Essay Revenue Management Techniques in Hospitality Industry –
The hospitality industry is part of a larger enterprise known as the travel and tourism industry. It is one of the oldest industries in the world. In early days, traders, explorers, missionaries and pilgrims needed a break in their journeys requiring food, shelter and rest. People opened their homes and kitchens to these weary travellers, and an industry was born. Although accommodation today is varied and their services have changed and expanded over the ages, one thing about the hospitality industry has remained the same, guests are always welcome! From a friendly greeting at the door, room service, breakfast, to a host of facilities' the hospitality industry offers travellers a home away from …show more content…
Flexibility Cancellations and rescheduling are allowed at a low penalty.
High penalty for cancellation and schedule change Time of purchase Bookings are made very close to date Bookings are made quite early
Privileges Are rewarded loyalty privileges No privileges
Size of Business provided Corporate business customers booking frequently
Self funding vacationers booking rarely
Point of sale Phisical delivery and confirmations By email or phone.
Changing Demand over Time eg. Perishable Products
Segment 1 S2 S3
IIMK Part VII – Tourism Infrastructure, Technology & Operations IIML
Conference on Tourism in India – Challenges Ahead, 15-17 May 2008, IIMK 272
Demand forecasting: Pricing and demand are inter-related and need to be coordinated. In the hospitality industry, demand for a room is cyclic in nature and follows a trend. Revenue management models help pinpoint demand by minimising uncertainity and producing the best possible forecast.
Allocation: the revenue management also puts light on the allocation of inventory (hotel rooms) among different segments. For example , if a hotel has two price categories of rooms, say Rs.4500 and Rs. 6000.
Since the pricing is different for the two rooms, these rooms are each targeted at a different customer set.
Based on the historical preference pattern of customers in each segment, it would be possible to estimate the number of customers who would be willing to