In recent years the digital revolution has created a lot of new advantages for every industry and sector across the globe. The ability to interact more easily and effectively with customers and consumers has given businesses a new edge which has never before been available. Although interaction between brands and customers has increased significantly, there is also a new level of disconnect that has become apparent due to companies being able to hide behind a screen.
Theunis Jansen Van Rensburg, Head of Commercial Analytics at Wonga explains how businesses are facing the new challenge of earning the loyalty of their customers without the warmth of human interaction. Many businesses are capitalising on the ease and convenience offered by digital platforms, but often this is compromising the level of customer service that is provided.
Previously, it was necessary for a customer to walk into a branch or store or pick up a phone to have any kind of interaction with a business. This allowed them to talk to a member of staff and experience the business first hand. In this digital age, this is no longer the case as the majority of the interactions a customer will have with a company is online, whether it is a website visit, via email or instant messenger conversation.
It does not have to be the case that digital technologies are detrimental to a business’ customer service levels, and if used correctly, they can actually be used to enhance and improve the experience. Artificial intelligence and machine learning are great tools for offering an all-round great experience for customers.
Machine learning is a pioneering technology that uses large-scale data analytics to create dynamic and predictive computer applications that can be applied to business problems. Using statistical methodologies, the systems ‘learn’ by continuously applying these methods to new data without the programmer explicitly needing to tell it to do so. The large data sets that are used for machine learning make it possible for the computer to extract both anomalies and patterns from the information. It is an evolution of artificial intelligence practices such as pattern recognition and computational learning theory. Machine learning assists computer programmes to make faster and more accurate data-driven decisions.
Making computer processes more efficient, reliable and cost effective, machine learning has a number of benefits for businesses and customer service. The technology is driving innovation in every sector and will only continue to grow in popularity as it progresses. Big brands in the technology industry are already using machine learning and artificial intelligence to offer their customers the best possible user experience. Real-world examples of machine learning are all around you, even if you haven’t realised it. Personalised Amazon recommendations based on previous purchase history of users. Apple’s voice recognition system, Siri, uses it to imitate human interaction. Facebook use the tech to tag individuals in photos. Google Maps analyses the traffic speed using location data from smartphones. PayPal uses machine learning algorithms to combat fraud, etc.
Machine learning can be used to provide customers with applications that understand what they want and then help them to achieve that goal. This gives businesses the opportunity to provide contextually relevant insights by using similar customer segments that are fully personalised and specific for each individual. There a number of ways that machine learning can be used to improve customer service levels:
Call Reason Predictions
Call reason predictions are a new and intuitive technology that uses machine learning to impact the level of service provided to customers. The number of customer calls being made on a daily basis is always increasing, and as a result the number of calls being routed to incorrect departments is an increasing issue. Customers are also experiencing longer wait times than ever, leading to an increase in call drop-offs and unsatisfied customers. Machine learning can help ease this issue by predicting the reason for a customers’ call based on the time of day the call is being made and other variables. This allows the call to be routed directly to the correct department for that specific customers’ needs, resulting in shorter calls and more satisfied customers.
Machine learning can also be used to assist customers without them having to pick up the phone, with the rise of chat bots and conversational interfaces. Chat bots are virtual assistants that are built using natural language processing engines combined with credit specific customer interactions. Allowing customers to get information and contact quickly and easily will improve service levels and offer a unique edge over other companies that require their customers to get in touch using traditional methods. As well as improving the customer experience, these virtual assistants save valuable work force time, which allows businesses to save money and have their teams focus their time on other aspects of the business.
Machine learning is a powerful tool for developing more detailed insights into customers and potential sales prospects. Utilising a wide range of available data, marketers are given much more to work with than they have ever had in the past. Customers can be more accurately segmented according to their profiles and probable needs, offering new opportunities for up-selling and cross-selling. This in turn improves the customer experience by offering them the exact products that are relevant to their needs.
This type of technology is improving at a fast pace and is already being used by many large companies across the globe. It is becoming apparent to many industries with large amounts of data that machine learning has a wealth of benefits for improving customer service levels within the digital world. It is a tool that until recently was available only to high technology and computer science companies, but it is rapidly coming within reach of businesses all over the globe. We can expect to see a huge increase in the amount of machine learning and artificial intelligence applications being used in the future.