Enterprise pre-sales forums: A preliminary study of metadata and content

Asynchronous discussion forums are one of the artifacts of the internet age. They occur in a wide variety of applications from distance learning to technical support. Technical support forums have also proliferated in enterprises, and today form a salient feature of many technical interactions in large enterprises. Two interconnected example applications where such forums may be employed are the following: customer pre-sales, where sales teams attempt to answer queries of potential customers; and internal forums where technical staff attempt to provide assistance to sales teams on urgent issues that require immediate attention. In this paper, we report a study of an internal technical support forum for pre-sales in a large Fortune-10 global enterprise. The data being generated on such forums is fast evolving, requires quick and intelligent human (assisted by machine) responses, and is of high value to the enterprise since it directly affects sales. Owing to this, it poses unique challenges. We conduct a two-fold study of the forum. First, we study the metadata in the forum messages to understand the temporal, participant, and length profiles of messages. Second, we use text mining to detect trends in forums using clustering and information-theoretic techniques. To our knowledge, this is the first study of an enterprise internal technical support forum. As a focal point in our study, we describe the problem of identifying "hot" or "urgent" issues early, so that management can take requisite steps to deal with emerging problems. Our results are surprising: we show that threads that bring urgent issues to light have temporal, length, and content profiles that resemble that of non-urgent threads. Therefore, the detection of such threads via metadata and content analysis is difficult. We present a solution to this problem based on participant profiles.