Building and hiring high-performing growth teams

Executive overview

Most founders hire for growth by pattern-matching to someone they've seen succeed — missing the underlying competencies that actually drive results. Adam Fishman's growth competency model provides a structured framework for evaluating, hiring, and developing growth talent across four skill domains.

Onboarding is the single highest-leverage surface in any product: 100% of users touch it, motivation is at its peak, and improvements compound directly into retention — often shifting cohort curves by 10–20 percentage points.

The biggest growth hiring mistake is not knowing what you actually need before you start looking.

The four growth competencies

1. Growth execution — channel fluency, experimentation, and productizing learnings (translating experiments into durable product changes)
2. Customer knowledge — data fluency, user psychology, and developing insight through ongoing research
3. Growth strategy — growth loop modeling, capital allocation and forecasting, prioritization and roadmapping
4. Communication and influence — strategic communication, team leadership, stakeholder management

No individual is a 10/10 across all four. The goal is a balanced team with no gaps in the portfolio.

Hiring growth talent

• Founders often seek a clone of a known growth leader — that's pattern matching, not first-principles hiring
• For early hires, prioritise customer knowledge and growth execution above strategy and influence; the latter are developed with time
• Internal transfers are underrated: they arrive with domain knowledge and a head start on one or more competencies
• Specialist skills (SEO, paid growth) are best sourced externally, and only once foundational growth infrastructure exists
• Without senior growth expertise in-house, junior hires need access to advisors, coaching, or structured external education — otherwise they run through walls instead of finding the door

Using the model to evaluate companies (PMF for candidates)

PMF here stands for People, Mission, Financials — three non-negotiable criteria for evaluating a role:

• People: Can you have hard conversations, disagree productively, and respect your peer set?
• Mission: Does the company's success translate into meaningfully better outcomes for its customers and the world?
• Financials: Has the company exercised fiscal discipline, or is it one demand shock away from layoffs?

Settling for two out of three has consistently led to poor outcomes. Strategies to evaluate before accepting:

• Attend an executive meeting or offsite as part of due diligence
• Ask the CEO: "Tell me about the last strategic disagreement — what was it, and how did you resolve it?"
• Run backchannel references on people you'd work for — not just the names the company provides
• Contact former employees not on the interview circuit; their incentive to sell you is gone

Onboarding as a retention lever

• Onboarding is the only product surface 100% of users touch — and where motivation is highest
• Think of brand as the promise and the product as the delivery; misalignment here causes the most damage
• Onboarding's biggest impact is on retention, not conversion — filtering out poor-fit users early improves cohort curves even if raw conversion dips
• At Patreon: connecting high-potential creators with a human at the right moment in onboarding improved their first- and second-month revenue by 25%, which flowed directly into LTV
• Productizing those human interactions (opinionated defaults, guided tier structures) made the gains scalable

Patreon onboarding in practice

• Identified high-potential creators by asking them to connect social accounts; a data science model scored follower count and engagement rate per channel
• Creators above the threshold were routed out of self-serve onboarding and handed to a human contact
• Learnings from those human interactions became opinionated defaults: pre-set tier counts, price points, and page structure that made the right choice the path of least resistance
• Creators could override defaults, but friction was intentionally added to wrong choices
• The sequence — human learning, then productization — is the canonical example of productizing learnings

Proxy metrics and iteration pace

• Don't wait 90 days to measure retention impact; find leading indicators tied to long-run success
• At Patreon: velocity to first patron and first $100 processed were proxies for long-term creator LTV
• Qualitative sampling of new signups — spot-checking who is onboarding and what they're doing — provides early confidence without waiting for cohort data
• Redesign onboarding only when you have a net-new insight, not for its own sake
• Micro-optimization rarely moves the needle once the fundamentals are sound; large improvements come from fundamentally new understanding of the customer