Signal Harbor

Overview

Signal Harbor is an operational intelligence suite that transforms noisy logistics data into clear, action-ready insights. It serves supply chain managers at mid-market companies who previously relied on spreadsheets and gut instinct to make routing and inventory decisions.

Role

Product Strategy Lead. Defined the product vision, conducted competitive analysis, and owned the pricing and packaging strategy. Reported directly to the CEO and presented quarterly roadmap updates to the board.

Goals

Reduce inventory carrying costs for customers by 15% within 6 months of adoption. Achieve 85% weekly active usage among licensed users. Build a self-serve onboarding flow that eliminates the need for implementation consultants.

Constraints

Customers had data in 40+ ERP and WMS systems with no standard format. The ML team was 2 data scientists — no dedicated ML engineers. Competitors had 5-year head starts with enterprise sales teams 10x our size.

Process

Ran a 6-week design sprint focused on the "aha moment" — the first insight a user sees. Built 15 connector integrations using a plugin architecture so adding new ERP sources took days, not months. Launched a free tier to build bottom-up adoption inside target companies.

Key Decisions

Positioned as "analytics for operators" — not "AI platform" — to differentiate from enterprise competitors. Built anomaly detection first (immediate value) before predictive forecasting (higher ceiling but longer time-to-value). Chose a usage-based pricing model tied to data volume processed.

Results

Customers reported an average 18.3% reduction in carrying costs. Weekly active usage reached 91% among paid accounts. Self-serve onboarding reduced time-to-value from 6 weeks (with consultants) to 4 days. ARR grew from $0 to $1.8M in the first year.

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