Marketing Intelligence System

> The Client

A global technology company whose marketing team's revenue contribution was systematically undervalued due to last-touch attribution.

> The Challenge

The marketing team had no lead scoring model. No segmentation beyond basic lists. Attribution was last-touch only, which meant the channels that closed deals got all the credit while the channels that generated awareness and consideration got none. The result: budget decisions based on incomplete data, marketing's contribution to revenue underreported, and no way to distinguish a high-fit, high-engagement prospect from someone who downloaded one whitepaper six months ago. The team needed scoring, segmentation, and attribution built from the ground up.

> The Approach

Designed a three-score model separating Fit (firmographic match to ICP), Engagement (behavioral signals across touchpoints), and Product (usage signals indicating commercial intent). Used W-shaped attribution to distribute credit across four key moments: first touch, lead creation, opportunity creation, and close. The entire architecture was designed through a multi-agent research methodology: 7 research sessions across two phases, producing 50,000+ words of analysis before writing a single line of implementation code. Each session used 5-8 specialized research agents working in parallel. The scoring model was implemented in HubSpot using existing infrastructure at zero additional software cost.

> Methodology Phases Applied

> The Results

First lead scoring model live in HubSpot. MQL workflow automated with score-based routing. PQL experiment designed with product data integration (pending data engineering access). W-shaped attribution model quantifying previously invisible marketing touches across the full buyer journey. The research methodology itself became a reusable pattern: structured multi-agent analysis as a prerequisite to implementation, producing better architecture decisions than traditional consulting approaches.