Scaling GTM Operations with AI-Powered Attribution

Marketing attribution in complex B2B sales cycles is notoriously difficult. This MarTech company was running campaigns across 12+ channels but had no reliable way to measure true impact.

Last-touch attribution was giving all credit to the final interaction before conversion, leading to massive overinvestment in bottom-funnel tactics while top-of-funnel awareness was underfunded.

Campaign optimization cycles took 30+ days because data aggregation was manual, and by the time insights were available, the market had already shifted.

We implemented a custom multi-touch attribution model using machine learning to analyze the entire customer journey and assign probabilistic credit to each touchpoint.

The AI system integrated data from all marketing channels, CRM interactions, and sales activities into a unified customer journey view updated in real-time.

Predictive analytics identified which campaign combinations were most likely to convert, enabling proactive budget reallocation rather than reactive adjustments.

Natural language reporting automatically generated weekly insights and recommendations, eliminating hours of manual analysis and democratizing data access across the org.

Marketing ROI improved by 3x within 6 months as budget was reallocated based on AI-identified high-performing channels and campaign sequences.

Wasted ad spend was reduced by 45% by eliminating investment in channels that appeared valuable under last-touch but showed minimal influence in the full journey analysis.

Campaign optimization cycles dropped from 30 days to 3 days, enabling rapid testing and iteration that kept the company ahead of competitors.

Sales and marketing alignment improved dramatically with shared visibility into which activities truly influenced pipeline, ending years of finger-pointing and misaligned incentives.