of integrating machine learning in mobile apps, you can avoid debilitating your customers by approaching them with products and services that they have no interest in. Rather you can concentrate all your energy towards generating ads that cater to each user’s unique fancies and whims.
Machine Learning app development companies today can easily consolidate data intelligently that will in return save time and money went into inappropriate advertising and improve the brand reputation of any company.
For instance, Coca-Cola is known for customizing its ads as per the demographic. It does so by having information about what situations prompt customers to talk about the brand and has, hence, defined the best way to serve advertisements.
Improved security level
Besides making a very effective marketing tool, Artificial Intelligence and machine learning for mobile apps can also streamline and secure app authentication. Features such as Image recognition or Audio recognition makes it possible for users to set up their biometric data as a security authentication step in their mobile devices. ML also aids you in establishing access rights for your customers as well.
Apps such as ZoOm Login and BioID have invested in ML and AI application development to allow users to use their fingerprints and Face IDs to set up security locks to various websites and apps. In fact, BioID even offers a periocular eye recognition for partially visible faces.
Now that we have looked at the different areas in which application of AI and ML can be incorporated in the mobile app, it is now time to look at the platforms which will make it possible, which we in our capacity has experienced AI software development company have been relying on, before we head on to the strategy that a business should devise to ensure a smooth implementation.
User engagement
The AI development services and solutions engage organizations to offer balanced customer support and a span of features. Few apps provide small incentives to the customers so that they utilize the application consistently. Also just for entertainment purposes, chatty AI assistants are there to help the users and hold a discussion at any hour.
If you are still confused and want to clear your doubts, you can read more about how to implement AI and Machine Learning in your existing application
