What do we love most about service? When we're treated individually. This increases both our loyalty and engagement, and the average check grows behind them. But to what extent is such an attitude possible if the service is streaming and is designed for a large number of clients? Big Data technologies allow us to study them and make the best customized offer for a specific person. World Class took advantage of this technique and received a positive result.

Sport has long been something more than a workout, it's a way of life. This idea was also confirmed by World Class studies. After analyzing its customer base, the company came to the conclusion that people who, in addition to a subscription, also buy other fitness club services, often return to renew their annual membership. Further, the company considered the structure of purchases and identified priority areas for proposals: group training, personal training, participation in club sports events and SPA-salon services. It was these services that were chosen by customers who renewed their sports membership.

The next step is big data. As a contractor for the development of the recommendation system, World Class attracted the Data Studio.

The team was tasked with creating a recommendation system based on the accumulated data on the use of additional fitness club services: SPA services, individual and group training, sports events.

In practice, this approach is implemented through matrix solving using Python libraries.

To solve the World Class problem, a giant matrix was built, each row of which is a client, and each column is a service from a fitness club. The Python library then ran the matrix factorization formula, resulting in client and service vectors. Further, in order to make an individual offer to the client, collaborative filtering compared the vector of the selected client with the vectors of other fitness club members, and found the most similar ones. As a result of the comparison, a person received as an offer services that were used by people with vectors similar to his.

But, of course, the project was not without its challenges. For a certain number of clients there was no information about the services used. Therefore, the system took into account not only past customer purchases, but also compared profiles in the personal account: gender, age, membership type, visits. Also, for a certain sample, the proposals were made randomly, and as a result of using the service, the system was retrained as a result.

The recommendation system gives recommendations, but they still need to be conveyed to customers correctly. Various channels were used for the proposals: a personal account on the website, a mobile application, Wi-Fi in clubs, verbal recommendations in the fitness club space itself and at the reception desk. Negotiating with electronic channels turned out to be easier than with people.

From curious cases: the system recommended a hair coloring service for a man, the employees rejected the offer, and later it turned out that the man really resorted to hair coloring. So, in order to motivate the staff, the KPIs of the club employees were changed.

Now let's move on to numbers, a little dry, but pleasant. The consumption of value-added services increased by an average of 10% per customer. Outflow decreased by 14%. Most importantly, the club's clients who used the recommendations began to use 58% more services of the company as a whole.