5 Tips on How to Use Machine Learning in Mobile Apps?
According to a study, “The global machine learning market size is expected to reach USD 96.7 billion by 2025”.
Innovating in software applications is your chance to make huge profits and a positive impact on people’s quality of life. Here’re a few amazing case studies of companies with their software products:
- Neural Network Library – AI Neural Network
- Hit Factor – Machine Learning Image Recognition App
- High Speed Vehicle Recognition – Machine Learning Image Recognition Application
INTRODUCTION
As William Gibson said, “The future is already here – it’s just not evenly distributed.”
Startup founders, aspiring entrepreneurs, CEOs of large corporations, and project managers – all of us regularly consume information about trending technologies. We’re looking for a new tech wave we can ride to win the market and outperform competitors. One of the latest tech waves gaining significant traction is AI – artificial intelligence.
Some people still have their doubts about it. Why is it important after all?
The world is moving into the higher resolution era faster every year. Twenty years ago, we had bulky computers and a modem-based internet; these days we have personal supercomputers in our pockets, Wi-Fi internet connection and many more! The software world and customer experience are no different. This new era requires a super-personalized experience for the people you serve. That’s why AI is the future. It allows you to learn about people’s behavior and build highly personalized experiences for them.
However, very often people imagine SkyNet, or Iron Man’s personal assistant, Jarvis, or something that’s really far out and scary.
And I bet you’re smarter than that. You think about how you can apply this new technology in the very near future, and start gaining significant long-term benefits from it.
I want to focus your attention specifically on Machine Learning and mobile application markets. Firstly, mobile apps overtook desktop usage a long time ago.
Secondly, the mobile market drives more revenue to consumer-focused companies. Take Facebook, for example – 67% of their revenue comes from mobile ads. E-commerce is moving the same way.
Now Machine Learning. Venture Scanning reports that Machine Learning is a leading category among funded startups, with over $2 billion in funding. That’s 3x more than the funding of the next category of Natural Language Processing.
Machine Learning is poisoned to lead the AI revolution because it’s the core of artificial intelligence. According to Wikipedia, Machine learning is:
the subfield of computer science that “gives computers the ability to learn without being explicitly programmed” (Arthur Samuel, 1959). Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible; example applications include spam filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), search engines and computer vision.
You may think that machine learning is for developers only. Not quite. In opposite, this topic should be interesting for startup founders and companies’ executives. Understanding even the basics of Machine Learning will help you think broader and come up with innovative solutions for your business. By the way, here is a good android machine learning app where you can learn a lot about machine learning.
In addition to this, our mobile phones are now so powerful that they can run Machine Learning algorithms without an Internet connection. Here are 5 tips on how to use machine learning in mobile apps:
- Machine learning in data mining for mobile applications
- Machine learning in mobile finance apps
- Machine learning in e-commerce mobile apps
- Machine learning in the healthcare mobile market
- Machine learning for fitness trackers and mobile apps
Let’s go over the most popular machine learning solutions providers, companies such as Google, IBM, Amazon, and Microsoft.
1. Machine learning in data mining for mobile applications
According to Wikipedia, data mining allows the analysis of big data and the discovery of useful, non-obvious patterns and connections within significant sets of data. It consists of data storage, maintenance, and the actual data analysis. Machine Learning provides both a set of tools and the learning algorithms necessary to find all possible connections within the data sets.
Let’s say you want to build a mobile app for the travel industry, or you already have one. With decent traffic, there would probably be a ton of people using it every day. It’s simply impossible for a human to analyze all the possible variations and find obscure customer behavior patterns. Instead, you can collect all the data about your clients, structure it by gender, Facebook connected accounts, how they fill out their profile, how often they visit your app, how often they go for vacation, etc. Once you have all these data tables in your database, you can apply Machine Learning. You can build your own custom solution, or use the ones straight out of the box from Google, IBM, or Amazon, to analyze the data and gain valuable insights about your travel mobile app users.
For example, you might learn that people under 35 who live in New York and connect their Facebook profiles to your service travel 3 times more often than the same group of people from California. From that, you can build your user tests, and figure out that you simply need to add more destinations near California to increase conversion for users from that area. This is just one example, but there are many more.
Once you start learning these insights about your customers, you can apply Machine Learning to make suggestions and show your users very personalized offers, thus improving conversion rates even more. In time, you will be way ahead of your competitors. Read on.
2. Machine learning in mobile finance apps
The finance market is mostly concerned about security, earnings, investment, and lending. Mobile apps play a big role here, either as standalone applications, banks’ storefronts in consumer pockets, credit planning solutions, and much more.
For example, your “smart bank” can analyze the history of previous transactions, the schedule of your customers’ credit card payments, their latest social media activity (yes, companies buy and sell this data), and offer your clients unique deals which are built automatically, based on the collected and analyzed data.
Another example is the automated investment robot. This has been in use for a long time. Technical analysis is nothing new. However, now robots can review all the market data and offer a service to help build your portfolio and invest.
3. Machine learning for the eCommerce app
eCommerce machine learning applications are the future. Stores like Amazon use Machine Learning to suggest products for their clients. You may say that you can install the plugin, or you already have a suggestion system in place. However, you probably underestimate it. If you test it yourself, you’ll see that Amazon’s suggestion system adjusts on the fly, while you are browsing. If you keep clicking on new pages, it learns that you aren’t interested in certain products and will start suggesting others. Moreover, it learns not just from you, but from the combined experience of all the people who live in your neighborhood, and from many other social factors you might not even have considered. All this helps to provide the best-personalized experience.
You may think Amazon uses this technology because they are a big company and have the resources for it. However, solutions from Amazon itself, Google, IBM, Microsoft, and some startups, make Machine Learning e-commerce advantages available to any type of company. For example, at DevTeamSpace, we use open-source APIs and SDKs, and tools from the companies mentioned above, to help clients build custom smart e-commerce solutions. Here are just a few options that are available for your e-commerce mobile apps:
PRODUCT SEARCH
This is one of the most important features of a mobile e-commerce app. One reason for this is the size of the screen. You can only display a few products on a mobile screen and users have to scroll down if they don’t like what they see. So, the relevance of your product to a particular search query needs to be really high. Machine Learning can help your mobile app learn from users day in and day out, so it not only displays the most relevant products at the top, but starts to better understand the text query itself, counts all the screen scrolls and clicks, and learns to suggest the most relevant products in addition to the search results.
PRODUCT PROMOTION AND RECOMMENDATION
Another way to increase your mobile store revenue is to offer extremely relevant promotions and complementary goods before and after the purchase. You see this on Amazon or other large stores, in the form of “this item fits to…”, or “people who buy this also bought this…”.
This type of solution is based on mobile app content analysis, customer behavior, and purchase patterns. The predictive analysis makes the challenge easier, so your app recommendations and promotions become more and more relevant with every visit. These solutions can increase your e-commerce app revenue by up to 12%.
TREND FORECASTS
It’s always hard to predict what will be the next hot thing before it goes on sale and blows up in the media, blogs, and news, and all of a sudden, everyone is selling it in their mobile stores. The market is very competitive now, and the most successful are those who discovered the next big thing earlier than others.
However, with Machine Learning, you can game the system, because it allows you to aggregate the trends and sales information from different open sources (celebrity bloggers, YouTube product reviews, social media, designer reports, etc.) and build a forecast in real-time. Taking this even further, you can build a system which adds a new inventory automatically, based on the forecast.
FRAUD PREVENTION
Annual fraud costs reached $32 billion, which is 38% more than in previous before. {{add a source}} On average, adult American online shoppers change their credit cards every 4 months due to fraud activities. This affects your mobile e-commerce app. And Machine Learning can help here, too. It plays a critical role in building a defense system by monitoring online activities and triggering alarms.
4. Machine learning in the healthcare mobile market
If you are passionate about healthcare or have a healthcare-related business, you should use Machine Learning. For example, IBM Watson has access to a database with tens of thousands of cases relating to cancer and can sometimes diagnose a patient even better than a highly-trained professional. You can lear more here – http://www.ibm.com/watson/health/
Other tracking applications can measure your daily water intake or a number of activities, and use the data from thousands of people with diabetes to learn and provide them with valuable trending data. For example, it can show you that if you don’t work out a certain amount of days, or if you consume less water than usual, your sugar level could rise and place you at risk.
5. Machine learning for fitness trackers and mobile apps
The fitness industry is awash with mobile apps that analyze your daily activities, steps, jogging rhythm and much more. However, they rarely provide you any insight or push you to achieve your goal. In the very near future, these kinds of apps will be able to analyze all the anonymous user data and provide trending information, suggestions for achieving your goals and how to change your diet/activities to achieve them faster.
To develop a mobile app with machine learning, you would probably end up using one of these major Machine Learning APIs and SDKs:
Amazon Machine Learning
Google Machine Learning Cloud Platform
Intel Machine Learning solution
IBM Machine Learning APIs and SDK
Conclusion
Now you know that Machine Learning is already here and is becoming mainstream in software world. The question you may have in mind is “where to hire the developers with relevant expertise?” If you spend some time searching and googling, you can find different services with different price structures.
