Machine learning, where software algorithms use data and artificial intelligence to improve with experience, has become a powerful tool for businesses. But what do companies use it for? Here we look at some of the most common ways machine learning is deployed today.
- Decision making
Machine learning’s ability to quickly absorb new data to continually provide up-to-date insights and predictions has been used by many organisations to help them make decisions. Algorithms are trained to use the most relevant data sets and are able to analyse them at incredible speed to model possible scenarios. This helps decision makers to find the best course of action, for example, machine learning modelling has been used extensively during the pandemic to help the UK government predict the impact of restrictions like lockdowns, mask wearing and vaccination.
A feature of many websites, chatbots are artificial intelligence online chat assistants used to replace human customer service personnel. Unlike their human counterparts, a chatbot can deal with multiple conversations simultaneously, speeding up the pace of customer service while reducing staffing costs.
The reason chatbots have been able to do this is because machine learning and natural language processing enable them to better understand language inputted by users and respond much like a human, which users like. Because of machine learning, the more they are interacted with, the better they understand and communicate. Today, chatbots not only respond in written text; they have evolved into the voice assistants many of us use on a daily basis, such as Google Assistant, Alexa, and Siri.
Acquiring new customers can be costly, so it makes sense to hold on to as many existing customers as possible. Today, businesses are getting more successful at doing this by using machine learning to identify when a customer might leave and to create strategies that can prevent it. Algorithms analyse customer churn by finding trends in their historical data, and machine learning is used to predict which customers are most likely to leave and find ways to turn that around.
Getting product or service pricing right can be crucial in highly competitive markets. Today, machine learning is helping businesses optimise dynamic pricing strategies in line with demand. By analysing pricing data in conjunction with a wide range of variables, businesses can understand the best prices to charge at different times, whether it’s the time of year or the time of day. This can influence prices in almost every sector.
Personalised customer recommendations
Customer recommendation engines are becoming widely used in sectors like eCommerce and the finance industry to provide customers with information about products and services that are relevant and appropriate for them. Consumers like this because it improves the customer experience, businesses like it because it leads to improved sales.
Machine learning is used by recommendation engines to analyse customer data, for example, segmentation data, customer journey data and any personal data the user provides. In addition, it will take into consideration things like product availability and consumer trends, in order to recommend the best products and services.
One of the most common uses of machine learning is to improve automated processes. While automation relies heavily on artificial intelligence to run operations, the introduction of machine learning empowers the system to learn over time. In this way, it can improve efficiency, increase safety and reduce downtime. Indeed, when using natural language processing, it can improve the automation of processes where data is stored in unstructured formats, such as in financial and legal documentation.
Machine learning is exceptionally effective in identifying patterns and spotting irregularities. For this reason, it has a powerful role in protecting systems and networks from cybercrime. It can, for example, use data from historical attacks to recognise new types of spam, phishing and malware, and analyse web traffic to spot signs of emerging DDoS and brute force hacking attacks. Every time a new attack is discovered, it learns from it to help it become more effective in the future.
Machine learning is used widely within online industries, at eukhost, for example, we use it to protect our data centres and customer accounts. The antivirus and antispam software that most people have on their devices will also make use of machine learning in this way.
Cloud-based machine learning applications are being widely adopted as the pace of digital transformation increases. They enable companies to make far more valuable use of their data in operations across the entire business. Hopefully, the examples in this post will give you an indication of how you can benefit from machine learning in your own company.