Service Analytics
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ItemProcess Mining for Advanced Service Analytics – From Process Efficiency to Customer Encounter and Experience( 2022-01-04)With the ongoing trend of servitization nurtured through digital technologies, the analysis of services as a starting point for improvement is gaining more and more importance. Service analytics has been defined as a concept to analyze the data generated during service execution to create value for providers and customers. To create more useful insights from the data, there is a continuous need for more advanced solutions for service analytics. One promising technology is process mining which has its origins in business process management. Our work provides insights into how process mining is currently used to analyze service processes and how it could be used along the service process. We find that process mining is increasingly applied for the analysis of the providers' internal operations, but more emphasis should be put on analyzing the customer interaction and experience.
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ItemImproving Support Ticket Systems Using Machine Learning: A Literature Review( 2022-01-04)Processing customer support requests via a support ticket system is a key-element for companies to provide support to their customers in an organized and professional way. However, distributing and processing such tickets is much work, increasing the cost for the support providing company and stretching the resolution time. The advancing potential of Machine Learning has led to the goal of automating those support ticket systems. Against this background, we conducted a Literature Review aiming at determining the present state-of-the-art technology in the field of automated support ticket systems. We provide an overview about present trends and topics discussed in this field. During the Literature Review, we found creating an automated incident management tool being the majority topic in the field followed by request escalation and customer sentiment prediction and identified Random Forrest and Support Vector Machine as best performing algorithms for classification in the field.
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ItemFake or Credible? Towards Designing Services to Support Users’ Credibility Assessment of News Content( 2022-01-04)Fake news has become omnipresent in digitalized areas such as social media platforms. While being disseminated online, it also poses a threat to individuals and societies offline, for example, in the context of democratic elections. Research and practice have investigated the detection of fake news with behavioral science or method-related perspectives. However, to date, we lack design knowledge on presenting fake news warnings to users to support their individual news credibility assessment. We present the journey through the first design cycle on developing a fake news detection service focusing on the user interface design. The design is grounded in concepts from the field of source credibility theory and instantiated in a prototype that was qualitatively evaluated. The 13 participants communicated their interest in a lightweight application that aids in the news credibility assessment and rated the design features as useful as well as desirable.
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ItemComparative Analysis of Classical and Deep Learning-based Natural Language Processing for Prioritizing Customer Complaints( 2022-01-04)Recent advancements in natural language processing have been shown to be very effective for different text mining tasks and thus have provided the opportunity to enhance service research. To improve the customer service experience, this paper compares several natural language processing approaches in order to automatically prioritize incoming customer complaints for service agents. This can help companies to reduce customers’ friction and enable effective resource allocations. Our paper uses state- of-the-art feature engineering techniques (e.g., term frequency, TF-IDF and Word2Vec) to identify key words that could enable machine to prioritize complainers. We experimented with many classical machine learning classification algorithms, such as Random Forests, Support Vector Machines, Decision Trees and Logistic Regression, as well as with deep learning-based classifiers, such as convolutional neural networks, bidirectional long short-term memory, and the pre-trained language model BERT to compare the model performance. Our findings show that the pre-trained language model BERT and TF- IDF in combination with Logistic Regression yields the highest macro averaged F1-score across the multiple classes and is therefore most capable of predicting the priority group of incoming customer complaints.
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ItemAnalyzing the Impact of Complaints on Customer Satisfaction in the Travel Industry( 2022-01-04)Customer satisfaction is crucial for the long term success of any travel service provider. Therefore, identifying situations that can lead to customer dissatisfaction is critical. The strongest evidence of customers dissatisfaction are their complaints. While complaints do not occur very often, they often lead to loss of customer goodwill which can cost travel providers millions of dollars in compensation and future revenue. In this paper, we describe an approach to proactively identify high value and high risk customers that have the highest propensity to complain, thereby empowering customer service teams with information to deliver a more timely, relevant and impactful service experience. We use three key aspects in this approach: (i) specialized feature engineering for the travel industry; (ii) handling extremely imbalanced data and (iii) adaptation of binary classification, anomaly detection and learning to rank models to our specific task. This research is an important step towards more individualized understanding of customer behavior, and potential service enhancements to further increase customer satisfaction.
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ItemA2Log: Attentive Augmented Log Anomaly Detection( 2022-01-04)Anomaly detection becomes increasingly important for the dependability and serviceability of IT services. As log lines record events during the execution of IT services, they are a primary source for diagnostics. Thereby, unsupervised methods provide a significant benefit since not all anomalies can be known at training time. Existing unsupervised methods need anomaly examples to obtain a suitable decision boundary required for the anomaly detection task. This requirement poses practical limitations. Therefore, we develop A2Log, which is an unsupervised anomaly detection method consisting of two steps: Anomaly scoring and anomaly decision. First, we utilize a self-attention neural network to perform the scoring for each log message. Second, we set the decision boundary based on data augmentation of the available normal training data. The method is evaluated on three publicly available datasets and one industry dataset. We show that our approach outperforms existing methods. Furthermore, we utilize available anomaly examples to set optimal decision boundaries to acquire strong baselines. We show that our approach, which determines decision boundaries without utilizing anomaly examples, can reach scores of the strong baselines.
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ItemIntroduction to the Minitrack on Service Analytics( 2022-01-04)