Increasingly, more and more businesses are harnessing the power of machine learning to effectively inform and automate processes, revolutionising customer relationships. We take a look at some of the top business uses for machine learning from last year…
1. Chatbots
Businesses are becoming much savvier to match consumer demands and are investing heavily into utilising machine learning technology to create human-like interaction. 2016 really was the year that the Chatbot took off. Social media giant Facebook now has over 11,000 chatbots through it’s messenger app and banking firm RBS recently installed their own version of a smart chatbot, Luvo to directly hook up customers to staff to provide fast and efficient support.
2. Recommendation Engines
Upselling is key to any business, but particularly for those in e-Commerce, the importance of timing and relevance is crucial. Perhaps the most obvious example of this is from retail giant Amazon; their refined recommendation engine revolutionised upselling paving the way for other business to follow suit. More recently, online entertainment company Netflix are winning the personalisation battle with their ultra slick algorithm. Netflix's recommendation engine is finely tuned to match content with the exact people who will be interested; this hyper-specified categorization is continually being improved on and adds to their global scale ambitions.
3. Virtual Assistants
Siri, Cortana and Alexa are just some examples of how advancements in machine learning and the development of virtual assistants have allowed tech giants such as Google, Apple and Amazon to battle it out. But it isn’t just the tech whizzes that are making the most of this technology, more and more we are seeing businesses in a variety of industries beginning to see the advantages of investing in such technology. Hospitality company Radisson Blu recently launched Edward, the virtual assistant who can be contacted for tips on local trips to log issues and complaints or to understand hotel amenities.
4. Autonomous Cars
Both car manufacturers and service providers are rapidly working towards autonomous vehicles that use machine learning techniques to drive without the aid of a human controlling the car. The most common machine learning algorithms being used in autonomous vehicles are based on object tracking. These algorithms are aimed at improving the accuracy of pinpointing and distinguishing between objects, so that the car knows whether it is a person, a tree or another car in front of them. Nividi and Aldi have both pledged to produce totally driverless cars by 2020 and Swedish brand Volvo have already piloted similar projects.
5. Language Translation
Microsoft and Google are both working on advanced language translation tools that will be able to translate speech real time. This is improving constantly due to the nature of machine learning and Microsoft’s Skype can already translate across seven different languages.
6. Better Diagnosis
Perhaps one of the most transformational ways machine learning is being used is by the healthcare industry to revolutionise personal, patient care. Although doctors and nurses will never fully be replaced, these advancements in technology have certainly paved the way for some pretty innovative changes.
7. Complex Pricing
AutoTraders Data Science and Insights teams are innovatively using machine learning to monitor and analyse data in order to automate pricing for its sales across their thousands of used cars. Their custom built algorithm teaches their systems to set the value of any given car using variables such as the specific model or what special features they have, ensuring customers receive only accurate information.
8. Real-time Infrastructure Updates
Companies such as Engie are utilising real-time sensors and machine learning for their efficiency to predict when high value assets could potentially break down or require maintenance. For example, if a sensor on a turbine shows the temperature is too high the system can automatically book in an engineer to check it over. These preventative measures mean companies can effectively get ahead of any unnecessary break downs or maintenance issues.
9. Fraud
Increasingly, banking institutions and online retail giants have turned to machine learning to flag any anomalies in customer behaviours before any malpractice takes place. Paypal uses machine learning technology that monitors a users' purchase history. Once a pattern is spotted, it can implement new rules to avoid any future scams. As a result, PayPal has a significantly reduced revenue fraud rate of just 0.32% compared with the industry average of 1.32%.
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