Machine learning has gone from the sci-fi era to a major component of modern business, especially as companies in almost every industry use various machine learning technologies. For example, the healthcare industry uses machine learning business applications to get more accurate diagnoses and provide better treatment for their patients.
Retailers are also using machine learning to send the right goods and products to the right stores before they run out of stock. Medical researchers are also no slouch when it comes to using machine learning, as many introduce newer and more effective drugs using this technology. Many use cases are emerging across industries as machine learning is implemented in logistics, manufacturing, hospitality, travel and tourism, energy and utilities.
Real-time chatbot systems
Chatbots are one of the main forms of automation. They have bridged the communication gap between humans and technology by allowing us to communicate with machines which can then perform actions based on the demands or demands expressed by individuals. The first generations of chatbots were designed to follow scripted rules that instructed bots on what actions to take based on certain keywords.
However, ML (machine learning) and NLP (natural language processing), which are another part of the AI tech body, allow chatbots to be more productive and interactive. These new sets of chatbots better meet user needs and communicate more and more like real human beings. Here are some noteworthy examples of contemporary chatbots: Alexa, Google Assistant, Siri, Watson Assistant, and the chat platforms on the runner request service.
Help with the decision
This is another area where machine learning business applications can help organizations turn most of the data they have into useful, actionable information that delivers value. In this field, algorithms that have been trained on multiple sets of relevant historical data and data are able to analyze information and process many possible scenarios at a scale and at a speed that humans cannot recommend the best line of action. conduct to adopt. Decision support systems are used in several industrial sectors, including some: the health industry, the agricultural sector and businesses.
Customer recommendation engines
ML powers customer recommendation engines designed to deliver personalized experiences and improve the overall customer experience. Here, the algorithms analyze data points on each customer, including the customer’s previous purchases, and other data sets like demographic trends, an organization’s current inventory, and other purchase histories. customers in order to know what services and products to offer as recommendations to each. particular client. Here are some examples of companies whose business models are based on recommendation engines: Amazon, Walmart, Netflix, and YouTube.
Modeling of customer churn rate
Companies are also using machine learning and AI to identify when a customer’s loyalty starts to decline and to find strategies to resolve it. In this use case, enhanced machine learning business applications help businesses tackle one of the longest and most common business problems – customer attrition.
In this way, algorithms identify trends in massive sales volumes, historical and demographic data to identify and understand the reason for a company’s loss of customers. The organization can then use the capabilities of ML to evaluate patterns among existing customers to find out which customers are likely to drop out of the business and go elsewhere, to identify the reasons behind those customers’ decision. to leave and then determine the necessary actions that the company needs to take. in order to retain them.
The following companies are examples of companies that use churn modeling: The Wall Street Journal, Bloomberg News, The New York Times, Spotify, HBO, Amazon, Netflix, Salesforce, and Adobe.
Dynamic or on-demand pricing strategies
Companies can begin to extract their historical price data as well as data sets on a plethora of other variables to understand how particular dynamics – from the season to the weather to the time of day – influence demand for products and services.
ML algorithms can learn from this data and combine this information with more consumer and market data to help companies dynamically price their products based on these large and abundant variables – a tactic that ultimately allows companies to maximize their income.
The most obvious example of on-demand or dynamic pricing can be seen in the transportation sector. Soaring prices at Bolt and Uber are one example.
Customer segmentation and market research
Machine learning business applications not only help companies set prices; they also help businesses deliver the right goods and services to the right areas at the right time through customer segmentation and predictive inventory planning.
For example, retailers use ML to predict which inventory will sell the most in which of its outlets based on the seasonal conditions influencing a certain outlet, the demographics of that area, and other data points, like new trends on social networks. This machine learning app can be used by anyone! From the insurance industry to Starbucks.
Machine learning’s ability to decipher patterns – and immediately spot anomalies that manifest themselves outside of those trends – makes it a great tool for identifying fraudulent activity.
In fact, companies in the financial industry have successfully used ML in this aspect for years. The use of commercial machine applications in fraud detection can be seen in the following industries: retail, gaming, travel and financial services.
Image classification and recognition
Companies have started to look to neural networks, deep learning, and machine learning to help them make sense of images. The application of this machine learning technology is wide – from Facebook’s intention to tag images posted on its platform, to the willingness of security teams to detect criminal activity in real time, to the need for cars. automated to see the route.
While some ML use cases have high specialization, many companies are embracing the technology to help them manage routine business processes, such as software development and financial transactions. According to Guptill, “The most common use cases in my experience (so far) are in corporate finance organizations, manufacturing systems and processes, and most importantly, software development and testing. .
And almost all of the cases occur as part of grunt work ”. ML is used by multiple business departments to improve efficiency, including operational teams, companies and finance departments, and IT departments who can use machine learning as a component of its software test automation to significantly increase and improve this. process.
ML with natural language processing will automatically gather crucial structured information from documents, even if the necessary data is stored in semi-structured or unstructured formats. Businesses can use this ML application to process everything from invoices to tax documents to legal contracts, improving the accuracy and efficiency of these processes and freeing human employees from monotonous and repetitive tasks.