People deal with the results of machine learning every day: smart algorithms in social networks, chatbots in online stores, and facial recognition cameras in public places are all part of life. Companies continue to incorporate technology into their business processes to analyze risk, understand audiences, and reduce costs.
Artificial intelligence is increasingly seen as an auxiliary tool to help optimize business processes and reduce costs. This software can independently make decisions and act even in those situations that the developers did not originally foresee. For example, to detect a new type of fraud.
Machine learning (ML) is a part of artificial intelligence. It is used in systems that collect large amounts of data. For example, intelligent energy management systems collect data from sensors at various facilities. The data is then analyzed using algorithms and the results are sent to decision-makers in the company to understand energy consumption and maintenance needs better.
The main difference between ML and standardized algorithms is its adaptability, because it is constantly evolving and learning. The more data the algorithm consumes, its analytics and forecasts will be more accurate. The technology is being actively implemented in many areas: agriculture, medical research, the stock market, traffic monitoring, production control, and so on.
Uses of Machine Learning in Business
Here are ways to boost your business with machine learning.
- Chatbots speed up customer service
Now on almost every site you can find a virtual consultant who filters requests and answers user questions. This allows you to reduce the burden on staff and the payroll fund, due to which the business can save. In addition, machine learning helps to develop the knowledge base of a chatbot, since the more questions, the more data. And six months or a year after the introduction of the system, a virtual consultant can answer almost all questions, leaving the opportunity to connect the operator for more complex issues.
- Decision Support Systems Reduce Errors
The technology is often used in the manufacturing sector. For example, statistics of breakdowns and devices can be loaded into the system. By analyzing this data, the neural network will be able to make a prediction about the failure or breakdown of the device and suggest when preventive maintenance should be carried out in order to avoid costly repairs in the event of a breakdown. The second example is the rise or fall of stock market players. This tool is popular with professional investors.
- Data analysis helps to check resumes and documents quickly
HR professionals are often overwhelmed with hundreds and thousands of resumes when doing mass recruiting. Machine learning helps standardize the hiring process to speed up the organization and analysis of recruitment letters. In addition, the technology allows you to create a profile of an ideal candidate based on data about current employees. As a result, the speed of hiring increases and the volume of routine tasks for HR specialists decreases.
- Personalization enhances customer service.
Machine learning helps bring customer service and communication to the next level. It increases user and customer engagement and satisfaction by providing a company with more data and helping to pinpoint communication based on the interests and actions of a particular person. For example, offers personalized products based on recent purchases or calculates an individual discount based on interaction with a brand.
- Pricing Strategies That Change Over Time
Companies can use historical price data and datasets on various other variables to better understand how different factors affect consumer spending, such as time of day, climate, and seasons. Machine learning algorithms can integrate this data with other market and customer data to help companies value their products dynamically based on broad and varied variables. This method allows companies to optimize revenues.
- Fraud Detection
The ability of machine learning to recognize trends and detect violations that deviate from these patterns makes it a valuable tool for identifying illegal practices. As a result, financial institutions have been effectively using machine learning in this area for years.
- Customer Recommendation Mechanisms
Customer recommendation engines that use machine learning to improve the customer experience and create personalized experiences are based on machine learning. Here, the algorithms analyze data about a single customer, such as past purchases and other data sources, such as a company’s current stock, demographics, and purchase histories of other customers, to determine which solutions to recommend to each individual consumer.
- Target Customers with Segmentation
The secret of successful marketing is to offer the customer the most appropriate product at the most appropriate moment. Until recently, marketers had to rely on their own intuition when distributing customers into segments for targeted marketing.
Today, machine learning allows data scientists to use clustering and classification algorithms to sort shoppers into distinct groups based on certain variations according to certain characteristics, such as demographics, site behavior and preferences. Matching these characteristics with behavioral algorithms helps to develop accurate, specialized marketing campaigns that drive sales more effectively than generic campaigns.
- Forecasting performance
One of the most valuable features of machine learning is prediction. Previously, business decisions were made on the basis of past performance. Today, machine learning uses sophisticated analytical tools to make predictions. Instead of relying on outdated data, companies can make decisions proactively.
- Image classification
In addition to retail, financial services, and online sales, machine learning can be used in a wide variety of scenarios. It is used very effectively in the scientific, energy and construction industries, as well as in healthcare. For example, machine learning algorithms can be used in pattern classification to assign labels from a predefined set of categories to patterns in input data. This makes it possible to create 3D building plans from 2D drawings, make it easier to tag social media photos, complement diagnosis, and more.
Final Words on the Uses of Machine Learning in Business
ML is constantly evolving and has huge potential for business use, but most companies are still in the early stages of adopting the technology. A new round of growth will occur when the main tools for developing machine learning become available to every company and it will be possible to implement the technology regardless of the complexity of the technical base and experience. Due to this, a business of any size will be able to afford ML algorithms to solve their problems and optimize business processes.