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Some Use Cases of Machine Learning in App Development

Estimated read time: 7 minutes

Are you interested in knowing how to use machine learning in app development? In this article, we will discuss some major use cases of machine learning in mobile applications.

Machine Learning and Mobile Application Development 

Some of the ML use cases in mobile app development are as follows:

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 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, and structure it by gender, Facebook-connected accounts, how they fill out their profile, how often they visit your app, how often they go on 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. Machine learning analyzes the data and gains 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.

Snapchat, for example, relies on computer vision to identify individuals in photographs, something which allows it to improve the accuracy of the services that it offers. The machine learning algorithms it uses are now so accurate that they can even identify individuals from blurry pictures and old photos.

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 most concerned about data security, earnings, investment, and lending. Mobile apps play a big role here, such 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, and their latest social media activity (yes, companies buy and sell this data), and offer your clients unique deals that 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. Check out Unicornbay.

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 of this helps to provide the best-personalized experience.

You may think Amazon uses this technology of unsupervised and supervised machine learning algorithms because they are a big company and have the resources for it.

However, solutions from Amazon itself, Google, IBM, Microsoft, TensorFlow, and some smaller startups, make the advantages of Machine Learning e-commerce available to companies of any size.

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 technology to identify buying and spending habits on Amazon and 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 really increase your e-commerce app revenue.

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 that adds a new inventory automatically, based on the forecast.

Fraud Prevention

There were 2.8 million fraud reports received by FTC in 2021., Moreover, fraud losses were more than $5.8 billion which is 70% more than the previous year. Read more details here.

56% of online customers change their shopping habits like closing accounts, changing credit cards, etc. after a fraudulent event. This affects your mobile e-commerce app. This report on credit card fraud statistics gives more details on this. 

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 learn more here.

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 suggestions based on collected data.

For example, it can show you that if you don’t work out for 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 devices that analyze your daily activities, steps, jogging rhythm, and much more. However, they rarely provide you with 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.

Ready to Use Machine Learning in App Development?

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

Now you know that Machine Learning is already here and is becoming mainstream in the software world for various business needs. The question you may have in mind is “Where to hire the developers with relevant expertise?

If you spend some time searching online for mobile app development services for your business processes, you will find different services with different price structures for machine learning application development. 

Since machine learning technology is one of the cutting-edge fields of software development, finding mobile app developers who are up to date with the latest ML technologies like artificial neural networks, natural language processing, artificial intelligence, and deep learning is essential.

These ML developers should also be familiar with machine learning tools, frameworks, and methods like data preparation, training data, and model training to develop robust and market-competitive ML applications. 

DevTeam.Space has years of experience developing machine learning and AI applications for companies worldwide via its field-expert software developers and data scientists.

To see just how good our developers are, simply fill out a project specification form and we will get back to you to discuss how we can help you with machine learning app development.

Frequently Asked Questions on Machine Learning App Development

What is an ML application?

ML stands for Machine Learning. Machine learning development uses machine learning in order to improve its functions like speech recognition, behavioral data analysis, predictive analytics, etc. ML algorithms are able to use gathered data to learn in such a way as to make better decisions or provide more relevant information, etc.

What are the applications of machine learning?

Machine learning techniques can be used to improve literally any feature or process. Provided that data can be collected and digitized, a machine learning algorithm can analyze the data in order to identify patterns or trends which can be used to improve the process. For example, using machine learning capabilities to understand user behavior.

How do machine learning models integrate with Android apps?

The process to integrate machine learning into the mobile app development process is fundamentally the same. Developers must create the ML program with tools such as Python, Java, and SciKit and then integrate it into the mobile application. sure to hire the best ML developers from a company such as DevTeam.Space as ML is a complex technology that requires expert-level programmers.


Alexey Semeney

Founder of DevTeam.Space

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