Chateau de Montana : applying data analytics to simulate room price of a repositioned hotel / Prashant Das.

Format:

Book

Author/Creator:

Das, Prashant, author.

Series:

SAGE Business cases.

SAGE Business cases

Language:

English

Subjects (All):

Hotel management--Case studies.

Hotel management.

Pricing--Data processing--Case studies.

Pricing.

Physical Description:

1 online resource : illustrations.

Place of Publication:

London : Indian Institute of Management, Ahmedabad, 2024.

Summary:

Olga Mitireva and Yulia Belopilskaya (the consultants) had been commissioned by Nicolas Dupont, the owner of Chateau de Montana, a struggling (and old) boutique hotel in Crans-Montana Ski Resort (Switzerland) to assess whether his hotel with enhanced amenities can fetch the higher room rates that he was targeting. The hotel was underperforming in terms of room rates. Duponts' plan was to invest in capital expenditure by offering new amenities, alter some of the services offered by the hotel and assure that in different seasons, the room rates would be enhanced. Room rates are a composite of different attributes offered with the hotel room and amenities offered by the hotel. However, the rates are decided by market forces. The equilibrium prices are reflected in the room rates published by online travel agencies (e.g. Tripadvisor.com). The consultants, Mitireva and Belopilskaya, decided to extract the pricing pattern from comparable listings and apply hedonic pricing. The case provides data on room prices and detailed attributes of 194 hotels in Crans-Montana and similar localities. The case exposes the students to the scope of data analytics in pricing and expects the students to have a basic understanding of regression modeling using any tool (MS Excel R, SPSS, Python, etc.). Students critique the cognitive limitations of analyzing comparable room prices from other hotels and apply ordinary least square (OLS) regression modeling to simulate the price of the (counterfactual) repositioned hotel room. Ideally, students should be asked to revise their understanding of OLS regression in advance before coming to the session. Those who are well versed with advanced tools (R, SPSS, Python, Stata, etc.) should be encouraged to be ready with the software installation (and licensing, if applicable). Else, the case can also be discussed in Excel.

Notes:

Description based on XML content.

ISBN:

1-0719-4205-0

9781071942055

OCLC:

1417358554