Dataset Open Access

Industrial Benchmark Dataset for Customer Escalation Prediction

Nguyen, An; Foerstel, Stefan; Kittler, Thomas; Kurzyukov, Andrey; Schwinn, Leo; Zanca, Dario; Hipp, Tobias; Da Jun, Sun; Schrapp, Michael; Rothgang, Eva; Eskofier, Bjoern

Dublin Core Export

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Nguyen, An</dc:creator>
  <dc:creator>Foerstel, Stefan</dc:creator>
  <dc:creator>Kittler, Thomas</dc:creator>
  <dc:creator>Kurzyukov, Andrey</dc:creator>
  <dc:creator>Schwinn, Leo</dc:creator>
  <dc:creator>Zanca, Dario</dc:creator>
  <dc:creator>Hipp, Tobias</dc:creator>
  <dc:creator>Da Jun, Sun</dc:creator>
  <dc:creator>Schrapp, Michael</dc:creator>
  <dc:creator>Rothgang, Eva</dc:creator>
  <dc:creator>Eskofier, Bjoern</dc:creator>
  <dc:description>This is a real-world industrial benchmark dataset from a major medical device manufacturer for the prediction of customer escalations. The dataset contains features derived from IoT (machine log) and enterprise data including labels for escalation from a fleet of thousands of customers of high-end medical devices. 

The dataset accompanies the publication "System Design for a Data-driven and Explainable Customer Sentiment Monitor" (submitted). We provide an anonymized version of data collected over a period of two years.

The dataset should fuel the research and development of new machine learning algorithms to better cope with real-world data challenges including sparse and noisy labels, and concept drifts. Additional challenges is the optimal fusion of enterprise and log based features for the prediction task. Thereby, interpretability of designed prediction models should be ensured  in order to have practical relevancy. 

Supporting software

Kindly use the corresponding GitHub repository ( to design and benchmark your algorithms. 


Citation and Contact

If you use this dataset please cite the following publication:


  author={Nguyen, An and Foerstel, Stefan and Kittler, Thomas and Kurzyukov, Andrey and Schwinn, Leo and Zanca, Dario and Hipp, Tobias and Jun, Sun Da and Schrapp, Michael and Rothgang, Eva and Eskofier, Bjoern},
  journal={IEEE Access}, 
  title={System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data}, 


If you would like to get in touch, please contact
  <dc:subject>machine learning, imbalanced data, industrial benchmark</dc:subject>
  <dc:title>Industrial Benchmark Dataset for Customer Escalation Prediction</dc:title>
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