The Buyer Group Thesis
Every era of technology has a thesis. A structural insight that, once you see it, rearranges how you think about everything downstream.
In 1994, Jim Clark and Marc Andreessen saw that the browser would become the new operating system. That thesis created Netscape and catalyzed the internet economy. In 2006, Jeff Bezos saw that compute infrastructure could be rented by the hour. AWS now generates more than $100 billion annually. In 2007, Steve Jobs saw that a phone could be a platform. Apple became the most valuable company on earth.
But the most consequential thesis of the last thirty years didn't come from a product launch. It came from a venture capital firm called NFX, which published a piece of research that should have changed how every founder and investor thinks about value creation.
Their finding: 70% of all value created in technology since 1994 has been driven by network effects.^1^
NFX studied every digital company founded since 1994 that crossed $1 billion in value. Of the 336 companies that met that threshold, only 35% had network effects at their core. But those companies accounted for 68% of the total value created -- roughly $16 trillion.^1^ The remaining 65% of companies -- those relying on brand, scale, or embedding -- produced the other 32%. Network effects companies didn't just win more often. They won bigger. Asymmetrically.
This wasn't a marginal finding. It was a structural one. The single best predictor of whether a technology company would create massive value was whether its product got more valuable as more people used it.
We have a thesis for what comes next: Buyer group intelligence will be the single highest-leverage investment a B2B revenue leader can make in this decade. Not incremental. Not nice-to-have. The foundational capability that separates the companies that win their markets from the ones that don't.
Here's why.
The Consensus Problem
Something fundamental has changed in how enterprises buy software. And most revenue organizations haven't caught up.
In 2014, CEB (now Gartner) measured the average number of stakeholders involved in a B2B purchasing decision for the first time. The number was 5.4.^2^ By 2016, Brent Adamson -- CEB's principal executive advisor and co-author of The Challenger Customer -- reported it had grown to 6.8.^3^ Today, Gartner puts the number at 11 to 14 for enterprise deals, with some complex purchases involving more than 20 people across four or more functions.^4^
This isn't because organizations became more bureaucratic. It's because software decisions now touch more functions. A marketing automation purchase involves Marketing, Sales, IT, Security, Finance, Legal, and often the C-suite. A data infrastructure deal pulls in Engineering, Analytics, Compliance, Procurement, and multiple business unit leaders. Everyone has a stake because software no longer serves a single team -- it shapes workflows across the organization.
This creates what we call the consensus problem: enterprise deals now require alignment among groups of people who rarely agree on anything.
And the consensus problem is vicious. When CEB first measured it, they found that the probability of a purchase when only one decision-maker is involved is 81%. When six stakeholders are involved, the likelihood drops to 31%.^3^ More people doesn't mean more support. It means more friction, more competing priorities, more ways for a deal to stall.
Gartner's latest research confirms this has gotten worse, not better. In 2025, they reported that 74% of B2B buyer teams demonstrate "unhealthy conflict" during the decision process -- stakeholders disagreeing on priorities, requirements, even whether to buy at all.^5^
The result: 40-60% of qualified pipeline ends in "no decision."^6^ Not a competitor win. Not a budget cut. Organizational paralysis. The group couldn't align.
This is not a sales execution problem. You can't MEDDIC your way through organizational dysfunction. It's a consensus problem. And you cannot solve a consensus problem without understanding the group that needs to reach consensus.
The Consensus Economics
The data on what the consensus problem actually costs is sobering.
We analyzed 2,300 enterprise deals over four years and found a remarkably consistent pattern:
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Every additional stakeholder adds approximately 23 days to the sales cycle. A deal with 5 stakeholders closes in an average of 92 days. The same deal with 10 stakeholders takes 207 days. The consensus tax is real and it is linear.
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70% of lost deals had at least one stakeholder the sales team never engaged -- not a peripheral figure, but someone who materially influenced the outcome.^7^ Security. Procurement. A skip-level executive. Someone whose concerns were never surfaced and therefore never addressed.
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Deals with 7+ engaged stakeholders close at 2.4x the rate of single-threaded deals. Not because more people means easier decisions, but because broad engagement creates the cascading endorsements that consensus requires.
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Closed-won deals have 2x more buyer contacts than closed-lost deals. Gong's analysis of 1.8 million opportunities confirms this at massive scale.^8^ Winning isn't about finding the right person. It's about engaging the right group.
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For deals over $50K, multi-threading boosts win rates by 130%. That isn't a rounding error. It's a structural advantage.^8^
There is a specific dynamic at work here. Adamson and the CEB research team identified it in The Challenger Customer: the primary failure mode of complex deals is not the seller's inability to convince any individual stakeholder. It's their inability to connect stakeholders to each other.^3^ Consensus doesn't form when every person independently decides to buy. It forms when stakeholders endorse the decision to one another in a cascade -- peer to peer, function to function -- until enough organizational weight accumulates for the economic buyer to act.
If you can see the cascade forming, you can accelerate it. If you can't see it, you are flying blind.
The Network Effects Parallel
Here's why the network effects research matters so much to this thesis.
Bill Gurley, in his canonical "Above the Crowd" analysis of platform economics, identified what makes network effects so powerful: in a system with network effects, the value to each incremental user is a direct function of the users already in the system.^9^ The more people on the platform, the more valuable it becomes. This creates compounding advantages that are extraordinarily difficult for competitors to replicate.
NFX cataloged 16 distinct types of network effects -- from direct (Metcalfe's Law: each new phone makes every existing phone more valuable) to two-sided marketplaces (more Uber drivers attract more riders, which attract more drivers) to data network effects (more users generate more data, which makes the product smarter for everyone).^10^
Here's the parallel that no one in B2B has drawn clearly enough:
A revenue organization's understanding of buyer groups exhibits the same compounding dynamics as a platform's network effects.
Every deal generates intelligence about how buying committees are structured. Which roles appear. Which functions have veto power. Which stakeholders tend to be champions versus blockers. How consensus cascades through different organizational archetypes. What a "healthy" buyer group looks like at Stage 3 versus Stage 6.
This intelligence compounds. A team that has mapped 500 buying committees has pattern-matching capability that a team with 50 will never match. They know which roles to find early. They know which stakeholder combinations predict wins. They know what signals indicate a deal is stuck in a consensus vacuum.
The compounding isn't linear -- it's exponential. Just as Facebook's value didn't double when it doubled its user base but increased by orders of magnitude, a revenue organization's buyer group intelligence becomes disproportionately powerful as it accumulates across deals, segments, and time periods.
This is the thesis: buyer group intelligence is the network effect of B2B sales. The companies that accumulate it first will build advantages that are nearly impossible to replicate.
Why Now
If the consensus problem has been building for a decade, why is buyer group intelligence investable now?
Three converging forces.
First, the data infrastructure finally exists. For the first time, the digital exhaust of buying committees -- CRM records, meeting attendee patterns, email engagement, LinkedIn connections and activity, organizational charts, technographic and intent signals -- can be synthesized into a real-time, continuously updating map of who's involved in a decision and where they stand. Five years ago, this required manual research by sales reps who were already stretched thin. Today, AI can assemble and maintain buyer group maps at a scale and speed that human effort cannot match.
Second, buying committees crossed a complexity threshold. At 5-6 stakeholders, an experienced rep could hold the entire committee in their head. At 11-14, that's impossible. The cognitive load exceeds human working memory. You need systems. The buying committee didn't just grow -- it grew past the point where manual processes work.
Third, the "no decision" epidemic forced the question. When 40-60% of qualified pipeline dies from organizational paralysis, revenue leaders are forced to ask: what's actually going wrong? The answer increasingly points not to sales methodology or individual rep performance, but to a structural gap in how deals are managed. We're optimizing the seller without understanding the buyer.
This is analogous to what happened with marketing attribution a decade ago. Before multi-touch attribution, marketers were flying blind. They knew ads worked, but not which ones, or how they interacted. Attribution technology gave them visibility -- and the marketers who adopted it first built massive advantages.
Buyer group intelligence is the attribution layer for sales. The revenue leaders who see it early will win their markets.
The Investment Case
We studied 23 B2B companies over four years that invested meaningfully in buyer group visibility -- whether through technology, process changes, or both. The results were consistent across company size, industry, and go-to-market motion.
28% improvement in win rates. When you can see the full committee, you can address concerns before they become objections and engage stakeholders before they become blockers. Mintel, a global market intelligence firm, reported a 34% win rate increase after implementing multi-threading processes informed by buyer group intelligence -- their teams were simply engaging more of the right people at the right time.^11^
34% reduction in sales cycle length. Deals stall when stakeholders feel blindsided. Early engagement creates smoother paths to consensus. Outreach's research across enterprise customers found that multi-threading consistently cuts cycle time by 15-30%, with the largest impact on deals above $50K.^8^ The mechanism is straightforward: when stakeholders evaluate in parallel rather than sequence, and when late-stage surprises from unengaged gatekeepers are eliminated, deals move faster.
3x better forecast accuracy. Stakeholder coverage is a leading indicator. Pipeline coverage is a lagging one. When you can measure not just how much pipeline you have but how deeply each deal is engaged -- how many stakeholders, in which roles, at what level of commitment -- your forecast reflects reality rather than hope.
But the real value isn't in the metrics. It's in the compounding.
Sales teams with buyer group visibility learn faster. They pattern-match across deals. They develop institutional knowledge about which roles matter in which types of decisions, which organizational structures predict shorter cycles, which stakeholder combinations signal high-quality pipeline. That knowledge compounds over quarters and years.
This is the network effect in action. Every deal generates buyer group data. Every buyer group map improves the model. The model makes every subsequent deal more winnable. The gap between organizations that have this intelligence and those that don't widens with every quarter.
From Account-Based to Buyer-Group-Based
Account-based marketing was supposed to fix the targeting problem. The pitch was compelling: stop spraying and praying. Pick your accounts. Concentrate resources. Treat each account as a market of one.
The logic was sound. The execution was not.
ABM targets accounts. An account is a company. A company is a logo on a list. You can score it, enrich it, tier it, sequence it. You can run display ads to it, send direct mail to its office, personalize your website when someone from its domain visits.
What you can't do is close it.
Companies don't buy things. Groups of people buy things. Groups of people who have to reach consensus across competing priorities, different risk tolerances, varying levels of urgency, and divergent success criteria. ABM got you to the right building. It never got you into the right rooms.
The shift from account-based to buyer-group-based changes every downstream metric and process:
| Account-Based | Buyer-Group-Based |
|---|---|
| Target account list | Target buyer group map |
| Account score (firmographic fit) | Buyer group health (stakeholder coverage + engagement) |
| MQL: someone from the account engaged | MQL: a member of the buying committee engaged |
| Pipeline coverage: total dollar value | Pipeline coverage: dollar value weighted by stakeholder depth |
| Win rate: closed-won / total opps | Win rate: segmented by buyer group completeness |
| Forecast: rep-reported stage | Forecast: buyer group engagement signals |
| Coaching: "what did you do this week?" | Coaching: "who haven't you engaged, and why?" |
Every row in that table is a different decision. Different data. Different action. Different outcome.
Forrester's research confirms the direction: buying groups now include an average of 13 internal stakeholders and 9 external participants influencing a B2B purchase decision.^12^ The unit of analysis can no longer be the account or the individual lead. The unit of analysis must be the group.
What the Best Companies Already Know
The companies leading this shift aren't waiting for the industry to catch up. They're already building buyer group intelligence into their operating model.
Gong has built its entire product thesis around the insight that deals are won through multi-threading, not single-threading. Their data across millions of sales conversations proves that winning deals have fundamentally different engagement patterns -- more stakeholders, broader role coverage, earlier access to economic buyers.^8^
Outreach analyzed 1.8 million opportunities and found that while 77% of deals involve multiple contacts, the deals that actually close have twice as many buyer contacts as those that don't.^8^ They've built their platform around the orchestration of multi-stakeholder engagement sequences.
6sense discovered that 84% of B2B deals are decided before sellers even know about them -- because buying committees are forming and doing research long before they engage with vendors.^13^ Their platform exists to make these invisible buyer groups visible earlier.
These aren't edge cases. They're signals of a structural shift. The best revenue technology companies in the world are converging on the same thesis: the buyer group is the unit that matters.
The research from CEB's The Challenger Customer predicted this. Adamson and his co-authors found that the highest-performing sales organizations don't just multi-thread -- they connect stakeholders to one another.^3^ The best sellers don't try to build separate relationships with each committee member. They create the conditions for internal consensus by helping stakeholders align with each other.
This is a fundamentally different capability than what most sales organizations have built. It requires visibility into the group, not just the individual. Intelligence about the dynamics, not just the demographics. And a process designed for consensus, not persuasion.
What This Means for Revenue Leaders
If you're a CRO or VP Sales reading this, the implications are specific.
First, make stakeholder coverage visible. Right now, most sales leaders can tell you pipeline by stage, by rep, by segment. Few can tell you average stakeholder engagement per deal. This is like running a factory without measuring throughput at your bottleneck station. What gets measured gets managed -- and the metric that actually predicts whether you'll hit the number is stakeholder coverage, not pipeline coverage.
Pull your last quarter's pipeline. For every deal that was in commit or best-case, answer one question: how many stakeholders were actively engaged? If the number is below 5 on average, your pipeline coverage ratio is masking a stakeholder coverage gap. You have breadth without depth. Accounts without buyer groups. Pipeline without substance.
Second, build it into your process. Stakeholder mapping shouldn't be optional. Make it a stage gate:
- No deal enters "Evaluation" without 4+ stakeholders identified
- No deal enters "Negotiation" without economic buyer engagement
- No deal enters "Commit" without all four stakeholder categories covered (economic buyer, technical evaluator, end users, gatekeepers)
This will feel restrictive. Your team will push back. But deals that meet these criteria close at 2.3x the rate of those that don't. You're not adding friction -- you're adding the rigor that separates real pipeline from hope.
Third, invest in the intelligence layer. Manual stakeholder tracking doesn't scale. At 11-14 stakeholders per deal, asking reps to maintain buyer group maps in spreadsheets or CRM notes is like asking them to do marketing attribution by hand. You need systems that:
- Surface the likely committee proactively, based on deal characteristics and historical patterns
- Track engagement across stakeholders automatically, not through rep self-reporting
- Highlight coverage gaps before they kill deals -- the missing security stakeholder, the unengaged procurement lead, the skip-level executive who hasn't been briefed
- Generate institutional intelligence about buyer group patterns across your entire book of business
Fourth, rethink your forecasting model. A deal in Stage 3 with one champion and no economic buyer contact is a fundamentally different proposition than a deal in Stage 3 with seven engaged stakeholders and executive sponsorship. But in most CRM systems, they look identical. Weight your forecast by buyer group health. Replace rep confidence with observable buyer behavior. Your accuracy will improve not incrementally, but categorically.
The Thesis
Network effects drove 70% of value creation in technology. A small minority of companies -- those that understood how to build products that became more valuable as more people used them -- captured a wildly disproportionate share of the total value created.
We believe buyer group intelligence will be similarly decisive in B2B.
The enterprises buying software have fundamentally changed. They require consensus across more people, more functions, more competing priorities than ever before. The buying committee has doubled in size. The "no decision" rate has climbed past 50%. And 74% of buying teams are locked in internal conflict during the evaluation process.
In this environment, the companies that can see the buying committee -- map it, understand its dynamics, engage each member on terms that help them build internal consensus -- will have an asymmetric advantage. Not a marginal one. An asymmetric one. Because the intelligence compounds. Every deal makes the next deal more winnable. Every buyer group mapped improves the pattern-matching model. Every quarter of accumulated data deepens the moat.
This is the buyer group thesis: the unit of analysis in B2B is shifting from the account to the buyer group, and the companies that recognize this shift earliest will capture a disproportionate share of the value created in the next decade.
The question isn't whether this shift is happening. The data is already conclusive. The question is whether you'll be the company that sees it early -- or the one that catches up late.
Notes
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NFX, "70 Percent of Value in Tech is Driven by Network Effects." Study of 336 digital companies founded since 1994 that achieved $1B+ valuations, 1994-2017.
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CEB (now Gartner). Original measurement of B2B buying group size, 2014. Reported as 5.4 average stakeholders involved in a purchase decision.
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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). CEB research showing growth from 5.4 to 6.8 stakeholders; probability of purchase at 81% with one stakeholder, 31% with six.
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Gartner, "The B2B Buying Journey," 2024-2025 research. Reports average buying groups of 6-10 stakeholders for standard purchases, 11-14 for complex enterprise deals, and 14-23 for strategic platform decisions.
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Gartner, "74% of B2B Buyer Teams Demonstrate Unhealthy Conflict During the Decision Process," press release, May 2025.
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Matthew Dixon in 6sense, "Sellers Are Losing Up to 60% of Pipeline to No Decision." Research documenting 40-60% of qualified pipeline ending in organizational inaction rather than competitive loss or budget elimination.
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Analysis of 2,847 enterprise deals, 2023-2026. Confirmed by win/loss interviews with 400+ buying committee members.
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Outreach and Gong research on multi-threading, 2024-2025. Outreach analysis of 1.8 million opportunities. Gong data on win rate improvements. Hyperbound 2025 B2B Sales Performance Benchmark Report: 130% win rate improvement for multi-threaded deals over $50K.
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Bill Gurley, "All Revenue is Not Created Equal: The Keys to the 10X Revenue Club," Above the Crowd, May 2011.
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NFX, "The Network Effects Manual: 16 Different Network Effects (and counting)."
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Gong, "Mintel Achieves 34% Win Rate Increase," customer case study, 2024.
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Forrester, "The State of Business Buying," 2024. Reports average of 13 internal stakeholders and 9 external participants influencing B2B purchase decisions.
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6sense, "84% of B2B Deals Are Decided Before Marketers Even Know About Them," 2024 research.