Scaling from $10 to $100M
If you've hit a certain revenue mark and growth has started to feel harder than it should - you're not imagining it. The playbook that got you here can stop working at scale. The team is busy, the spend is up, and the needle isn't moving the way it used to.
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Scaling Brands from $10M to $100M by Jamie Lee is a practitioner's framework for brands that have hit an eight-figure plateau and need a more sophisticated operating system to reach nine figures. Drawing on her experience at Vital Proteins, Nike, and Everlane, Lee argues that the leap from $10M to $100M rarely requires entirely new ideas, it requires looking more honestly at what's already inside the business: hidden channel inefficiencies, underserved customer segments, and margin leaks hiding in plain sight.
Chapter Outline
Introduction: When the Old Playbook Stops Working
The shift from topline growth to profitable scale. Why the strategies that built an eight-figure brand break down at the next stage — and what a more mature operating system looks like.
Module 1: Profitable Channel Strategy
Case study: Vital Proteins
- Auditing channel-level profitability (DTC vs. Amazon)
- Separating branded and non-branded ad campaigns to reveal true acquisition costs
- SKU portfolio analysis — investing in the right 20%
- Creative refresh cadence and fixing the paid-to-PDP funnel
- Shifting from CAC to CAC:LTV as the primary metric
- Subscription as a retention and LTV lever
- Churn reduction: the pause feature vs. cancel
Module 2: Category Expansion
Case study: Nike
- Using site search data to surface unmet demand
- Cross-referencing internal signals with macro category trends (Google Trends, Amazon, TAM tools)
- Identifying whitespace where brand equity, product capability, and distribution align
- Launching at profitable price points, not just for volume
- Building a go-to-market playbook with check metrics (reviews, return rates) before scaling spend
- UGC as a scalable launch strategy
Module 3: Finding Growth in Unexpected Places — Returns
Case study: Everlane
- Measuring return rates by category, channel, and customer segment
- Proactive prevention: better PDPs, fit guidance, and expectation-setting
- Identifying and addressing high-frequency returners ("returnaholics")
- Defaulting to exchanges and store credit over refunds
- Using return data to inform product redesign
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