The GTB Stack
Open any revenue leader's tech stack diagram and you'll see the same architecture. CRM at the center -- Salesforce, usually. A marketing automation platform feeding it -- HubSpot, Marketo, Pardot. A sales engagement platform on top -- Outreach, Salesloft. Intent data layered in -- Bombora, 6sense, G2. Conversation intelligence recording everything -- Gong, Chorus. Enrichment filling the gaps -- ZoomInfo, Apollo, Clearbit.
It is an impressive collection of technology. It is also built on the wrong unit of analysis.
Every tool in that stack models one of two things: a contact or an account. The CRM stores contacts grouped by accounts. The MAP scores contacts and routes them as leads. The SEP sequences contacts one at a time. The intent platform detects signals at the account level. Each tool is excellent at what it does. None of them model what actually decides whether you win or lose a deal.
Deals are decided by buyer groups -- committees of six to twenty people who must reach internal alignment before they can act.^1^ The tech stack has no object for this. No data model. No workflow. No measurement layer. The buyer group is the most consequential entity in B2B sales, and it doesn't exist in the system of record.
That's the gap. And closing it requires not replacing every tool, but adding layers that none of them provide.
The Current Stack: Optimized for the Wrong Thing
The modern revenue tech stack emerged in a specific era. Salesforce launched in 1999 as a contact and opportunity tracker. Marketo and HubSpot followed in the late 2000s to automate lead generation. Outreach and Salesloft arrived in the mid-2010s to scale outbound prospecting. Each generation solved the problem of the moment: first, organizing customer data; then, generating demand; then, scaling rep activity.
The implicit model behind all of these tools is a funnel. Leads enter at the top. Some convert to opportunities. Opportunities move through stages. A percentage close. The entire architecture is optimized for moving individual contacts through a linear process.
This model made sense when buying was simpler. When a VP could sign a $50K contract without a committee review. When procurement wasn't involved below $100K. When security didn't have veto power over software purchases.
That world is gone. Gartner reports that the average complex B2B purchase now involves eleven stakeholders, sometimes exceeding twenty.^1^ Forrester puts the number at thirteen, spanning two or more departments in 89% of purchases.^2^ Yet the technology stack still treats each of those thirteen people as a separate contact record, living in isolation, connected to an account but not to each other.
The stack doesn't know they're a group. It doesn't know who influences whom. It doesn't know whether the group is converging toward a decision or fragmenting into conflict. The buyer group -- the entity that actually decides -- is invisible.
Five Layers of a GTB Stack
If you were designing a revenue technology stack from scratch today, oriented around the buyer group instead of the lead, it would have five layers. Each addresses a question the current stack cannot answer.
Layer 1: Identity -- Who Is in the Buying Committee?
What exists today: Enrichment tools tell you who works at a company. Intent platforms tell you an account is showing research activity. LinkedIn surfaces people by title. These tools answer "who exists at this account?" They do not answer "who will be involved in this purchasing decision?"
A company with 5,000 employees might have 200 people whose titles suggest relevance. The actual buying committee is twelve of them. Knowing the 200 gives you a haystack. Knowing the twelve gives you a deal.
What's needed: A layer that identifies the likely buying committee from patterns across prior deals. What roles typically appear in purchasing decisions for your product category? Which functions tend to surface late and should be anticipated early? An identity layer that says: "Based on 400 closed deals in your segment, the buying committee typically includes a VP of Engineering, a CISO, a procurement manager, two to three end-user team leads, and an executive sponsor. Here's who those people likely are at this account."
No single tool does this today. The identity layer stitches together enrichment data, CRM history, and deal patterns into a map of the committee -- not a contact list, but a buying group.
Layer 2: Relationship -- Who Influences Whom?
What exists today: CRM contact roles provide flat labels -- "Decision Maker," "Influencer," "Champion." LinkedIn shows reporting structures. Conversation intelligence captures meeting attendance. None of this tells you how influence actually flows inside the buying committee.
What's needed: A layer that maps the influence topology of the buyer group. Not the org chart -- the actual decision architecture. Who does the economic buyer rely on for technical judgment? Who has informal veto power despite lacking a senior title? Where are the alliances and where are the fault lines?
This is where the CEB research -- showing that consensus forms through cascading endorsements between stakeholders, not through top-down decree -- becomes operationally relevant.^3^ If the VP of Engineering trusts the lead architect's technical assessment, and the CFO defers to the VP of Engineering on build-vs-buy decisions, you know the cascade path. Convince the architect and you've started a chain reaction. Miss the architect and the cascade never begins.
Today, this intelligence lives entirely in the rep's head -- if it exists at all. The relationship layer makes it visible, persistent, and shareable.
Layer 3: Engagement -- Are You Multi-Threaded or Single-Threaded?
What exists today: Activity logs, email tracking, call recordings, content downloads -- fragmented across five tools, attributed inconsistently, and almost never aggregated at the buyer group level. The CRM can tell you the rep had a meeting with the account. It usually cannot tell you that four of twelve committee members have been engaged, that procurement has had zero contact, and that the security lead attended a webinar six weeks ago but was never followed up with.
What's needed: A layer that measures engagement at the buyer group level. Which committee members have been engaged? At what depth? How recently? And critically -- which have not been engaged?
Gong's analysis found that winning deals have 2x more buyer contacts than losing deals.^4^ Outreach's data showed multi-threaded deals close 51 days faster.^5^ Yet 70% of opportunities had only one point of contact.^6^ The engagement layer reveals coverage gaps before they kill deals.
The unit of measurement shifts. Instead of "how many emails did the rep send?" the question becomes "what percentage of the buying committee has been meaningfully engaged?" Instead of "how many meetings this week?" it becomes "which roles are still dark?"
Layer 4: Intelligence -- What Does Each Person Care About?
What exists today: Basic CRM fields. Persona-based marketing content. Rep notes, when they're entered. Conversation transcripts that capture what was discussed but don't synthesize it into decision profiles.
What's needed: A layer that maps each committee member's decision framework. The CFO cares about ROI and total cost of ownership. The CISO cares about data residency and compliance. The VP of Engineering cares about integration architecture. The end-user managers care about adoption friction. The procurement lead cares about contract terms and vendor risk. Each person brings a different framework to the same purchase.
Gartner's 2025 research found that content tailored for individual-level relevance actually hurts group consensus -- creating a 59% negative impact on alignment.^7^ But content designed for buying-group-level relevance improves consensus by 20%.^7^ The intelligence layer isn't just about knowing what each person cares about individually. It's about understanding how their interests intersect and where they conflict -- so you can help the group converge.
Layer 5: Orchestration -- What's the Right Sequence Across the Group?
What exists today: Sales engagement platforms orchestrate sequences for individual contacts. Marketing automation orchestrates nurture campaigns for segments. Neither orchestrates engagement across a buyer group as a coordinated effort.
The result: reps engage whichever stakeholder responds, in whatever order they become available, with no systematic approach to sequencing for maximum cascade effect.
What's needed: A layer that coordinates engagement across the buyer group with the deliberateness of a campaign. Which stakeholders should be engaged first to seed the consensus cascade? Which in parallel? When should the economic buyer be brought in -- not by calendar date, but by the state of organizational readiness?
Our data shows that stakeholders engaged in the first third of the sales cycle become blockers 12% of the time, while those engaged in the final third become blockers 61% of the time.^8^ The orchestration layer operationalizes this. It recommends: "Engage the lead architect this week. Their endorsement gives the CISO confidence on security, which unblocks the CFO conversation. Loop in procurement by week four to avoid the late-stakeholder penalty."
This is the most ambitious component of the GTB stack -- and the one that, once built, creates the largest competitive advantage.
What's Actually Blocking This
If the GTB stack is so clearly needed, why doesn't it exist yet?
The data model problem. CRM databases are built on the Contact-Account-Opportunity triad. Adding "Buyer Group" as a first-class object requires either extending the CRM's data model or building an adjacent system that maintains its own buyer group graph. The foundation assumes buying is an individual activity.
The integration problem. The five layers require synthesizing data from CRM, marketing automation, sales engagement, conversation intelligence, enrichment, and intent platforms. Each has its own data model, its own API, its own definition of a "contact." Building a unified buyer group view means reconciling all of them.
The measurement problem. Revenue organizations measure what the stack makes measurable: leads generated, emails sent, pipeline created. Buyer group health -- committee completeness, stakeholder coverage, cascade formation -- doesn't show up in reporting because the data structures don't support it.
These are real barriers. They're also temporary. The convergence of AI-driven data synthesis, graph-based relationship modeling, and buyer intelligence platforms is eroding all three. At Adrata, we're building toward this architecture because the GTB stack isn't a marginal improvement -- it's a generational shift in how revenue organizations operate.
Where the Stack Is Heading
In the near term, the identity and engagement layers will mature first. AI can already synthesize CRM data, enrichment sources, and deal patterns to generate probable buyer group maps. Measuring multi-threading at the buyer group level is an analytics problem with known inputs. These layers don't require inventing new technology -- they require connecting existing data in a new way.
The relationship and intelligence layers will follow. Conversation intelligence transcripts contain enormous signal about stakeholder dynamics -- who defers to whom, what concerns surface, where tension lives. Mining that signal for influence maps and decision profiles is a natural language processing problem that current models can handle. The bottleneck isn't capability. It's the deliberate choice to orient these models around buyer groups rather than individual contacts.
The orchestration layer is the furthest out and the highest value. True orchestration requires all four preceding layers to be functional and enough historical data to pattern-match effectively. This is a machine learning problem that improves with scale. Early adopters will build advantages that compound.
The end state is a revenue operating system where the buyer group is the primary object -- as central to the stack as the contact is today. Pipeline reviews filtered by buyer group health. Forecasts weighted by stakeholder coverage. Coaching oriented around "who haven't you engaged?" rather than "how many calls did you make?"
The shift from lead-based to account-based took roughly a decade, from Marketo's founding in 2006 to ABM becoming mainstream by 2016. The shift from account-based to buyer-group-based is beginning now. The companies that build and adopt the GTB stack first will have a structural advantage -- not because the tools are better, but because the unit of analysis is right.
The tech stack you have was designed for leads. The tech stack you need is designed for the committee.
Notes
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Gartner, "The B2B Buying Journey" (2024-2025). Average buying group for complex purchases involves 11 stakeholders, scaling to 20 for enterprise decisions.
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Forrester, "The State of Business Buying, 2024." Average of 13 stakeholders involved in purchasing decisions; 89% of purchases involve two or more departments.
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Brent Adamson, Matthew Dixon, Pat Spenner, Nick Toman, The Challenger Customer: Selling to the Hidden Influencer Who Can Multiply Your Results (Portfolio/Penguin, 2015). Research showing consensus forms through cascading endorsements between stakeholders, not through individual persuasion.
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Gong multi-threading research (2024-2025). Analysis across millions of sales interactions showing winning deals have 2x more buyer contacts than losing deals. For deals over $50K, multi-threading boosts win rates by 130%.
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Outreach multi-threading research (2024-2025). Analysis of 1.8 million opportunities showing multi-threaded deals close 51 days faster and have twice as many buyer contacts as closed-lost deals.
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UserGems, "How Much Is Multithreading Worth to Your Pipeline and Revenue?" Analysis of 5,000+ opportunities. 70% of opportunities had only one point of contact, despite multi-threaded deals showing 5x win rate improvement.
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Gartner, "Sales Survey Finds 74% of B2B Buyer Teams Demonstrate Unhealthy Conflict During the Decision Process," press release, May 2025. Survey of 632 B2B buyers. Individual-level content creates 59% negative impact on consensus; group-level relevance improves consensus by 20%.
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Analysis of 2,300 enterprise deals with complete stakeholder engagement data, 2022-2026. Stakeholder engagement timing vs. blocker probability: first third 12%, middle third 34%, final third 61%.