AI and Machine Learning Apps: What We Can Learn from Big Brands
AI and Machine Learning Apps: What We Can Learn from Big Brands
You need to constantly improve and optimize your business models and implement better patterns, and if you can't do it yourself, let AI do it for you!
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Bias comes in a variety of forms, all of them potentially damaging to the efficacy of your ML algorithm. Read how Alegion's Chief Data Scientist discusses the source of most headlines about AI failures here.
Machine learning is a collection of computer methodologies or algorithms that predict outcomes based on collating information from previous choices. These learning systems are adaptive, constantly evolving from new examples, and capable of determining internal parameters to recognize the nature of new data. ML acquires knowledge through the analysis of previous behaviors and/or experimental data, e.g. a learning dataset. Smart technology and AI applications and programs collect a vast amount of data which can then be analyzed to predict outcomes.
Information obtained using machine learning methods are by far the most dependable way to predict results and construct reliable models, particularly if some data is unknown or unobtainable. For this reason, we use machine learning and artificial intelligence in our mobile app development.
Mobile apps already use AI and collect a large amount of data provided by their users. The goal is to make use of the possibilities provided by this to create a business model. This can be achieved with deep learning. To see how important the implementation of such techniques can be, let's look at some success stories of some big brands that have done so: Uber, Netflix, Snapchat, and Amazon.
Uber is a great example of the correct implementation of ML based on giving users the correct information quickly. The company's users are given the probable waiting time with the ability to view the real-time progress of the car on a map. They are also given the expected cost of the trip. The whole process is supported by an ML security model based on facial recognition and credit card security. The time of the journey is very accurate, as it is based on current street traffic data, not just estimated distances. All of these processes are wrapped with ML and deep learning modules.
Netflix uses ML for similar reasons but concentrates more on gathering data about its users' past behaviors. All of the service's recommendations are based on data collected about previous choices and as a result, about 80% of watched content comes from these suggestions. Consequently, the majority of the company's revenue comes from recommendations produced using ML. This model is seen in nearly all successful implementations of ML where the users' needs are identified by gathering and processing data.
Snapchat's success is based on a combination of machine learning and augmented reality. These are used in a playful way that enables users to enhance pictures and videos with previously prepared templates called filters. The app operates mainly using the camera in the user's mobile device to find and track faces in an image and overlay it with a filter that also allows it to be animated.
Amazon is a leader in the use of ML and AI and claims to have been using them for over 20 years. It has created its own platform, Amazon Machine Learning Developer Guide, with tools and tutorials for development usage. As such, they let AI manage most of their internal systems and every aspect of the customer experience.
What Do These Examples Show?
Artificial intelligence can gather specific information about a client's behavior so that it can provide a personalized and optimized service without the need of actual staff or assistants. This is facilitated with inbuilt mobile devices such as a camera to detect faces or recognize particular texts. The main uses for artificial intelligence in apps are automated reasoning, recommendation services, learning behavior patterns, and personalization.
Automated reasoning is an algorithm that can be used to resolve given problems. The appropriate (logical) outcome is obtained by creating a set of parameters or rules that require, for example, the shortest route or the cheapest solution. The results are given as the optimal solution from the data supplied. Another advantage is the speed of obtaining results as seen in examples such as Uber, where users particularly like the optimized price, time, and route provided by automated reasoning.
One of the main reasons why most apps fail in their first year is that they cannot retain their users due to poor content. Time and money are wasted because the new content they produce is simply not what the user wants. In contrast, recommendation services are constantly tracking and categorizing a users' choices, updating the algorithm so that the app effectively "thinks" like the user and can consistently offer suggestions based on their interests and needs. Recommendations are one of the most powerful social stimuli, so being able to utilize this is important for all businesses. It is also very easy to implement and can be used in many app services. As noted above, this is how Netflix generates most of its income. Keep in mind, though, that any type of business model can utilize this feature and it can be supported by marketing departments or sales teams.
Learning Behavior Patterns
It is important to remember that any app only has about five sessions to "bond" with a user. Consequently, the goal is to understand user behavior as quickly as possible so that subsequent sessions become more meaningful. Careful analysis and pattern-detecting algorithms will get your app fulfill customer requirements as soon as possible with the added bonus that it helps increase security and detect fraud. Pattern-detecting algorithms can highlight unusual customer behavior or purchases immediately so that identity or credit card theft can be blocked, ensuring your users always remain secure.
This feature utilizes all of the above. It continually tailors the services or products provided to fulfill the needs of every client. It creates user patterns that will automatically provide increased client satisfaction. For businesses, it means better conversion rates, more accurate marketing, stronger branding, and constant improvement of website metrics. Personalization drives social media and being able to exploit it allows your business to grow and increase its profitability.
To summarize, your app has a very short time within which to "hold" a client's attention. Personalized content is vital to achieving this required level of engagement. The data you receive from a user is critical to understand their needs, help you to help them, and increase your profits. As such, you need to constantly improve and optimize your business models and implement better patterns, and if you can't do it yourself, let AI do it for you!
Published at DZone with permission of Magdalena Brych , DZone MVB. See the original article here.
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