By Nirav Shastri

In today’s time and age, where we call for convenience, the automation offered by machine learning and artificial intelligence seems to be quite comforting.

Every single person is exposed to machine learning in one way or the other. How? Well, you may have used Netflix, iPhone, Echo or Google’s search engine at one point or the other in your life.

Some interesting facts about machine learning:

  • According to Call Credit, 12.5% of the staff time is invested in data collection, which is close to five hours of the 40-hour work week.
  • 41% of the consumers believe that the reason for their better life will be AI.

As a consumer of machine learning, the one thing you ought to know is that machine learning is different from artificial intelligence. Though both aim to improve and automate your life, they are two different things and aspects of technology.

So, what is machine learning? It is basically an AI application that allows the systems where it has been incorporated to learn from their experience and improve what it offers. An excellent example is the insights Amazon has collected, based on which they offer same day shipping to their customers.

So, what good will machine learning do in a mobile app, such as Amazon? Let’s look at it from your mobile app experience. When you logged into a particular app and started browsing the same, did you not receive alerts for items that you looked into from the application. Once you left the app, you began receiving discounts or even items similar to the ones you looked for in the app. Personalization is the key to conversions, and one of the reasons personalization occurs is because you have machine learning incorporated into your application

You observe and recognize the patterns of the human brain, and react accordingly, so that you are able to win them over.

Netflix is an excellent example of personalization via machine learning. Based on what you have watched, the application recommends movies and series that you should watch. Interesting, isn’t it? This increases engagement and allows the mobile app to flourish.

The funding for machine learning mobile apps are also increasing by the day. Statistics by Venture Scanning state that close to $2 billion in funding has been awarded to machine learning apps alone. 

However, the main question here is how to incorporate machine learning to your mobile app. We have curated a few ideas here, which should help you plan your mobile app. This is three times the funding awarded to apps based on natural language processing.

1. Reasoning App Solutions

This is the first kind of app solution that you can introduce with the help of machine learning. You will be able to identify and optimize the solutions for greater experiences.

For instance, based on your destination, Uber suggests the best route you can take and shortens the distance you will be traveling as a result.

Using reasoning techniques, Gboard helps you type on the mobile with ease. They tend to predict the next word you want to type, which is based on a deep analysis of your history and through insights from your past communication. You not only end up typing faster, but also find the right emojis and words to end the sentences.

Reasoning app solutions are one way of integrating ML into your next mobile app. It could be predictive analytics that you offer to enrich experiences or just plain optimization of your existing experiences.

2. Recommendation App Solutions

The second major type of app solution that you can offer the audience is the recommendation app solutions. One of the best examples of this type of app solution is Netflix. They offer recommendations and personalize the solutions based on your browsing methods and what you have been viewing.

For instance, based on the series or movies you have seen, Netflix channelizes its insights and builds a recommendation for you. This insight building exercise that helps apps recommend or personalize the solutions is what makes them stand apart and differentiate from their competitors.

3. Behavioral App Solutions

Based on a deep analysis of how you work, what you do and other habits you may have, the app will offer solutions that can enrich your experiences.

For instances, an app can track how you spend and based on the spending habit and income you get at the end of the day, the app will help you with saving solutions. This is one type of finance app that you can devise with machine learning.

Based on social media activity or activity along any other channel, the apps can help with suggestions that can help.

Things to Consider when Developing Machine Learning Apps

There are two major things that you might need to consider before you set out to redefine app solutions with machine learning.

  1. Identify the problem you are attempting to solve with the help of machine learning. When you say you want to offer recommendation based solutions, then identify what type of issue will be solved as a result. The answer to this question will define the path for developing the mobile app. You will also be able to understand how best to apply machine learning to your app solution
  2. The next most important consideration is technology that you will use to incorporate machine learning. A lot of tech giants have created their own libraries and tools that will help you envision a machine learning app. TensorFlow by Google, CoreML for iOS, Microsoft Cognitive Services and Amazon Machine Learning Services are just a few to choose from.

Once you have the type of solution you want to offer, the problem you aim to solve and the machine learning technology that you will be using known, then it becomes easy to create your next machine learning mobile app.

Summing Up

It is important to envision the type of solution you want to award your target audience before proceeding with the actual development. You should ideally identify the gaps that exist and know which issues can be resolved with machine learning. In-depth analysis and building insights using the analysis should help you define appropriate solutions for the market.

Featured photo credit: Depositphotos