The Priceline’s Case Analysis – a Recommendation for Mr. White

Mr. White’s 10 Room Priceline Analysis and Recommendation

Priceline is an Online Travel Agent (OTA) that stands out from traditional companies in its category such as Expedia, Travelocity, Booking.com, Orbitz, among others. Priceline differentiates itself with an opaque approach to bookings with its bid style model to hotel reservations. In the traditional OTA model, customers get to compare prices for their hotel accommodations, but in Priceline, they get to name their willingness to pay (and selecting potential brands they do not desire in addition to hotel star level desired) with no comparison to an array of options.  

Priceline.com

Groundwork & Assumptions

Before we go on to the analysis, we wanted to lay the groundwork of this analysis by stating the following assumptions:

  1. Mr. White’s current Average Daily Rate (ADR) for the ten rooms is $75 in Priceline
  2. The goal is to maximize revenue
  3. In the Priceline model, if a bid is lower than what we have set for the rate, the bid would be rejected. 

After a discussion about the Priceline business model, we decided to run an analysis to determine the price that would maximize revenue for Mr. White’s ten rooms. We decided to run 1000 trials for each of the following ADR’s given the demand provided by Priceline: $70, $75, $80, $85, $90, $95. We decided this range to view the results of a lower ADR at $70 and with increments of $5 from Mr. White’s current ADR. After running the trials maximizing for revenue, we saw the following results:

Results Table

Recommendation to Mr. White

From the results of our analysis, we recommend Mr. White to adjust his ADR to $90 because our model showed maximize revenue potential at that rate given the demand report provided by Priceline. The results for revenue for the ten rooms are above in the table. If all ten rooms are available at the time of the auction our model showed that given the demand data available there would be more customers or bidders with successful bids, then there are rooms available leaving unmet demand. The assumption was that the revenue would be maximized at a price that reduced the number of successful bids on average (of 1k trials) to around ten. Thus having the price high enough to increase revenue per room but low enough to continue to sell the maximum number of rooms. The ideal strategy we hypothesized is to minimize unmet demand by setting a price just high enough to limit the number of successful bids on average to around 10. Not set the price so high that there are the right amount of unsold rooms. We concluded that we were most sensitive to not having a great deal of unmet demand even if that means on average having a few unsold rooms.  

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