The Best Ways of Applying AI in Mobile Apps
While mobile apps continue to be a prime focus for the enterprise, there is an increasing interest in artificial intelligence technologies. Gartner predicts that intelligent apps will be one of the top ten strategic trends for 2017. When an app claims to be powered by “artificial intelligence” it feels like you’re in the future. What does it really mean, though?
Who used it better?
It wasn’t long ago when both Google and Microsoft added neural networks to their translation apps. Spotify is challenging Apple Music app claiming to use AI-powered recommendations. Period & Ovulation Tracker Flo uses a neural network to outperform the competitors while predicting women’s cycles and ovulation dates. One more breakthrough example of applying AI in mobile apps, called Prisma, uses this complex technology that has been around for quite a while to help users turn their photos and videos into art.
For these apps, AI became a reality thanks to recent technological advancements in natural language processing, machine learning, predictive modeling, sensors, and cloud solutions.
What are the best tasks for AI in mobile apps?
Neural networks aren’t the right solution for everything, but they excel at dealing with complex data. Most of the time applying AI in mobile is still associated with incorporation of personal assistants, however, there is a larger perspective to that – AI is used to study our habits inside of various apps and better serve us. Thanks to AI solutions, app creators and marketers are now closer to understanding user behavior on the app through actions, likes, preferences, purchases and more. Deep Learning is the science to ‘teach’ the machines recognize patterns and then apply these ‘learnings’ to solve various complex queries. It also allows to learn user’s behavior patterns in order to make the future sessions personalized and seamless.
Another feature that takes a significant place in mobile is sentiment analysis used for app analytics. For example, Appbot provides its users with such useful piece of information as sentiment analysis of app reviews from Apple Store and Google Play store. It can be used to gain an understanding of the attitudes, opinions (positive, negative or neutral) and emotions expressed by customers. This understanding of the customer allows to provide the most relevant content and features.
Acquiring new users for your app is tough, time-consuming & expensive. But selling more to existing customers and gaining the maximum revenue from a mutually beneficial relationship, is one of the main drivers of growth for any company.
The main use cases for applying AI in mobile apps could be identified as:
- automated reasoning – it is the art and science of getting computers to apply logical reasoning to solve problems, for example, to prove theorems and solve puzzles. This way AI machines beat humans at chess, stock trading and Jeopardy. Uber uses automated reasoning in order to optimize routes and get the riders to their destinations faster. The algorithm takes millions of bits of data from Uber Drivers who have traveled similar routes and learns from their trips.
- recommendation services – this is the simplest and most effective application of AI in mobile apps that can be used in almost any solution. The reason why most apps fail within a year of launch is that they fail to provide relevant content to continuously engage users. You may be providing fresh content regularly, but if it isn’t something that is interesting to the end user than it isn’t worth the time you spend creating it. By monitoring the choices users make and inserting them into a learning algorithm, apps make recommendations that users are likely to be interested in. This is a powerful source of revenue for such entertainment app like Netflix. Yet any business that upsells or cross-sells content can utilize this type of AI, even if it’s currently a manual process handled by the sales or marketing team.
- learning behavior patterns – most platforms have the capability to learn users’ behavior patterns in order to make the next session more seamless. For example, Snaptravel is a half-bot, half-human hotel booking service. It uses natural language processing and machine learning to have realistic conversations with users suited to their preferences. If a user stumps the bot with a request, a human agent intervenes and teaches the bot how to not make the same mistake next time. Another classic example of AI learning your behavior is fraud detection for online payments. Pattern-detecting algorithms go through your credit card statements and purchases as they happen, and can detect if you’ve made a recent purchase out of the norm of your behavior.
First 1-5 app sessions are crucial for retaining new customers. You’re much more likely to make these sessions memorable if you use AI technology to learn their behavior and make each app session more valuable than the previous one. Data is a privilege, and you owe it to your customers to use it to improve the experience for them. The challenges that face AI may somewhat mirror those of mobile – i.e. security, adoption, usage, performance, integration, and data management.
Introducing AI to your app involves a lot of hard work. Most companies have to start with transforming their IT organizations for a digital, rapidly-evolving market and dealing with more tactical issues such as securing mobile access to data, backend integration of apps with legacy systems, implementing API-based architectures, and adopting agile development methods. But once the process started the result will follow.
InData Labs provides data science & AI consulting to help startups and enterprises build breakthrough AI products, start applying AI in mobile apps and leverage their data to improve operations, performance, and profitability.