A Survey of Text Mining Techniques and Applications <s> 1) Customer Relationship Management (CRM): <s> In order to delineate the state of the art of the main TM applications a two-step strategy has been pursued: first of all, some of the main European and Italian companies offering TM solutions were contacted, in order to collect information on the characteristics of the applications; secondly, a detailed search on the web was made to collect further information about users or developers and applications. On the basis of the material collected, a synthetic grid was built to collocate, from more than 300 cases analysed, the 100 ones that we considered most relevant for the typology of function and sector of activity. The joint analysis of the different case studies has given an adequate picture of TM applications according to the possible types of results that can be obtained, the main specifications of the sectors of applications and the type of functions. Finally it is possible to classify the applications matching the level of customisation (followed in the tools development) and the level of integration (between users and developers). This matching produces four different situations: standardisation, outsourcing, internalisation, synergism. <s> BIB001 </s> A Survey of Text Mining Techniques and Applications <s> 1) Customer Relationship Management (CRM): <s> We present our experiences in building and deploying a text mining solution in services industry settings, specifically in contact centers. We describe the voice of customer (VoC) and customer satisfaction (C-Sat) analysis settings and outline several unique research challenges brought about by this confluence of text mining and industrial services research. We describe our system for integrated text classification, business intelligence and interactive text labeling for C-Sat analysis. We recount invaluable lessons learned as computer science researchers in services research engagements. The system has been deployed in multiple accounts in contact centers and can be extended to any industrial CRM service practice to analyze unstructured text data. <s> BIB002
In CRM BIB001 domain the most widespread applications are related to the management of the contents of clients' messages. This kind of analysis often aims at automatically rerouting specific requests to the appropriate service or at supplying immediate answers to the most frequently asked questions. Services research has emerged as a green field area for application of advances in computer science and IT. CRM practices, particularly contact centers (call centers) in our context, have emerged as hotbeds for application of innovations in the areas of knowledge management, analytics, and data mining. Unstructured text documents produced from a variety of sources in today contact centers have exploded in terms of the sheer volume generated. Companies are increasingly looking to understand and analyze this content to derive operational and business insights. The customer, the end consumer of products and services, is receiving increased attention. Analytics and business intelligence (BI) applications revolving around the customer has led to emergence of areas like customer experience management, customer relationship management, and customer service quality. These are becoming critical to competitive growth, and sometimes even, survival. Applications with such customer focus are most evident in services companies, especially CRM practices and contact centers. We focus on the particular application of C-Sat BIB002 (Customer Satisfaction) analysis in contact centers depicted in Figure 19 . Customers interacting with contact centers, typically fill out feedback surveys with free form text comments which are directly indicative of their (dis)satisfaction with products, services, or interactions. Currently, only manual analysis is done, if at all, on a small sample of available data. Various problem buckets identified by the analysis can directly lead to actions like improving a particular agent's accent, or imparting product training for other agents. Text classification can help automate this making it consistent and exhaustive.