Built the partner analytics function at Atlassian from resale tracking to lifecycle value.
At HubSpot, a 500k+ MAU ecosystem had no shared definition of success — five product teams, each optimizing for activity. At Atlassian, a global partner ecosystem was being measured like a resale channel. In both cases I rebuilt the measurement logic at the layer where value actually flows, and that changed what the org optimized for.
- Based in
- Pleasantville, NY
- Targeting
- Senior Manager · Director · Data Strategy
- Open to
- PLG companies with ecosystem or marketplace layers
Partner contribution was invisible. The ecosystem was measured like a resale channel — activity, deal registrations, sourced revenue — none of which captured what partners did for customer retention or cloud migration.
Five product teams shared a 500k+ MAU ecosystem with no shared definition of success. Each optimized for its own activity metrics.
The integration marketplace had no personalization. Every user saw the same generic catalog regardless of use case, stack, or behavior.
IBM had roughly 20,000 sellers mapped to accounts using coverage logic that had never been empirically tested.
Red Hat was acquired with the assumption that its open-source marketplace would become a major distribution channel. No one had tested whether that was true.
Five agents. One workflow.
Built on agent.ai. Each one analyzes a different layer: strategy, profit, AI planning, adoption, measurement. The natural sequence: surface the opportunity, identify what to build, drive adoption, prove the return.
Customermaxxing
Analyzes a company from the customer's perspective, applying the full marketing lifecycle (opportunity, target markets, mix, operations) to surface the gaps that matter. Useful before deciding where to build.
Profitsmaxxing
Audits revenue growth levers, cost structure, and margin potential through a profit strategist's lens. Finds where a business is leaving money on the table and why.
WSIAI: What Should I AI?
Individual-level AI strategy tool. Given your company and job title, it maps where your time is going, brainstorms specific buildable agents across six categories, and returns a prioritized playbook — not 'use AI more,' but a concrete agent specification.
AI Uptake: Agent Adoption
Diagnoses adoption failures using STP analysis and a 4 Ps marketing mix adapted for AI, where 'price' means trust deficit and behavioral change, not money. Identifies the specific gap and the fastest path to closing it.
AI Payback: Agent Measurement
Segments users by role, maps each role to an outcome category (Output, Efficiency, Quality, Engagement), builds explicit dollar-value formulas with sourced benchmarks, and produces a measurement playbook for replacing estimates with real data.
I'm looking to talk to analytics leaders at PLG companies figuring out how to measure their partner or marketplace layer.