Predictive Customer Data Analytics : The Value of Public Statistical Data and the Geographic Model Transferability
Hopf, Konstantin; Riechel, Sascha Jonas; Sodenkamp, Mariya; u. a. (2018): „Predictive Customer Data Analytics : The Value of Public Statistical Data and the Geographic Model Transferability“. Bamberg: opus.
38. International Conference on Information Systems (ICIS), South Korea 2017
Year of publication:
Ursprünglich in: AIS electronic library, 38. International Conference on Information Systems (ICIS 2017, Datascience, 9), South Korea; http://aisel.aisnet.org/icis2017/DataScience/Presentations/9/
Year of first publication:
Companies pay high prices for detailed customer information (e.g., income, household type) for gaining insights and conducting targeted marketing campaigns. We argue that companies can utilize predictive analytics artifacts to derive such information from existing customer data in combination with freely available data sources, such as open government data. In this study, we use a machine learning artifact for a specific yet highly relevant case from the utility industry, trained on data of 7,504 energy customers and investigate two important aspects for predictive business analytics: First, we identified the sparsely available open government statistics and found that even that limited amount of open data can increase our artifact’s performance. Second, we applied the predictive models, trained with a regional customer dataset, on households in other geographic regions with acceptable performance loss. The results support the development of systems aiding managerial decision-making, predictive marketing and showcase the value of open data.
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Supervised Machine Learning
Open Government Data
Public Sector Information
January 29, 2018