THE SITUATION
Marketing and RevOps were both looking at campaign performance. They were rarely looking at the same numbers.
The client had built substantial campaign infrastructure, but the underlying data lived in static dashboards, refreshed on lag, and interpreted differently depending on who pulled the report. Attribution models were calculated inconsistently across teams, making it difficult to align on what was actually driving pipeline.
When questions arose, the answer required a BI request and a wait. By the time the insight arrived, the moment to act had passed.
THE SOLUTION
GrowthArc unified the client’s campaign, CRM, and attribution data into a single semantic model, then built a conversational AI agent on top of it. The agent translates plain-language questions into governed, accurate queries in real time, without BI intermediation.
The goal was not to replace existing reporting tools but to make them the structured complement to a more flexible intelligence layer that both Marketing and RevOps could use from the same source of truth.
WHAT WE BUILT
The Marketing ROI Catalyst is a Snowflake Cortex-powered agent that gives business users direct access to campaign performance data through natural language, without BI intermediation or schema knowledge.
Four proprietary components power it. The Autonomous Reasoning Engine goes beyond reporting numbers to explaining why funnel shifts occur. Closed-Loop Observability captures user feedback signals to continuously refine query logic.
Dynamic Semantic Mapping adapts to new campaign tags and hierarchies in real time. The Zero-Trust SQL Gatekeeper validates every AI-generated query against hardened logic gates before execution.
The result is a reasoning system that gets more accurate with use, not just a faster way to pull reports.
THE OUTCOME
Revenue attribution accuracy improved by approximately 40%, giving Marketing and RevOps a consistent view of what is sourcing and influencing pipeline. Questions that previously required BI intermediation are now answered in seconds. The 70% reduction in ETL complexity means the data foundation is easier to maintain as campaign structures grow.
Both teams now work from the same campaign reality. That shift, from siloed interpretation to shared intelligence, is where the compounding value sits.
~40%
Attribution Accuracy Gain
Improvement in attribution consistency, eliminating model-level discrepancies between Marketing and RevOps.
80%
Faster Insight Delivery
Reduction in time-to-answer for campaign performance questions compared to BI queue workflows.
70%
ETL Complexity Reduced
Data unification cut pipeline complexity, making the foundation easier to maintain and scale.
FUTURE OUTLOOK
The foundation built here extends beyond campaign analytics. As the semantic model matures and the agent accumulates more feedback, the same architecture can be extended across other GTM functions, bringing governed real-time intelligence to sales operations, customer success, and revenue forecasting.
Analytical access scales across teams without scaling the BI function to match.
