With  4,437  hotels  and  647,161  rooms,  InterContinental  Hotel  Group  (IHG)  is  the world’s largest hotel group in terms of number of rooms. About 85 percent of its hotels are  franchised,  14  percent  are  managed,  and  1 percent are owned directly  by  the InterContinental Hotel group. Some of the hotel brands that belong to this group are Holiday Inn, Holiday Express, Staybridge Suites, and Crowne Plaza. Revenue generated from their rooms amounts to around $20 billion. Before the optimization model was implemented, pricing decisions were made based on a complex myriad of variables, some of which were day of the week, seasonality, occupancy level, competition, and customer feedback. These decisions were  made without  the  use  of  analytics. Price decisions were made with the assumption that demand was independent of the price charged  for  a  room.  This  fundamental  flaw  worked  well  in  normal  economic conditions. However, when the hospitality industry suffered a decline in revenue, with the challenge posed by the widespread use of the Internet, which introduced multiple distribution channels, IHG started considering and exploring alternative and effective revenue generation methods. The main aim was to increase the revenue per available room (RevPAR). 

Methodology/solution

IHG rolled out their retail price optimization system to help increase their RevPAR. The large number of hotels was a big challenge to this task. Pricing decisions numbered over 273 million (or 76,000 per hotel) per day. The project resulted in a change of their fundamental  business  flow.  The  final  model  included  a  demand  forecast  model, market response model, competitor rates model, and an optimization price model. For each hotel, a price response is calculated by the market response model based on historical  data.  Price  and  competitor  rates  were  used  to  estimate  the  demand  for rooms. The objective function used in this computation turned out to be nonlinear. The input data for the competitor rates model were derived from third-party sources. Decision  variables  used  to  determine  the  best  rates  for  each  hotel  were based  on factors  like  estimated  demand,  hotel  capacity,  current  bookings,  and  prices  being charged  by  competitors.  IHG’s  price  optimization  system  is  packaged  in  a  Web application called Preformism.  

Results/benefits  

There has been widespread adoption of the retail price optimization model by hotel managers globally. PERFORM is used by over 4,000 users worldwide. The retail price optimization  model  was  tested  in  a  couple  of  IHG’s  hotels  and  the  results  were compared  with  hotels  where  the  model  had  not  been  implemented  yet.  It  was recognized  that  there  was  a  2.7  percent  increase  in  RevPAR  for  hotels  where  the optimization model had been implemented.

Questions for the Opening vignette  

1.  Describe  the  challenges  faced  by  IHG  during  development  of  their  retail  price optimization system.  

2. Besides the hotel business in the hospitality industry, explain at least three other areas where an optimization model could be used.  

3. What other methods could be used to solve IHG’s price optimization problem?  

What we can Learn from this vignette  

IHG has been doing business using manual price optimization methods for a long time and it seems to have worked for them. However, sometimes business environments change, which renders existing methods of running a business obsolete. IHG used data analytics  and  mathematical  optimization  methods  to  revolutionize  revenue management. The price optimization model was a combination of different operations research  methods.  What  is  also  important  is  that  such  decisions  are  eventually implemented using a decision system that is available to each client hotel. They do not have to know anything about the underlying decision methods to be able to use the recommendations made by the system.

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