With technological advances and digital progress, now there are distinct tactics possible to attract and retain customers. One such technological trend in the industry currently is Machine Learning. This technology makes mobile platforms more user-friendly, enhances the customer experience, maintains customer loyalty, and helps in developing consistent omnichannel skills. Let's have a look at some of the best use-cases of machine learning in mobile apps: 1. Customized Experience Machine learning can assist you with the continual learning process. With the help of Machine Learning, you can identify users based on their interests and further design the appearance of your app based on the user’s requirements. Moreover, algorithms can examine multiple sources of information ranging from social media activities to credit ratings and based on the analysis pop recommendations right onto user’s devices. You can have an in-depth knowledge of your customers interests with the help of machine learning, such as: Who are your customers? What exactly do they require? What services are they looking for? What hobbies, preferences, and pain points they possess? What words they’re using to describe your products? Based on the aggregate data collected from the above questions, you can distinguish and structure your customers. You can also find a specific approach to connect each customer group by accordingly adapting the tone of your content. Machine learning enables you to provide your users with the most relevant and enticing content. This will ensure users remain engaged with your app. For instance, in 2013, Netflix app launched the ‘House of Cards’ and used big data to choose the main character (Kevin Spacey) and one of the executive producers (David Fincher) for this show. This depicts the perfect use case of machine learning in various aspects of mobile apps. 2. Sophisticated Research Another compelling use case of machine learning solutions is that it enables you to deliver excellent and more contextual outcomes. It makes the process of searching more intuitive and easily accessible to your customers. Machine learning algorithms analyze customer’s queries and prioritize the results based on the specific requirements of a particular customer. With the help of a mobile app, you can collect all available data about your customers like search histories, and typical actions. You can further use this data combined with behavioural data and search requests. Based on the results received, you can show customized results of your product listings to your visitors. For instance, Reddit app is implementing machine learning to improvise search performance for hundreds of millions of community members. 3. Foreseen User Experience Another use case of machine learning in mobile apps is that it helps marketers understand user’s preferences and behaviour patterns by scrutinizing various data such as: Age Gender Location Search requests Frequency of app usage By collecting and analyzing all the data mentioned above, you can improve the effectiveness of your app and your marketing efforts. Suppose you analyze that the number of females using your app is more than that of males and most of them are under 40 years of age. Based on this information, you can design your net campaign to either attract a male audience or target your entire marketing campaign at women under 40. Machine learning also helps in curating personalized recommendations that enhance customer engagement and time spent on your app. For instance, Amazon’s app suggestion mechanism is designed in such a manner that while customers are browsing, a machine learning algorithm constantly analyzes their preferences. 4. Significant Advertisements The success of a product or an app depends upon the significant advertisements. Consumers are focusing more on the products that are useful as well as contextually relevant and personalized. Machine learning aids an organization’s target display advertisements and personalized messaging more accurately. Such accurate results help app developers to generate ads focusing on data about each customer’s unique interests and buying propensities. By implementing machine learning, you can predict how a specific customer will react to a given promotion so you can display only specific ads that interest them. This helps you save time and capital and also lets you focus on improving the brand’s reputation. For instance, Coca-Cola pays close attention to how its products are represented across social media. The company implements image recognition technology to identify when people have posted images of its products or those of competitors on Facebook, Instagram, and Twitter. This information helps Coca-Cola to understand what situations prompt customers to talk about its brand. Ads designed in such a way have higher chances of being clicked on than ads created using other methods of targeted advertising. 5. Enhanced Security Machine learning can streamline and secure the app authentication process. Video, audio, and voice recognition features allow customers to authenticate using their biometric data such as face or fingerprint. Machine learning also aids you in determining access rights for your customers as well. Apps such as Zoom Login and BioID utilize machine learning and enable easy log in to other websites and apps with ultra-secure, selfie-style face authentication. For instance, big companies like Uber leverage machine learning for implementing face detection technology to address any fraudulent behavior. With the help of this technology, they can identify fraudsters who use stolen credit cards. Some of the big names in the banking sector and financial industries are also implementing machine learning algorithms to scrutinize customer’s previous transactions, social media activities, and borrowing history. With machine learning, following distinct features related to security can be accessed: Image recognition Shipping cost estimation Product tagging automation Wallet management Logistics optimization Business intelligence 6. Profound User Engagement Machine learning tools enable you to offer robust customer support, a wide range of features, and entertainment that binds your customers to your app daily. Certain profound features of machine learning can help you keep your customers engaged. Sufficient Support Amazon and Facebook app utilize a machine-learning algorithm to engage users with intelligent request handling. Machine learning systems can quickly analyze vast sets of data and make quick decisions. Various companies use machine learning to design conversational UX, or virtual assistants known as AI chatbots that assist in making calls, writing long emails, or waiting on the phone until somebody responds. You can even utilize machine learning algorithms to send smart notifications and voice notifications about the current deals going on. By performing these tasks, this technology helps your brand to become more proactive and keep your customers engaged. In short Machine learning tools enables you to offer solid customer support, a wide range of features, and entertainment that binds your customers to your app on a daily basis. Other Effective Use-Cases: Machine learning intelligence enables you to promote your app with a built-in translator. These processes help international customers communicate within your app without the involvement of third-party online translators. Airbnb app uses Cloud Translation API to translate listings, reviews, and conversations between its users, as almost 60 percent of its users use the app in different languages. Wrapping Up: Machine learning technology can authorize your mobile app with an efficient customization engine, cutting-edge search mechanisms, quick and reliable authentication, and fraud protection. OpenXcell Technolabs provides excellent mobile app development services. Leveraging the trending machine learning technology, the company delivers app solutions that are Emotionally Intelligent, Free to Explore, Pre-Trained, and comes with Autonomous Reasoning. Apart from machine learning, the company encompasses skilled, innovative talents for Cloud Computing, IoT, Artificial Intelligence, and more to upscale your customer base for mobile app development. OpenXcell Technolabs holds expertise in developing mobile apps that implement machine learning. This technology can help generate proactive customer interactions, monitor data to get insights, and boost lead generation metrics. If you want to implement machine learning algorithms to your mobile app, OpenXcell Technolabs is the one-stop destination for all such requirements.
With technological advances and digital progress, now there are distinct tactics possible to attract and retain customers. One such technological trend in the industry currently is Machine Learning. This technology makes mobile platforms more user-friendly, enhances the customer experience, maintains customer loyalty, and helps in developing consistent omnichannel skills. Let's have a look at some of the best use-cases of machine learning in mobile apps: 1. Customized Experience Machine learning can assist you with the continual learning process. With the help of Machine Learning, you can identify users based on their interests and further design the appearance of your app based on the user’s requirements. Moreover, algorithms can examine multiple sources of information ranging from social media activities to credit ratings and based on the analysis pop recommendations right onto user’s devices. You can have an in-depth knowledge of your customers interests with the help of machine learning, such as: Who are your customers? What exactly do they require? What services are they looking for? What hobbies, preferences, and pain points they possess? What words they’re using to describe your products? Based on the aggregate data collected from the above questions, you can distinguish and structure your customers. You can also find a specific approach to connect each customer group by accordingly adapting the tone of your content. Machine learning enables you to provide your users with the most relevant and enticing content. This will ensure users remain engaged with your app. For instance, in 2013, Netflix app launched the ‘House of Cards’ and used big data to choose the main character (Kevin Spacey) and one of the executive producers (David Fincher) for this show. This depicts the perfect use case of machine learning in various aspects of mobile apps. 2. Sophisticated Research Another compelling use case of machine learning solutions is that it enables you to deliver excellent and more contextual outcomes. It makes the process of searching more intuitive and easily accessible to your customers. Machine learning algorithms analyze customer’s queries and prioritize the results based on the specific requirements of a particular customer. With the help of a mobile app, you can collect all available data about your customers like search histories, and typical actions. You can further use this data combined with behavioural data and search requests. Based on the results received, you can show customized results of your product listings to your visitors. For instance, Reddit app is implementing machine learning to improvise search performance for hundreds of millions of community members. 3. Foreseen User Experience Another use case of machine learning in mobile apps is that it helps marketers understand user’s preferences and behaviour patterns by scrutinizing various data such as: Age Gender Location Search requests Frequency of app usage By collecting and analyzing all the data mentioned above, you can improve the effectiveness of your app and your marketing efforts. Suppose you analyze that the number of females using your app is more than that of males and most of them are under 40 years of age. Based on this information, you can design your net campaign to either attract a male audience or target your entire marketing campaign at women under 40. Machine learning also helps in curating personalized recommendations that enhance customer engagement and time spent on your app. For instance, Amazon’s app suggestion mechanism is designed in such a manner that while customers are browsing, a machine learning algorithm constantly analyzes their preferences. 4. Significant Advertisements The success of a product or an app depends upon the significant advertisements. Consumers are focusing more on the products that are useful as well as contextually relevant and personalized. Machine learning aids an organization’s target display advertisements and personalized messaging more accurately. Such accurate results help app developers to generate ads focusing on data about each customer’s unique interests and buying propensities. By implementing machine learning, you can predict how a specific customer will react to a given promotion so you can display only specific ads that interest them. This helps you save time and capital and also lets you focus on improving the brand’s reputation. For instance, Coca-Cola pays close attention to how its products are represented across social media. The company implements image recognition technology to identify when people have posted images of its products or those of competitors on Facebook, Instagram, and Twitter. This information helps Coca-Cola to understand what situations prompt customers to talk about its brand. Ads designed in such a way have higher chances of being clicked on than ads created using other methods of targeted advertising. 5. Enhanced Security Machine learning can streamline and secure the app authentication process. Video, audio, and voice recognition features allow customers to authenticate using their biometric data such as face or fingerprint. Machine learning also aids you in determining access rights for your customers as well. Apps such as Zoom Login and BioID utilize machine learning and enable easy log in to other websites and apps with ultra-secure, selfie-style face authentication. For instance, big companies like Uber leverage machine learning for implementing face detection technology to address any fraudulent behavior. With the help of this technology, they can identify fraudsters who use stolen credit cards. Some of the big names in the banking sector and financial industries are also implementing machine learning algorithms to scrutinize customer’s previous transactions, social media activities, and borrowing history. With machine learning, following distinct features related to security can be accessed: Image recognition Shipping cost estimation Product tagging automation Wallet management Logistics optimization Business intelligence 6. Profound User Engagement Machine learning tools enable you to offer robust customer support, a wide range of features, and entertainment that binds your customers to your app daily. Certain profound features of machine learning can help you keep your customers engaged. Sufficient Support Amazon and Facebook app utilize a machine-learning algorithm to engage users with intelligent request handling. Machine learning systems can quickly analyze vast sets of data and make quick decisions. Various companies use machine learning to design conversational UX, or virtual assistants known as AI chatbots that assist in making calls, writing long emails, or waiting on the phone until somebody responds. You can even utilize machine learning algorithms to send smart notifications and voice notifications about the current deals going on. By performing these tasks, this technology helps your brand to become more proactive and keep your customers engaged. In short Machine learning tools enables you to offer solid customer support, a wide range of features, and entertainment that binds your customers to your app on a daily basis. Other Effective Use-Cases: Machine learning intelligence enables you to promote your app with a built-in translator. These processes help international customers communicate within your app without the involvement of third-party online translators. Airbnb app uses Cloud Translation API to translate listings, reviews, and conversations between its users, as almost 60 percent of its users use the app in different languages. Wrapping Up: Machine learning technology can authorize your mobile app with an efficient customization engine, cutting-edge search mechanisms, quick and reliable authentication, and fraud protection. OpenXcell Technolabs provides excellent mobile app development services. Leveraging the trending machine learning technology, the company delivers app solutions that are Emotionally Intelligent, Free to Explore, Pre-Trained, and comes with Autonomous Reasoning. Apart from machine learning, the company encompasses skilled, innovative talents for Cloud Computing, IoT, Artificial Intelligence, and more to upscale your customer base for mobile app development. OpenXcell Technolabs holds expertise in developing mobile apps that implement machine learning. This technology can help generate proactive customer interactions, monitor data to get insights, and boost lead generation metrics. If you want to implement machine learning algorithms to your mobile app, OpenXcell Technolabs is the one-stop destination for all such requirements.
With technological advances and digital progress, now there are distinct tactics possible to attract and retain customers. One such technological trend in the industry currently is Machine Learning. This technology makes mobile platforms more user-friendly, enhances the customer experience, maintains customer loyalty, and helps in developing consistent omnichannel skills.
Let's have a look at some of the best use-cases of machine learning in mobile apps:
1. Customized Experience
Machine learning can assist you with the continual learning process. With the help of Machine Learning, you can identify users based on their interests and further design the appearance of your app based on the user’s requirements. Moreover, algorithms can examine multiple sources of information ranging from social media activities to credit ratings and based on the analysis pop recommendations right onto user’s devices.
You can have an in-depth knowledge of your customers interests with the help of machine learning, such as:
- Who are your customers?
- What exactly do they require?
- What services are they looking for?
- What hobbies, preferences, and pain points they possess?
- What words they’re using to describe your products?
Based on the aggregate data collected from the above questions, you can distinguish and structure your customers. You can also find a specific approach to connect each customer group by accordingly adapting the tone of your content. Machine learning enables you to provide your users with the most relevant and enticing content. This will ensure users remain engaged with your app.
For instance, in 2013, Netflix app launched the ‘House of Cards’ and used big data to choose the main character (Kevin Spacey) and one of the executive producers (David Fincher) for this show. This depicts the perfect use case of machine learning in various aspects of mobile apps.
2. Sophisticated Research
Another compelling use case of machine learning solutions is that it enables you to deliver excellent and more contextual outcomes. It makes the process of searching more intuitive and easily accessible to your customers. Machine learning algorithms analyze customer’s queries and prioritize the results based on the specific requirements of a particular customer.
With the help of a mobile app, you can collect all available data about your customers like search histories, and typical actions. You can further use this data combined with behavioural data and search requests. Based on the results received, you can show customized results of your product listings to your visitors.
For instance, Reddit app is implementing machine learning to improvise search performance for hundreds of millions of community members.
3. Foreseen User Experience
Another use case of machine learning in mobile apps is that it helps marketers understand user’s preferences and behaviour patterns by scrutinizing various data such as:
- Age
- Gender
- Location
- Search requests
- Frequency of app usage
By collecting and analyzing all the data mentioned above, you can improve the effectiveness of your app and your marketing efforts. Suppose you analyze that the number of females using your app is more than that of males and most of them are under 40 years of age. Based on this information, you can design your net campaign to either attract a male audience or target your entire marketing campaign at women under 40.
Machine learning also helps in curating personalized recommendations that enhance customer engagement and time spent on your app.
For instance, Amazon’s app suggestion mechanism is designed in such a manner that while customers are browsing, a machine learning algorithm constantly analyzes their preferences.
4. Significant Advertisements
The success of a product or an app depends upon the significant advertisements. Consumers are focusing more on the products that are useful as well as contextually relevant and personalized. Machine learning aids an organization’s target display advertisements and personalized messaging more accurately. Such accurate results help app developers to generate ads focusing on data about each customer’s unique interests and buying propensities.
By implementing machine learning, you can predict how a specific customer will react to a given promotion so you can display only specific ads that interest them. This helps you save time and capital and also lets you focus on improving the brand’s reputation.
For instance, Coca-Cola pays close attention to how its products are represented across social media. The company implements image recognition technology to identify when people have posted images of its products or those of competitors on Facebook, Instagram, and Twitter. This information helps Coca-Cola to understand what situations prompt customers to talk about its brand. Ads designed in such a way have higher chances of being clicked on than ads created using other methods of targeted advertising.
5. Enhanced Security
Machine learning can streamline and secure the app authentication process. Video, audio, and voice recognition features allow customers to authenticate using their biometric data such as face or fingerprint. Machine learning also aids you in determining access rights for your customers as well.
Apps such as Zoom Login and BioID utilize machine learning and enable easy log in to other websites and apps with ultra-secure, selfie-style face authentication.
For instance, big companies like Uber leverage machine learning for implementing face detection technology to address any fraudulent behavior. With the help of this technology, they can identify fraudsters who use stolen credit cards.
Some of the big names in the banking sector and financial industries are also implementing machine learning algorithms to scrutinize customer’s previous transactions, social media activities, and borrowing history.
With machine learning, following distinct features related to security can be accessed:
- Image recognition
- Shipping cost estimation
- Product tagging automation
- Wallet management
- Logistics optimization
- Business intelligence
6. Profound User Engagement
Machine learning tools enable you to offer robust customer support, a wide range of features, and entertainment that binds your customers to your app daily. Certain profound features of machine learning can help you keep your customers engaged.
Sufficient Support
Amazon and Facebook app utilize a machine-learning algorithm to engage users with intelligent request handling. Machine learning systems can quickly analyze vast sets of data and make quick decisions.
Various companies use machine learning to design conversational UX, or virtual assistants known as AI chatbots that assist in making calls, writing long emails, or waiting on the phone until somebody responds. You can even utilize machine learning algorithms to send smart notifications and voice notifications about the current deals going on. By performing these tasks, this technology helps your brand to become more proactive and keep your customers engaged. In short Machine learning tools enables you to offer solid customer support, a wide range of features, and entertainment that binds your customers to your app on a daily basis.
Other Effective Use-Cases:
Machine learning intelligence enables you to promote your app with a built-in translator. These processes help international customers communicate within your app without the involvement of third-party online translators.
Airbnb app uses Cloud Translation API to translate listings, reviews, and conversations between its users, as almost 60 percent of its users use the app in different languages.
Wrapping Up:
Machine learning technology can authorize your mobile app with an efficient customization engine, cutting-edge search mechanisms, quick and reliable authentication, and fraud protection. OpenXcell Technolabs provides excellent mobile app development services. Leveraging the trending machine learning technology, the company delivers app solutions that are Emotionally Intelligent, Free to Explore, Pre-Trained, and comes with Autonomous Reasoning. Apart from machine learning, the company encompasses skilled, innovative talents for Cloud Computing, IoT, Artificial Intelligence, and more to upscale your customer base for mobile app development. OpenXcell Technolabs holds expertise in developing mobile apps that implement machine learning. This technology can help generate proactive customer interactions, monitor data to get insights, and boost lead generation metrics. If you want to implement machine learning algorithms to your mobile app, OpenXcell Technolabs is the one-stop destination for all such requirements.
