Former operator turned investor focused on growth-stage technology and consumer startups, Brandon Silvia brings product and GTM experience to early growth investing. He has led cross-functional teams in product management, marketing and commercial strategy, and is active in deal sourcing, diligence and board engagement for portfolio companies. Known for scaling customer acquisition and monetization models, he advises founders on go-to-market playbooks and unit-economics optimization while allocating capital across SaaS and direct-to-consumer opportunities.
Former operator turned investor focused on growth-stage technology and consumer startups, Brandon Silvia brings product and GTM experience to early growth investing. He has led cross-functional teams in product management, marketing and commercial strategy, and is active in deal sourcing, diligence and board engagement for portfolio companies. Known for scaling customer acquisition and monetization models, he advises founders on go-to-market playbooks and unit-economics optimization while allocating capital across SaaS and direct-to-consumer opportunities.
Operator-led growth investor focused on early growth technology and consumer businesses, deploying capital where product‑market fit is evident and GTM levers can be scaled. Prefers SaaS and direct‑to‑consumer models with clear unit economics, emphasizing CAC payback, LTV expansion and monetization paths. Investment style is hands‑on: sourcing and diligence informed by product and marketing experience, active board engagement, and playbook-driven value creation. Capital allocation favors concentrated, conviction bets with staged follow‑ons and KPI‑based risk management.
Operator-led growth investor focused on early growth technology and consumer businesses, deploying capital where product‑market fit is evident and GTM levers can be scaled. Prefers SaaS and direct‑to‑consumer models with clear unit economics, emphasizing CAC payback, LTV expansion and monetization paths. Investment style is hands‑on: sourcing and diligence informed by product and marketing experience, active board engagement, and playbook-driven value creation. Capital allocation favors concentrated, conviction bets with staged follow‑ons and KPI‑based risk management.
| Trades 840 | Longs Won 638/840 75% | Profit Factor 38.81 |
| Profitability | Shorts Won 0/0 0% | Standard Deviation $332,112.99 |
| Average Win $102,645.26 | Best Trade (Jul 17) $6.07M | Sharpe Ratio -10.24 |
| Average Loss -$8,353.9 | Worst Trade (Mar 30) -$192,071.64 | Z-Score 14.06 (100%) |
| Commissions $0 | Avg. Trade Length 1y 10m 4d | Expectancy $75,952.6 |
| Loss Size | 100% | 90% | 80% | 70% | 60% | 50% | 40% | 30% | 20% | 10% |
| Probability of Loss | <0.01% | <0.01% | <0.01% | <0.01% | <0.01% | <0.01% | <0.01% | <0.01% | <0.01% | <0.01% |
| Consecutive Losing Trades | 20,000 | 18,000 | 16,000 | 14,000 | 12,000 | 10,000 | 8,000 | 6,000 | 4,000 | 2,000 |