A global talent technology company relied on a rigid, rules-based system to determine who within customer organizations held real purchasing authority. This legacy approach depended heavily on job titles, admin flags, and seniority assumptions—factors that often misrepresented actual influence.

As job titles became more diverse and organizational structures more complex, the rules engine struggled. Marketing campaigns routinely targeted the wrong personas, sales spent valuable time hunting for the right contact, and product teams lacked clarity on which user types drove platform adoption.

The company requested a regression-based refinement to improve accuracy. However, Steven recognized that linear models could not capture the nonlinear behavioral patterns, organizational context, and interaction effects required to correctly identify decision makers at scale.

Steven proposed a full modernization: replacing the rules engine with an advanced machine learning model designed specifically to uncover the hidden signals that define real decision-making authority.

He began by aligning stakeholders across Product, Marketing, and Sales on a shared, operational definition of a “decision maker”—not just by title, but by strategic influence, budget ownership, and platform engagement patterns.Using this foundation, Steven engineered a multi-layer feature set incorporating:

• Semantic interpretation of job titles and roles
• Behavioral signals from hiring, sourcing, learning, and analytics tools
• Firmographic attributes such as company size, industry, and seat mix
• Network influence indicators derived from professional connections and collaboration patterns

He then developed a stacked ensemble modeling architecture combining gradient-boosted trees, regularized regression for interpretability, and embedding-based methods to accurately classify ambiguous or emerging role types. The resulting model produced a high-precision probability score for every member’s likelihood of being a purchasing decision maker.

The machine learning model replaced the legacy rules-based system and delivered a substantial increase in accuracy, coverage, and business value. Marketing campaigns became significantly more targeted, reducing spend waste and improving engagement rates. Sales teams could immediately identify the true buying group within each account, accelerating outreach and shortening deal cycles. Product teams gained deeper insight into how different personas interact with the platform, enabling more personalized feature rollouts and onboarding journeys.

The initiative equipped the company with a modern, scalable intelligence layer that reshaped how it understands organizational influence. It became a foundational component of future AI-driven go-to-market strategy, customer insights, and product adoption analytics.