GrowthLoop, a pioneer in agentic AI-powered marketing solutions, and EVERSANA®, a leading independent provider of global services to the life sciences industry, today announced an expansion of their Composable AI Decisioning solution to include biopharmaceutical marketing, improving when and how marketers engage healthcare professionals. Building on GrowthLoop’s composable CDP and AI Studio, the AI decisioning applies continuous experimentation to identify which marketing actions improve healthcare professional engagement and use those findings to inform AI decisioning and guide subsequent interactions.
The work addresses a practical challenge for biopharma marketers more broadly. First-party data can show past engagement metrics, but marketers also need evidence of which marketing actions make a difference, for which audiences, and under what circumstances — causal data. Powered by continuous experimentation, the GrowthLoop team, together with partners including EVERSANA, is building a Causal Context Graph, a knowledge graph unique to each organization that continuously captures healthcare professional context and learnings from prior interactions to provide reliable, verifiable feedback for AI decisioning.
“Our pharmaceutical clients need more than just another performance report or audience segment. They need to understand which marketing decisions result in a measurable, impactful difference for the metrics that matter most to their bottom line and the marketing decisions that are most likely to yield the highest returns in the future,” said Faruk Capan, Chief Innovation Officer, EVERSANA. “GrowthLoop’s expanded AI decisioning capabilities are a huge step for the industry.”
Experiments reveal opportunities to improve engagement
In one campaign, GrowthLoop’s decisioning capabilities identified opportunities to improve send timing and channel selection. The results were measured using a weighted composite engagement score that combines digital engagement and prescription activity, allowing life sciences customers to bridge optimization across clinical and digital outcomes:
- Experimentation can uncover opportunities that predictive models alone may miss. Existing models predicted audiences would respond better to afternoon sends, but GrowthLoop’s experimentation and AI decisioning uncovered that morning sends delivered a 13% higher weighted composite engagement score than afternoon sends.
- Channel selection can make a meaningful difference for specific audiences. Digital display advertising, such as banner ads on websites or apps, delivered a 52% higher weighted composite engagement score per user than email for audiences sourced through peer-to-peer (P2P) programs. Other audience sources showed no difference, indicating an opportunity to shift P2P-sourced audiences toward display advertising rather than change the channel mix across all audiences.
GrowthLoop’s enhanced capabilities are especially important in highly regulated industries, where teams must adhere to detailed restrictions for both messaging cadence and data security. With its warehouse-native architecture and journey orchestration tool, GrowthLoop was able to support the team’s specific goals while maintaining industry compliance.
Connecting immediate signals with longer-term objectives
To evaluate campaign performance, customers can use a weighted score combining email views, email engagement, and prescription activity. Prescription activity receives the greatest weight because it is the primary business objective. But prescription data is often delayed by months and provides less frequent feedback, so GrowthLoop’s multi-objective optimization also uses digital engagement signals to help clients learn from campaigns as they progress.
That learning builds across repeated interactions. Each time a healthcare professional moves through a campaign journey, the system uses those learnings to inform subsequent choices, including send timing, the interval between touchpoints, and channel selection.
“Frontier AI has made remarkable progress in math and coding because those fields are verifiable: you can run tests at scale and check the proof. But AI can’t solve enterprise growth yet, because — unlike math and coding — marketing is not a verifiable domain.” said Anthony Rotio, Co-Founder and Co-CEO of GrowthLoop. “Historically, marketing has lacked causal evidence of what actually engages a customer or changes an action. But these results demonstrate how GrowthLoop is making marketing verifiable and applying causal learnings to move the needle for the business.”
GrowthLoop’s composable CDP foundation brings audience segmentation, customer journey orchestration, campaign activation, and performance analysis together on the data cloud. With AI Studio, Composable AI Decisioning, experimentation tools, and newly released Engagement Suite, it creates a Compound Marketing Engine — a closed-loop marketing system that learns and improves with every campaign.
Building on this closed-loop system, GrowthLoop’s roadmap includes real-time decisioning for known and unknown audiences using customer context already available in the data warehouse, with the ability to incorporate new in-session behavior planned for a later phase.
See this case study on stage live at Martech World Forum, New York, November 4-5, 2026
Rotio and EVERSANA’s Head of AI Agency Abid Rahman will showcase their work together at Martech World Forum, in a session titled “AI is superhuman at math and code. Why not marketing?” They will share practical starting points for experimentation and explain how they built the Causal Context Graph to support AI-powered campaign optimization.
