Business Analytics for Sales Pipeline Management in the Software Industry: A Machine Learning Perspective

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dc.contributor.authorEitle, Verena
dc.contributor.authorBuxmann, Peter
dc.date.accessioned2019-01-02T23:48:06Z
dc.date.available2019-01-02T23:48:06Z
dc.date.issued2019-01-08
dc.description.abstractThis study proposes a model designed to help sales representatives in the software industry to manage the complex sales pipeline. By integrating business analytics in the form of machine learning into lead and opportunity management, data-driven qualification support reduces the high degree of arbitrariness caused by professional expertise and experiences. Through the case study of a software provider, we developed an artifact consisting of three models to map the end-to-end sales pipeline process using real business data from the company’s CRM system. The results show a superiority of the CatBoost and Random Forest algorithm over other supervised classifiers such as Support Vector Machine, XGBoost, and Decision Tree as the baseline. The study also reveals that the probability of either winning or losing a sales deal in the early lead stage is more difficult to predict than analyzing the lead and opportunity phases separately. Furthermore, an explanation functionality for individual predictions is provided.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2019.125
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59541
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAnalytics and AI for Industry - Specific Applications
dc.subjectDecision Analytics, Mobile Services, and Service Science
dc.subjectLead and Opportunity Management, Machine Learning, Sales Pipeline Process, Software Industry, Supervised Classifiers
dc.titleBusiness Analytics for Sales Pipeline Management in the Software Industry: A Machine Learning Perspective
dc.typeConference Paper
dc.type.dcmiText

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