UK FITNESS SCENE
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Evolution of the internet By Dr Paul Bedford
THE evolution of the internet and its ability to help operators learn where and when to support customers is a magnificent tool. Imagine you’re considering buying a health club membership online. It’s not an off the cuff decision, it will impact on your time and money, so it’s one you’re unlikely to undertake without some level of research. You probably want to check out a few health club websites to see which one suits you best. Having invested significant time on a website, it should have a good idea of your general preferences; recognising whether you’re primarily interested in classes or PT, mind body activities or high intensity classes. Most importantly, the site should be able to leverage this information to subtly personalise your experience. But what if you could take that a little further and personalise your members’ day-to-day experience within the club too? Machine learning is truly revolutionising business at the one-to-one level. However many people are confused by the term ‘machine learning’ and frequently confuse it with ‘artificial intelligence’ (AI). Machine learning is programming computers to make intelligent decisions and to draw conclusions without human involvement. It’s used to decide what experience to give each customer at the one-to-one level. Artificial Intelligence takes this a step further. It’s the development of computer systems able to perform tasks that usually require human intelligence, such as visual perception, speech recognition, translation between languages and decision making. Up close and personal There are two main types of personalisation, where customers are put into groups or clusters based on their attributes or behaviours: rulebased and machine-learning. Rule-based personalisation allows operators to deliver experiences to specific groups of customers based on the manual creation and manipulation of business rules. Categorising customers to tailor their experience is a method used by a number of software products, designed to increase interaction with customers most at risk of leaving.
Dr Paul Bedford A segment created using just one or two data points, such as male, female, individual or corporate members, is called a broad segment, meaning it fits many customers, while those combining multiple data points are narrow segments and often leverage “AND” and “OR” logic to identify very specific groups. In these cases, the segment size will be much smaller, for instance, male, corporate members over 45 who do yoga. Operators must decide for themselves whether the effort to manually design an experience for a very small group of people is worthwhile, as the more rules you add, the more complicated it becomes. The club would need to set up and manage hundreds or even thousands of rules to narrow segment and personalise the rest of its customers. Speeding it up Machine learning is revolutionising the speed, scale and depth of data analysis to yield invaluable insights into customers. Predictive segments, also known as automatic clustering, can identify groups of customers with
similar behaviours and affinities, enabling more relevant insights and targeting than rule-based segmentation, essentially allowing operators to create smarter segments more quickly and easily. Machine learning can also help operators identify behaviour differences among existing customers that may not be easy to see from a manual data inspection. For example, the key behaviour differences between a customer that stops using the club versus one who is retained. Operators can then examine high value versus low value customers that quit and, with those insights, react more quickly to business trends and create new opportunities to advance their business. For example, creating a ‘churn risk’ segment for people that exhibit similar behaviours to customers who leave and a ‘potential high-value customer’ group for those similar to existing highvalue customers. Using these segments, operators can create targeted experiences, through digital and physical channels, to encourage high churn risk customers to stay and potential high-value customers to purchase. Predictive segmentation enables operators to identify valuable customers and learn what makes those customers unique. Without that knowledge, they rely on best guesses. Machine-learning presents operators with the most relevant content or experience for each and every customer.
Unique to you Essentially, machine learning personalisation provides a highly scalable way to provide unique human and digital experiences to your customers – right down to the individual level. Everything from the amount of support and interaction required by a customer to the recommending of classes or personal trainers and food and beverage offers. Operators can even modify website navigation, search results and digital content. Popularised by household names like Amazon and Netflix, machine learning algorithms aren’t just for giant e-commerce companies; health club operators of any size can jump on the band wagon. One algorithm can do the work of thousands of rules, personalising who to interact with and what content to share with them based on their behaviours and individual preferences, ultimately resulting in simpler deployments.