How Can Entrepreneurs Leverage Applications Of Machine Learning In Mobile App Startups

2. How Can Entrepreneurs Leverage Applications of Machine Learning in Mobile App Startups? Machine learning (ML) is one of the most exciting technologies that mankind has come across. In recent years, the advancements in machine learning have seen some interesting applications across industries. Entrepreneurs planning mobile app startups are continuously exploring the ways through which they can leverage ML and unlock a range of benefits. If you are an entrepreneur planning to build a mobile app and are excited about the concept of machine learning, here are the applications of machine learning across different industries. By learning these applications, you can not only plan on creating your app but also create an app that has the potential to learn from the data sets of your customers. Let us head into the article. Applications Of Machine Learning In Mobile Apps Across Industries Whether your mobile app startup targets healthcare, retail, manufacturing, banking, transport, or eLearning, machine learning can deliver a range of benefits. Here are the applications of Machine Learning across industries that you must explore before sharing your idea with app developers for startups.

3. Machine Learning in Healthcare Apps The healthcare industry has transformed itself extensively in the modern era. The industry is predicted to grow from $583 billion in 2022 to $819 billion in 2027, according to a report by McKinsey & Company. Over time, we have separated how data is utilized in the healthcare industry. Through advanced data analysis, patients can get personalized treatment plans. How has this been possible? It is all just because of machine learning. Machine learning algorithms can analyze vast amounts of medical data to identify patterns and predict outcomes associated with them. This leads to enhancements in diagnostic accuracy and patient care since the analyzed data sets can present reports about possible failure cases in surgery and what not. A notable example of the application of machine learning in healthcare apps is Ada, which uses ML to analyze medical history and user inputs to provide accurate insights into healthcare. Machine Learning in Manufacturing Apps For a long time, the manufacturing industry has relied on a manual workforce and ignored the importance of successful digital transformation. But now this is not the case, as we can notice some solid advancements in manufacturing where machine learning is one of the most popular advancements. As the aim of the manufacturing industry is to enhance efficiency in operations, whether it is about machines or any form of digital system, through machine learning-rich apps, manufacturers can analyze data in real-time, allowing them to predict possible machine failures. This in turn leads to optimisation of production and reduces any form of downtime. The growth rate of ML implementation in manufacturing is more than 30%, so you can imagine how fast ML is enriching the apps in the manufacturing industry. Machine Learning in Retail Apps Another industry that has been leveraging machine learning is the retail industry. And, as consumers shift their buying habits from offline mode to online mode, retail apps are becoming increasingly popular. Not to forget, not only retail apps, but smarter retail apps, are the new base of research for entrepreneurs. From inventory management to maximizing customer experiences, machine learning in retail apps has a range of benefits to deliver for businesses. Customers get personalized

4. recommendations, and businesses can promote optimized pricing and improve inventory predictions. With machine learning, retail companies saw an over 10% revenue increase, which is quite an interesting insight. A popular app leveraging ML in retail has been Amazon. Through the past purchases of the customers, the app leverages machine learning to analyze the data and deploy a personalized shopping experience. Machine Learning in Agriculture Apps Managing crops, controlling pests, predicting yields, and gathering the right information have always been challenges in the agriculture industry. Although a lot of it has been possible through IoT-based devices and the advent of digitalization, bringing the internet into the hands of farmers, there is still a lot to be done, which is possible with ML. Machine learning in agriculture apps has made the analysis of smart sensors easy. Further, the forecasting of weather has become more accurate with these advanced algorithms. By 2026, we can expect that intelligence in the agriculture field will reach $4 billion. And, for a notable example, you can refer to the CropX app, which highly utilizes machine learning to optimize irrigation and nutrient management through data. Machine Learning in Banking Apps Banking apps are one of the most crucial to secure since they involve the funds of users. From enhancing fraud detection to personalized financial services and customer support, there has always been a desire to enhance all of them. However, with machine learning, a lot has become possible, and that with the utmost efficiency. Now banks can reduce their fraud detection costs by up to 70% by using ML. Not only do these customers get highly personalized financial insights with expense tracking and dedicated fraud alerts, Additionally, customers can get in-built advice using intelligent chatbots about whether they should invest in specific stocks or not. ML is a game changer for the banking domain. Machine Learning In eLearning Apps Every school, college, or organization working in the learning industry wants to offer the most convenient way for learners. And mobile apps have made it possible to the fullest degree. However, enhancing personalized learning, administrative efficiency, and student

5. engagement has been a challenge even after offering mobile versions of learning. However, machine learning is gradually solving it. Students get personalized lesson plans, which keep them engaged through gamification, and unlock the best administrative efficiency for the school, colleges and parents. Take an example of eGurukul, where the apps have solidly embedded machine learning to offer the best possible experience to the learners. Machine Learning In Transportation Apps For a long time, transportation has always been just about going to the relevant taxi or bus stands and getting on board. However, mobile apps in travel have changed how customers used to travel with the introduction of on-demand travel. And machine learning is going to take it to the next level. By analyzing the traffic in real-time, machine learning algorithms can generate the most suitable and fastest routes. If you have used Uber, the app has been leveraging machine learning for a long time to optimize the ride experience both for customers and for drivers. The algorithms continuously study the patterns of drivers and riders to offer the best collaborative model that reduces the turnaround time for both parties. How Can You Leverage Machine Learning In A Mobile App Startup?

6. Now that we have detailed information about the applications of machine learning in diverse industries, let us guide you through the process of leveraging the same in a mobile app startup. Gather As Much Data As You Can The first step in leveraging machine learning in mobile app startup is to gather as much data as you can, just like machines do before analyzing them. This data shall be researched on the basis of the following: ●Identification of market needs ranging from what is going on in the market to what are the key challenges ●Determine what challenge you are going to solve through your solution, on which you want to apply ML for enhancements. ●Organize, prepare, and analyze the data sets. Now that you have pools of data sets with you, it is time to organize them. Store your data efficiently through proper conventional naming, and filter it as much as you can. Since you are planning a mobile app startup, you must have data related to users. Prepare and analyze the user behavior, transactional data, or any other data you have collected. Make sure that you prepare this analysis in the form of right sheets and docx. Consult An App Development Partner Leveraging machine learning is a complex process, for sure, but that can be made easy with a dedicated app development partner. Present your insights to the company and discuss the possible ways they can benefit your app through machine learning algorithms. Once you finalize your deal with the machine learning development company, you must keep in constant touch with them during the development phase. This will involve: ●Development and integration of ML models in your app ●Testing and deployment of the final build Additionally, ensure to consider any ethical considerations according to your region. For example, the EU has strong data governance that mobile apps have to comply with in order to utilize the data sets of users.

7. Prepare Your Market-Entering Strategy. Do not forget to share how your mobile app startup leverages machine learning algorithms better than most. Ensure that you highlight the intelligent aspects of your mobile app that will help you stand apart from the competition. It is because customers in the modern world know how intelligent apps reduce their efforts to a minimum compared to apps that do not leverage ML. Concluding Thoughts The impact of machine learning on mobile apps is noticeable. If you have noticed Google Maps lately, it has the capability of analyzing the routes and keeps on suggesting the best possible routes to you, even if you have already set the destination. This is ML working behind the scenes in the backend. And, planning to leverage your mobile app with ML is definitely one of the great ways to step into the modern mobile app startup world. However, make sure that you consult and hire one of the top mobile app development companies that has solid experience in transforming ideas into intelligent products. Source: https://ripenapps.com/blog/leveraging-applications- of-machine-learning-in-mobile-app-startups/