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

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Title:Business Analytics for Sales Pipeline Management in the Software Industry: A Machine Learning Perspective
Authors:Eitle, Verena
Buxmann, Peter
Keywords:Analytics and AI for Industry - Specific Applications
Decision Analytics, Mobile Services, and Service Science
Lead and Opportunity Management, Machine Learning, Sales Pipeline Process, Software Industry, Supervised Classifiers
Date Issued:08 Jan 2019
Abstract:This 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.
Pages/Duration:10 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Analytics and AI for Industry - Specific Applications

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