EO StudioHow to Steal Customers From Giants 600x Bigger than Your Startup | Pigment, Eléonore Crespo
CHAPTERS
- 0:00 – 0:30
Pigment’s “climb Everest” strategy: beat SAP/Oracle by going enterprise-first
Eléonore Crespo frames Pigment’s ambition as competing with legacy giants by choosing the hardest path early: building an enterprise-ready platform from day one. She explains how winning credibility with respected “fast-forward” customers creates compounding trust and referrals over time.
- •Pigment competes directly with entrenched players like SAP and Oracle
- •Decision-makers follow signals from other top decision-makers (social proof)
- •Strategy: earn trust via marquee customers who will publicly endorse the platform
- •Choosing the hard route early can create a long-term compound advantage
- 0:30 – 1:30
What Pigment is: an AI performance management platform for cross-functional planning
Crespo defines Pigment as a unified platform that brings together finance, HR, sales, and supply chain data to improve decision-making. She also shares key growth context, including capital raised and ARR trajectory.
- •Pigment unifies multiple business data sources into one platform
- •Goal: help companies make better strategic decisions on that data
- •Raised $400M+ since inception
- •Company tracking ~2x ARR growth year-over-year
- 1:30 – 2:01
Origins and motivation: building a high-impact company from Europe
Crespo describes how being in France shaped her early view of entrepreneurship, and how emerging European unicorns influenced her ambitions. She highlights her co-founder as a major inspiration and explains why entrepreneurship was always part of her long-term plan.
- •Europe/France felt like a smaller ecosystem at the time, but early unicorns were inspiring
- •Co-founder’s prior unicorn experience motivated her path
- •She deliberately studied entrepreneurship as a minor
- •Desire for large-scale impact drove timing and career choices
- 2:01 – 3:02
The Google lesson: even top innovators run critical planning on spreadsheets
Working with Google’s CFO organization, Crespo expected world-class internal planning systems but found heavy reliance on Google Sheets. The gap between spreadsheet flexibility and enterprise-scale planning revealed a major, unsolved problem.
- •Exposure to strategic planning at Google EMEA/Alphabet level
- •Surprise: core forecasting, budgeting, and margin work done in spreadsheets
- •Spreadsheets weren’t built for multi-country, multi-business scale
- •Insight: if Google struggles, many companies likely do too
- 3:02 – 3:32
Market validation at Index Ventures: best tech companies still struggled with planning
At Index, Crespo saw leading founders and late-stage companies wrestling with the same performance management and planning challenges—pre-IPO and post-IPO alike. This reinforced that the problem was widespread and persistent even among tech-forward teams.
- •Worked alongside founders/companies like Figma, Datadog, Revolut
- •Planning and finance decision-making remained painful despite strong tech adoption
- •Key challenges: margin improvement, quota optimization, territory planning
- •Consistent signal across stages: teams were not well-equipped for these decisions
- 3:32 – 4:33
Why Excel breaks at enterprise scale: governance, security, and billions of cells
Crespo explains why Excel persists but fails beyond a certain organizational complexity. She uses Coca-Cola as an example to show how scale demands centralized data, governance, and secure collaboration—capabilities spreadsheets can’t reliably provide.
- •Excel is flexible for quick models but hits limits at scale
- •Enterprises need governance, security, and consistent modeling standards
- •Complex orgs require analysis across products, countries, units, and contracts
- •Spreadsheets can’t handle “billions of rows/cells” planning needs
- 4:33 – 5:03
Founding Pigment to disrupt legacy EPM: must-have platform, not a nice-to-have tool
Pigment was created to solve this systemic planning problem in a category dominated by older platforms. Crespo emphasizes the importance of building something indispensable rather than incremental, especially when competing against long-standing incumbents.
- •The opportunity was “everywhere” and represented a massive market
- •Category reality: competing with entrenched, decades-old platforms
- •Goal: build a must-have platform rather than a nice-to-have feature
- •Positioning depends on delivering measurable decision-making value
- 5:03 – 6:04
Enterprise trust and early distribution: winning logos to create a network effect
Because Pigment serves CFOs and strategic decision-makers, trust is central. Crespo describes how securing endorsements from forward-looking companies creates a network effect—others follow once respected leaders validate the product.
- •Target buyer: CFOs and strategic finance teams—high trust requirements
- •Credibility comes from respected customers willing to vouch publicly
- •Decision-makers watch peers at companies like Anthropic/OpenAI
- •Customer validation becomes a scalable go-to-market lever
- 6:04 – 7:04
Why enterprise sales is harder than founders expect: platform breadth and adoption scale
Crespo outlines why “enterprise-ready” is often misunderstood: in enterprise performance management, value requires an end-to-end platform, not a single feature. Early Pigment use cases involved dozens or hundreds of users, forcing a higher bar across UX, engine, and completeness.
- •Founders often underestimate how hard it is to sell to large companies
- •EPM requires an end-to-end platform with breadth, not a narrow product
- •Early deployments can involve many teams and many use cases at once
- •Higher expectations across computation, UX, governance, and reliability
- 7:04 – 7:34
Building the product moat: elastic computation engine + top-tier UX (2019→2021)
Pigment invested heavily in foundational technology and usability, accepting a longer build cycle to meet enterprise standards. Crespo points to their “elastic Canva” computation engine and adoption-focused UX as core differentiators.
- •Needed a superior platform “from all angles” to compete with incumbents
- •Built an “elastic Canva” computation engine for flexible modeling
- •Prioritized exceptional UX to drive adoption across diverse users
- •Timeline: started end of 2019; product truly ready in 2021
- 7:34 – 8:35
Using investors strategically: door-opening, US hiring, and first flagship customers
Crespo explains how Pigment selected investors for practical help—introductions, credibility, and building a US footprint. Early customer wins (e.g., Figma, Brex, Carta) were pivotal inflection points that later produced compounding market trust.
- •Investor selection based on operational leverage, not just capital
- •VC backing helped open doors with large US tech companies
- •Early lighthouse wins: Figma, Brex; Carta CFO became an early angel
- •Hard early traction created long-term compounding benefits
- 8:35 – 10:05
Pre-launch fundraising and the central thesis: ‘your team is the deck’
Pigment raised ~€25M pre-launch largely on credibility, narrative, and team quality rather than product maturity. Crespo stresses that when you lack analyst coverage and references, the only scalable proof is hiring world-class people who can build trust with customers and investors.
- •Raised ~25M before launch without a traditional fundraising process
- •Pre-launch fundraising requires a strong story and credibility
- •When you lack brand/analyst validation, the team becomes the key asset
- •Hiring experts from the ecosystem helps customers trust a young product
- 10:05 – 12:42
Hiring system: values, calibration, and the two stages that reveal everything
Crespo shares Pigment’s hiring approach: define values early, benchmark excellence via market “calibration,” and emphasize two decisive steps—case studies and deep reference checks. She closes by acknowledging inevitable hiring mistakes and the importance of learning loops.
- •Write and preserve company values from day one (evolve as needed)
- •Calibration: meet top performers (CFOs/CROs/sales) before hiring a role
- •Two critical stages: case study (tests doing/learning) and background checks beyond provided references
- •Hiring mistakes cascade—focus on understanding why and raising the bar next time
