Customer online shopping experience data analytics: Integrated customer segmentation and customised services prediction model

Abstract

Purpose

The purpose of this paper is to develop a customer online behaviour analysis tool, segment high-value customers, analyse their online purchasing behaviour and predict their next purchases from an online air travel corporation.

Design/methodology/approach

An operations review of the customer online shopping process of an online travel agency (OTA) is conducted. A customer online shopping behaviour analysis tool is developed. The tool integrates competitors’ pricing data mining, customer segmentation and predictive analysis. The impacts of competitors’ price changes on customer purchasing decisions regarding the OTA’s products are evaluated. The integrated model for mining pricing data, identifying potential customers and predicting their next purchases helps the OTA recommend tailored product packages to its individual customers with reference to their travel patterns.

Findings

In the customer segmentation analysis, 110,840 customers are identified and segmented based on their purchasing behaviour. The relationship between the purchasing behaviour in an OTA and the price changes of different OTAs are analysed. There is a significant relationship between the flight duration time and the purchase lead time. The next travel destinations of segmented high-value customers are predicted with reference to their travel patterns and the significance of the relationships between destination pairs.

Practical implications

The developed model contributes to pricing evaluation, customer segmentation and package customization for online customers.

Originality/value

This study provides novel method and insights into customer behaviour towards OTAs through an integrated model of customer segmentation, customer behaviour and prediction analysis.

Citation

Wong, E. and Wei, Y. (2018), "Customer online shopping experience data analytics: Integrated customer segmentation and customised services prediction model", International Journal of Retail & Distribution Management, Vol. 46 No. 4, pp. 406-420. https://doi.org/10.1108/IJRDM-06-2017-0130