The Use of Machine Learning in Mobile AppsPosted by: Mariya Parackal | On: 29th Jul, 2020 | Mobile Development
With the advent of eCommerce and mobile applications, purchasing products and getting these delivered to your home has become simpler.
Customers now have the entire ‘market’ at their fingertips. But sometimes customers feel that mobile applications do not give them the actual feeling of going to a shop and making a purchase. You can quickly solve this problem with the help of Machine Learning (ML) and Artificial Intelligence (AI). Machine learning can help customers get a more personalized experience, even when making purchases from mobile applications.
Other than that, machine learning also has many other applications about mobile apps. It can help improve the user experience and do the search for products more intuitive. It uses cognitive technology to analyze and evaluate customer behavior and develop reports for managers.
Thus, machine learning has dual-use. It is applicable for business purposes and customers as well. Some of the conventional methods of machine learning for mobile applications are as follows:
- Improve consumer experience
One of the primary applications of machine learning is to improve user experience. Machine bots, Chatbots are some of the typical forms of machine learning. With the help of these applications, customers get a personalized feeling while shopping using a mobile app.
Any query they post in the chatbox is automatically answered as if by a live person at the other end. This is like a personal assistant that is available for twenty-four hours.
Moreover, along with chatbots, you can have other machine learning applications like search strings that lead directly to Frequently Asked Questions. These can make it easier for customers to have their queries addressed immediately.
Often machine learning is less expensive than hiring customer care executives who will manually evaluate and answer the questions
- Providing relevant information
When a customer is accessing your mobile application, you must provide them with all the relevant information not just about the product, but of similar products. For example, it like browsing through a rack of shirts. On a rack of shirts, you will get to see similar-looking shirts and not only one shirt.
With the help of machine learning relevant advertisements, links for related products and other relevant details will be displayed automatically. This will improve user-experience extensively and make it easier for customers to identify and purchase suitable products. It will also make your company’s mobile application, one of the most frequently downloaded apps because of its ease.
- Tagging and identifying products for loyal customers
If you have a loyal customer base, you must keep in touch with them. However, calling them up can prove to be cumbersome. But with the help of machine learning, you can keep them updated. Update the latest addition to your inventory of products through an automated messaging system.
Based on their previous purchase history, you can send them messages about the latest price of the products or similar items that you might have added. This will help your customers feel associated with your company. It will also help in the process of identifying products suitable for your loyal customers.
You can also identify appropriate vouchers, discount coupons, and personalized sales announcements. You can send all these to your loyal customer base. Thus, depending on data generated by the machine learning application you have installed on the mobile app, launched by your firm. You can identify and segment your loyal customer base.
- Data generation for user behavior
A significant part of the success of mobile commerce or an eCommerce business depends on predicting customer behavior. This happens with large scale data mining. Data science starts with identifying a suitable customer base, then segmenting it based on its buying behavior.
It will depend on several variables like gender, previous buying history, location, search requests, number of times the customer uses the application, and other factors. With the help of machine learning, you can compile all of this data and develop charts and dashboards to identify customer behavior. This is important if you are looking to make a predictive analysis.
For any business organization to succeed, a certain amount of predictive analysis is essential. Based on this analysis, you will be able to identify the products that you need to stock up, the products that are fast-moving, and the products that you will need to buy in the near future. All of this becomes important if you want to reduce operational expenses and improve your profit margin.
Thus, with the help of machine learning, you can increase your profitability. Hence, machine learning on mobile applications is essential not for the customer but also for the entrepreneur.
- Increased security
Machine learning can also help improve the security features of your mobile application. It can run algorithms that can identify suspicious activities and prevent any breach. It can also prevent any malicious threats, and information shared by your customers will be safe.
Machine learning will help reduce the number of person-hours that you need to put to run the algorithms, identify the threats, and remove these. Since all of these will happen automatically, you will not have to run it physically, which will also reduce your expense.
Thus, ML in mobile applications can prove to be beneficial for customers and entrepreneurs. Some of the most common mobile apps that are heavily dependent on machine learning are as follows.
- Snapchat, which uses facial recognition machine learning applications.
- Oval money, which evaluates spending behavior.
- Google Maps, which uses data analysis as a part of the machine learning application.
- Dango, which can be integrated with various messengers.
- Netflix, which can predict customers’ viewing preferences with the help of ML applications.
- Dinner Ideas, which suggests a perfect recipe
- Tinder, which finds a perfect match with reinforcement learning to get smart photos
- Spotify, which uses Collaborative Filtering, Natural Language Processing, and Audio model to get personalized music recommendations.
- Yelp, which can show the most popular things recommended by other users
- Facebook, which suggests new friends as ‘people you may know’ and ML is used for Newsfeed, targeted ads, and facial recognition.
- eBay, ML used to understand what users are looking for.
ML in Industry-Specific Mobile Applications
- AI powered financial assistant
- Fitness mobile apps with ML
- Healthcare mobile apps with ML
- Transportation mobile apps with ML
- Online retail mobile apps with ML
Therefore, machine learning has already achieved real-life applications in various mobile apps. ML algorithms can improve customer experience, customer loyalty, increase customer engagement, and many more. Its demand increased in the field of eCommerce and mobile commerce. It reduces the dependency on manual labor. Thereby making it less expensive, but no less proficient than having professionals handling all aspects of it.