Love the face recognition feature on your smartphone? Liked the recommended series on Netflix? It’s the magic of machine learning technology. Want to use it too? We’ve covered 5 tips on how to use machine learning in mobile applications to help you stay ahead of the curve. 

• Machine Learning & Data Mining

Data mining is a process companies use to generate new useful chunks of information from a large amount of data that they already have. However, arriving at all the potential combinations and behavioral patterns solely through data mining is impossible for humans. That’s where Machine Learning can be useful. It uses data mining techniques, along with other algorithms, to find out patterns in such raw data and make better future decisions.

Let’s assume you are an established entity offering fresh baked goods through a dedicated mobile app. You already have a decent database of your customer details, their preferences, order frequency, etc. It is enough to provide the basis for using a Machine Learning solution.

With ML, you can create relevant use cases to better serve your customers, such as making apt suggestions of your new recipe based on their past orders or providing them with customized offers on their purchases. It improves their overall experience while increasing your rate of conversion simultaneously. A study also revealed that the integration of ML into business operations has helped 75% of entities record higher sales.

• Machine Learning & E-Commerce App

A Statista report indicates that ML apps successfully garnered about 28.5 billion USD in March this year. That shows the wonders ML can do to your business, especially if you own an e-commerce store.

When people shop online, they still yearn for human-like experiences. For instance, a quick resolution of their queries. That’s the reason why online stores invest in chatbots, an ML-based technology. These chatbots are programmed to communicate with customers, identify and understand their issue, and resolve it - all in a language that seems natural & more human. As a result, customer satisfaction increases, positively affecting your sales.  

Apart from that, Machine Learning can also be used in the following ways:

1. Product Suggestion & Promotion – ML can be best utilized to suggest products based on the customer’s most recent purchase, interests, and behaviors with their every visit, in real-time. We’ve witnessed giants like Amazon use it.
2. On-site Search – Several studies have iterated that optimized on-site search feature can raise conversion rate by up to 50%. That alone explains the need to have 100% accurate search results, that is achievable by Machine Learning algorithms.
   
3. Fraud Identification & Prevention – With a booming e-commerce industry, the rate of frauds have skyrocketed too. Machine Learning can help develop an intelligent defense mechanism that can monitor online activities, detect the suspicious ones, and raise an alarm, helping the firm curb the menace altogether.
   
4. Trend Prediction & Analysis – As Machine Learning can help curate patterns & customer behavior from the available data in real-time, it becomes possible for the e-commerce company to forecast the next big trend & be on top.
   

• Machine Learning & Finance App

The mobile app era has persuaded many leading financial corporations, as well as banks, to have their own standalone or storefront-based mobile application. But how can Machine Learning be useful in this sector?

ML comes in as a handy tool to make accurate future market trend predictions, market crashes, and bubbles. It can also be utilized for identifying the credit score of borrowers to make informed lending decisions. On top of it all, ML can replace and automate manual repetitive work processes, increasing productivity to a great extent.

There are now smart robot advisors as well in the finance sector that help clients make the right investment choices packed with greater ROI. Such robo-advisors consider the market situation, clients’ risk-bearing capacity, expected returns, and investment duration to build the portfolio accordingly. For instance, Wealthfront.

• Machine Learning & Healthcare App

Healthcare sector has ventured into the mobile application arena since the past few years. There are many healthcare & fitness apps in the market today, ranging from apps which track the number of your everyday activities to the ones that keep track of your diet.

If healthcare apps begin using machine learning, it can provide substantial health-related information – from disease detection, diagnosis, along with decision-making and personalized treatment.

Hospital systems can use Machine Learning algorithms to -

Predict chronic illnesses

Forecast the likelihood of patients developing chronic diseases

Diagnose heart disease

Detect and prevent cancer

Offer preventive and/or treatment measures, tips that are patient-specific, and much more.

It can also be used for precise prediction of a patient’s length-of-stay at the hospital.

• Machine Learning & Media and Entertainment App

Today’s industry of media and entertainment is abuzz with a variety of applications that provide viewers with on-demand content. Understanding the importance of ML, a majority of these apps incorporate ML algorithms to increase customer engagement & satisfaction. For example, Netflix. We all have watched at least one Netflix show that the platform had recommended to us. The popular content platform does that through its machine learning technology.

In addition to that, ML can also be used for analyzing user behavior and interests in real-time to stand out from the competitors. It can also help build custom artworks for the mobile app.

5 Machine Learning Tips for Developers

• The predictions made by a machine learning algorithm become more accurate with more data. Hence, it is recommended that a developer uses all the information available to him and feed it to the algorithm.
• Data and research play a major role in machine learning so make sure that you conduct a thorough research and use the correct data. If not done correctly, it can hamper the predictions.
• Machine learning requires proper testing and careful evaluation.
• If you want your machine learning algorithm to succeed, it is important to select the aptest ML method.
• When you choose a simple machine learning model, the learning process becomes easier. With an easier learning process, the chances of errors reduce and the predictions become more accurate.

Wondering Where to Start?

There are a number of platforms for building a mobile application with machine learning, such as TensorFlow and OpenCV. Learn more about the best machine learning frameworks and You can also resort to major APIs and SDKs, including the solutions from Google and Amazon.

Final Thoughts

Now that you know how businesses can incorporate Machine Learning in their mobile app, consider taking the first step today. Above 5 tips on how to use machine learning in mobile applications to help you stay ahead of the curve.

Author bio:

Varsha Solanki is a Digital Marketing Strategist at Space-O Canada, a Software development company. She has 3 years of experience in the Information technology industry. She spends her time reading about new trends in digital marketing and the latest technologies.