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How to Use Machine Learning in a Mobile App?

If you want to name one technology with gigantic promises to bring machine further closer to the human endeavors and intelligence, it is machine learning. With computers continuing to be smarter and efficient in interacting with humans, we are slowly entering into a new era in which machines would have more control on human actions than ever before.  

No, it is not to be dubbed altogether a futuristic idea. Machine learning is already here, and we are benefited by it almost every day. While shopping a product on Amazon it learns from your purchases, browsing history, preference of price range and accordingly suggests products that you are more likely to prefer. Similarly, Google search engine learns from your latest searches, location, and account information and accordingly tweak the search results a little for your preference. Your browser app remembers certain preferences based on your uses and accordingly adjusts to them. But these machine learning examples only showcases only the elemental scope of this technology. It is needless to say that the real promise of machine learning is a lot bigger than this.  

Mobile apps by incorporating machine learning are allowing better user interactions and user experience. Learning from user behavior and use patterns and accordingly adjusting to the user situations and preferences is paving the way for customization in mobile apps. Machine learning is paving the way for better utilization of resources for app marketers as well. With advanced device sensors and relocation capabilities of today's machines, mobile apps are continuing to get smarter. So, this is high time we evaluate the future scope of machine learning for mobile apps.  

As an app development company, you should know how machine learning can add value to mobile apps.

1. Product search 

You have already experienced it many top e-commerce sites. Just when you search or buy a product, based on your previous purchases and browsing history, some products are suggested. The same is happening across other business websites as well. Some of the tools that make an app understand the user behavior include query understanding, ranking, user favorite determination, etc. Knowing the user intent also require knowing the user situation, location and constraints if there is any.  

Offering relevant most results corresponding to product search and offering most succinct suggestions related to the search is most important for e-commerce mobile apps, just because small screen real estate make users more impatient and less attentive to scroll down all the way to the last result. So, just to prevent your visitors go away without finding what they are looking for, you need to allow machine learning to fine tune the search results and suggestions.  

2. Customizing mobile apps  

These days every business is focusing on personalization to create a stronger bond with customers and drive sales by delivering up to the customer-intents, contexts and situations. When a business customizes business offerings and messages for a customer it works great by making the customer feel important. This customization is increasingly being used by mobile apps to drive more traction and engagement and to enhance retention.  

The machine learning algorithm can help drawing relevant insights about the user preferences, user behavior patterns, and user situations by analyzing the user data fetched from the device or profile information of the user. The user data may include the personal information of the user, search history, user reaction to contents, etc. On the basis of the user preferences as decided by the machine learning algorithm, a set of contents can be offered to the respective user.  

Let’s see how this customization works thanks to machine learning. Suppose you have a popular app for frequent travelers. Your app is visited by several thousand people every day. All these users do not use your app for the same reason. While some prefer it for flight information, other prefer it for on the go travel tips while still there are users who like the app to get introduced with latest travel gadgets. For all these users you cannot cater your app in the same way and so you need customization for your users. A machine learning algorithm backing the app from within can make it easier by collecting volumes of customer information, classifying users, segmenting users by user preferences and behaviors and deciding over the app look and feel for every different user. If you cannot put your customer under the scanner of analytics to draw insights, machine learning SDKs from large corporations like Google, IBM, or Amazon, can help you. 

3. Product promotion and recommendation 

Most online businesses know that it is the customer reviews that sell products better than media ads. A machine learning algorithm can easily grab all types of reviews and further can tell the business about the reviews that are most influential in driving sales. Thus thanks to machine learning input an app can easily know the best reviews that push their business and bad reviews that pull their business.  

4. Machine learning for healthcare apps  

Healthcare apps already understand the huge potential of machine learning for the sector and respective apps. If a machine learning algorithm has access to millions of healthcare database corresponding to every different disease, it can actually suggest a perfect path of treatment and medication. For example, IBM Watson having such a robust database of cancer patients can actually make a better diagnosis of the disease than even the qualified medical professionals.  

Similarly, fitness tracking and consumer healthcare apps by tracking the regular health and fitness data of millions of people can offer valuable trends concerning lifestyle related diseases and accordingly can recommend treatments.  

5. Machine learning for security and fraud control 

Finally, machine learning can really help making security arrangements and fraud control mechanism better and stronger. Machine learning algorithm within crucial apps can evaluate user behavior and all sorts of irregularities to assess the most probable frauds and security vulnerabilities in the making. While a whopping $32 billion worth of frauds occur every year making an increasing number of financial transactions vulnerable, an app with machine learning algorithm can detect such frauds and help to build a better defense system. 

To conclude, 

So, the promise of machine learning for mobile apps will continue to grow and nothing can stop it from becoming the trend to have the biggest influence on the mobile apps of future. The spectrum of usability for machine learning will encompass every app niches in the time to come.


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