Machine learning has moved beyond its hype and presented several industries with multiple use cases. Today, business leaders are considering to integrate machine learning in their mobile apps to improve customer experience, enhance efficiency, and increase revenue. For instance, e-commerce businesses use machine learning to suggest relevant products to customers during their purchase. Customers may buy these products along with their ongoing purchase, leading to increased revenue. Likewise, other businesses can utilize machine learning in their mobile apps to increase their revenue and also develop effective marketing strategies. Hence, business leaders must consider incorporating machine learning during mobile app development.
Machine learning models analyze several sources of data like personal details, social media, past purchases, and other public datasets. Based on the collected data, machine learning systems can classify users to help businesses gain insight into their customers’ interests, pain points, financial situations, and product opinions. Using such detailed classification, businesses can structure their customers into different groups to offer personalized content and marketing campaigns. In this manner, businesses can provide relevant content with their mobile apps. For example, e-commerce businesses can send notifications about relevant product recommendations to their customers using data such as past purchases, product wish lists, location data, and other personal details. Similarly, other businesses can provide personalized experiences that cater to their customers.
During mobile app development, developers can integrate machine learning algorithms to optimize the search functionality. For this purpose, machine learning systems collect and analyze customer data such as search history and user behavior. With such data, mobile apps can rank products and services to display results that match the given search query. For example, streaming services use optimized search to display relevant search results for customers based on their past viewing experience. Using this technology, mobile apps can deliver contextual search results that can make the search functionality more intuitive. Also, machine learning can be used to incorporate voice-enabled search and spelling corrections in mobile apps.
Machine learning can be used in healthcare apps to offer a user-specific app experience. Machine learning-enabled healthcare apps can monitor a user’s height, weight, diet, sleep patterns, exercise routines, and medical conditions. With the help of this data, mobile apps can analyze every user’s health in real-time. Based on such detailed analysis, healthcare apps can suggest personalized workouts and diets for their users. Also, these apps can display news and other articles that may be relevant to the users. For example, a user who is suffering from obesity may receive blogs about different diets and workout routines that can help reduce weight. Hence, incorporating machine learning is a necessity for healthcare mobile app development.
Delivering relevant ads to customers can be a complicated task. Machine learning can help simplify this process by offering targeted advertisements. For this purpose, machine learning models generate ads that are based on every customer’s interests and purchase patterns. Machine learning algorithms help businesses predict how customers would react to a certain kind of promotion. Hence, businesses can display specific ads to a particular demographic of customers who would be interested in the showcased product or service. Using this technology, business leaders can analyze which ads are relevant to certain demographics of audience and develop relevant ads for them. With this approach, business leaders can generate effective advertising and marketing content.
Machine learning can be used to streamline user authentication process in mobile apps. Machine learning helps in developing precise authentication models using facial, fingerprint, and voice recognition. For this purpose, machine learning models analyze biometric data of a user and their access rights for a mobile app. For instance, banking and finance apps can use fingerprint authentication to identify users.
Machine learning can also be used for background app monitoring. Machine learning systems can analyze app behavior to detect any suspicious activity. Using this approach, machine learning algorithms can identify unidentified malware in real-time. Therefore, incorporating machine learning in the mobile app development process can help businesses deliver secure mobile apps.
Chatbots have become a necessity for businesses to deliver a great customer experience. Chatbots enable organizations to offer 24/7 customer support. Using machine learning, chatbots can understand the context in a conversation and resolve queries precisely. For this purpose, machine learning algorithms utilize natural language processing and sentiment analysis. Using this technology, businesses can also recommend relevant products or services.
Machine learning models can be trained using large volumes of images to identify objects and recognize people. Mobile apps can use in-built cameras to detect facial expressions and scan barcodes and QR codes. Social media platforms such as Snapchat and Instagram use machine learning for various filters that can analyze a user’s facial expressions. Also, Facebook uses machine learning to identify different people in a picture to help users tag their family and friends. Additionally, banking and finance apps can utilize machine learning to scan credentials on credit and debit cards. In this manner, businesses can deliver faster checkouts using mobile apps.
To implement machine learning in mobile apps, business leaders will require an experienced team of developers on-board. However, hiring and managing such a team can be tedious and expensive for an organization. Hence, businesses can outsource machine learning-based mobile app development to the experts at Digital Fractal. Digital Fractal offers customized and robust apps for every business.