The client is a leading travel and leisure company providing timeshare properties to its members across the world.
By late '90s the market for timeshare services became extremely competitive because holiday-makers now had an impressive line-up of hotel brands to choose from. For an average holiday-maker, the market offered more choice. For timeshare companies, it meant intense competition and, more effort to engage and retain customers.
The client wanted to improve the engagement levels with its members and increase the efficacy of its sales and marketing programs. To achieve this, it needed to segment its customer base well and direct the right messages to the right segments.
The client engaged with WNS to address four challenges:
How to identify marketing messages that would improve the current levels of engagement with customers?
- How to forecast customer behavior and target marketing campaigns accordingly?
- How to capture attitudinal information of its entire customer base so as to improve sales?
- How to forecast a customer's probability to attrite and hence, develop appropriate retention programs?
The WNS Solution
For each of the challenges, the WNS analytics team delivered an analytics solution which enabled the client to develop more impactful marketing campaigns.
To improve the current levels of customer engagement
The member base was divided into segments according to the current level of their engagement, i.e. number of transactions. The lifecycle segmentation technique was used to segment the entire member base into the following categories: Most valued customers, growable customers, marginal customers, and loss-making customers. The client used this data to design loyalty programs for most valued customers, and 'care and nurture' initiatives for growable customers. In the case of loss-making customers, the client reduced the cost of servicing them. The differential marketing of the customer base improved the efficacy of its marketing messages, and helped move customers to higher levels of engagement.
To forecast customer behavior and target marketing campaigns accordingly
To address this challenge, members were segmented according to the frequency and volume of the transactions they made. The WNS team applied the propensity model, which used transactional data to understand member behavior. The client could now forecast whether a member was likely to make a booking or was only going to enquire about a property. This insight helped the client optimize its communication programs and target only the members who were likely to get influenced by the communication.
The model developed by WNS helped the client save 30 percent of its previous marketing outlay. The savings were used to design and deploy campaigns for members who were highly likely to make a booking. This helped increase the overall conversion rate for the campaigns.
To capture attitudinal information of its entire customer base so as to improve sales
Transactional data throws up characteristics that are transient in nature, whereas customer attitudinal data is of a more permanent nature, which can help you derive tangible benefits. The client wanted to understand attitudinal trends of its members so that it could communicate with them more effectively and thereby improve sales.
A survey was conducted for 600 members; and the responses were used to classify them into five clusters. The five behavioral clusters were: New members, high transactors, multiple fixed ownerships, dormant members and at-risk members. The team then conducted a Principal Component analysis to identify the key characteristics of each segment. Thereafter, it used data stitching to extend this information to classify the entire member base (extrapolate the information received from the responses to the entire customer base).
To forecast a customer's probability to attrite and thereby develop appropriate retention programs
As much as 20 percent of the client's members did not renew their subscriptions. These members would remain inactive for a long period of time and eventually attrite. The WNS team used the Logistic Regression model to help the client identify and target members likely to attrite with appropriate retention offers.
The client used the information to target members more effectively with separate retention programs. This resulted in substantial savings in direct marketing costs and helped reduce attrition.
Benefits delivered by the WNS team
In a cluttered market, where there is a sharper focus on marketing spends, the WNS Analytics team delivered solutions that increased the effectiveness of the client's marketing campaigns. Some benefits delivered by the WNS team to the client include:
A better understanding of members and their buying behavior
Reduction in customer attrition
Increased efficacy of marketing messages based on characteristics of members.
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