Commercializing New Predictive Metrics in B2B Markets: A Longitudinal Case

Predictive metrics increasingly influence organizational decision-making by shaping how firms evaluate alternatives, justify investments, and allocate resources. Although research has examined innovation commercialization, analytics adoption, and legitimacy-building, comparatively little is known about how predictive metrics become accepted as organizational decision tools in business-to-business (B2B) markets, where offerings are difficult to evaluate ex ante and must gain acceptance among multiple organizational stakeholders. This study examines the commercialization of a predictive media-quality metric through a longitudinal qualitative case study of Adelaide Metrics between 2019 and 2025. Drawing on six semi-structured executive interviews, internal and public documentation, third-party validation studies, and observations of industry forums, the study uses triangulated process analysis to examine how an analytical signal was transformed into a market-facing product and progressively embedded within organizational decision-making. The findings show that commercialization depended not only on technical development or predictive performance but also on four interdependent mechanisms: interpretive packaging, buyer enablement, workflow integration, and legitimacy transfer through third-party validation and ecosystem participation. These mechanisms evolved over time and together informed a six-stage framework describing how predictive metrics move from internal analytical artifacts to accepted organizational decision tools. The study contributes to innovation and new product development research by explaining how commercialization differs for probabilistic, credence-like analytical offerings whose value cannot be directly observed before adoption. It also extends research on analytics commercialization by demonstrating that interpretability, organizational embedding, and institutional legitimacy are co-produced through commercialization activities rather than emerging automatically from analytical accuracy. More broadly, the findings suggest that the proposed framework may inform research on the commercialization of predictive metrics beyond advertising, including other B2B contexts where organizations adopt predictive decision-support tools.