More and more focus is concentrated around Artificial Intelligence (AI) and Machine Learning (ML) as they have become a powerhouse in the world of technologies. ML is used in education, the financial industry (banking and cybersecurity), social media applications, gaming, e-commerce, among many other domains. Moreover, as of 2018, 32% of companies use machine learning technology for business analytics, 25% to increase their security, 16% to boost their sales and marketing operations, and 10% apply it for better customer services. In the following article, we will explain the uses of machine learning mechanisms, what applications are suitable for these technologies, and provide a list of corporations that managed to implement ML successfully.
How Many Companies Use Machine Learning?
In the last four years, the number of big companies using machine learning has increased from 10% in 2016 to 37% in 2019. In fact, by the year 2021, the ML market is forecasted to reach $58 billion in revenues and be present in more than 91% of all-purpose applications.
Companies using machine learning and AI have increased their sales by improving customer satisfaction and app acceptance. They have also experienced many other benefits.
Most people do not even notice how AI and machine learning technology surround us in everyday life. But it is due to this technology that Facebook ads seem to be reading your thoughts. Virtual assistants are popping up on countless websites. And we are regularly presented with suggestions online that are similar to items searched or purchased in the past.
Let’s explore how companies use machine learning to optimize their business operations to gain a competitive edge in a thriving marketplace.
Ways of Implementing Machine Learning Technologies
Chatbots and virtual assistants are not the only ways where ML comes in handy. Continue reading to learn the scope of services you may gain with ML and AI technologies to help your business grow and prosper.
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1. ML in Data Analytics
Whether entering the market or expanding an existing position, organizations require taking a broader look at data. Data analytics includes the following elements: gathering, getting useful insights for the betterment of business, and interpreting this data into the specific strategies.
The clustering algorithms of ML assist in collecting the data from various sources. After that, you classify it to apply it to particular sources: group the data and maximize its use. Finally, dimensional reduction helps to visualize the information and present it comprehensively.
Data analytics is inevitable for:
sales - it helps monitor the progress and understand sales weaknesses;
products analysis - their use, demand, customer response;
target audience segmentation - define what factors drive product promotion, what customers need/expect from a product/service, what’s the loopholes;
market research - through the use of numerous ML tools, you may get a clear picture of your competitors, the economic situation on the market, and how you may solidify your position.
2. Predictive Analytics
When inaccurately treated, risks lead to failures and business breakdown. With this in mind, forecasting, fraud prevention measures, and market prediction is a set of ‘must’ actions. Machine learning helps compare current and historical data to identify progress and apply actions accordingly. For companies that deal with stock prices, it is essential to calculate market volatility and make corresponding decisions.
Predictive analytics is widely used in supply chain management, when business companies may fully control the product’s flow. However, in most cases, this approach is handy to detect and predict fraudulent actions. Most of the malware occurs due to automated algorithms, and with ML, a system may expose fraud payments, spam emails, bot traffic, and other related actions.
3. Face (Image) and Voice Recognition, Visual Search
One of the most demanding sectors where companies use machine learning technology is computer vision. The feature that is eventually created is used in multiple domains, including eCommerce, search engines, and social platforms.
For instance, a user can easily find necessary products by uploading the picture or choosing from the ones available on the platform’s database. Other examples include logging in with face detection, searching for goods via voice typing, and handwriting to add a signature.
4. Speech Recognition - Personal Virtual Assistants, AI Assistants
Having occupied hands, users these days are striving to have voice recognition features in their daily applications. This feature’s mechanism works as follows: using a set of sound samples within the application database, the system analysis, processes, and conducts an action/response for the spoken message.
In 2020, we can find this feature in Google Search and Assistant (Alexa and Siri), online translators, and healthcare applications.
5. Smart Houses
Another great application of machine learning is home security, surveillance, and distant control. These AI and ML-based features are installed in cameras, movement detectors, and sound identifiers.
These days, the owners of big houses and small apartments no longer need to ask neighbors to watch their place while on vacation. The cameras and other devices are synchronized with smartphones and can be accessed anytime from anywhere.
6 Leading Companies in Machine Learning
To better understand how companies use machine learning and what applications have ML-driven features, we will show you a list of top companies using machine learning to facilitate the life of users and boost their business.
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Undoubtedly, social media platforms actively utilize machine learning in their operations, and Facebook is one example. Its Messenger contains a vast number of chatbots and allows any ML developer to submit its own chatbot. Moreover, Facebook is using AI tools to clear out spam messages and content of poor quality.
Another way Facebook is using machine learning is to deliver advertisements. It’s not just about posting random ads but about finding customized approaches to provide pop up ads aimed at the particular needs of users. They analyze customers’ behavior, interests, and needs to give maximum useful advertisements to the users.
The features based on machine learning help increase user experience and compete with other similar applications.
One of the largest databases of movies, documentaries, and series does not stand on the sidelines of technologies. There are many ways Netflix is actively using AI and ML tools to deliver excellent services for customers across the world.
Recommendations. Once a user finishes watching a movie. the platform will offer dozens of other options considering the genre, actors, length, and storyline. Even if the user only watched the trailer to the movie, Netflix will provide a chain of other movies to hold you on the platform and continue buying the subscription.
Big Data Processing. Since the platform is holding a large amount of data, it would take much more time to process it manually. With this in mind, ML algorithms help to process and analyze all the data on the server and give the best results to users.
Audience Exploration. To meet user’s expectations, Netflix is constantly researching their behavior: what device is used for watching the movie, how often they use the platform, what subscription they prefer, if they liked a film, user’s location (to offer some local options), etc.
3. E-Commerce Platforms
Amazon, AliExpress, Shopify, and other globally recognized applications have implemented machine learning technologies to spread their services across the world by giving a great variety of features. How did they manage to implement these? Let’s see.
Faster and better search engine. By simply typing the keyword in a search line, a user will get dozens of products to choose from. Moreover, machine learning tools recognize the synonyms if the search doesn’t match the word in the database. The search can also be facilitated by filtering “By Featured or By Relevance” based on the user's profile and recent actions in the application.
Chatbots. This feature is more complex as it requires the system to adapt a “human-like” conversation to respond to the messages. Typically, chatbots for eCommerce platforms discover users’ need to offer coupons, next purchase reminders, and identify potential buying opportunities.
Protection from fraudulent actions. Since the platforms mentioned above are operating globally (thus executing billions of requests), machine learning is inevitable to detect malware or suspicious transactions. They can identify stolen credit cards or situations when a client tries to get a retracting payment after the delivery has been successful.
IBM is one of the first technological corporations that has transformed from a traditional business model to presenting its Watson ML technology applications. A set of features within Watson allows corporations to conduct predictive and automated analysis, get a deep knowledge from the data, and establish an undisruptive relationship with customers and employees, all while concentrating on innovations and fueling the business growth with AI technologies.
One of the largest market shares within the world of technologies is in Salesforce’s operations. Its Einstein product allows businesses within different industries to utilize CRM software to build strong and reliable connections with customers. AI tools within Einstein help businesses predict outcomes and define risks to create a chain of mitigating actions.
Another beneficial feature of Salesforce Einstein is that it helps discover customer’s behavior and mood, automatically direct inquiries, and organizing overall workflow.
Last in this list of tech companies using machine learning is Google. Recently, they introduced a neural network. This product helps process natural language, recognize speech, and translating per request.
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Industries of Machine Learning Presence
Soon, it will be hard to find an industry that doesn’t implement machine learning technologies. Let’s discover which of them are already leveraging their business with this powerful technology.
The introduction and the use of mHealth (Mobile Health) applications is growing at a steady and dynamic pace. From 2013 to 2017, the number of downloads has increased from 1.7 to 3.7 billion times globally. The revenues from these platforms in the USA are $2.4 billion and predicted to rise to $11 billion by 2025. mHealth applications introduce the following machine learning features: voice recognition, online assistance via chatbots, alerts on the upcoming doctor appointment, recommendations on the visits, and others.
2. Financial Industry
Machine learning is used for a variety of functions in banking domains, especially to detect fraudulent behavior, analyze client’s transactions, and prevent malware occurrences. For businesses, ML tools introduce a facilitated method for data analytics and processing, defining investment opportunities for third-party members, and have an efficient CRM to operate with clients and manage employees.
As of 2018, 84% of companies dealing with marketing are actively integrating machine learning and AI technologies into their operations. It helps them conduct risk analysis, predict sales by analyzing customers’ behavior and economic situation, increase Sales Qualified Lead generation, and reduce revenue churn.
4. Automotive Sphere
The machine learning technologies that automotive companies use improve overall operations and marketing processes and identify weaknesses after the product’s release. These companies also conduct predictive analysis and identify current trends on the market to deliver products that match local expectations.
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The immense potential of machine learning technologies allows companies in dozens of industries to transform their operations, ease analytical processes, and boost business. Machine learning is a powerful tool that can convert your business, improve your operations, and deliver top-notch experiences to your clients.
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