After smart devices such as smartphones, smartwatches, and smart speakers, we are now moving towards a future with smart apps. These apps leverage different technologies to deliver a much more personalized and engaging user experience. In a highly competitive mobile app market where there are millions of mobile apps and thousands of them being added every day, smart features and technologies will help your app stand out from the crowd and get the limelight it
Do We Really Need Smart Apps?
Every user is different and so do their needs. As a app development company or app developer, you cannot create a separate mobile app for every user, but you can add smart features which allows the app to learn from user preferences and personalize their mobile app experience.
Unlike traditional apps, smart apps tend to learn from user behavior and tailor the experience according to user preference. Additionally, these apps can also take care of repetitive tasks and also offer conversational AI. As the technology matures with the passage of time, we might see these apps deliver accurate results with perfection on a consistent basis, offering users a uniform and personalized experience.
In this article, you will learn about seven smart technologies that can take your mobile app experience to the next level.
1. Chatbots
According to chatbots statistics, 85% of customer interaction will be handled without human agents by 2021. With chatbots capable of handling the majority (80%) of questions and helping businesses reduce operational costs by 30%, you can easily see why so many businesses are adopting chatbots.
It not only reduces the burden of the shoulders of your customer support, but it automates customer support. More importantly, it allows businesses to collect customer insights, deliver round the clock customer support, improves customer satisfaction by delivering rapid response to their queries and help you close more leads and generate more sales.
2. Machine Learning
Whether you are a business, app developer or app user, using machine learning in mobile apps can benefit all. Machine learning algorithms can reduce the workload of programmers by anticipating all the use cases, contingencies, and possibilities. It can also help in identifying trends, needs, and patterns.
Since apps with machine learning capabilities are more effective when it comes to fulfilling user needs, they are more profitable too. It gives businesses power to add new data, products and other information in real time, keeping the mobile app fresh and up to date. It is the ability to predict trends and identify patterns that help you to take advantage of those trends.
3. Biometrics
Most smartphones today come with a fingerprint sensor and face unlock feature. Some even use iris scanning and other biometric technologies. Add to that the gesture controls, voice controls and sensory recognition capabilities and you get different ways to interact with your devices. With hardware already present, app developers can take advantage of it to evaluate human behavior. You can even use this technology to measure the dimensions of different body parts. Apart from being used as a user authentication method, there are many applications of these technologies that app developers can take advantage of.
4. Emotion Recognition
There are some people who can read the emotion on your face just by looking at your face. We all have someone in our friends and family circle who can do that. Thankfully, there is also a technology alternative too and it is called emotion recognition.
Emotion recognition combines sophisticated image processing with audio-based information for recognizing your emotions. It even captures and analyzes subtle speech signals as well as the tone of your voice to depict your mood. Even though this technology is still in its infancy, many startups are experimenting with it.
5. Speech Recognition
One of the most popular smart technologies that is already being used extensively in mobile apps is speech recognition. We already have Siri, Google Assistant and Cortana, voice assistants that can decode and convert human speech into computer understandable language. app development companies also include this feature into mobile apps. In short, it provides users with the convenient way to interact with your mobile apps and get things done.
6. Text Recognition
Another sub branch of natural language processing is text recognition. With users being exposed to tons of data daily, it is harder to extract relevant information from huge data sets. That is where text recognition can come in handy. Even though this technology is mostly being used for security issues and fraud detection, it can also be used for data entry automation, document indexing in search engines as well as automatic number plate detection. More importantly, it can also be used to assist disabled people such as those who are visually impaired.
7. Image Recognition
Computer vision, a branch of artificial intelligence is used to analyze visual source data. It can also be used for object recognition, visual geolocation, code recognition and applications in medical image analysis as well as in industrial automation. This is not all, this technology has also found its applications in gaming, ecommerce, and automotive industry. In fact, social media giants such as Facebook and Twitter have benefited from it and managed to increase their user engagement and optimize their mobile advertising
An app equipped with image recognition capabilities first understands different patterns and compares different objects present in an image. Additionally, it even detects how the image will look when you change the position of the object in three-dimensional space. This allows social media advertisers to get a better idea about their target audience’s lifestyles. They can analyze that visual data and identify your friends, interests, and brands you prefer and tweak their ads accordingly.
Here is a three-step process on how image processing works.
- Collect and organize data
- Create a predictive model for image recognition
- Recognize images
The only downside is that image recognition and processing algorithms are resource hungry. If you can look past it, image recognition is a technology you should include in your mobile apps.
Which of these mobile app technologies do you use in your mobile apps? Let us know in the comments section below.
