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Genting UK

Loyalty Scheme analysis

Background

Genting UK is the largest casino operator in the country with over 40 casinos located from Edinburgh to Plymouth. The company operates 2 brands in the provincial areas; Genting Casino and Genting Club.

In London it is the capital’s leading casino operator with five casinos which cater for all levels of the gaming market. This includes its flagship, Crockfords, located in the heart of Mayfair which is recognised as the world’s oldest private members gaming club, Colony Club, Maxims Casino Club, The Palm Beach and Cromwell Mint.

In 2010 Genting UK introduced a loyalty programme using a proprietary system from their Malaysian parent.  It was rolled out to 17 sites in order to test the effect of a loyalty scheme upon attendance, play and drop levels.  The scheme was re-launched in 2012 with many system enhancements, to improve both the customer & casino management experience. The program includes the installation of new card readers into all of the electronic gaming machines and a new kiosk for customer redemptions.

The requirement

Aszent’s task was to understand and quantify the business benefit / ROI for the new Genting Rewards loyalty scheme.

The challenges

Where it exists casino data can be perplexing to interpret as many items are collected electronically via the card reader, as well as input manually from the gaming tables.  This relies upon the Dealers calculating averages accurately and inputting them in a standard manner.   The data from Genting’s casinos is held on several different systems, with different customer keys so it was not possible to join up the data from one system to another.  We were also faced with cleaning thousands of orphaned records, where there appeared to be no record of a visit to the casinos, but transactions recorded.

What we did

Using FastStats as the analysis tool we undertook the entire methodology, from data quality management through to the production of the resultant tables and graphs.

1. Data quality management
In order to analyse the data we had to clean and structure the data to make it fit for purpose.  This involved attaining a thorough understanding of the data fields, the codes, the time periods to which the data belonged, et al and then determining an appropriate method for handling the orphaned records.   As with all data analysis studies calculations were made within the

2. Adjusting the data
After completing the data preparation we completed distribution studies to gain an understanding of the shape of the data under investigation.  It was at this point we noted that the data related to different time-periods, and also showed distinct peaks and troughs.  To attain a true picture of the impact of the data it was necessary to smooth it for seasonality and isolate periods of time so that both pre- and post-launch analysis could be undertaken for accurate measurement.

At this stage the a series of activities distribution studies of the data values before a logical segmentation approach was applied to identify player activity levels that would be understood easily within the organisation.

3. Analysis of the Levers
We undertook extensive and iterative data analysis to determine the effects of the loyalty scheme upon:

  • Frequency of visit
  • Drop levels per visit
  • Retention levels over time
  • Number of members

The data for the levers/KPIs were calculated at aggregate level for each month prior to, and post launch.  This enabled us to establish patterns of behaviour and calculate averages over each distinct period.

The results

The loyalty scheme did have an impact upon the levers/KPIs, but these were generated by the low activity segments:

Points earned increased by a factor of 4, a direct result of the increase in the average frequency of visits by members
Lower level activity segments accounted for the increases, which was a concern as these members are unlikely to move up the loyalty tiers and become “Players” or “High Rollers”

The reason for this is due to the change in the level of rewards on ER and slot machines, which awarded points on the same basis as table play.   Thus the analysis study enabled Genting to re-evaluate and reshape its loyalty scheme, so that it could reward its higher activity level players and members with service and experience that would not be accorded to the lower level members; they would continue to accrue points alone.

 

 

Aszent has proved invaluable in automating SeachStar’s web analytics and statistics; we’re able to save time and focus on our core business as a result of their innovation and dedication. They have delivered 100% customer delight & are hugely recommended for their intelligent solutions and genuine customer service. — Dan Fallon, MD, SearchStar


Call Aszent on 07887 653595