Enhanced loyalty by assessing customer value for a leading casino operator
Case Studies:Mu Sigma
Published On: 21 March 2015
What We Did: Enabled customer segmentation based on the share of wallet at client and competitors’ casino in order to develop a customized campaign plan. Also, developed effective up-sell/ cross-sell as well as promotion strategies to improve customer loyalty.
The Impact We Made: The customer value assessment framework has enabled the client to profile customers based on their share of wallet and target specific customers to increase engagement and loyalty. A 7% incremental revenue was achieved through up-sell strategy.
Summary - Delivering customized marketing strategy to drive loyalty
The marketing function of a leading casino operator wanted to devise customized promotion strategies for their customers. They wanted these strategies to be based on the customer’s share of wallet/ spend with the client and their competitors’. The intent was to up-sell to customers who have a low share of wallet with the client and also design loyalty program for customers with a high share of wallet as a means to enhance the relationship.
About The Client - Leading gaming corporation
The client is one of the leading gaming corporation in the world with annual revenues of over $10 billion. It operates over 50 casinos and hotels worldwide.
The Challenge - Understanding wallet size and customizing campaigns
The client had conducted a survey on a sample of customers selected on the basis of demographics and amount spent, to ensure proper mix. The survey was aimed to ascertain their wallet share at the client and competitors’ casinos over the past year. The client wanted to analyze survey results, identify customer categories and come up with recommendations on appropriate marketing strategy.
The Approach - Categorization and customization
Based on the survey conducted on the sample customer base, Mu Sigma identified gaming and non-gaming behavior of the customers. This process helped to identify factors affecting their share of wallet. Customers were bucketed as a proportion of share of wallet to market expenditure. A multinomial logistic regression model was performed to classify all the customers in to 3 categories:
Under indexed: Client’s share of wallet < Competitors’ share of wallet
Even share: Client’s share of wallet = Competitors’ share of wallet
Over indexed: Client’s share of wallet > Competitors’ share of wallet
The business was able to identify various factors such as types of casinos, total days spent at a casino, lodging facilities, complimentary offers, etc.
The Outcome - A more holistic view of share of wallet
Key factors affecting their customers’ share of wallet were identified like amount spend, marital status, designation, frequency of visits etc.
32% of the customers were identified as spending more at client casinos
“Under-indexed” customers were accurately targeted with appropriate offers to increase their engagement with client casinos, which led to 7% increment in revenue
“Over-indexed” customers were offered loyalty programs and other specific campaigns to further strengthen the relationship, which saw 44% overall conversion