Last week in my post, Revenue Management vs. Price Optimization: Part One, I explored the practice of practice of hospitality revenue management, its origins in the airlines and how it was changed for use in the hospitality industry. This week I will explore how price optimization has evolved and how hospitality companies can use price optimization to gain competitive advantage.

Price Optimization

Price optimization approaches were developed to suit industries such as the retail industry whose characteristics don’t match that of the revenue management approaches that I covered in my last post:

• Fixed available capacity – capacity cannot be increased in order to accommodate surplus demand
• Product perishability – the product loses its value to a customer after some point in time, and cannot be sold thereafter
• Advance reservations – advance reservations are accepted for the product

In these businesses, each product was made available at a single price to a broad market of customers.  This broad market of customers had different ability or “willingness to pay”, and the businesses had some capability to change the price of the product offered to the market over time.

Using a price optimization approach, demand for a product is estimated as a function of price, and the price itself becomes the decision variable (rather than the protection value, which I’ve described in part one) that is used in maximizing revenues or margin.  In these businesses there may be no advance reservations, or separation by segment, so there’s no one to “protect” sales for.  In addition, inventory itself may not be constrained – in such cases there’s no reason to “protect” inventory.  Since the market is not segmented by price, the problem becomes “what is the optimal price to charge the overall market in order to maximize return?”

Price optimization approaches have been widely adapted in retail, and other industries.  Decision support solutions that utilize price optimization approaches (like the SAS Revenue Optimization Suite for retail) need to estimate demand and its sensitivity to price (aka price elasticity) for a wide variety of products, and often from a wide variety of channels or stores.  The calculation of price sensitivity on an automated basis introduces new technical challenges that these solutions have had to overcome – and much research has been invested in this area over the last decade or so.

How/Why does price optimization apply to hospitality?

As I noted in my previous post, two important things happened following the initial development of revenue management methods:

1. Low cost airlines introduced fare structures with significantly reduced fencing, and expanded significantly – effectively invalidating the assumption of demand independence made in these revenue management models
2. Revenue management science has been introduced into markets where strict fences never existed.  Hotels, rental cars, and so on have rate structures that do not contain strict fences like the airlines once did – and so the assumption of demand independence is again problematic

Many hotels, in particular, fall into the second group – they simply never developed segmentation of pricing that would support the original revenue management approach.  Consider, for example, the roadside hotel – this business meets the primary conditions of revenue management (fixed capacity, perishable product, and advance reservations), but very likely does not have clearly fenced segments.  Rather, this business is choosing one of several possible rates to charge on any given date.  And that generally available rate services the vast majority of their customers.  I’m sure that there are other contributing factors here, but given this fundamental mismatch, is really any wonder that revenue management technologies have not seen broader adaption with this segment of hotels?

Does price optimization work for the hospitality industry?

This type of retail or “price-able” product represents a significant portion of today’s hospitality market – most hotels have such a product, and for many of them, it is a significant portion of their business.  For this type of product, a price optimization approach, where the selection of an optimal price is the primary output of the model, and price elasticity is directly modeled, would seem to be a better “fit” for the business problem.  Of course, you cannot simply take a solution that was designed for retail and port it over to another industry and expect it to work “as is” – that’s not going to work.  But, it is possible to adjust the price optimization approach to suit hospitality.

Not all hospitality products are price-able, however.  Many hospitality customers purchase rates that are controlled not through price, but via availability.  These type of products often behave more like traditional revenue management segments (i.e. they are clearly fenced from other products, and their rate is not or cannot be independently managed on a day to day basis).  In order to maximize revenue for a business that is selling both a retail product and these more traditional segmented products, we need a model that has elements of both price optimization and revenue management. A combined approach can bring the benefits of both price optimization and revenue management to predicting demand and optimizing revenue.

Next on the Analytic Hospitality Executive, Kelly McGuire will explore the evolution of the role of the revenue manager and why today’s revenue management systems are just not working as well anymore. For more information on trends in pricing, including price optimization, view the New Pricing Techniques for Hospitality and Gaming webcast.

McGill, J. and van Ryzin, G. (1999) Revenue management: research overview and prospects. Transportation Science 33: 233–256.

http://en.wikipedia.org/wiki/Revenue_management

Fiig, Isler, Hopperstad and Olsen (2012), Forecasting and optimization of fare families, Journal of Revenue and Pricing Management, Vol. 11, 3, 322-342

Koushik, D. Higbie, J. and Eister, C (2012), Retail Price Optimization at InterContinental Hotels Group, Interfaces, Vol. 42, No. 1, 45-57