Ghent University Academic Bibliography

Your filters: cql: keyword exact "customer acquisition"

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- Conference Paper
- C3
- open access
Profit-driven analytics : how to align machine learning with business objectives

Shimanto Rahman (UGent) , Matthias Bogaert (UGent) and Steve Muylle (UGent)

(2023) Flanders Make Conference on Machines, Vehicles and Production Technology (CMVPT) 2023, Abstracts.
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- Journal Article
- A1
- open access
Predicting donation behavior : acquisition modeling in the nonprofit sector using Facebook data

Lisa Schetgen (UGent) , Matthias Bogaert (UGent) and Dirk Van den Poel (UGent)

(2021) DECISION SUPPORT SYSTEMS. 141.
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- Conference Paper
- C3
Acquisition modeling in the nonprofit sector based on Facebook data

Lisa Schetgen (UGent) , Matthias Bogaert (UGent) and Dirk Van den Poel (UGent)

(2019) INFORMS Annual Meeting, Abstracts.
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The added value of social media data in B2B customer acquisition systems : a real-life experiment

Matthijs Meire (UGent) , Michel Ballings (UGent) and Dirk Van den Poel (UGent)

(2017) DECISION SUPPORT SYSTEMS. 104. p.26-37
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Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework

Jeroen D'Haen (UGent) and Dirk Van den Poel (UGent)

(2013) INDUSTRIAL MARKETING MANAGEMENT. 42(4). p.544-551
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Predicting customer profitability during acquisition: finding the optimal combination of data source and data mining technique

Jeroen D'Haen (UGent) , Dirk Van den Poel (UGent) and Dirk Thorleuchter (UGent)

(2013) EXPERT SYSTEMS WITH APPLICATIONS. 40(6). p.2007-2012

Academic Bibliography

Profit-driven analytics : how to align machine learning with business objectives

Predicting donation behavior : acquisition modeling in the nonprofit sector using Facebook data

Acquisition modeling in the nonprofit sector based on Facebook data

The added value of social media data in B2B customer acquisition systems : a real-life experiment

Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework

Predicting customer profitability during acquisition: finding the optimal combination of data source and data mining technique

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