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Understanding Airline Passenger Behavior through PNR, SOW and Webtrends Data Analysis

机译:通过PNR,SOW和Webtrends数据分析了解航空公司乘客行为

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This study investigates airline passenger behavior by analyzing three types of travel data: passenger name record (PNR), share of wallet (SOW) and webtrends. First, PNR archives the airline travel itinerary for individual passenger and a group of passengers traveling together. Usually, passengers and their accompaniers are close to each other, such as families, friends, lovers, colleagues and so on. Therefore, the social network between passengers and their accompaniers can be constructed through exploring the PNR history data. The PNR data analysis will help the airline company to identify who are influential passengers in their social circles. Second, SOW is a marketing term representing traveler's value and contribution to a company, which refers to the amount of the customer's total spending that a business captures in the products and services that it offers. This study measures SOW as a ratio of tickets purchase amount from an airline company to passenger's total travel times. With SOW data analysis, this study identifies who are potential high-value travelers, and suggest corresponding marketing segmentation and promotion strategies based on different SOW level. This study also analyzes webtrends data to explore passenger behavior of websites and mobile usage. Passenger's webtrends information includes mobile number, membership number, identity number, and other web browsing records. Connecting these webtrends data with other information sources, this study provides an overview and insights on individual passenger's website and mobile usage. Furthermore, this study configures the accessing event flow on WebTrends, and incorporates it into the sequence analysis of passenger events. All data sources are provided by a Chinese Airline company. This study demonstrates how to develop comprehensive understanding on passenger travel behavior and social network using PNR, SOW and Webtrends data. The findings shed new light on airline precision marketing and cust- mer relationship management.
机译:本研究通过分析三种旅行数据来调查航空公司行为:乘客姓名记录(PNR),钱包(母猪)和Webtrend的分享。首先,PNR档案航空公司旅行行程为个人乘客和一群乘客一起旅行。通常,乘客及其伴随彼此接近,例如家庭,朋友,恋人,同事等。因此,乘客与其伴奏之间的社交网络可以通过探索PNR历史数据来构建。 PNR数据分析将有助于航空公司识别谁是社交界的有影响力的乘客。其次,母猪是代表旅行者对公司的价值和贡献的营销术语,这是指客户总支出的数量,该公司在提供的产品和服务中捕获业务。本研究播种播种机票从航空公司购买金额与乘客总旅行时间的比率。通过播种数据分析,本研究确定了谁是潜在的高价值旅行者,并提出了基于不同播种层面的相应营销细分和推广策略。本研究还分析了Webtrends数据,以探索网站和移动使用情况的乘客行为。乘客的Webtrends信息包括手机号码,会员号码,身份证号码和其他Web浏览记录。本研究将这些WebTrends数据与其他信息源连接,提供了对个人乘客网站和移动使用情况的概述和见解。此外,该研究在Webtrend上配置了访问事件流,并将其包含到乘客事件的序列分析中。所有数据来源都由中国航空公司提供。本研究展示了如何使用PNR,SOW和Webtrend数据对乘客旅行行为和社交网络进行全面了解。调查结果阐述了航空公司精密营销和客户关系管理的新光。

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