Inside the Growth Twins Playbook @Pigment
À propos de Louis & Edouard
Edouard leads Growth from the US alongside roughly 35% of Pigment’s workforce, while Louis is based in Paris driving the European engine. They both started in sales roles at Kymono, then moved into growth roles before reuniting at Pigment.
8 years experience in Growth Outbound and 2 years in GTM AI
Plus belle réalisation
Built systems generating $100M+ in pipeline and AI-powered tools used by 100+ weekly internal users across EMEA and the US
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À propos de Pigment
Pigment is an enterprise software company operating in the EPM (Enterprise Performance Management) category, serving 500+ employee organizations across Europe and the US for financial planning, sales forecasting, supply chain, and workforce planning. Founded in 2021, the company has raised significant VC funding (Latest Series D: $145M - total raised: €397M). Today, North America represents roughly 60% of total revenue, with strong GTM teams based in the US.
Their references include: Fortune Global 500 companies like Coca-Cola, Unilever, and Danone, and fast-scaling tech leaders like Figma, Notion, Gong, Vinted, and Anthropic.
Outils utilisés
LinkedIn, ZoomInfo, lemlist, Surfe, Dropcontact, Cognism, Zerobounce and internal tools
Introduction
When I joined 4 years ago, we had less than 10 customers at Pigment, we were basically starting from scratch from a GTM perspective - Edouard Uguen
When Edouard started in 2022, the company was selling into a mature enterprise software market dominated by large EPM (Enterprise Performance Management) incumbents:
- Brand awareness was close to zero - generating no pull
- Most deals were competitive replacements into a crowded enterprise category
- Waiting for inbound momentum wasn’t realistic.
Outbound was the primary way to open markets and create pipeline. It initially drove roughly 90% of sourced revenue, now complemented by inbound and partner motions, both of which are growing significantly year over year.
Today Outbound remains a core engine for prospect & customer expansion and it is built as a signal-driven GTM infrastructure that continuously feeds qualified opportunities to sales reps.
This playbook breaks down how that infrastructure actually works.
Growth Team DNA
How Pigment actually runs pipeline generation
Core principles
The Growth Team builds the GTM infrastructure, systems, automation and AI that make pipeline scalable and runs high-leverage outbound programs, while the Sales team owns account strategy, relationship building, and turns opportunities into revenue.
What we believe in is the power of translating and amplifying what people already do well (often manually) or want to scale, by leveraging technical workflows and GenAI. It’s not just about automation; it’s about systematizing great practices.
The key roles
The Growth team operates like a revenue engineering squad, with technical profiles embedded directly in it. They are responsible for building the data infrastructure, capturing and enriching buying signals, orchestrating outbound motions, and even creating internal AI tools to scale execution.
On the Sales side, BDRs and AEs focus on what humans do best. They engage real conversations, turn interest into booked meetings, and convert opportunities into closed revenue.
In practice, Growth owns the entire pipeline creation system - from data foundations to activation and prioritization - while Sales owns conversion and deal execution.
Important note : From Growth to Product
A few weeks after I joined, I had two engineers. We started mapping our Total Addressable Market and building the whole outbound infrastructure
Edouard Uguen
At Pigment, Growth operates as a Product Team inside the Revenue organization, focused on building GTM systems rather than (only) running campaigns. The team develops internal infrastructure: a centralized GTM data warehouse, growth applications used daily by sales reps, signal-driven orchestration workflows, and AI tools for data hygiene, context aggregation, and signal interpretation.
We track usage constantly, improve tools based on rep behavior, and redesign anything that doesn’t get natural adoption. Our goal is to replicate what the best reps do every day.
Louis Uguen
These systems are treated like products - with users (reps), adoption metrics, feedback loops, and continuous iteration - and are optimized around one outcome: predictable pipeline generation. We even see a broader movement from Growth to GTM through a new appellation. Perhaps will we witness an evolution from Growth Twins to GTM Twins - who knows?
The Pigment Revenue Infrastructure
How to build your decision-driven GTM system
Pigment built a data → decision → activation pipeline where every GTM action is computed upstream before reaching sales reps.
The data warehouse ‘Palette’ is the control center.
Important note:
'Palette' is not a market tool - it was built internally at Pigment.
Layer 1 - Raw market reality, not “lead sources”
Pigment doesn't ask "which contacts fit our ICP?" They ask "which organizations are visibly changing right now?"
Three categories feed their data warehouse Palette continuously:
- Organizational structure: Firmographics, company growth, team sizes, tech stack evolutionTools : LinkedIn, Zoominfo, custom data enrichment
- People movement: New hires (especially within their persona), promotions, role changes, past Pigment users resurfacingTools : Zoominfo, LinkedIn*, custom tracking*
- Buying pressure: Job postings mentioning planning tools or complexity, website engagement patterns, reviews, competitor mentions, shared investors, previous user, G2
Pigment doesn't model "who fits the ICP." They model how fast a company is changing. A stable Fortune 500 company might technically fit the profile, but if nothing is moving, there's no buying pressure. A mid-market company with 3 new FP&A hires, a recent tech stack change, and job postings mentioning a need for planning tools? That's momentum.
Important note :
G2 and TrustRadius are B2B software review platforms where real users rate, compare, and discuss tools. They basically show you who is actively evaluating software - which makes them powerful momentum signals for outbound activation.
Layer 2 - Transformation layer: from raw signals to computable decisions
"Clean and reliable data is the foundation of everything. Without disciplined data hygiene and strong data architecture in your CRM and database, scaling becomes guesswork. You can’t build anything truly serious or sustainable on unreliable data - even if you try to force growth on top of it" - The Growth Twins
What unclean data looks like: company names spelled differently across sources, roles standardized nowhere, signals with no context, duplicates everywhere.
This is why data is first loaded into the warehouse, where it goes through a transformation layer. It is cleaned, normalized, unified (identity resolution across sources and systems), enriched, and interpreted.
- Deduplicate and standardize company records across all sources
- Parse job descriptions to extract intent: what tools are mentioned? what complexity are they describing?
- Classify competitor mentions: is this person actively using a competitor or just mentioning it?
- Enrich contact records with signal proximity (how close are they to a buying signal?)
- Score data quality: is this information reliable enough to act on?
This is what makes automation safe. And this is what most teams skip or do manually in spreadsheets.
Layer 3 - Palette, data warehouse as the brain
Palette is not storage. It’s a real-time intelligence layer that models market change and revenue readiness. It is basically the brain leveraging on clean data to operate.
It connects four realities:
- Companies = organizational momentum: size, stack, team growth, segment, geo
- Contacts = influence map: role, seniority, past deals, signal proximity, previous experience implementing a competitor tool
- Signals = change engine: parsed job posts, promotions, tech clues, engagement
- Activities = feedback loop: outreach activities, responses, meetings, signings
Palette continuously answers: Where is pressure building? Who is closest to the change? Is momentum accelerating or cooling?
And that’s something CRM cannot do. It tracks what happened. Palette predicts what's about to happen.
Layer 4 - Orchestration, execution & feedback
Once Palette has made the decision, it needs to activate without manual work.
This is where Hightouch enters the picture. Hightouch is the bridge between intelligence and execution.
Palette outputs audiences, which means accounts + contacts that meet the decision criteria. HighTouch syncs those audiences in real-time to:
- Salesforce - for CRM visibility
- Outreach/Gong Engage - for email sequencing
- LinkedIn - for multi-profile outreach
- Spreadsheets and internal tools - for manual review or sharing
This guarantees:
- One source of truth - all activation tools pull from Palette, not random CSVs
- Zero manual list building - no growth manager exporting and re-uploading
- Consistent prioritization - the same logic applies everywhere
- Continuous refresh - as signals change in Palette, audiences update automatically
Then Outreach runs. CRM tracks activities and engagements. Slack alerts sales reps within a channel per Account Executive. Results flow back into Palette to improve models. The system learns continuously.
What Pigment really built
Not a GTM stack but a real-time market response engine.
Market change → data processing → scoring → activation → human selling → learning loop.
Important note:
We are witnessing a shift from CRM to data warehouse as the control center of modern GTM systems - but why? - CRMs are built around static objects (accounts, contacts, opportunities) and overwrite state, while Palette stores every market change as timestamped events, preserving full signal history. - CRMs cannot compute momentum - frequency, acceleration, decay, and signal clustering over time - which requires time-series analysis natively handled in a warehouse. - CRMs struggle to normalize and join high-volume heterogeneous data sources (job boards, LinkedIn, web signals, reviews, AI-extracted insights), whereas a warehouse is designed for large-scale cross-source computation. - CRMs optimize ownership and activity tracking, while Palette models market dynamics and buying pressure, shifting GTM logic from lead management to momentum detection.
Pigment’s Outbound Motion
powered by the Infrastructure
The visual below shows how Pigment structures pipeline generation across five connected layers: audience building, outbound motions, engagement channels owned by the Growth team and territory logic, sales ownership by BDR & AE.
Let’s walk through how it actually operates:
Audience building - a real-time self-refreshing pipeline input
As we said earlier, Palette doesn’t start with who matched an ideal customer profile.
A company only enters outbound when multiple signals converge. This creates live, self-refreshing audiences ranked by urgency, continuously feeding the GTM system.
Outbound motions - momentum defines the play
Instead of one-off sequences, Pigment runs three persistent outbound programs:
- Pre-event acquisition: Audiences dynamically built around upcoming events (in-person, webinars, live product tour…). For context, we’re talking about over 70 events to run and fill in 2026.
- Micro-campaigns: Short, highly contextual plays launched on specific momentum clusters.
- Programmatic outbound: ****Always-on trigger-based flows reacting to market change.
These are always-on GTM programs maintained by live data.
Channels - leveraged as infrastructure
Email runs as a scalable delivery layer.
LinkedIn runs as a distributed engagement network using 100+ real profiles across the company.
This turns outreach from rep effort to system reach with a force multiplier.
Territories - momentum routing
Momentum-driven audiences are automatically split between:
- core BoB (Book of Business)accounts owned directly by AEs
- unassigned accounts
The result: full market coverage, clear ownership rules, and no overlaps, handoff friction, or missed opportunities.
Ownership & speed - to ensure conversion
The moment a prospect engages positively, it triggers routine, CRM updates, slack deal rooms alert for the right AE. Speed is essential to maximize conversion.
Because intelligence lives upstream inside Palette:
- Timing becomes event-driven, not cadence-driven
- Plays become contextual, not templated
- Activation becomes automatic, not manual
- Scaling becomes predictable with clean data, not chaotic,
Outbound stops being a manual sales activity. It becomes a real-time market response system.
AI at Pigment
How intelligence is embedded inside the GTM system
Pigment doesn’t use AI to replace humans. They leverage AI to have their sales reps living in meetings, and not spend 72% of their time on non-selling activities (source: Salesforce’s annual “State of Sales” research).
AI for signal structuring - the backbone of automation
AI converts unstructured inputs into clean GTM objects.
Examples:
- Job descriptions → capture tech stack, competitors in use, scope of transformation, and seniority level
- LinkedIn experience → competitor usage, implementation history
- Web content → buying intent indicators
This allows Pigment to classify signals automatically, weight their relevance based on Pigment’s value proposition and stack them into momentum models. Without this layer, real-time activation wouldn’t scale.
AI for data hygiene at warehouse scale
AI continuously:
- normalizes company names across sources
- standardizes roles and seniority for hyper-relevant segmentation
- de-duplicates contacts
- maps noisy inputs into clean dimensions
This is what keeps Palette reliable as the GTM brain. Clean data is what makes automation safe and GenAI reliable.
AI for content acceleration, not replacement
AI drafts:
- outreach messages aligned to detected signals
- follow-up responses
- contextual summaries for reps
But nothing goes out automatically. Every message stays human-validated.
Newton - Pigment’s internal GTM intelligence layer leveraging Context
Newton is an internal AI-powered account planning and context engine connected directly to Palette. It continuously aggregates and interprets:
- CRM data (accounts, opportunities, deal history)
- Gong call transcripts and deal conversations
- past wins and losses
- internal playbooks and best practices
- real-time market signals from the warehouse
Instead of reps hunting across tools, Newton answers in one place:
- what changed in this account
- why it matters now
- which narrative to use
- what similar deals looked like
Newton turns raw GTM data into actionable deal context.
Adoption KPIs:
- 80+ weekly active users, covering 70% of the target AEs & BDRs audience
- 12K+ GTM chatbot queries from sales reps since June 2025
Important note on understanding Context or “CQ”:
AI has long focused on raw intelligence (IQ): faster models, better reasoning, more computation. But intelligence alone doesn’t create competitive advantage without context (CQ). Without deep GTM context - your data, your signals, your deal history, your market dynamics - AI produces generic outputs. Your context is as critical as the model itself. Real performance comes from multiplying IQ × CQ: powerful models embedded inside rich, proprietary GTM systems.
Where Pigment deliberately keeps humans in control
- deciding narrative tone
- handling live conversations
- qualification and negotiation
- deal strategy
AI accelerates preparation and insight. Humans own persuasion.
What Pigment is optimizing next in 2026
Maximizing time spent with customers
Goal: Reduce everything that isn’t conversation.
Automation + AI to push revenue per rep and sales efficiency
Creativity as a GTM moat
Now that data is easy, automation is easy and tools are everywhere: Differentiation = creativity.
New plays. New hooks. New experiences. Not just better sequences.
What you should take away from this
Technology has been leveled. The same data sources, the same automation platforms, the same AI models are available to everyone. What once felt like an edge is now infrastructure.
Real differentiation comes from what you build on top of it.
The advantage lies in how well you transform raw signals into sharp insight, how clearly you articulate a point of view, and how decisively you execute. The companies pulling ahead are not the ones experimenting with the most AI features. They are the ones redesigning how their GTM engine works - embedding intelligence into workflows, scaling the behaviors of their best performers, and removing friction from every step of execution.
The goal is not to automate everything. It is to elevate human judgment. It is to give reps better context, better timing, and better narratives. It is to turn scattered data into conviction, and conviction into pipeline.
In a landscape where technology is increasingly commoditized, creativity becomes the multiplier. Not creativity as random experimentation, but creativity engineered into systems. Structured. Repeatable. Scalable.
That is the new moat.
Not the tools you use - but how intelligently you use them to create leverage.
And leverage is what wins.
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Growth Twins Substack
