Machine Learning In Mobile Applications: The Next Wave Of Enterprise Mobility

Artificial Intelligence (AI) and machine learning (ML) have transformed the mobile app development landscape. The mobile app developer relies on cognitive technology like ML to write powerful algorithms to create intelligent apps that can understand human behavior, assist users, and entertain them. Today we are going to explore such technology used in mobile app development. 

All About Mobile Apps and Market Trends

Mobile app developers create informative, productive, and entertaining apps for popular handheld devices like smartphones, wearable devices, personal and enterprise digital assistants. They also use the latest technologies and tools such as Java, Kotlin, Swift, PhoneGap, ReactJS, Xamarin, Flutter, etc. to build user-friendly mobile apps. 

Social media platforms and digital services are experiencing geometric progression in recent years. The smartphone’s power, productivity, and value must exceed the buyer’s expectation to be a successful product. Certainly, this trend paves the way for a need for more advanced mobile apps, which can provide heightened performance without requiring too much space or power.

With this in mind, the following state-of-the-art features will become standard in the next decade:

• Market-friendly accelerated mobile pages.
• Augmented and Virtual Reality apps for entertainment apps and games.
• AI apps for advanced analytics and user assistance.
• ML techniques to create complex, cognitive interfaces.
• Cloud-driven apps for superior storage and connectivity.
• Enterprise applications to connect productive teams.
• Wearable devices for health and fitness benefits.
• Intuitive m-commerce apps for shopping ease.
• The confluence of smartphones and IoT devices.

The market potential of these mobile apps spans multiple sectors. By 2020, VR and AR apps growth hit an $18.8 billion market value. There has been a 300% increase in AI and ML investments. The big players in this sector include Google, eBay, Microsoft, Intel, Yahoo, IBM, Facebook, and AOL. 

Another key point, after 2019, more than 66% of business owners increased their investment in enterprise mobile apps. These apps’ market value will peak to the US $430 billion in 2021. The future of mobile development heavily focuses on security features too.

Growth Prospects

The growth opportunities across various industries are very positive. Integrated, location-based apps will become popular. Notably, Wi-Fi will go beyond traditional hotspots, and customized chatbots will also rise. Google and Apple Pay will overpower debit/credit card transactions for mCommerce. Around 65% of the media time spent accounts for mobile app usage.

1. The market for mobile apps will surpass US$ 407 billion by 2026.
2. The estimated CAGR for the next 5 years is at 18.4%.
3. The focus will be on Healthcare, Engineering, Real Estate, and Retail sectors.
4. Apps for Sports, Gaming, Military, and Education will gain traction.
5. By 2021, the number of connected devices will exceed 25 billion.
6. Further, by 2022, IoT investments will rise to more than US$ 22 billion.
7. Advertising, cloud, and non-cloud services will propel growth.
8. Apps ensure faster access, compelling graphics, and HDD displays.

Technology and Mobile App Development: Two Sides of the Same Coin

• AR and VR are major elements for app development. Millions of people used AR and VR for games and eCommerce apps. By 2024, the growth expectation is $191 billion. The manufacturing sector and health sector are the major areas where AR and VR powered mobile apps will become a huge hit.
• Security is one of the leading variables to decide the success of an application. There are numerous reasons why security is becoming a prime factor, and blockchain usage helps a mobile app developer improve tracking and security. The growth of blockchain enables the growth of more sophisticated payment and money transfer apps.
• Next to security, space is of major concern. Manufacturers try to enhance the mobile device’s primary storage limit in every new model, and yet, users find it insufficient. The use of the cloud allows apps to utilize less space. The trend of favoring apps with a cloud base started as early as 2014. Apps with heavy databases depend on AWS or similar services for backup. Such technology paves the way for enhanced collaboration and improved productivity in the future.
• AMP allows app developers to stay competitive. It helps the developers to advertise, sell, and market products or services on the app, with ease. According to An Akamai study, even a delay of 100 milliseconds reduces the conversion rate by 7%.
• Cross-platform application development reduces the time, complexity, and cost required to bring an app to the market. Consequently, this trend supports the app developers to generate better apps within a shorter time-frame. 
• Billions of devices integrate and sync with the help of IoT and development of mobile apps for smart homes, smart automobiles and healthcare will see a positive trend.
• Lastly, 5G technology, everyone is waiting for it. It helps to transfer data one hundred times faster than today. Such a faster data channel will enhance the development of ultra-performing apps, which would make a whirl in healthcare. The combination of 5G and virtual reality would bring new changes to 3D gaming apps and healthcare applications.

How Has Machine Learning Technology Revamped the Usage of Mobile Apps?

The AI subfield is getting recognition for constant, data-dependent learning. It uses real-time analytics to spot behavior, anomalies, differences, and regularities. ML is having a substantial impact on the development of 21st-century mobile apps. The shift from ML-powered computer applications towards smartphone apps has been rapid and highly productive.

With this in mind, machine learning apps revamp smartphone usage with the following efficient approaches:

• The modern mobile users expect simplicity, convenience, joy, and functionality; Through machine learning app, they record hobbies, anticipate wishes, manage calendars, notify events, and recommend solutions.
• Restless searchers want quick, relevant, and informative results. ML tools are effective in tracking typical actions and storing historical data. These tools correct spelling, respond to voice searches and offer lists of related outcomes.
• M-commerce is a boon for those who want to buy and sell on the move. ML algorithms enhance the customer experience and recommend the best products. They assist in digital marketing through data analysis, real-time ads, and purchase pattern recognition to businesses.
• Business leaders expect top-of-the shelf app functions. ML algorithms help with metrics, insight analysis, and intelligence gathering. They facilitate data accuracy, decision-making, product delivery, personalized services, and secure connectivity.
• ML-powered mobile experiences are seamless, smoother, and intuitive. Their practical approach is suitable for high-speed smart devices. The machine learning in mobile applications helps developers build diverse, powerful apps through data filtration, algorithmic training, model selection, parameter tuning, and predictions.

Rise of ML-based Mobile Apps

AI and ML technologies will bring forth more stunning mobile apps. The investments in ML will reach US$58 billion by 2021. The industry’s growth will peak at US$20.83 billion, with a CAGR of 44.06% by 2024. Experts estimate the neural network market to grow to US$23 billion by the year 2024.

Machine learning applications will revitalize the eCommerce industry. Security, Finance, Photo, and Video apps will grow by leaps and bounds. To put it differently, IoT devices are smartphone-controlled so the market potential for ML-powered mobile apps is also huge. 

Top Machine Learning Applications

Without tangible results, investors will not continue their patronage. Thankfully, multiple applications satisfy their trust. These diverse ML-powered mobile apps are powerful and user friendly. Some of the top ML-powered mobile apps are:

1 Snapchat

This messaging app has excellent face detection abilities. Its filters combine ML algorithms with augmented reality. Photos get processed as data sets with individual color pixels. Repeated scanning, contrasting clues, and grayscale values get processed to identify faces. To emphasize, it also creates 3D masks using data training, scaling, rotation, alignment, etc.

2 Google Maps

Google studies and analyzes thousands of data sets. They use training models to aggregate location data. And later logical regression gets applied to make parking lot of predictions and related services.

3 Tinder

This dating app uses Smart Photos to find perfect matches and soul mates. Profile swipes get analyzed to reorder popular photos. The system continually learns and improves itself using inputs.

4 Netflix

This streaming service uses regression and learning algorithms too. It classifies content based on genre, actors, reviews, year, etc. The app learns from user action and behavior to recommend the movie and personalized content.

5 Oval Money

This financial app stores and processes user’s spending habits. It analyzes transaction data to offer convenient strategies to save money. In addition, its intelligent system also relies on tracking to improve financial literacy. And it helps the user make sound and safe investments at the right time.

Machine Learning in Mobile App Development

The current mobile app developers prioritize machine learning application projects for various reasons. The top smartphone brands have become very powerful, thanks to ML-powered apps. The internet is ideal for testing out powerful machine learning algorithms. 

How mobile app developers use ML to generate innovative apps?

ML helps reduce the gap between understanding user behavior and utilizing it to create a customized solution. App developers embed ML into mobile applications to create customized applications for each individual. ML depends on continuous learning. Based on the user’s everyday activity, the ML program learns and re-learns to create a customized solution. This advancement helps to create an adaptive app that can help to achieve an ideal omnichannel experience.

1. ML helps in predictive analysis. This technology helps an application to process a massive amount of data and derive quantitative predictions that are customizable based on the user’s requirements.
2. Developers can train ML modules to filter out spam and potentially insecure sites or emails. This technology leads to a pro-active security measure.
3. Character recognition and NLP, combined with predictive analysis, will help create applications with the ability to read and understand language. It is a milestone in ML-based apps, and it helps to generate an array of different applications for different niches.

Have a look at these different machine learning techniques applied in diverse fields by mobile app developers:

• Data Mining

  The mining of large datasets helps to identify patterns and connections. This process requires the collection, storage, maintenance, and analyses of relevant data. A good example of using the mining technique of ML is the travel app. The algorithm helps to collect client data and categorize it. Gender, age, social media profiles, and others are some of the variables to design custom applications and services. The operators receive business intelligence to modify tours and schedules. 

• Tracking

  Tracking financial and banking data facilitates smarter businesses. The module uses the user’s credit and transaction data to offer friendly deals. This tracking feature helps to create customer-focused marketing and targeting for various products and services.

• Analyses

  Data analytics is essential for eCommerce and m-commerce. Big Data analyses help retail giants like Amazon satisfy their customers.

• Search

  ML techniques help improve search outcomes for products and services. They learn to display the most relevant results based on the search query, screen scrolls, clicks, etc. It is possible to tailor the available information based on the searcher’s preference. The programs understand the searchers’ past behaviors, history, and other activities to deliver a customized ranked search result. 

• Monitoring

  Data monitoring and ML-powered defense systems improve security and antivirus apps. With the help of programmed modules, the user can trail online activities and install alarms to trigger if any fraudulent activity appears. 

• Security

  Voice recognition, biometrics, and audiovisual data can be helpful in-app authentication. Certainly, security becomes possible in a platform where real-time changes take place to the input. This application is useful in a secured data transfer app, file storage, and others.

How Mobile App Developers Benefit From Machine Learning Techniques? 

The machine learning technique of pattern recognition is a dynamic learning experience. On the other side, predictive analytics is applicable to financial, marketing, and banking data, face detection, image, and object recognition methods are essential for reliable security. The percentage of mobile app developers relying on ML techniques at various mobile app development stages is increasing. And therefore, developers benefit from these sophisticated processes in the following way:

1 Creating wireframes for apps

ML helps to develop wireframes for apps. It uses big data to create a wireframe that incorporates strategies from R&D and conceptualization. The developers can use ML in running technical feasibility tests at each stage with high speed and accuracy.

2 Customer segmentation

Mobile app developers can create apps with an ability to provide personalized services for their customers. Notably, they can use the data from these apps to create personalized messages for customers and others using ML techniques. ML provides a growing in-depth knowledge of customer behavior, which is essential for strategy formulation and feedback. 

3 Profit

ML helps developers to collect and monitor the app usage data. Enterprises can identify profitable opportunities through app insights. They can collect information about users’ spending on mobile apps. The improvised personalization helps to allure the customers into seeing the products that have a very high probability of attracting them. Henceforth, users stay on the ML app for a considerably longer time compared to other applications.

4 Fraud control

ML techniques help a developer to create apps with crucial security features. A fraud-control app with pro-active security features will have higher trustworthiness among the users. It improves the brand value of the app. Another key point, a fast authentication process is very crucial while developing eCommerce sites, which is possible through ML.

5 Cost-effective development process

On-device ML solution helps in reducing the complexity of data processing and thereby requires lower bandwidth. It benefits the developer in avoiding expensive cloud services. It is possible to use a small team to scale the development without the need for cloud infrastructure.

6 Effective app testing

ML helps developers to generate a customized module for testing their ML apps or use a general AI- testing module. Either way, the module helps to run exploratory tests to find random behaviors without the team spending a massive amount of time running unnecessary steps.

7 Efficiency

Mobile app development can enjoy heightened accuracy without the team sacrificing long labor-intensive working hours or complicated processes. It is possible to reduce manual and numerical error to almost zero and add more innovation to curb real-time problems.

8 Virtual assistant

A virtual assistant might not be helpful in the development phase. The ML-powered chatbots help to communicate with a wide customer base with a small team. Manual errors can reduce to zero, and it becomes possible to provide an ample amount of information to the customers within a short time and with no space for miscommunication.

Why Use Machine Learning Technology For Your Enterprise Mobile Apps?

Machine learning started as a pattern recognition program, and today, the programmers train the modules to conduct a specific task. ML techniques can help to read big data and generate big-picture analysis within seconds. It is possible to adapt the ML module to accept new input and self-learn to generate a continuum of information. Information is the key to the growth of any business or development. The concept of a self-driving car is possible only with machine learning. Users receive online product recommendations during online shopping because of machine learning.

Machine learning can help to monitor, create, understand, and even react intelligently. For example, while composing a mail, if you write about attaching a document in the body of the mail and forget to attach it, the application sends out a message reminding you that you forgot to attach the document. ML can learn, understand, and use this information to predict our behavior. Why is machine learning important?

• For personalized experiences

  Advanced ML algorithms analyze information from social media platforms. They facilitate a continuous learning process using digital units. To put it differently, customers who use apps can experience a personalized experience. They will receive recommendations and ratings in their social media app immediately.

• Increase customer engagement

  Machine learning apps increase customer engagement. Customer engagement and experience can be enhanced through the function of information categorization. It is possible to convey the app’s real intent with approached buyers.

• Superior search

  In the data-led world, a high-speed search with efficient UX is vital. ML-based apps facilitate seamless and rapid online searches; they improve contextual results and optimize search outcomes too. The customer receives a balanced list through the history of data access, analysis, and ranking.

• Visual authentication

  ML methods include facial recognition and word translation. Developers can write apps with visual and auto-recognition. ML enables account access and a secure authentication process. M-commerce apps become more reliable, convenient, and user-friendly.

• Advanced data-mining

  Big Data is the new norm in the world of mobile communications. Refined data mining techniques effortlessly analyze large information volumes. Data categorization and multiple user profile configuration become more efficient. Right strategies and robust data create first-rate mobile apps.

• Consumer behavior

  Consumer’s online behavior, hobbies, and interests are valuable. ML algorithms exploit these preferences to assess customer’s attitudes. They use the insights to enhance advertising strategies, and lower and upper sales funnels to achieve higher profits. Location, search, gender, and app usage data analysis is possible with ML methods. Result-driven strategies ensure customer satisfaction, brand equity, and market reputation. 

• Online security

  Mobile apps help streamline and secure audiovisual data. Voice recognition, biometrics, and digital fingerprints improve security. Banking, m-retail, and financial sectors benefit from selfie-style account access. They prevent identity theft and stop breaches in business, personal, and customer data security. Automated product tags, logistical optimization, and insightful business intelligence are some of the other benefits.

Conclusion

Summing up the discussion, the next-gen mobile apps will be smarter and powerful. App developers will employ the finest machine learning techniques. They apply predictive analytics, data mining, and facial recognition techniques and rely on neural networks and reinforced learning algorithms. The effective ML processes, including mining, tracking, analyzing, monitoring, searching, and predictions, benefit sectors like health, finance, m-commerce, banking, education, and entertainment. The next-generation mobile apps will have refined security, search, predictive, and customization features. And their UI/UX will be sharper, intuitive, informative, authentic, and entertaining. Are you planning to create a new app for your business? Try to create a profitable and self-learning app with ML techniques.

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