Industrial Benchmark Dataset for Customer Escalation Prediction

这是来自一家主要医疗设备制造商的真实世界工业基准数据集，用于预测客户升级。该数据集包含来自物联网（机器日志）和企业数据的特征，包括来自数千名高端医疗设备客户的升级标签。该数据集随附出版物“数据驱动和可解释的客户情绪监视器的系统设计”（已提交）。我们提供两年内收集的匿名数据版本。该数据集应推动新机器学习算法的研究和开发，以更好地应对现实世界的数据挑战，包括稀疏和嘈杂的标签以及概念漂移。其他挑战是用于预测任务的企业和基于日志的特征的最佳融合。因此，应确保设计的预测模型的可解释性，以具有实际相关性。支持软件 请使用相应的 GitHub 存储库 (https://github.com/annguy/customer-sentiment-monitor) 来设计和基准测试您的算法。引用和联系 如果您使用此数据集，请引用以下出版物：@ARTICLE{9520354, 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}, year={ 2021}, volume={9}, number={}, pages={117140-117152}, doi={10.1109/ACCESS.2021.3106791}} 如果您想取得联系，请联系 [e-mail] .

This is a real-world industrial benchmark dataset from a leading medical device manufacturer, developed for customer upgrade prediction tasks. The dataset incorporates features from both Internet of Things (IoT, machine logs) and enterprise data, alongside upgrade labels collected from thousands of high-end medical device customers. This dataset is associated with the submitted publication titled "System Design for a Data-Driven and Explainable Customer Sentiment Monitor". We provide an anonymized version of the dataset, which was compiled over a two-year collection period. This work aims to advance research and development of novel machine learning algorithms to better address real-world data challenges, including sparse and noisy labels as well as concept drift. A key additional challenge is the optimal fusion of enterprise and log-based features for the prediction task. Therefore, the interpretability of the designed predictive models must be prioritized to ensure practical relevance. Supporting software: Please use the corresponding GitHub repository (https://github.com/annguy/customer-sentiment-monitor) to design and benchmark your algorithms. Citation and Contact: If you utilize this dataset, please cite the following publication: @ARTICLE{9520354, 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}, year={2021}, volume={9}, number={}, pages={117140-117152}, doi={10.1109/ACCESS.2021.3106791}} If you wish to contact the authors, please send an email to [e-mail].

搜集汇总

背景与挑战

背景概述

该工业基准数据集源自一家医疗设备制造商，包含物联网日志和企业数据，用于预测客户升级。它旨在推动机器学习算法研究，以应对现实世界数据挑战，如稀疏标签和概念漂移，并强调模型可解释性。数据集随附相关出版物和软件支持，发布于2021年。