H酒店顾客在线预订行为分析及营销策略研究

发布时间：2018-03-05 18:19

本文选题：在线预订　切入点：营销策略　出处：《沈阳师范大学》2015年硕士论文　论文类型：学位论文

【摘要】：随着国内经济和旅游业的发展,酒店行业的竞争日趋激烈。在线预订作为现代旅游业主要预订方式,是酒店企业销售业绩中重要构成因素。为保证企业的市场竞争力,优化企业营销策略,投入大量资源分析顾客在线预订行为,对顾客信息进行聚类分析和数据挖掘成为酒店业管理层关注的重点。现阶段,酒店从业人员在顾客信息日常运营和管理过程中需要处理的数据规模成倍增长,这为聚类分析和数据挖掘工作提出了新的挑战。采用传统的单机处理方式进行数据单机处理,不仅需要花费大量的时间和人力资源,同时由于单机的处理能力有限,日益增长的信息量最终会导致数据处理的时间超过企业可接受的范围。因此,酒店管理体系中需要构建一种高效的信息处理方式,对海量的顾客信息数据进行收集和整理,研究顾客的预订行为。一般来说,有两种途径。第一种是提高单机处理效率,并增加内部存储。这样的方式效果明显,可以在短时间内满足企业信息处理的需求,但设备的更新和存储单元的购置通常需要花费较大成本。第二种也是本文所采用的分布式并行处理的思想,将廉价的小型机或者微型机构成一个处理集群,将K-means算法运用到分布式体系中,以期对H酒店顾客预订行为进行分析和研究。全文共分为六个章节。第一章为本文研究背景、研究意义、研究现状进行阐述。第二章介绍了本文涉及到的相关理论和概念。第三章为H酒店背景介绍及其顾客在线预订行为现状的分析,提出改进H酒店在线预订管理的必要性。第四章和第五章全文核心章节,第四章对H酒店的顾客资料进行数据收集、数据预处理、变量选择、聚类分析等流程,最后对得出的结果进行论述。第五章采用行为事件访谈法对第四章中分类出的不同类别顾客的预订行为影响因素进行探究。第六章,根据第四章和第五章的分析结果,提出了基于顾客群细分的营销策略,包括个性化服务策略、新型促销模式、新型产品策略等。最后,本文总结了该系统设计过程中存在的不足,并就相关问题进行了展望。
[Abstract]:With the development of domestic economy and tourism, the competition of hotel industry is becoming more and more intense. As the main booking method of modern tourism industry, online booking is an important factor in the sales performance of hotel enterprises. Optimizing enterprise marketing strategy, investing a lot of resources to analyze customer online reservation behavior, clustering analysis of customer information and data mining have become the focus of hotel management. In the daily operation and management of customer information, the scale of data needed to be processed by hotel employees has increased exponentially, which presents a new challenge for clustering analysis and data mining. Not only does it take a lot of time and human resources, but also because of the limited processing capacity of a single machine, the growing amount of information eventually leads to data processing times that exceed what is acceptable to the enterprise. In the hotel management system, we need to build an efficient information processing method, collect and organize the massive customer information data, study customer reservation behavior. Generally speaking, there are two ways. The first is to improve the efficiency of single machine processing. And increase internal storage. The effect of this way is obvious and can meet the needs of enterprise information processing in a short period of time. The second is the idea of distributed parallel processing, which combines cheap minicomputers or microstructures into a processing cluster. The K-means algorithm is applied to the distributed system in order to analyze and study the customer reservation behavior of H Hotel. The whole paper is divided into six chapters. Chapter one is the background of this paper, the significance of the study, The second chapter introduces the related theories and concepts involved in this paper. The third chapter is the background introduction of H Hotel and the analysis of customer online reservation behavior. This paper puts forward the necessity of improving H hotel online reservation management. Chapter 4th and 5th are the core chapters of this paper. Chapter 4th deals with the processes of data collection, data preprocessing, variable selection, cluster analysis and so on. Finally, the results are discussed. Chapter 5th uses behavioral event interview method to explore the influencing factors of different types of customer reservation behavior in Chapter 4th. Chapter 6th, according to the analysis results of Chapter 4th and Chapter 5th. The marketing strategy based on customer group segmentation is put forward, including personalized service strategy, new promotion mode, new product strategy and so on. Finally, this paper summarizes the shortcomings in the design of the system, and looks forward to the related problems.
【学位授予单位】：沈阳师范大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：F274;F724.6;F719

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