Rapid growth of the services industry over the past few years has led to increased number of research efforts in the area of service quality improvement. However, analyzing service quality and determining the factors inuencing consumer's perception of service quality is a challenging problem. Our work explores a data driven approach to analyze the comparative inuence of the two primary aspects 'experience' and 'outcome', on service quality. With our novel approach, we analyze a large number of customer satisfaction feedback records using text analytics techniques. We apply simple statistical and machine learning techniques to study the dynamics between occurrence frequencies of keywords related to both experience and outcome in user comments and the corresponding customer satisfaction scores. Based on our analysis we observe that in the context of customer support centers, service experience has stronger inuence on perceived customer satisfaction and service quality.
[Show abstract][Hide abstract] ABSTRACT: textlessptextgreaterThe attainment of quality in products and services has become a pivotal concern of the 1980s. While quality in tangible goods has been described and measured by marketers, quality in services is largely undefined and unresearched. The authors attempt to rectify this situation by reporting the insights obtained in an extensive exploratory investigation of quality in four service businesses and by developing a model of service quality. Propositions and recommendations to stimulate future research about service quality are offered.
Journal of Marketing 01/1985; 49(4):41-50. DOI:10.2307/1251430 · 5.47 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Text classification has matured well as a research discipline over the years. At the same time, business intelligence over databases has long been a source of insights for enterprises. With the growing importance of the services industry, customer relationship management and contact center operations have become very important. Specifically, the voice of the customer and customer satisfaction (C-Sat) have emerged as invaluable sources of insights about how an enterprise's products and services are percieved by customers. In this demonstration, we present the IBM Technology to Automate Customer Satisfaction analysis (ITACS) system that combines text classification technology, and a business intelligence solution along with an interactive document labeling interface for automating C-Sat analysis. This system has been successfully deployed in client accounts in large contact centers and can be extended to any services industry setting for analyzing unstructured text data. This demonstration will highlight the importance of intervention and interactivity in real-world text classification settings. We will point out unique research challenges in this domain regarding label-sets, measuring accuracy, and interpretability of results and we will discuss solutions and open questions.
Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, August 24-27, 2008; 01/2008
[Show abstract][Hide abstract] ABSTRACT: Data analytics tools and frameworks abound, yet rapid deployment of analytics solutions that deliver actionable in- sights from business data remains a challenge. The pri- mary reason is that on-field practitioners are required to be both technically proficient and knowledgeable about the business. The recent abundance of unstructured business data has thrown up new opportunities for analytics, but has also multiplied the deployment challenge, since interpreta- tion of concepts derived from textual sources require a deep understanding of the business. In such a scenario, a man- aged service for analytics comes up as the best alternative. A managed analytics service is centered around a business analyst who acts as a liaison between the business and the technology. This calls for new tools that assist the analyst to be efficient in the tasks that she needs to execute. Also, the analytics needs to be repeatable, in that the delivered insights should not depend heavily on the expertise of spe- cific analysts. These factors lead us to identify new areas that open up for KDD research in terms of 'time-to-insight' and repeatability for these analysts. We present our analyt- ics framework in the form of a managed service offering for CRM analytics. We describe different analyst-centric tools using a case study from real-life engagements and demon- strate their effectiveness.
Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28 - July 1, 2009; 01/2009
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.