RCI Uses SAS to Forecast Demand, Predict Timeshare Values

  • SAS
  • 12.22.08
Group RCI, a global leader in leisure real estate, is bucking economic trends and retaining members using predictive analytics from SAS, a leader in business analytics.

Serving millions of vacationers yearly, RCI measures return on investment by network growth, based on matching supply with demand. With SAS predictive analytics – SAS Enterprise Miner™ and SAS Forecast Server – RCI can better satisfy customers. 

RCI Global Vacation Network’s members own time shares they want to swap or luxury properties they want to lease. SAS enables RCI to estimate the value of swaps and rentals and forecast demand. RCI can distribute inventory more effectively, increasing member satisfaction and revenue. RCI also uses SAS to identify cross-sell and up-sell opportunities and to create targeted mailing lists.

Executives agree that SAS delivers significant ROI. Implementing SAS has accelerated exchange membership growth in key geographies, such as Europe and Mexico. Rising to the challenge, RCI distributes scarce-supply properties to the right customers.

“We see lots of interesting problems on the business side,” said Jeremy TerBush, director of analytics within revenue management at Group RCI. “With less powerful software tools, we hit roadblocks. The SAS Business Analytics Framework equips RCI to solve any sort of business problem.”

RCI generates millions of Web and call center transactions annually and has more than a terabyte of real-time information for data mining. Through SAS' user-friendly tools, the right information is presented to the right people at the right time, driving business decisions and company success.

“People know what’s happening within the network thanks to the systems, alerts and pricing tools we’ve built using SAS,” said Sean Joseph Lowe, senior VP revenue management and analytics at RCI.

For supply, demand and business forecasting, SAS Forecast Server simplifies the process at RCI. “It’s absolutely essential that we forecast accurately,” Lowe continued. “SAS quickly generates models for one set of information we’re forecasting, spots the best fit and ranks them so we can decide which will be most successful. We refine our forecasts in much deeper detail, improving forecasting accuracy for regional financial teams.”

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