The ultimate idea of every business is to create new and retain the older users with their mobile apps. Though it is not exactly true, it still forms a primary basis for fetching new business and fulfilling the needs of the users simultaneously. Mobile applications are focused upon bringing discretion and interpretation of business and thought-process that it wants to lay in front of its users. Why would one download and install a mobile app that is dedicated for a purpose when almost everything is available on Google search? This is a classical form of data collection which has now been morphed into sincere mobile applications that are dedicated to particular user needs which simultaneously emphasize a better understanding of users’ minds. From machine learning to artificial intelligence, this tussle of making intelligent mobile apps has gone to extremes.
Frankly, it is bizarre for mobile app developers to acts as arbiters of mobile technology. While it might sound flakey but artificial intelligence and more appropriately machine learning has an upper-hand in turning mobile apps into gold mines for app users recently. While once users had to spend multiple minutes to wait for a customer support associate to be online, the evolution of virtual assistants has unhesitatingly overruled them without exceptions.
#Machine Learning Key-terms
- Machine learning involves educating computer systems to recognize the patterns in the way human brains understand them.
- Artificial Intelligence makes a computer work in the way human beings work. It eases out the tasks that normally require human interaction like – Speech recognition, Visual perception, Decision-making, and Translation between languages.
- A neural network involves interconnection of various networks or nodes or artificial neurons. The output of one neuron acts as input for the next neuron in the network.
- Deep Learning can be understood as a way to make neurons understand how to solve a particular programming problem.
- Linear Regression implies a linear relationship between single input variables.
- Logistic Regression implies modeling a binomial outcome with one or more explanatory variables.
How does machine learning help?
Machine learning involves accessing data and uses it for self-learning purpose. It is used to extract essential excerpts out of it and use it to enhance user experience. Let’s have a sneak-peak into how machine learning has innovated and eventually transformed mobile applications:
1. Personalization
Machine learning can help identify users and group them according to their needs from the mobile apps. It can be helpful in collecting information and coming upon a decision of how the app looks. ML can be useful in determining:
- Who is the target audience?
- What can they afford?
- What are their requirements?
- Are there any particular points that the mobile apps need to resolve?
- The content or words used over the mobile apps to interact with the customers
Structuring out the customers and finding the right approach to satisfy their needs, providing relevant content on the app makes the app enticing in front of the user and gives an impression of communicating with the user in real time. Examples: Taco Bell, Uber, UberEats, ImpromDo, Migraine Buddy, Optimize Fitness.
2. Searching for Products and Items
Right from optimizing the search on the app to providing clear and relevant results that are contextual and intuitive makes it easy for the customers to stay connected and interested in the app for a longer time. The cognitive approach helps in grouping the articles, scripts, documents, FAQ’s, videos and graphs to provide smart and immediate answers to searches.
3. Product Recommendation and User Behavior
ML even helps marketers to understand user preferences and purchase patterns on the app. They are then prompted for a recommendation based on their search behavior, purchase history, and last minute buys. These recommendations are based on their age, gender, location, search requests and frequency of app usage etc. Examples: Youboox, JJ Foodservice, Qloo,
4. Improved Fraud Control and Security/ Fast authentication
Face and fingerprint sensors enabled with AI features helps in determining essential security features within the apps. These measures might include image recognition, shipping cost estimation, product tagging automation, wallet management, logistics optimization, and business intelligence. Example of an app that deploys these: Zoom Login, BioID etc.
5. Trend Forecasting/Entertainment
AI enabled filters in smartphone apps helps in forecasting weather. Funny filters help in animating photos in the picture gallery, detect customer’s face, localizing facial features and adding filters etc.
Some other important features of ML include evaluating real estate property, giving customers a virtual tour, making them aware of various setups with augmented reality and making relevant purchase decisions.
How are developers deploying artificial intelligence to renovate mobile apps?
Out of the plethora of potential uses for machine learning, deploying them to create real-like mobile applications is one of the most innovative and sustainable usages.
- Machine learning can be used as part of artificial intelligence
- It can be used for predictive analysis (Processing large volumes of data used for predictions)
- Machine learning can be used for inducing security and filtering (Helpful to applications that require some form of protection in case of ever-changing inputs. It can even detect and block spammers without developer’s explicit programming instructions)
Above three methods are simply few of the many potential uses of machine learning for mobile apps. It’s easy to conceive optical character recognition (OCR) from machine learning capabilities as it helps the developer to skip some possible variations from the original algorithm.
Machine learning empowers an OCR application to identify and remember the characters (which the developers have skipped or might not have considered)
Machine learning concept also goes for natural language processing (NLP) apps. Besides reducing the time and efforts to develop it, it eventually reduces the time spent in updating and fine-tuning a number of different algorithm elements.
Almost every technology is benefitted from machine learning; this includes everything from predictive analysis and natural language processing to augmented reality, artificial intelligence and virtual reality.
Some of the most prevalent examples of machine learning are:
- Netflix
- Tinder
- Oval Money
- Snapchat
- Google Maps
- Impromp Do
- Dango
Final Cut
Machine-learning essentially empowers the mobile app with enough personalization features to make it more usable, efficient and effective. It is one of those technologies that is in accordance with the latest style and is fast and secure. Does an app require anything more than this? It is one of those sharp boundaries that set your app apart from that of your competitors. Develop a mobile app with the required boost of AI and ML for your business and make it leap out. Drop us a line and we’ll be on our toes to assist you!