Research of discovering high-value passengers of airline based on PNR data

Weidong Cao, Liang Bai and Xia

Abstract

In the case of solving the problem of that the evaluation index of frequent flyer program is single, cannot identify high-value passengers accurately, present a method of discovering high-value passengers combines Map/Reduce and data mining. Processing gigabytes of PNR data on Hadoop by Map/Reduce parallelly, according to the improved RFD model and analytic hierarchy process, determine the customer value indexes and the weight of each index, identify the high-value passengers by data mining, and make an experiment on a real PNR dataset. The experimental result shows that, the method can effectively identify the high-value passengers of airline and provide a favorable basis for airlines to make effective decisions.