How Machine Learning is Used in Business (Use Cases)
How Machine Learning is Used in Business (Use Cases). Primarily speaking, machine learning is nothing more than learning systems. It is primarily an area of artificial intelligence that has been devoted to algorithms. Primarily allowing machines to identify patterns, this is a capability that organizations can use in many ways. Thanks to this, it leads to the improvement of their activities for the better.
In this article, we learn what machine learning is. In addition, we learn about the benefits of their use cases.
Shall we start with How Machine Learning is Used in Business (Use Cases).
What is Machine learning ?
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All in all, it has gained fantastic recognition in recent years due to its applications in many industries. From credit score card fraud detection to targeted social media advertising. Additionally, it’s being used effectively for tasks that humans used to do quickly, but now be computerized using algorithms that use vast databases.
Moreover, it’s a great way to automate complex tasks beyond rule based automation. Traditionally, developing machine learning solutions has been an expensive and time consuming process. But thanks to no code machine learning tools like Levity, this technology is now easily accessible to companies of all sizes.
Why is Machine Learning important for business?
Firstly, most companies that process large amounts of data have discovered the benefits of using machine learning technology. Furthermore, this is quickly becoming important for organizations looking to stay at the forefront of social forecasting. Or companies looking to outperform their competitors on the latest trends and lucrative opportunities.
Secondly, the most common and recognizable application of machine learning for business is chatbots. By implementing this technology, the company is able to handle customer requests around the clock without increasing workforce. Additionally with Facebook messenger -a popular platform where businesses program chatbots to complete tasks, understand questions, and direct customers where they need to go.
Thirdly, online retailers such as Amazon, ASOS, and eBay are using machine learning to recommend products they might be interested in. Also this is a branch of the art called customer behaviour modelling. Using data collected about customer habits, companies segment what users with similar online behaviour want to see.
Business Benefits of Machine Learning (ML)
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Easy spam detection
Generally, spam detection is one of the first problems solved by machine learning. Certainly several years ago, email providers used rule based methods to filter spam. But with machine learning, spam filters use brain like neural networks to create new rules to remove spam emails. Moreover, neutral networks recognize phishing messages and spam by evaluating rules across vast computer networks.
Facilitates accurate Medical Predictions and Diagnoses
Well, in healthcare, machine learning easily helps to identify high risk patients, make near accurate diagnoses. Especially, it recommends the best possible medication, and predict future diagnosis scenario. Also, they are primarily based on available data sets of anonymous patient records and the symptoms they present.
All in all, a near accurate diagnosis and the best medical advice helps patients recover faster without the need for external medications. In this way, ML enables healthcare to improve patient health at minimal cost.
Rules and models for money get more accurate
Following, machine learning has also had a huge impact on the financial industry. Additionally, portfolio management and algorithmic trading are two of the most popular machine learning applications in finance.
Some studies claim that machine learning is to be used to continually evaluate data to find and analyse anomalies and subtleties. In this way, financial models and rules are to be made more accurate.
Fraud detection
When it comes to fraud detection, machine learning is a powerful tool due to its ability to recognize patterns and quickly spot outliers. Financial companies have been using machine learning in this area for many years.
Even more, machine learning also has its opportunities to exploit fraud, and here are the areas:
- Gaming.
- Retail.
- Travel.
- The provision of monetary services.
Simplifies time intensive documentation in data entry
Well, automated data entry tasks can be performed on computers. In turn, it frees human resources to focus on more important tasks. Therefore, automating data entry presents several challenges, the most important of which are data replication and accuracy. Altogether, predictive modelling and machine learning methods greatly solve this problem.
Real time chatbot agents
Adding AI machine learning and natural language processing (NLP) make chatbots more engaging and productive. Another example of machine learning algorithms are at the heart of digital assistants such as Amazon’s Siri, Google Assistant, and Alexa, and these technologies. Those are used to replace traditional chatbots in new customer service and interaction platforms.
Chatbots are one of the most popular machine learning applications in the workplace. Here are some examples of highly rated chatbots:
- Listen, search, and share music with the music streaming service’s bot for Facebook Messenger.
- Automatic plate recognition, where the rider’s licence plate and car model are supplied via chat platforms or phone calls so they locate their transport.
Business Use Cases of ML
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Detect and block cyber attacks
Reliance on digital technology has increased in recent years. It certainly made our lives easier, but it also made us more vulnerable to attack. Both, machine learning and business intelligence are the most useful for detecting and preventing cyber attacks before they even start. Businesses greatly benefit from identifying and preventing emerging threats at an early stage.
Description of collected statistics
Business analytics collects data, statistics, and visualizations to gain insight into what drives your business. So, it uses machine learning to model different elements of a business that systematically “explains” these statistics. Instead of relying on outdated assumptions when trying to explain your data, let your machine learning models surprise you, or at least challenge those assumptions.
Fraudulent transaction detection
Online fraud causes billions of dollars in losses worldwide each year. Many applications designed to protect against potential online fraud are based on rules that cannot keep up with the ever-changing tactics of hackers, malware, or intruders.
Personal recommendations that increase customer loyalty
Certainly, machine learning helps deliver personalized experiences that lead to better customer experiences, conversions and revenue.
For example, a purchase offer or recommendation for an e commerce platform. Initially, each user’s interaction history (purchase, product view, search, etc.) is analyzed and compared to other similar users. In this way, online stores can more accurately present the content they are interested. In to achieve their users’ goals (eg maximizing sales, eliminating perishable inventory, promoting product lines, etc.).
Improve customer service and reduce costs
Machine learning helps turn contact centres (phone or other channels, chat, WhatsApp, etc.). Into profit centres by reducing interaction latency (calls, messages, etc.). Increasing agent productivity and satisfaction, reducing costs, and identifying businesses, can be opportunity for improvement for business as a whole.
By applying machine learning to your contact center, you improve the customer experience through continuous improvement through data analysis. The vast amount of data coming into the Contact Center is an important resource for incremental adjustments and improvements to implemented algorithms.
Business Applications of Machine Learning
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Text parsing
Algorithms also be taught to understand and process human generated text. This process is called text analytics and refers to natural language processing. By teaching AI the rules of language and grammar, it processes large amounts of data in less time. Text analytics is useful for analysing existing data and gathering new data from user generated or competitive content.
Concurrently, through the interesting application of text analysis, computers interprets large amounts of text in the same way that humans do. Allows companies to use fast search engines for basic tasks and more advanced algorithms for additional requirements such as bibliography. This improves the company’s bottom line by reducing the need for low skilled text analysis staff.
Predictive modeling
A category of machine learning solutions that extract large amounts of data to predict the outcome of potential scenarios. Then use these forecasts to make informed business decisions. Predictive modelling algorithms essentially provide predictions about the future based on historical data. So businesses take business action against those predictions.
Customer service
Train an algorithm to act as a customer service manager by deploying natural language processing trained on common customer complaints. AI has taken over the customer service sector with more available chatbots and natural language processing solutions on the market.
Recommendation engines
It collects user data and uses deep learning and neural networks to train an algorithm to recommend something to users. Known as recommendation engines, these algorithms are commonly used to collect and store data about user preferences. Knowing what users like and dislike can help shape preferred consumption or purchasing patterns. This model is used to provide personalized recommendations to users.
Thank you for reading How Machine Learning is Used in Business (Use Cases). We shall conclude this article now.
How Machine Learning is Used in Business (Use Cases) Conclusion
In summary, machine learning is fast becoming a core technology that is being organically deployed across all business sectors to solve complex business problems while improving organizational efficiency and scalability.
Despite all the complexities involved in properly implementing ML, companies are willing to undertake this time consuming and relatively expensive process because it offers measurable and significant benefits over traditional analytics methods in the long term.
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