Maximizing Network Value at Wyndham Exchange and Rentals · hang on to their home weeks. But there...

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Maximizing Network Value at Wyndham Exchange and RentalsPredictive Analytics

Featuring:

Jeremy TerBush, Vice President of Revenue Management and Analytics at Wyndham Exchange and Rentals

Kelly A. McGuire, PhD, Executive Director of the Hospitality and Travel Global Practice at SAS

MaxiMizing network Value at wyndhaM exchange and rentals

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The concept of vacation time-sharing has been around since the 1960s. While shared vacation ownership remains a thriving industry, the original business model has long been passé.

Under the original concept, you owned the right to use a vacation property for a designated week. The property management agency might have offered flex weeks that shifted from year to year, so every owner could have a shot at the most desirable weeks – but the location and week were basically set for you. If you got tired of going back to the same place year after year, you didn’t have many good options. You could try to rent the space to someone else, but there was no really good channel to do that.

“Resort developers and vacation owners both needed more flexibility,” said Jeremy TerBush, Vice President of Revenue Management and Analytics at Wyndham Exchange and Rentals, a worldwide leader in vacation exchange. “If you owned an Orlando week, and you were tired of taking the kids to Disney World, you needed a mechanism that would allow you to trade that Orlando week for a chance to take the family skiing or to the beach. The industry needed a trusted environment for owners to trade amongst themselves.”

RCI Group (formerly Resort Condominiums International, now a division of Wyndham Exchange and Rentals) pioneered the vacation ownership exchange business concept in 1974. The company started by organizing trades out of a shoebox full of index cards – and has grown to deliver vacations to more than 3.8 million members in about 100 countries. The portfolio includes more than 30 brands, 4,000 affiliated resorts and 87,000 vacation properties. In any given year, Wyndham Exchange and Rentals facilitates nearly 2 million exchanges. The shoe box is long gone, replaced by mainframe computing and distributed servers.

With a formal channel for exchanging vacation weeks, owners now have a world of options – literally.

■ Find out how the worldwide leader in vacation exchange uses predictive analytics to improve the value of its network, make more effective revenue management decisions in less time and deliver a better customer experience.

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The Vacation Exchange Business Model

When you purchase a share in a Wyndham vacation property, you are typically enrolled in the member network for a modest annual subscription fee. You can choose to return to your deeded property every year, or you can exchange it for a comparable offering at another location or another time.

The core business model can be explained in three simple steps: deposit, request and confirm.

• Deposit. When members decide that they want a change of scenery, they can deposit their week into the RCI Spacebank (the network of available inventory) and grant RCI the right to manage the usage of that week on their behalf.

• Request. Owners who have made deposits may make a request online or through a call center agent, and can specify what they’re looking for: a certain destination or type of property and the week.

• Confirm. If a desirable match is available, the customer can book it on the spot. If nothing suitable is available, the system can continuously scan inventory as it enters the network and try to find a match. When the right property becomes available, the customer can complete the exchange.

At first glance, this business model appears similar to that of a hotel in many ways. The primary assets in the system are accommodations for available time periods. Just like hotel room nights, each resort week is a perishable commodity. “As soon as the start date of that week comes and goes, if we haven’t exchanged somebody into that week, the value of that week is gone and there’s nothing we can do with it,” said TerBush. “From that perspective, it is similar to a hotel or airline, so it is a good fit at a high level for revenue management practices.”

However, there are some notable differences as well:

Inventory can be highly variable. Unlike a hotel with a fixed number of rooms, “we get our inventory when members decide that they want to deposit their weeks into our system,” said TerBush. “So we won’t know from one week to the next, or one year to the next, how much inventory we’re going to have on the shelf to sell. That means we have another core variable that we need to predict in order to set prices correctly.”

Potential demand is constrained. Unlike a hotel that caters to an unlimited public, this is a closed network of members who trade within the circle of RCI properties. “Unlike a typical open-market system, we do have an idea of how much potential demand and potential inventory we can have in our system.

“Given these unique factors, the goal for us is to maximize the number of fair trades that we can generate within the network – matching people who have given similar types of inventory in the system with like inventory within the system.”


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Transactions don’t indicate relative value. There is a fixed transaction fee for any given trade, which does not relate to the value being traded. Whether you’re trading a week in Nebraska or a week in Hawaii, the transaction fee is the same.

“In order to gauge the economic value to the network of any given trade, we need to look beyond the financial aspect of the transaction,” said TerBush. “We need to look at what value the person has brought into the network with their deposit and what value are they taking out of the network when they book a piece of inventory. We refer to that internally as trading power – an important factor in maximizing the value of the exchange network.”

Why the concern about maximizing the value of the network?

If each transaction brings RCI Group the same revenue, regardless of the value of the properties being traded, a logical question might be, “Why worry about maximizing the value of the properties being exchanged through the network?”

Sure, RCI Group gets the same fee for each exchange, but the behind-the-scenes value is what really drives the volume and quality of those transactions, said TerBush. He provided a hypothetical example.

Suppose you have an exchange network that contains two types of inventory: Green weeks that are very desirable and Red weeks that are not so desirable. Imagine that the network has no internal valuation process, so there are no distinctions made between Green and Red properties. Members trade their weeks freely on a first-come, first-served basis.

Initially, you’re going to have a mix of Red and Green deposits – probably more Red than Green, because owners of the more desirable Green properties are more likely to hang on to their home weeks. But there will be some Green trades in the system. For example, owners who have prime weeks in Key West and New York might like to try Paris and Hawaii for a change.

“If trading is a free-for-all, the low-value Red depositors will move fast to book the high-value Green inventory from the network,” said TerBush. “It’s a great deal for them; they’ve gotten much better space than what they brought into the network. However, when my Green guy comes in and looks for space, he doesn’t see anything that seems to be equitable compared to the value that he traded in. He sees nothing but Red space. Over time, the network starts to degrade, because the members bringing the best value into the network don’t see the value proposition in return, and they leave the network. Eventually the mix of inventory turns pure Red, and even the Red owners become dissatisfied because they used to see Green properties in the mix. They get disenfranchised and start to leave the network.”

■ Data sources found

in social media:

• Unstructured text data.

• Images/audio/video.

• Ratings on travel review sites.

• Demographics.

• Social network connections.

• Impact as an influencer.


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Figure 1. If internal valuation isn’t handled appropriately, the network will degrade and lose members.

“This is a bit of an extreme example, but it’s not really hard to imagine something like this happening with a network like this,” said TerBush. “You need to pay very close attention to the way you’re matching up demand with supply to ensure that you’re keeping customers happy and that they continue to deposit with you and renew their memberships into the future. That’s why accurate valuation is one of the key things we focus on from a revenue management perspective.”

Optimizing Vacation Exchange

The deposit-request-confirm business model at Wyndham Exchange and Rentals worked well for more than 35 years, but with the dip in the economy, exchanges were flat; occupancy was slipping. It was time for an update to processes and policies that had been in place for decades.

“We conducted focus groups across our member base and got a really good idea of the types of improvements members were looking for,” said TerBush. This feedback called for improvements in three key areas:

Deposits Deposits Over Time

Inventory Mix

Exchanges

If desirable (green) and less-desirable (red) inventory are valued the same (unfairly).

I’m not depositing any more.

Why should I take this? My deposit is worth more.

Great deal for me!

No more good stuff to get.I’m out of here.

RCI

RCI

RCI

RCI

Importance of Valuation


Transparency in value assessments. “We had always disclosed the fact that there was an internal valuation process behind the scenes – that trading power – but members wanted to know more about how valuation was determined,” said TerBush. “When a member came into the system and deposited an Orlando two-bedroom condo week, we didn’t tell them anything about the value associated with that week. The only way members could discern the value we put on properties was when they searched the inventory and saw what was returned. Even then, they couldn’t see the inventory that wasn’t returned – the inventory we deemed more valuable and not equitable for them to trade into. Members told us they wanted to know more about the value we associated with their inventory, which makes sense.”

Deposit credits. “Historically, when a member came into the system and made a deposit and exchange, there might be a differential in value between what they exchanged into versus what they deposited, but it was strictly a one-for-one trading system,” said TerBush. “If I brought in a deposit that was maybe worth a 20 in trading power value and traded it for something that was valued at 15, I never saw that difference in value. It just disappeared in the trade. Members said, ‘We’d like to get that differential back, because we feel like sometimes we’re trading down, and when we do, we’d like to get change back.’ Again, that seemed like a good idea.”

Combination credits. “With the deposit credit concept, it also made sense for members to be able to combine deposit credits into new and more powerful deposits,” said TerBush. “We wanted to maintain the overall one-to-one feel to the system, so it is actually a different transaction to combine multiple deposits into another one, but now a member can perhaps combine two off-season weeks and trade them for one higher-value week – such as trading two Nebraska weeks for that dream Hawaii week.”

The ‘Before’ View: Big Improvements Require Big Changes

Transparent valuation, deposit credits, combining credits overhaul to legacy processes. The revenue management team was glad for the call to action, because these were enhancements they had wanted to make for years but didn’t previously have the capital or the business case to justify.

“A lot of our yield management processes were developed in the early ‘90s,” said TerBush. “They were quite good at the time but they were built into our mainframe system. We’ve made a number of changes over the last 15 years or so, but it was always extremely cost-prohibitive to make any sort of change. Going into the mid-2000s, we certainly understood that a lot of things could be improved – and we had a lot of great ideas about how to improve them – but because a lot of those processes lived in the old mainframe environment, it was very challenging to justify the case to make improvements.”

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Now, with member satisfaction and network growth on the line, management agreed that it was time to make enhancements that would overcome some serious process limitations:

• Lack of granularity. Our trading power valuation processes were not enough to differentiate between the really scarce space and the pretty scarce space,” said TerBush. “We didn’t have enough shades between Red and Green to manage the network efficiently enough, particularly if values would be transparent to members.”

• Nested allocation model. “We had a nested allocation model where, within the same SKU level of inventory, we might have 20 deposits, and those 20 deposits could be spread over multiple levels, similar to fare classes within an airline,” said TerBush. “As demand buckets were filled up, in terms of exchanges, the different exchange classes would fill up and be closed. That type of model was fine when everything was opaque. However, when we’re in the new transparent model and start to give out deposit credits, we needed to know how much change to give back.”

• Weak view of relative value. “Our approach for valuing inventory was good in terms of ranking one unit to another, to say A is better than B, but it didn’t do a good job gauging relative value, for instance, to say A is twice as good as B,” said TerBush. “When you start to do things that feel more like currency, like giving credit back and combining deposit credits, you have to have a valuation scheme that supports that kind of feature.”

The ‘After’ View

Fast-forward to June 2009 and November 2010. Wyndham Exchange and Rentals has implemented technology enhancements to support the member-requested process changes.

■ “We’ve built most of the reporting

capabilities using the SAS BI

environment – using the SAS BI

portal and stored processes. We’ve

found these tools to be extremely

effective to develop the right

mechanisms to steer analysts in the

right direction.”



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Before After

Valuation Decisions

Valuation decisions were made at a very low level of granularity via a black box process on the legacy mainframe system. It was very difficult to make any changes to the valuation mechanisms locked away in the mainframe.

Valuation is now a completely modular process that takes place outside the legacy system, assigns values at 60 levels of differentiation, and sends the outputs back into the reservation system.

Forecasting

Forecasting processes were simple and naïve, and done at an aggregate level, too high to capture the true dynamics of supply and demand.

Robust, state-of-the-art forecasting algorithms implemented in SAS® generate forecasts at a micro level, much better able to capture the supply and demand characteristics for an area.

Optimization

There was no optimization for the process, only heuristic rules hard-coded in the mainframe system, with basic build-up curve type models.

A state-of-the-art optimization module was custom built for the nuances of RCI’s unique revenue management situation.

Analyst Interface

The legacy user interface screen showed analysts the current pricing recommendations at a very low level of detail and without much flexibility in managing those prices.

The IT group developed an intuitive, user-friendly Web interface to serve up recommendations from the algorithms to the inventory management pricing team to fine-tune pricing decisions.

Reporting

The former system had limited reporting and no alerts to guide day-to-day decision making.

Analysts can customize extensive reporting, so they can define their own alerts to help them focus their energy on the right place on any given day.

Inventory Updates

Changes were blunt, with a very big impact on what members saw when they looked at inventory from day to day.

With valuation at many more levels, updates reflect small, precise changes that make more sense to members when they search inventory.

A User-Friendly Interface for the Human Element

Figure 2 below shows the interface developed by the company’s IT department for analysts to review output of the algorithms and make overrides based on their market-specific knowledge, said TerBush.

“Algorithms are key to the process, but algorithms can only be so smart. Here the analysts can review the outputs and make updates at multiple levels of aggregation – or they can choose from various scenarios that come out of the optimization. In addition to our base case, we run eight different scenarios looking at different sensitivities in our supply and demand forecast to create nine different pricing recommendations for inventory analysts. We try to steer analysts into a range that makes sense in terms of everything that we know from the analytics perspective, but the human element is key in pricing decisions as well.”

■ “We have a large analytic

environment primarily powered by

SAS that continuously analyzes the

valuation results and improves

on them, independently of IT, so it is

much more cost-effective to manage

these processes today.”


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Figure 2. Analysts can review and update pricing recommendations in a user-friendly Web interface.

Predictive Analytics on a Large Scale

The new analytic environment – running SAS Forecast Server in grid in a SAS 9.2 environment with three 64-bit UNIX servers – has redefined the possibilities in terms of scale, processing efficiency and flexibility.

“We have approximately 2 million SKUs coming into the network every day,” said TerBush. “Our forecasting is a combination of time series and regression and other econometric models built in SAS. At a high level using these models, we’re forecasting on 36 million data points. After that, we need to spread those forecasts down to a lower level, ultimately to get to the level of resort start week and unit configuration. To do that spreading is another forecasting process, for which we use a custom regression model.

Analyst Interface

■ “This is an incredibly complex

decision process, involving millions

of forecasts and thousands of

optimization problems daily to help

the revenue management team

understand supply and demand and

optimally manage trades based on

consumer preferences and available

inventory. It’s a great example of a

nontraditional application of revenue

management techniques.”



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“If you add up the 36 million data points and all the spreading processes, it ends up being approximately 1.4 billion forecasts we’re doing every day to support this process. The forecasting time takes only about four hours, which is pretty good when you’re talking about doing more than a billion forecasts. It is certainly not something that we could have done 10 years ago with the technology that was around then. It’s really incredible, the things that we can do with SAS from the forecasting side.

“On the optimization side, we’re using a combination of linear programming and metaheuristic methods for the optimization, and every day we’re solving approximately 6,000 problems. At any given time, we’re running eight problems in parallel to speed that up. That part of the processing takes about five hours to complete.

“Total run time, including all the data manipulation, is about 12 hours, which for us works pretty well, because pricing decisions need to be made on a daily basis. So, provided we can get this to fit into a window of less than 24 hours, this works very well for us.”

Closing Thoughts

In the 18 months that followed the first stage of the multiphase implementation, RCI Group got more than it expected. It improved the efficiency of matching up demand and supply, which had tangible effects on the value of the network and its transactions.

“Within a 12-month period following the June 2009 implementation, we saw an increase of 4.6 percent in our AWR [average weekly rate], which is analogous to ADR [average daily rate] on the hotel side,” said TerBush. “Clearly our RevPAR metric goes up by a similar amount: 4.6 percent.

“We also stemmed the decline in our occupancy that we had been seeing for the five years before the implementation. These were very encouraging results, actually a little bit better than what we had expected.”

■ “RCI Group faced a very complex

business problem, but SAS Forecast

Server can do that large-scale

forecasting in a reasonable amount

of time, and also enables analysts

to interact with the forecast,

identify outliers, and pick the best

forecasting method for the type of

forecast involved. This is a really

good example of how important it is

to have the right tools for the job.”


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About the Presenters


Jeremy TerBush is responsible for the Wyndham Exchange and Rentals Analytics Group, including valuations, trading power maintenance, consumer analytics, inventory reconciliation and business intelligence. Since joining Wyndham Exchange and Rentals (formerly RCI Group) in 2002, TerBush has held a variety of roles within revenue management and IT with an increasing level of responsibility, leading teams in the areas of operations research, business intelligence, operations and management consulting.

TerBush has a master’s degree in mathematics from Purdue University and an MBA from the Kelley School of Business.


Kelly A. McGuire, PhD, has 20 years of experience in the hospitality industry, both in operations and IT. Before joining SAS, McGuire consulted with Harrah’s Entertainment to develop restaurant revenue management strategies for casinos in the organization’s major markets. She also was a senior consultant at Radiant Systems. McGuire earned her PhD from the Cornell School of Hotel Administration, focusing on nontraditional applications of revenue management. McGuire’s research has been published in Cornell Hospitality Quarterly, and she has conducted several executive seminars on capacity management and revenue management.


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