What changed in B2B buying and why the old model broke

What actually changed in B2B buying?

The winning vendor was on the buyer’s Day One shortlist in 95% of purchases in 6sense’s 2025 study of buyers who had spent at least $25,000 in the previous two years. The Day One shortlist is the one buyers hold at the start of the buying journey. It is in place before any enquiry, drawn from earlier experience and research the supplier cannot see, in channels its CRM does not reach. Buyers fill in a contact form only after substantial evaluation.

The demand generation model most B2B technology companies run has a structural problem. It was designed for a buyer who entered through visible channels and followed an individual, linear path to purchase. That buyer doesn’t exist any more, and hasn’t for several years. Group decisions, anonymous research and AI-mediated discovery have replaced that buying process.

Five shifts happened together. Each would stress the traditional model; together, they broke it.

Buying groups expanded

6sense’s 2025 research explainer puts the average buying group at 10.1 members. In separate research, Forrester’s 2026 State of Business Buying release describes a typical business buying decision involving 13 internal stakeholders and 9 external influencers. Procurement professionals are decision-makers in 53% of business buying cycles, engaging from the start.

A programme built for an individual buyer, tracking one form fill, one nurture sequence and one sales-qualified lead (SQL), cannot account for the wider buying group. A single enthusiastic researcher downloading three whitepapers generates MQL signals; the CFO and procurement lead generate none. The CFO controls the budget; the procurement lead runs the contract process and will block the deal if the paperwork isn’t right.

Buyers arrive with preferences already formed

6sense’s 2025 report, the source of the 95% Day One shortlist finding above, also describes the pre-contact favourite winning roughly 80% of the time. Buyers have completed an average of 8.6 prior category purchase journeys, according to the 6sense report. They are not starting from scratch; they are pattern-matching against previous experience.

This reframes the purpose of demand generation. Capturing buyers who enter the funnel is not enough on its own. Demand generation also has to get the vendor onto a shortlist built where it cannot see, by buyers who intend to make contact only when they are ready.

The buying timeline shifted

The average buying cycle among the buyers surveyed by 6sense in 2025 runs 10.1 months. First direct seller contact happens at months seven or eight. Buyers complete 61% of their journey before first direct contact with sellers. The early-engagement window on which traditional demand generation relied, reaching buyers early and nurturing them through, has effectively closed.

Buyers have more tools for conducting their own research and can decide when they need help from a vendor.

Rep-free preference, with a paradox

Seventy-five per cent of B2B buyers prefer a rep-free purchasing experience, according to the 2022 Gartner B2B Buyer Survey, cited in its 2024 research summary. Buyers who purchase through self-service digital commerce are 1.65 times as likely to experience purchase regret as those using traditional sales channels. Buyers want control. They do not want isolation.

The demand generation system needs to be present without being intrusive: available when the buyer is ready, invisible when they are not. That is a harder design problem than sending the right email at the right time.

AI entered the research stack

Ninety-four per cent of business buyers in Forrester’s 2025 Buyers’ Journey Survey reported using AI in their buying process. In a separate US study, 60% of surveyed B2B buyers used AI tools during their purchase journey (Google/NRG, A Playbook for Earning Trust in Today’s B2B Buyer Journey, October 2025). The private AI conversation generates no direct vendor CRM record. A later referral may create a website visit without revealing the research that shaped the shortlist (see AI search and AI agents in B2B buying).

Where did the buying journey go? Three layers of unobserved research

B2B buying decisions are made mainly through unobserved research.

The 6sense 2025 report describes the buying journey in two phases. The Selection Phase is where buyers research, evaluate, build shortlists, and form preferences. It is largely anonymous and almost entirely invisible to vendor analytics. The Validation Phase is where buyers make direct contact with sellers, attend demos, and run procurement processes. It is largely confirmatory. This is what your CRM captures.

Three layers of unobserved research can shape selection before an identifiable enquiry.

Layer 1: Anonymous web activity. In its August 2022 survey of B2B marketers, 6sense describes roughly 97% of the respondents’ website traffic as anonymous, based on their reported form-fill rates. Anonymous visitors read your content, compare you against competitors, and shortlist or dismiss you without ever submitting a form. Analytics shows activity but cannot show the anonymous visitor’s intent, competitive context or decision stage.

Layer 2: Closed peer networks. Buying decisions are shaped by conversations in Slack communities, LinkedIn direct messages, analyst briefings, and board-level peer introductions. These interactions are often the highest-trust inputs in the entire buying process. A recommendation from a peer who has used your product is worth more than any amount of retargeting. These private conversations generate no vendor-side data.

Layer 3: AI-mediated research. A buyer asks ChatGPT which demand generation platforms are worth evaluating. Gemini returns a shortlisted comparison. Perplexity summarises analyst commentary. The buyer forms a view. Your name is either on the list or it is not. This is the fastest-growing layer.

Trying to track the untrackable does not solve the problem of unobserved research. The response is to be present, credible and correctly positioned in all three layers before active evaluation begins. This is an infrastructure problem, not a campaign problem.

What does the old model assume?

The traditional demand generation model was built on a set of assumptions about buyer behaviour. Each of those assumptions has been invalidated by the data.

Each row is a mismatch between how the model operates and how buyers actually behave. An individual-form-fill metric rewards contact volume over quality and leaves the buying group, anonymous research and earlier preference formation outside its measurement system. Marketing teams optimise for the form fills they are measured on. Together, these incentives create a demand generation system optimising for outcomes with almost no relationship to revenue.

Why the MQL model broke, and what the data says

MQLs were a reasonable proxy for intent when individual buyers engaged visibly. The buying process changed and the metric did not. Five failures reinforce each other.

The individual-lead problem. MQLs measure one person’s engagement in an era when 10 or more people make the decision. The metric measures a single finger and calls it a hand.

The volume incentive. Marketing teams are rewarded for hitting MQL targets. This creates an incentive to optimise for form fills: gated content, aggressive retargeting, lead capture at the expense of brand presence. The tactics that generate MQLs are, in many cases, the same tactics that damage the supplier’s standing in unobserved research. You trade long-term credibility for short-term numbers.

The attribution blind spot. MQL attribution tracks visible touchpoints. When 97% of website visitors are anonymous and first direct seller contact happens at month seven or eight of a ten-month buying cycle, the vast majority of the journey is invisible to attribution. Last-touch models credit the last visible action, the form fill, while ignoring the months of anonymous research, peer conversations and AI-mediated discovery that actually drove the decision. The leads passed to sales are then credited to the campaign, although they came from a process the campaign never touched. The form fill looks like causation. It is usually correlation at best.

The sales-marketing disconnect. CMOs report hitting MQL targets. Sales reports that the leads are poor quality. Both observations are accurate. MQL targets are being met; the leads do not represent genuine buying intent. The metric has become disconnected from revenue, and both functions are optimising against the wrong thing.

The economics are broken too. The industry’s standard commercial model compounds the problem in a way that rarely gets discussed. Binary de-duplication treats any repeat lead submission as a non-billable duplicate. It suppresses one of the most reliable buying signals available, and one of the few that could indicate genuine escalating interest, before anyone can act on it. When someone engages with your content for the second time eight weeks after the first, it is escalating interest rather than an identical duplicate.

LeadScale’s Repeat Frequency Model (RFM) classifies repeat engagement into four categories: a Replica (an identical submission, genuinely worthless); a Repeat (a new enquiry from a known individual within a defined time window, signalling increasing intent); a Reactivation (engagement after extended dormancy, full value); and Net-New (a previously unknown individual). The traditional commercial model treats categories two and three as category one. The economic incentive structure of the lead generation industry is directly misaligned with the behavioural reality it is supposed to measure.

Three operational observations from Robin Caller’s client work make the consequences concrete.

The tenure paradox. “Look at your best customers. Many of them pre-date your current marketing operation. They found you despite your demand generation, not because of it.”

The nurturing investment gap. “It’s like BMW spending millions on brand awareness and then staffing the showroom with one person on a Saturday afternoon. Heavy investment in getting people interested. Thin provision for when they’re actually ready.”

The universal servicing failure. “Most B2B companies service every inbound lead identically, regardless of ICP fit. The result is that roughly 40% of servicing resource goes to accounts that will never close. That is not a sales execution problem. That is a system design problem.”

Where this leaves the programme

The failures these five shifts expose are symptoms of a demand generation model built for a buying process that no longer exists. Robin Caller, LeadScale’s CEO, describes the required transition as moving from Campaign Thinking to Infrastructure Thinking. Campaign Thinking treats demand generation as a sequence of executional decisions: which channels, which messages, which offers. Infrastructure Thinking treats it as a governed system architecture: where signals, personas, content, and compliance operate as interconnected components rather than discrete campaigns. Together, the shifts make Campaign Thinking structurally incapable of generating pipeline. The transition is not a strategic preference. It is the structural reality of B2B buying in 2025 and 2026.

If reporting still centres on MQL volume, the gap between the measure and the buying process widens every quarter. Closing that gap means connecting activity to accounts and buying groups, supporting research before contact and distinguishing meaningful repeat engagement from identical submissions. The final FAQ lists account-level metrics that can replace MQL volume in that reporting.

The system foundations cover the data, rules and handovers needed to put it into practice. AI search and AI agents in B2B buying examines how AI search and AI agents accelerate these shifts and change how content needs to perform.