The Twenty Minute VCThis is why I don't believe in sales quotas | Figma CRO
CHAPTERS
- 0:00 – 0:30
Figma’s GTM contrarianism: no traditional CS, no traditional SDRs, quotas are “made up”
Shaunt opens with several contrarian GTM choices at Figma—eschewing traditional Customer Success and SDR structures and challenging common quota-setting logic. The framing sets up the episode’s core themes: building sales on top of PLG and managing performance with more nuance than just quota attainment.
- •Figma does not run a traditional CS org; much of “CS-like” work sits elsewhere
- •Figma also avoids a classic SDR model, despite Shaunt’s prior experience scaling SDR teams
- •Quotas exist, but Shaunt argues the way most companies justify them is often flawed
- •Teaser of Figma’s emphasis on focus/specialization and strategic selling
- 0:30 – 2:49
How Shaunt accidentally got into sales (the mall kiosk origin story)
Shaunt recounts falling into sales through a summer job selling cell phones at a mall kiosk. With minimal training and a sink-or-swim environment, he learned by doing—an early lesson in self-direction and adaptability.
- •Sales wasn’t an academic path; he “fell into it” while studying advertising
- •Got hired on the spot at a mall kiosk after buying his first cell phone there
- •Received almost no onboarding—just pamphlets, plan guidance, and "go sell"
- •Early exposure to learning fast, handling ambiguity, and earning through performance
- 2:49 – 4:27
The #1 trait in sales: curiosity—balanced with being prescriptive
Asked what advice he’d give his younger self, Shaunt says curiosity is foundational—deeply understanding the buyer’s situation. But in modern enterprise sales, he argues curiosity must be paired with a prescriptive point of view that delivers insights quickly to time-poor customers.
- •Curiosity: understand the customer’s context and constraints
- •Modern buyers expect insights and recommendations, not just questions
- •Best practice is balancing curiosity with being prescriptive
- •Figma trains reps to teach customers what top performers do differently
- 4:27 – 6:11
Is sales less important in a PLG world? Why Figma says no
Shaunt explains why PLG doesn’t reduce the need for sales—especially as companies scale and expand use cases. Figma’s self-serve roots drove massive customer counts early, but sales becomes critical to help customers realize the platform’s broader potential.
- •Figma spent years perfecting product before monetizing; growth took off via self-serve
- •Many customers start with credit card + link-sharing viral adoption
- •At similar revenue scale, Figma had far more customers than typical SaaS peers
- •Sales becomes the engine to close the gap between current usage and potential value
- 6:11 – 7:44
Figma’s sales motion evolution: from inbound upgrades to outbound into existing users
The conversation shifts to how Figma’s sales organization changed over time. What began as responding to self-serve customers for tier upgrades became a largely outbound motion—still aimed at existing users, but now driven by proactive insights and new champion-building.
- •Early sales: mostly upgrade conversations with existing self-serve accounts
- •Today: global segmentation (SMB, mid-market, enterprise, strategic)
- •Majority of mid-market+ sales activity is outbound
- •Outbound targets an existing installed base with new POVs and champions
- 7:44 – 9:46
Why Figma doesn’t run traditional CS: expansion as a ‘hunting’ motion
Shaunt explains the rationale for not having a classic CS team: customers self-define value early, but that often leaves a large “value gap.” Closing that gap requires education, new stakeholder mapping, and champion creation—work that looks more like hunting than farming.
- •Customers often under-utilize Figma relative to what Figma believes is possible
- •Expansion requires proactive education and reframing value
- •Often involves activating new champions/executive buyers, not just supporting existing ones
- •Figma decided much of this belongs in a sales/hunting motion vs. classic CS
- 9:46 – 11:26
Platform vs point solution selling: win on value narrative, not feature battles
Harry raises the common challenge of selling a platform when point solutions may win on individual features. Shaunt argues top sellers connect to business direction and value drivers, using storytelling and honesty about where customers get the most value.
- •Great sellers avoid feature-by-feature combat
- •Anchor on where the customer is going and what they’re trying to achieve
- •Tell a coherent platform value story when that’s the real differentiator
- •Be honest internally about where the solution truly delivers superior value
- 11:26 – 13:14
Seat-based pricing: not dead (but AI credits are coming)
Shaunt pushes back on the narrative that seat-based pricing is obsolete, noting strong net retention in Figma’s seat-based model. He acknowledges monetization is evolving—especially with AI—pointing to credit-based usage as an additive layer rather than a full replacement.
- •Figma hasn’t seen evidence that seat-based pricing is “dead” in their buyer set
- •Net retention improved meaningfully while primarily seat-based
- •AI usage monetization via credits is being introduced
- •Pricing model viability varies by function (e.g., labor-replacement vs builder tools)
- 13:14 – 15:51
Does the SDR role have a future? Figma’s non-traditional approach
Shaunt explains why Figma doesn’t use traditional SDRs and why AEs must own pipeline generation regardless. He also describes how the SDR-like capacity, when used, is redeployed toward transactional renewals and targeted tasks to free strategic AEs for higher-leverage work.
- •AEs must be responsible for pipeline generation—“full stop”
- •Hard to isolate incremental value between SDR vs AE in many models
- •Figma’s focus is expansion within a large customer base, not net-new logos
- •SDR-like roles are being repositioned toward transactional renewals and talent pipeline
- 15:51 – 18:30
Operating model & segmentation: self-serve vs SMB PLG vs sales-led enterprise
Shaunt outlines Figma’s “three businesses” framework and the operational rhythm that supports it. SMB owns the upgrade-centric PLG motion; mid-market/enterprise/strategic runs a classic SaaS cadence (PG, pipeline reviews, weekly forecasting), but often within “customer” accounts.
- •Three motions: self-serve, SMB PLG (0–500 employees), and sales-led (MM/ENT/Strat)
- •SMB intercepts self-serve accounts using product signals and maturity indicators
- •Sales-led reps map orgs and build a vision of best-in-class deployment vs current state
- •Traditional operating cadence: PG, pipeline reviews, and weekly forecast calls
- 18:30 – 21:46
When to intercept PLG users + how to build enterprise champions
Shaunt advises erring earlier on intercepting PLG customers because persuasion and timing often require multiple cycles. For champion-building, he emphasizes delivering relevant insights that improve the champion’s effectiveness—not just product enthusiasm.
- •Interception timing is contextual; earlier outreach rarely hurts
- •Common pattern: “lose” an upgrade, then win it months later
- •Champion-building hinges on teaching the customer something valuable
- •Enterprise champions require insights tied to their job, not just love for the product
- 21:46 – 26:53
Sales quotas: why the standard math is wrong, and why Figma keeps quotas low for strategic work
Shaunt argues quotas are often reverse-engineered to create a false sense of coverage rather than to reflect the work required. He proposes quotas should reflect a philosophy: what job you need done, how hard it is, and how you want to reward it—leading Figma to offer comparatively more attainable quotas for harder, more strategic selling.
- •Traditional quota coverage math can be a “made up” comfort blanket
- •Quotas should be tied to job-to-be-done and reward philosophy, not just top-down targets
- •Early-stage/transactional motions can sustain higher quotas; strategic motions merit lower quotas
- •Figma aims to reward complex, multi-stakeholder strategic work (often 3–4x OTE quotas)
- 26:53 – 33:42
Hiring winners: deal experience beats industry experience + what Shaunt screens for
Shaunt prioritizes candidates who have navigated complex deal cycles over those who simply know an industry. He also details hiring signals: avoiding chronically “jumpy” resumes, testing perseverance, using take-home exercises, and relying heavily on backchannel references.
- •If forced to choose: complex deal experience > industry experience (industry can be taught)
- •Red flag: repeated short tenures that suggest pattern risk at scale
- •Take-home: discovery + light demo workflow to test curiosity and perseverance
- •Backchanneling is critical; offer-stage enthusiasm matters (candidate must be “all in”)
- 33:42 – 39:28
Slow hire vs fast hire, and the mercenary vs missionary red flag
Shaunt prefers hiring slowly over filling seats with mediocre talent, arguing wrong hires cost more time than vacancy. He also describes a common failure mode: hiring candidates who optimize for title/money (mercenary) rather than growth, craft, and mission (missionary).
- •Better to miss hiring plan than accept a “maybe B-player” out of urgency
- •Interviewing is imperfect; success also depends on leadership, enablement, and environment
- •Some struggling reps can thrive with the right coaching if inputs/behaviors are strong
- •Mercenary signals (excess focus on comp/title leverage) predict churn when things get hard
- 39:28 – 46:57
Ramping enterprise reps + enablement, comms challenges, and the next wave of sales tech/agents
Shaunt explains enterprise ramp by getting reps into accounts early, then layering structured onboarding around ecosystem, positioning, and process. He candidly notes knowledge distribution is messy across tools and channels, and he’s increasingly focused on adopting tech—potentially agentic solutions—to reduce rep friction, even if the org isn’t yet expert at ‘training agents.’
- •Enterprise ramp: throw reps into accounts early to learn stakeholders and context
- •Rebuilding onboarding: more in-person classroom training and cross-functional thought leaders
- •Enablement pillars: ecosystem landscape, customer patterns, positioning, sales process/rhythm
- •Knowledge sync is fragmented across Slack, enablement platforms, CRM—searchability is a gap
- •Growing interest in agentic tools to remove busywork; uncertainty about org readiness to train agents
- 46:57 – 54:47
Performance management beyond quota: behaviors, competencies, lone wolves, and when to fire
Shaunt’s “hottest take” is that quota attainment alone is a lagging indicator that can enable lazy leadership. He advocates a written performance framework spanning results, behaviors, and competencies; discusses when lone-wolf reps are acceptable; and outlines when patience vs fast exits are warranted.
- •Quota can be wrong; relying on it alone is a poor performance signal
- •Framework: results (incl. PG), behaviors (collaboration/attitude), competencies (discovery, MEDDIC, pipeline)
- •Leaders should diagnose *why* outcomes lag using real-time inputs
- •Lone wolves can fit if they don’t poison culture and don’t aspire to lead
- •High patience for hardworking, improving reps; low patience for bad attitudes or toxic behavior
- 54:47 – 1:05:06
Setting quotas early + verticalization: specialize early, but choose the right dimension
Shaunt shares principles for quota-setting in early-stage companies—start with what you’re trying to accomplish and whether the market is pulling or you’re pushing. On specialization, he recommends focus as early as possible, but verticalization by industry depends on product; at Figma, specialization is more often by motion, persona, or overlays.
- •Quota-setting starts with business reality: market pull vs proactive push
- •Strategic outbound motions may require more favorable quotas to attract top talent
- •Focus/specialization should happen early to avoid reps doing “14 different things”
- •Verticalization is not always industry-based; overlays/motions/personas may be better
- •Trigger for specialization: when the job becomes too broad for one rep to do well
