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Authors: Frederik Simon Bäumer and Bianca Buff

Affiliation: Semantic Information Processing Group, Paderborn University, Warburger Str. 100, 33098 Paderborn and Germany

Keyword(s): Customer Relationship Management, Web Mining, Local Grammars.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Change Detection ; Collaboration and e-Services ; Data Engineering ; Data Management and Quality ; e-Business ; Enterprise Information Systems ; Informatics in Control, Automation and Robotics ; Information Integration ; Integration/Interoperability ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Signal Processing, Sensors, Systems Modeling and Control ; Symbolic Systems ; Text Analytics

Abstract: Customer Relationship Management refers to the consistent orientation of a company towards its customers. Since this requires customer-specific data sets, techniques such as web mining are used to acquire information about customers and their behavior. In this case study, we show how web mining can be used to automatically collect information from clients’ websites for Customer Relationship Management systems in Business-to-Business environments. Here, we use tailored local grammars to extract relevant information in order to build up a data set that meets the required high quality standards. The evaluation shows that local grammars produce substantial high-quality results, but turn out to be too rigid in some cases. In summary, our case study demonstrates that web mining in combination with local grammars is suitable for Business-to-Business CRM as long as the information demand can be defined precisely and the requested information is available online.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Bäumer, F. and Buff, B. (2019). How to Boost Customer Relationship Management via Web Mining Benefiting from the Glass Customer’s Openness. In Proceedings of the 8th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-377-3; ISSN 2184-285X, SciTePress, pages 129-136. DOI: 10.5220/0007828301290136

@conference{data19,
author={Frederik Simon Bäumer. and Bianca Buff.},
title={How to Boost Customer Relationship Management via Web Mining Benefiting from the Glass Customer’s Openness},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - DATA},
year={2019},
pages={129-136},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007828301290136},
isbn={978-989-758-377-3},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - DATA
TI - How to Boost Customer Relationship Management via Web Mining Benefiting from the Glass Customer’s Openness
SN - 978-989-758-377-3
IS - 2184-285X
AU - Bäumer, F.
AU - Buff, B.
PY - 2019
SP - 129
EP - 136
DO - 10.5220/0007828301290136
PB - SciTePress