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Machine learning has been used in numerous fields, and now it is looking forward to leaving its imprint on mobile apps as well. This blog talks about the benefits of effectively integrating machine learning into mobile apps.
97% of mobile users use voice assistants such as Amazon Alexa, Siri, Google Assistant, or others, implicating that we all use ML integrated virtual smart assistants in our daily lives in one way or the other. Integrating ML into mobile applications has become more common, thanks to the cutting-edge features and high-end functionalities the software permits.
ML-powered mobile apps have become the first choice for businesses, as these apps can analyze data and help entrepreneurs develop a customer-centric sales strategy aimed at increasing revenue and ROI. Allied Market Research has predicted that the market for ML will reach $5,537 million in 2023 further demonstrating its growing prevalence.
If you are also thinking about building ML-based apps, you should look for a leading ML-enabled app development company that develops machine learning apps and simplify human to machine interaction.
What is ML App?
A mobile application that leverages machine learning technology is known as ML app. In ML apps, developers integrate machine learning technology into mobile apps to make it able to learn and improve from experience rather than using any explicit programming. AI-based apps or ML-based apps analyze the vast amount of unstructured data or consider the number of parameters to predict the outcomes.
What Are the Applications of Machine Learning?
Machine learning is everywhere. Numerous ML-powered mobile apps surround us. Here are a few examples of machine learning that we use every day and perhaps have no idea that ML drives them.
- Smart assistants
- Videos surveillance
- Malware filtering
- Online customer support
- Product recommendations, and more.
The Best Ways to Incorporate Machine Learning in Mobile Apps
It’s crucial to understand the right ways of integrating machine learning technology into mobile applications if you are planning to build ML-based apps for your business. Here are a few great ways to do so:
- Identify the purpose of ML and data types that apps need to predict.
- Collect data and make sure that the ML system will be able to understand that data.
- Data transformation to make it understandable by the ML system.
- Create additional features by combining two or more functions.
- Train the model.
- Determine and enhance model accuracy.
- Deploy the model in production.
Advantages of Incorporating Machine Learning in Mobile Apps
Also known as knowledge discovery and data discovery, data mining involves analyzing the vast set of data to collect useful and assemble it in different areas, including data warehouses and others. Machine learning (ML) offers a data algorithm that tends to enhance automatically through experience based on data. It follows the process of learning new algorithms that make it quite easier to discover connections within the data sets and collect the information with ease.
Advanced search is a significant feature of many mobile applications, especially for e-commerce apps. Machine learning is commonly utilized in mobile apps and can make advanced search more effective due to its ability to better understand user queries. It displays the most relevant products first, based on the search query, making it easier for users to find what they are looking for.
You may also like to read: Effective Machine Learning Ideas to Improve User Experience on Apps
Any business that wants to be successful should be able to use market trends to predict what consumers will want in the future. It’s vital for sustainable success, especially when it comes to staying ahead of competitors; however, ML in your mobile apps can help you with this.
When we talk about virtual assistants, Siri, Google Now, Amazon Alexa, and others probably come to mind. These virtual assistants have transformed personal assistance and have made it one of the hottest features for mobile applications.
Integrating machine learning into your mobile applications will make virtual personal assistants more specific and goal-driven. The reason is ML-powered bot can process the natural language to listen, contextualize, and ensure execution of the command to complete the task while enhancing user experience.
ML-Powered Chatbot for Customer Support
Due to heavy traffic, it’s impossible to provide dedicated customer support to everyone by a real person. In this case, integrating an ML-powered chatbot into your mobile application not only helps you in automating the customer support services but also improve your brand’s reputation due to the quick responses that ML provides. An exciting thing about ML-powered chatbot is it recognizes the writing style of customers to understand their queries and provides them the appropriate solutions accordingly.
Increase Sales through Customized Product Recommendations
Customized product recommendation to your customers is only possible if you know what they are looking for, their preferences, budget, etc. Using machine learning in mobile apps can simplify this challenge.
The machine learning algorithm analyzes previous searches that you have made to determine individual user preferences to recommend specific products. It can help in promoting your products and services in a way that can capture the attention of customers.
Fraud cases are a concern for every industry, especially banking and finance. ML uses data analysis to minimize loan defaults, fraudulent checks, credit card fraud, and more.
It also helps you determine a person’s ability to pay back a loan and the risk associated with giving the loan. E-commerce applications often take advantage of machine learning to find promotional discounts and offers.
Object and Facial Recognition
Facial recognition can be a beneficial feature for mobile applications to have. Facial recognition can help improve the security of your app while also making it quicker to login.
In addition to enhanced security, facial recognition can be used by healthcare providers to assess the health of patients by scanning an individual’s face.
Machine learning can augment the capabilities of your app with data mining, advanced search, forecast trends, personal assistant, ML-powered chatbots for customer support, increasing sales through product recommendations, and fraud detection. Just keep your requirements in view and implement accordingly while integrating machine learning technology into your mobile app.
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