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How to Boost Customer Relationship Management via Web Mining Benefiting from the Glass Customer's Openness

F. Bäumer, B. Buff, in: S. Hammoudi , C. Quix , J. Bernardino (Eds.), Proceedings of the 8th International Conference on Data Science, Technology and Applications, SciTePress, 2019, pp. 129–136.

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Konferenzbeitrag | Veröffentlicht | Englisch
Autor*in
Bäumer, FrederikFH Bielefeld ; Buff, Bianca
Herausgeber*in
Hammoudi , Slimane ; Quix , Christoph ; Bernardino , Jorge
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.
Erscheinungsjahr
Titel des Konferenzbandes
Proceedings of the 8th International Conference on Data Science, Technology and Applications
Band
1
Seite
129-136
Konferenz
8th International Conference on Data Science, Technology and Applications
Konferenzort
Prague, Czech Republic
Konferenzdatum
2019-07-26 – 2019-07-28
ISSN
FH-PUB-ID

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Bäumer, Frederik ; Buff, Bianca: How to Boost Customer Relationship Management via Web Mining Benefiting from the Glass Customer’s Openness. In: Hammoudi , S. ; Quix , C. ; Bernardino , J. (Hrsg.): Proceedings of the 8th International Conference on Data Science, Technology and Applications. Bd. 1 : SciTePress, 2019, S. 129–136
Bäumer F, Buff B. How to Boost Customer Relationship Management via Web Mining Benefiting from the Glass Customer’s Openness. In: Hammoudi S, Quix C, Bernardino J, eds. Proceedings of the 8th International Conference on Data Science, Technology and Applications. Vol 1. SciTePress; 2019:129-136. doi:10.5220/0007828301290136
Bäumer, F., & Buff, B. (2019). How to Boost Customer Relationship Management via Web Mining Benefiting from the Glass Customer’s Openness. In S. Hammoudi , C. Quix , & J. Bernardino (Eds.), Proceedings of the 8th International Conference on Data Science, Technology and Applications (Vol. 1, pp. 129–136). Prague, Czech Republic : SciTePress. https://doi.org/10.5220/0007828301290136
@inproceedings{Bäumer_Buff_2019, title={How to Boost Customer Relationship Management via Web Mining Benefiting from the Glass Customer’s Openness}, volume={1}, DOI={10.5220/0007828301290136}, booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications}, publisher={SciTePress}, author={Bäumer, Frederik and Buff, Bianca}, editor={Hammoudi , Slimane and Quix , Christoph and Bernardino , Jorge Editors}, year={2019}, pages={129–136} }
Bäumer, Frederik, and Bianca Buff. “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, edited by Slimane Hammoudi , Christoph Quix , and Jorge Bernardino , 1:129–36. SciTePress, 2019. https://doi.org/10.5220/0007828301290136.
F. Bäumer and B. Buff, “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, Prague, Czech Republic , 2019, vol. 1, pp. 129–136.
Bäumer, Frederik, and Bianca Buff. “How to Boost Customer Relationship Management via Web Mining Benefiting from the Glass Customer’s Openness.” Proceedings of the 8th International Conference on Data Science, Technology and Applications, edited by Slimane Hammoudi et al., vol. 1, SciTePress, 2019, pp. 129–36, doi:10.5220/0007828301290136.

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