Developing an integrated revenue management and customer relationship management approach in the hotel industry

被引:9
|
作者
Vaeztehrani A. [1 ]
Modarres M. [1 ]
Aref S. [2 ]
机构
[1] Department of Industrial Engineering, Sharif University of Technology, Azadi Ave., Tehran
[2] Department of Computer Science, University of Auckland, Private Bag 92019, Auckland
关键词
customer relationship management; hotel industry; loyalty programs; network revenue management; overbooking; stochastic dynamic programming;
D O I
10.1057/rpm.2014.22
中图分类号
学科分类号
摘要
Revenue management (RM) and customer relationship management (CRM) are the standard strategies of many hotels to increase their profitability. Although the objectives and time horizons of RM and CRM are different, they can be considered as complimentary business strategies. However, the integration has received little attention both practically and theoretically. In this study, we develop an approach to jointly make the capacity allocation and overbooking decisions considering CRM strategies over a hotel network. Hotel customers are divided based on their lifetime value into two major groups of occasional and loyal customers. Price discounts and room availability guarantee (RAG) are offered to loyal customers, who are the hotel's essential source of profit. The main problem is tackled by a stochastic dynamic programming model whose expected value of objective function is approximated by two deterministic linear programming-based algorithms. The computational results indicate that the loyalty programs may lead to decrease in short-term net revenue. However, another analysis is required to decide upon cost-effectiveness of loyalty programs in an extended planning horizon. On the basis of an estimated discount-RAG response function, the cost-effectiveness of different loyalty programs is compared, which shows potential increase in hotel expected net revenue up to 3.5 per cent. The analytical long-term evaluation of loyalty programs introduced is capable of determining the most appropriate loyalty program factors. Moreover, it suggests discount-RAG response function and the level of tightness as sensitive parameters. © 2015 Macmillan Publishers Ltd.
引用
收藏
页码:97 / 119
页数:22
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