Predictive value of comments in the service engagement process

Stephen Carman, Ray Strong, Anca Chandra, Sechan Oh, Scott Spangler, Laura Anderson, Bernard Jansen

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

From the point of view of service providers, a service engagement process begins at the time an opportunity is known and concludes when a proposal for service delivery is resolved (won, lost or canceled). As such, understanding the service engagement process is critical for many businesses. This paper reports an application of text analytics to predict the engagement outcomes of service engagement opportunities based on written text comments about the opportunities during the course of the engagement processes. The comments are attached to documents, which also contain formally prepared solution proposals for potential deals. We examine whether the comments provide value by predicting the outcome of the engagement. Our final data set was 1,000 engagements and approximately 20,000 comments. We designed and carried out two experiments: one building a general classifier that would predict outcomes from comments; and the other building a one-sided classifier that could provide an advance warning for a significant subset of the deals with one particular outcome. The classifier achieved a 96% precision (4 percent false positives) for the cancel class and also a 96% recall on the full set of training documents. Our experiments show the predictive value of comments or service providers during service engagement and provide an interesting indication of trend in the practice of providing comments.

Original languageEnglish
JournalProceedings of the ASIST Annual Meeting
Volume49
Issue number1
DOIs
Publication statusPublished - 2012
Externally publishedYes

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Keywords

  • Crowdsourcing
  • Data mining
  • Services science
  • Text analytics

ASJC Scopus subject areas

  • Information Systems
  • Library and Information Sciences

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