Marketing teams regularly collaborate with Commercial Insights to run budget scenarios that optimize ROI on spend. However, these processes have often been manual, fragmented, and reliant on external support, with limited transparency, resulting in slow turnaround times, low reusability, and inconsistent auditability.

This session will present a practical, scalable approach to transforming Commercial Spend Optimization through a standardized, platform-based model. Attendees will gain insights into how centralized workflows, improved visibility, and reusable scenario planning capabilities can accelerate decision-making, strengthen governance, and drive more efficient investment outcomes across marketing and commercial teams.

Multi-Touch Attribution (MTA) is critical for understanding omnichannel effectiveness in healthcare, yet many organizations struggle to scale beyond pilots. This session covers a practical, end-to-end approach to designing and operationalizing MTA using integrated data sources like claims, digital engagement, and field interactions.

Attendees will gain insights into key modeling considerations and how to embed MTA into workflows, enabling teams to translate insights into action. The session will also highlight the role of governance, cross-functional collaboration, and feedback loops in driving adoption and measurable impact.

In this poster presentation, we are showcasing a knowledge graph–centric framework that connects fragmented pharma commercial data by encoding entities, relationships, and business context into a unified knowledge graph for AI applications. This structured context powers GenAI, analytics, and agentic workflows while reducing data silos and dependencies across HCP engagement, omnichannel optimization, and commercial strategy.

In this poster presentation, we highlight how organizations can improve commercial launch readiness by adopting an AI-powered, automated data quality framework for Marketing Mix (MMX) data. Traditional manual validation processes often slow workflows, reduce KPI visibility, and delay decision-making. A scalable model combining an Automated Data Quality Engine with a real-time Watch Tower enables faster validation, proactive issue detection, and continuous monitoring across brands and channels. By improving data trust, governance, and transparency, teams can make quicker, more confident decisions during critical launch periods while focusing resources on strategic priorities.