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Automatic Classification of a Customer’s Vertical Market by Using Text Mining

  • The German logistics company Schenker AG categorizes its customers into so-called vertical markets. For instance; category “Automotive” is assigned to car manufacturer BMW AG. The classification allows the company to evaluate its revenue and profits on different customer segments which, in turn, has an impact on Schenker’s strategic planning. Until now the assignment is carried out manually which means that someone from sales department should perform some research on a customer’s public profile whenever a new customer is registered in the database. With the rapid growth of global trade in recent years and Schenker’s expansion to the Asian market with thousands of new customers the manual approach is no longer sustainable in a global market. This thesis provides an alternative solution based on scraping customer data available in the web and classification of the extracted content. We deal with three significant difficulties: find web data related to a given customer name (we do have a company name but no homepage URL in Schenker database), extract a predictive portion of the data without introducing too much noise and, finally, set up a classification algorithm. Most importantly, the whole process needs to be implemented automatically. For the classification task, we have identified two tree-based classification algorithms random forest and extreme gradient boosting (xgboost). Random forest performs better by using package ranger with an overall 52% accuracy and 87.8% multiclass area under the curve. On the other hand, xgboost takes less time to compute, but the accuracy is poor as compared to random forest.

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Metadaten
Author:Muhammad Ahsan Nawaz
Examiner:Agatha Kalhoff, Paul Kinsvater
Document Type:Master Thesis
Language:English
Date of Publication (online):2018/07/03
Date of first Publication:2018/07/03
Publishing Institution:Hochschule Rhein-Waal
Degree-granting institution:Hochschule Rhein-Waal
Date of final exam:2018/07/03
Release Date:2018/08/06
GND Keyword:Natural language processing; Text classification
Faculties and Institutes:Fakultät Kommunikation und Umwelt
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 51 Mathematik
0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):License LogoCC BY 4.0 International - Namensnennung