How casino giant Genting UK keeps the customers coming back

"New members who receive a communication are up to 71% more likely to make a return visit within one month compared to those who receive no communication. In addition, the value of new members we contact is 62% greater (per head) than those we don’t contact."

Over the past two years the UK’s largest casino operator, Genting UK, has gone from strength to strength with their forward thinking and progressive CRM and data strategy. Through the integration of people, processes and technology, with a clear focus on the customer, Genting won several prestigious awards in 2010 in recognition of their innovative and industry leading data management and CRM programme. This included the coveted Marketing Society Gold Medal award for CRM and the Best Data Quality Initiative 2010, at the Data Strategy Awards.However, not being happy to rest on our laurels and with challenging external factors such as increasing legislation in the sector, the growth in online gambling and not least our direct competition beginning to adopt similar techniques, we have further enhanced our use of data over the past year for both commercial benefit and making key business decisions.We have identified ‘optimal’ new members and we are now sending communications to them; we are measuring Net Promoter Scores (NPS) against individual customer records to track their visit and value; and we are applying the Latent Demand Model (LDM) to make informed decisions on how to tactically grow existing market places as well as identify new ones. These developments, described in more detail within this case study, provide us with a series of additional benefits above and beyond the direct CRM activity and as such the wider commercial benefit to the business.Main ReportPrior to all of these recent developments, our CRM strategy and insight provision capabilities consisted of looking solely at members’ previous visits and then sending them one of three offers to encourage further visits. This was not only problematic from a cost point of view due to the size of the database, but also from a technical point of view. Our membership database had the primary purpose to register admissions and revenue; it was not designed for data mining and therefore very limited. This limitation was not only from a consumer marketing perspective, but also meant that we weren’t able to provide business intelligence to the wider departments in the company.Building on our existing customer profiling and segmentation, we have identified a number of further data mining opportunities. These new opportunities not only extend our CRM programme by delivering new triggers, but also assist other business functions with their requirements. Specifically these data mining opportunities are:• Targeting of ‘optimal’ new members• Introduction of Net Promoter Score (NPS)• Development of a Latent Demand ModelNew MembersFrom the results we have seen to date, new members who receive a communication are up to 71% more likely to make a return visit within one month compared to those who receive no communication. In addition, the value of new members we contact is 62% greater (per head) than those we don’t contact. We have also been able to deliver a 3 fold increase in the number of new members who go on to join and play with the online business. These impressive results are also reflected in the 37% click through rates we see from the emails which are sent, a clear sign of good customer engagement and interest levels.Although these results are extremely welcome, it does mean we are treating all new customers the same. We have therefore assessed and scored each individual’s attributes so we can identify those which have the greatest potential to be of value in the future, based on those customers who are already visiting the club they joined. When a potential ‘higher’ value customer now joins any of our clubs, it triggers a communication with enhanced incentives so we can attempt to move the customer through the loyalty ladder faster. It is too early to attribute any direct results to this insight and specific trigger in our CRM programme; however it is through what we have learnt to date and the use of data that we are able to develop our data and CRM in such a tactical way. Net Promoter Score ProgrammeThe second development of note is the application of NPS into the business. This globally recognised programme to measure customer satisfaction and loyalty is integral to placing our customer at the heart of what we do and the decisions we make. Applying NPS across a business is no small feat, however we wanted to go one step further and also ensure that every customer’s evaluation and score was mapped back to their profile in the database. This delivers a number of additional benefits to the organisation. Not only are we seeing club and group-wide NPS scores delivered each day, week and month, but we can also see the NPS scores by loyalty segment. For example we can see how our ‘regular’ customers rate us compared to our ‘occasional’ customers, or existing customers compared to new customers, or males compared to females, etc. To deliver this we have automated e-surveys triggered at specific and relevant points throughout the customer journey.In addition to these e-surveys we also use iPads at our clubs to give us direct interaction with our customers. This makes the survey feel more interactive, personal and distanced from the normal ‘paper’ method that the majority of retailers would use. One of the great challenges with any programme that focuses on Customer Service is trying to monetise it. We all know we should be doing it, but trying to measure it in terms of benefits to the bottom line is not easy. By mapping a customer’s score to their profile in the database, we can see the correlations between an NPS score and a customer’s value, i.e. if we can increase someone’s NPS score (their value of our customer service), can we see the benefit in their visits and value thereafter. This means we now have a measurable and clear connection between our efforts in customer service and a customer’s value / return based on their feedback. To take this even further, we have also introduced an application internally known as ‘Data Calm’. This system allows our staff to log and monitor customer feedback, again linked back to our single customer view. This allows us to track the effect a resolution has on a customer’s RFM score, i.e. – we can track the impact that positive complaint handling has on a customer’s attendance and value. This additional level of measurement and insight can really help identify the ROI for our customer service initiative which will be a great development and one which we will be really focussing on through 2011.Latent Demand Model The third area of note in 2010 was the development of a Latent Demand Model (LDM). The LDM looks at each club’s key drive time area so we can understand the level of market penetration we are achieving. We also apply a series of weighting factors to take into account the competitor proposition and the demographic makeup of the market place. This information obviously provides us with a good level of insight, however we found the model could also be used for investment appraisals and marketing planning. To do this we drilled down further to identify the level of market penetration we had by ethnicity and age, i.e. it may be we are over-saturated with White European who are 25 to 45 years old, but under-saturated for Chinese customers who are 18 – 24 years old. Each age group and ethnicity is then valued based on our existing data on those groups in the database. By completing this more exhaustive approach we can clearly see the potential of each market place, both existing and new. We can also assess the acquisition cost to satisfy any latent demand based on the potential return we would get from the identified age and ethnicity group.The model is now being used for assessing investment appraisals within the business and also provides a far more effective and scientific way of applying drive time and market penetration profiles. We can now accurately demonstrate the likely returns based on the millions of transactions we already hold in our Single Customer View. What’s more, the model is dynamic and is updated every day so we always have the latest and most accurate information.ResultsBy building on our Single Customer view and CRM programme beyond our achievements in 2009 we have been able to deliver valuable business intelligence. We have continued to deliver outstanding results from a CRM programme that benefits the bottom line, plus we have also identified how we can play an instrumental part in evaluating customer service, loyalty and future investment proposals. It should also be noted that we have achieved this against a backdrop of declining marketing spend. This case study was awarded a Commendation for Customer Insight at The Drum Marketing Awards

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