SAS® predictive analytics helps Wyndham Exchange & Rentals generate $11 million in incremental revenue in first year
In traditional real estate, the three most important factors might be “location, location, location.” But in the world of vacation ownership exchange, the mantra is “value, value, value.” For RCI, a subsidiary of Wyndham Exchange & Rentals, the ability to analyze the preferences and patterns of its 3.7 million members and fairly estimate the value of their vacation ownership weeks within the RCI exchange system was the key to the generation of $11 million of revenue and a more than $10 million savings through building the solution in-house using SAS. Using a combination of SAS predictive analytics and grid computing, RCI has increased revenue in a challenging economic environment, while improving customer satisfaction and loyalty, and boosting its transaction volumes.
According to Srinidhi Melkote, Director of Analytics at Wyndham Exchange & Rentals, a new approach was needed. “We created a very sophisticated model to dynamically assign a value to each week of inventory based on a variety of factors,” he said. “It starts with supply and demand patterns, seasonality, resort popularity, the size of the property unit, and more – all at a very granular level. And all of this analysis and transaction activity was processing-intensive.”
The Wyndham Exchange & Rentals team created the RCI Weeks trading exchange application to manage its approximately 2 million distinct SKUs. The team uses SAS to pull transaction data from a variety of data sources and raw clickstream logs to create time-series analyses, regressions and other econometric models: 6 million region/week forecasts for supply forecasts, 15 million region/week/product forecasts for usage forecasts, and another 15 million region/week/product forecasts for demand forecasts. These analyses and forecasts enable Wyndham Exchange & Rentals to assign – on a daily basis – an updated exchange value to each vacation week in its inventory. As a result, RCI members have access to more exchange opportunities than they ever have in the past.
"We have about 25 times more supply and usage forecasts, since these must be disaggregated at a lower level of detail," explained Jeremy TerBush, Vice President of Analytics for Wyndham Exchange & Rentals. "And we have 60 times more demand forecasts since our demand is forecast at different price points. It's all done using custom regression models in SAS. Altogether, we have about 1.4 billion forecasts, which we create every day using SAS Forecast Server and SAS Grid Computing, that provide recommended values for our inventory. It takes us about four hours to complete this process."
$11 million of revenue generated in the first year
SAS helps Wyndham Exchange & Rentals synergize marketing strategies, allowing for trend spotting and cross- and up-selling opportunities. SAS segments customers by pulling data on travel patterns, and then the company uses this customer intelligence to recommend vacations that appeal to other members with similar patterns. This merchandising logic is also applied to Wyndham Exchange & Rentals' updated website, where member preferences trigger online suggestions for cross-sell opportunities.
During the first 12 months of operation, the RCI Weeks exchange platform generated $11 million in revenue, compared to recent historical volume trends, due to an increase in exchanges. Average weekly rates, an internal metric that Wyndham Exchange & Rentals uses to assess the network value generated by the exchange, increased 4.6 percent. Occupancy rates, which had been trending downward, rose 3 percent. And deposits (where members deposit a week of their timeshare so that RCI can offer it to others), which had also been declining, rose 6 percent compared to recent trends. "What's more, by developing the solution in-house leveraging SAS – as opposed to working with a consulting firm – we saved an additional $10 million to $15 million in direct costs over three years," said TerBush.
A hit with executives, customers and industry watchers
That success has led Wyndham Exchange & Rentals to be named the 2011 winner of the “INFORMS Revenue Management and Pricing Section Practice Award,” edging both Google and Walt Disney Parks & Resorts for the top honor.
"The superior matching and superior operation of the network from using SAS leads to returns in revenues," said TerBush. "That matching is critical. If you don't make sure that members holding the scarce space get an equitable exchange, you prejudice the ability to maintain future streams of the supply. SAS allows us to have the right information in front of the right people at the right time, driving business decisions and company success."
The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.
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VP of Analytics
Wyndham Exchange & Rentals
This leader in vacation timeshare exchange wanted to enable 3.7 million members to trade time at their vacation properties by properly forecasting supply and demand and assessing the value of those assets on a daily basis.
Through more accurate valuations and forecasts, Wyndham Exchanges & Rentals generated $11 million of revenue in the first year and saved $10 million to $15 million in projected three-year costs.
“"Previously, we spent 80 percent of our time pulling data from disparate systems. Now, with SAS, we're spending about 95 percent of our time making business decisions on assigning the right value to inventory. That increases member satisfaction and revenue."”
Director of Analytics
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