Modeling and Forecasting the Customer Activity for an European Travel Website

12 Pages Posted: 13 Jun 2019

See all articles by Henning Nobmann

Henning Nobmann

affiliation not provided to SSRN

Thomas Winter

Beuth University of Applied Sciences Berlin

Patrick Erdelt

affiliation not provided to SSRN

Nicola Winter

affiliation not provided to SSRN

Alwin Haensel

Haensel AMS

Date Written: May 30, 2019

Abstract

How can predictive analysis of customer data be used to identity the decrease or increase of customer activity over time during the product search and the booking process? Another question that arises in this context is how many data is necessary to carry out a reliable analysis?

We present a case study from an European travel website based on a session history of 15 months only here only a small part of the customers are known to the website and where a huge number of users regularly delete their browser histories.

We focus on the analysis of the users' web traffic while visiting the booking platform. Based on a classification algorithm we identify the non-contractual customers who are mostly anonymous to the website, and investigate the short-term customer activity with respect to customer churn on a more microscopic level.

Keywords: predictive analysis, booking platform, churn, e-commerce, neural networks, random forest

JEL Classification: C52, C53, C81

Suggested Citation

Nobmann, Henning and Winter, Thomas and Erdelt, Patrick and Winter, Nicola and Haensel, Alwin, Modeling and Forecasting the Customer Activity for an European Travel Website (May 30, 2019). Available at SSRN: https://ssrn.com/abstract=3396362 or http://dx.doi.org/10.2139/ssrn.3396362

Henning Nobmann

affiliation not provided to SSRN

Thomas Winter (Contact Author)

Beuth University of Applied Sciences Berlin ( email )

Luxemburger Str. 10
Berlin, D-13353
Germany

Patrick Erdelt

affiliation not provided to SSRN

Nicola Winter

affiliation not provided to SSRN

Alwin Haensel

Haensel AMS ( email )

Berlin
Germany

HOME PAGE: http://https://haensel-ams.com/

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