Opportunity Management for Business-to-Business (B2B) Service Organizations: A Theory-Informed Decision Support Framework

To meet sales targets with limited resources, business-to-business service firms must prioritize promising opportunities within large pipelines. Yet, both theory and practice indicate that such decisions often rely on intuition or ad hoc rules, resulting in suboptimal sales and operations planning. Drawing on the relationship management and organizational buying literature, we develop a theory-informed sales-operations framework that links buyer typology (e.g., new vs. rebid) and opportunity characteristics (e.g., size and relationship strength) to the firm’s bid and win decisions. Using archival data from a global on-site services provider encompassing 4,574 opportunities across 23 countries (2010–2021), we document a persistent tradeoff: while low-risk, relationship-based opportunities yield higher win probabilities, they are insufficient to achieve regional sales goals. We address this challenge through an ensemble machine-learning model that predicts win likelihood and a combinatorial optimization model that allocates bidding capacity strategically. The integrated framework improves predictive accuracy by 11% and could have increased realized sales by 21% while bidding on 38% fewer opportunities. Extensions incorporating stochastic programming and a Heckman-style two-stage correction enhance the framework’s robustness to uncertainty and data selection bias, providing managers with a rigorous, data-driven approach to opportunity management.