Inside Figma's $1B ARR Machine | Shaunt Voskanian
TL;DR
Figma CRO Shaunt Voskanian explains how the company reached $1B ARR by eliminating traditional CS and SDR teams entirely, instead building a sales-led outbound machine that expands existing PLG accounts through prescriptive education rather than inbound upgrades.
🏗️ Organizational Structure 3 insights
No Traditional CS or SDR Teams
Figma operates without dedicated Customer Success or Sales Development functions, requiring Account Executives to own pipeline generation and treat expansion as a "hunting" motion for new champions rather than farming existing relationships.
Three Distinct Business Segments
The go-to-market splits into self-serve (credit card purchases), PLG/SMB (0-500 employee companies), and sales-led (mid-market/enterprise/strategic) to ensure reps maintain extreme focus rather than juggling competing priorities.
AE Pipeline Ownership
Account Executives are fully responsible for their own pipeline generation, as leadership found it impossible to isolate incremental value from traditional SDR/AE splits in a hybrid PLG environment.
🎯 Sales Strategy & Motion 3 insights
Outbound Into Installed Base
Unlike typical PLG companies, Figma's sales team executes 100% outbound into existing customers who initially bought via self-serve, proactively educating them on unrealized value rather than waiting for upgrade requests.
Prescriptive Curiosity
Top performers balance deep customer research with prescriptive insights about how best-in-class customers use Figma differently, teaching new workflows rather than simply fulfilling stated needs.
Multi-Product Persona Expansion
The primary growth driver is introducing new products like FigJam and Dev Mode to different personas (developers, PMs) within existing design-led accounts rather than merely adding seats to the core product.
💰 Compensation & Economics 3 insights
Aggressive Quota Multipliers
Enterprise reps carry quotas set at 3-4x their on-target earnings, a ratio leadership accepts as appropriate given the compensation structure and account potential.
Seat-Based Pricing Resilience
Despite industry narratives predicting its death, Figma maintains 136% net dollar retention (up from 131%) under a seat-based model, though they are adding AI credit consumption for new features.
Quotas as Calibration Tools
Voskanian views quotas as "made up" constructs requiring iterative calibration rather than rigid mathematical formulas, adjusting targets based on realistic attainment expectations.
Bottom Line
Treat your existing PLG customer base as a prospecting goldmine by building specialized outbound sales teams that proactively educate users on unrealized platform value rather than relying on traditional CS or inbound SDR models.
More from 20VC with Harry Stebbings
View all
Nikesh Arora on The Future of Token Costs | Memory Becoming the Moat & Why Enterprise AI Isn't Ready
Nikesh Arora argues that while frontier AI models chase consumer breadth with high false-positive tolerance, enterprise AI adoption requires expensive depth and context to handle edge cases, predicting token costs will fall 90% and traditional G&A functions will halve within three years as AI transitions from passive software to opinionated agents.
Flexport CEO: Why Revenge and Patriotism are the Best Founder Traits
Flexport CEO Ryan Peterson reveals the company is on track for $450 million in net revenue and approaching profitability, while sharing contrarian views on why fear of losing drives founders, remote work is "white collar fraud," and AI dependency poses existential operational risks.
Anthropic's Fable Banned by US Government | Wix & Adobe Hit All-Time Lows | Mistral Raising at $20BN
SpaceX completed the largest IPO in history at
How Export Controls Helped Not Hurt China & Power is the Bottleneck to AI | Perplexity CEO
Perplexity CEO Aravind Srinivas argues his company forced Google's strategic pivot to AI mode while asserting that sustainable AI value lies not in frontier models but in orchestration interfaces that maximize token value per watt for high-spending power users running autonomous agents.