Defining the ICP, the buying group and the anti-target in demand generation

What defines the targeting layer of a demand generation system

Only 10% of a vendor’s total addressable market is actively in-market at any given time (6sense 2025 Buyer Experience Report). Spending against the other 90% without a qualification mechanism generates noise rather than demand. That is why the targeting layer requires three constructs (an ideal customer profile, a buying group map, and a formal anti-target) defined together before any campaign launches.

An ideal customer profile identifies the accounts to pursue. But an ICP on its own is incomplete.

It tells you which buildings to enter, but leaves unanswered who to speak to inside and which buildings to pass by.

The ICP defines where to invest. The buying group defines who to engage within those accounts. The anti-target defines where not to spend.

Define all three together, before commissioning content or committing media budget. They are three views of the same targeting decision.

Most organisations get the first one roughly right. They treat the second as a sales problem. And they ignore the third entirely. That sequencing failure explains a significant share of wasted B2B demand generation spend, more than technology gaps or attribution challenges.

What defines an ideal customer profile in demand generation?

An ideal customer profile defines the account-level characteristics of organisations most likely to buy, succeed with the product, and generate long-term commercial value. This account-level definition guides content strategy, campaign targeting, lead scoring and sales prioritisation. Getting it wrong carries the error into every downstream decision.

The ICP answers a single question: which organisations should receive investment? Everything that follows inherits the quality of that answer. A weak ICP does not simply reduce conversion rates. It actively damages pipeline economics.

Across 48 client engagements, LeadScale consistently sees conversion effort costing five to ten times the initial acquisition spend, once sales development representative (SDR) time, nurturing sequences, data enrichment, and sales cycles are factored in. Precision at account level is an economic imperative.

What an ICP contains

A functional ICP operates across four dimensions.

Firmographic fit covers the observable characteristics: industry vertical, company size, revenue band, geography, growth trajectory. These are useful starting filters. They are also where most teams stop, before examining the other three dimensions.

Technographic fit examines the technology environment. What platforms does the account run? What stack decisions have they already committed to? What integration constraints exist?

An account running a competing platform is not an ICP match; it is a conversion project with fundamentally different economics. Assess those switching economics explicitly.

Behavioural fit looks at engagement patterns and buying signals: content consumption, event attendance, website visits, third-party intent data. Use intent signals only after establishing firmographic and technographic fit. Intent without fit is a distraction.

Economic fit assesses whether the account can generate value that justifies the cost of acquisition and servicing. Its variables include deal size potential, contract duration, expansion likelihood and servicing complexity. These variables separate an account worth pursuing from one that will consume resources without producing margin.

Cross-product ICP scoring: where most exercises go wrong

Product teams build ICPs for individual product lines in isolation from other teams, hiding opportunities shared across the business.

Consider a major UK telecommunications provider with consumer pay-as-you-go, consumer pay-monthly, business mobile, broadband, and Internet of Things (IoT) divisions. Each product team builds its own ICP. Each defines its own ideal customer. None of them examine the overlap.

Bring the product ICPs together and look for overlap. An account qualifying for three product lines scores higher than one qualifying for one: the total cross-sell and upsell potential is three times greater, the lifetime value is higher, and acquisition cost is spread across several revenue streams.

A single individual might be relevant to a consumer mobile contract, a business mobile deployment for fifty handsets, a broadband upgrade, and even a specialist IoT product. Siloed teams never see the full picture. The details about that customer are identical across every division, but nobody connects them.

That is the difference between a £30-per-month consumer customer and a £15,000-per-year enterprise relationship from the same individual. The ICP that captures that distinction produces fundamentally different pipeline economics.

The same logic works in B2C. A household with three cars on the drive and two teenagers approaching driving age has a fundamentally different lifetime value from the empty-nest household next door, even if both sit in the same postcode and income band. Single-product ICPs miss this. Cross-product ICPs catch it.

Most ICP exercises fail not because the methodology is wrong, but because organisational structure prevents the methodology from working. Product silos produce product-level ICPs. Company-level targeting requires company-level ICP exercises, and those require someone with the authority to bring all the product teams into the same room.

How does an ICP differ from a buyer persona?

The ICP defines which organisations to target. It is an account-level construct. The buyer persona defines which people within those organisations influence the decision and what they care about. It is a person-level construct.

The two are routinely conflated, causing operational damage. Define the ICP first, then develop personas within its boundary.

An organisation that builds personas before defining its ICP is answering “who do we talk to?” before answering “which companies should we be talking to at all?” That sequence produces beautifully detailed persona documents aimed at accounts that will never close.

The practical test: if your ICP and your buyer persona look interchangeable, you have not built an ICP. You have built a persona and called it something else. Start with the account characteristics before describing individual roles.

How do you map the buying group within ICP-qualified accounts?

Identify the roles that make up the buying group, establish what each role evaluates and match material to those criteria. Programme design must specify these roles as well as the target accounts.

Once the ICP identifies which accounts to pursue, the buying group defines which people within those accounts collectively influence the purchase decision. This is a decision-making unit, not a contact list: larger, more experienced and more autonomous than most demand generation programmes account for.

6sense’s November 2025 explainer reports an average buying group of 10.1 members. In 6sense’s 2025 Buyer Experience Report, respondents averaged 8.6 prior purchase journeys in their category. The median respondent age was 39.6, or about 40. Buyers arrive with frameworks, preferences, and prior vendor experience already locked in.

These experienced evaluators assess suppliers in parallel, each on their own track. Demand generation needs to reach the group as a unit rather than picking off individuals and hoping the account follows.

What a buying group looks like in practice

The composition varies by deal complexity, but the roles repeat. A champion drives the evaluation internally. An economic buyer controls budget. Technical evaluators assess integration and architecture.

End users will live with the decision daily. And frequently there is at least one blocker: someone whose objections must be addressed even though they may never appear on a form fill or attend a webinar.

Forrester’s State of Business Buying 2026 release reports that a typical business buying decision involves 13 internal stakeholders and 9 external influencers: a wider lens that captures advisors, analysts, and peer influencers outside the organisation. The methodology differs from 6sense, but the direction is consistent: buying groups are growing, not shrinking.

Consider a pattern LeadScale sees repeatedly in defence and aerospace procurement: a Tier 1 account evaluating a critical component supplier on a multi-year contract worth £5 to 10 million. The buying group spans seven stakeholders across four departments.

Engineers need performance specifications and testing data. Finance cares about efficiency and total cost of ownership. Sustainability teams need environmental credentials and carbon reporting. Procurement wants compliance documentation, export licensing, and supply chain resilience evidence.

Each stakeholder needs different material, calibrated to their specific evaluation criteria. Sending every role the same white paper is broadcasting, rather than engaging a buying group.

How should demand generation engage the buying group?

Engage the group as a decision-making unit, with material matched to each role’s evaluation criteria. Lead-based programmes measure individual contacts. Buying groups operate as collective decision-making units. Measuring one while trying to influence the other produces a fundamental misalignment between what the programme measures and what actually drives the deal. Resolving that mismatch is a programme design task, rather than a CRM configuration change.

What changed in B2B buying examines how the structural shift in buyer behaviour broke the lead-based model. Buying groups increasingly evaluate suppliers inside AI research environments beyond the vendor’s direct view of their discussions.

Why is the anti-target the most economically significant targeting decision?

The anti-target is the least discussed and most consequential component of the targeting layer. An anti-target is a deliberately defined exclusion: an account type, segment, or use case that the organisation chooses not to pursue. Not because those accounts would never buy, but because the economics of pursuing them do not justify the investment.

Sales and marketing teams often know which industries or segments are a poor fit.

Informal exclusions leak. They are inconsistent across teams, invisible to automated systems and impossible to measure. A formal anti-target makes the exclusion explicit, measurable, and enforceable across every channel.

Why anti-targeting has become urgent

Two forces have made anti-targeting an operational requirement: downstream cost and reputational risk.

The first is economic. For every pound spent acquiring a lead, the downstream conversion cost runs five to ten times higher: nurturing, sales engagement, servicing, follow-up, all compounding against the original acquisition (LeadScale client analysis, 48 engagements). When a substantial proportion of acquired data is structurally unsuitable (wrong industry, wrong size, incompatible procurement process), the waste is not in the acquisition cost. It is in the multiplier applied against data that was never going to convert.

A UK residential windows and home improvement company discovered this during a 12-month lead audit. Roughly 40% of their acquired leads were structurally unsuitable, concentrated in segments where installation complexity and customer service costs outweighed the revenue per job. They could extract some sales from that segment, but servicing costs ran at 1.8 times the margin. Discarding the segment entirely and redirecting investment toward accounts with genuine structural fit produced a measurable reduction in wasted servicing spend and a corresponding improvement in cost-per-acquisition for the remaining portfolio (LeadScale engagement, 2023).

The second force is reputational risk, and it is accelerating. The Forrester 2026 State of Business Buying analysis confirms that buyers are increasingly sensitive to irrelevant outreach, and that poorly targeted contact erodes vendor credibility before a conversation even begins.

AI-driven outreach systems can send thousands of personalised messages per day, making the absence of formal anti-targeting a brand safety risk. A separate Gartner forecast predicts more than 2,000 “death by AI” legal claims by the end of 2026. The forecast concerns AI safety failures caused by insufficient risk controls.

Every avoidable contact with the wrong audience is a preventable harm. Formal anti-targeting seeks the total elimination of preventable waste by removing entire categories of unsuitable accounts from the targeting pool before any campaign activity begins, rather than filtering individual failures after acquisition.

How do anti-targets differ from negative personas?

An anti-target operates at the account level. It removes entire segments from investment based on structural fit: wrong industry, wrong size, incompatible procurement process, insufficient deal size to justify servicing costs. An anti-targeted account never enters the pipeline. It never consumes SDR time, never receives a nurturing sequence, never occupies a CRM record.

A negative persona operates at the person level within accounts that otherwise qualify. It filters out specific individual profiles: a serial form-filler who downloads everything but never engages sales, or a role that has no purchasing influence whatsoever. Negative personas refine engagement within valid accounts.

Both matter, but the anti-target stops the downstream conversion-cost multiplier from being applied at all. Across LeadScale’s cited five-to-ten-times range, excluding an unsuitable account avoids up to ten times its acquisition cost in subsequent conversion work.

What criteria should guide anti-targeting decisions?

Functional anti-target criteria typically fall into four categories:

• Structural misfit: the account’s industry, size, or operating model makes the product irrelevant.
• Economic misalignment: the deal size cannot justify the cost of acquisition and servicing.
• Procurement incompatibility: the account’s buying process is fundamentally misaligned with the sales cycle: government procurement timelines for a product requiring quarterly iteration, for example.
• Historical evidence: the segment has been pursued before and conversion data confirms the economics do not work.

The discipline is making these criteria explicit and enforcing them in the campaign targeting logic, not just the strategy document. An exclusion that never reaches the campaign lists has no effect.

Review the criteria as markets change. A segment that was structurally unfit two years ago may have evolved through new procurement processes or technology adoption.

Equally, a segment that converted reliably may have deteriorated as competition intensified. Anti-targeting is a continuous discipline, reviewed against conversion data at least quarterly, not a one-time exercise filed away after the strategy offsite.

How the three layers work as a system

The three constructs are a single targeting system, not independent exercises run on separate timelines by separate teams.

The ICP sets the boundary. It defines the universe of accounts where the product can deliver value, where deal economics justify investment, and where the conditions for long-term commercial success exist.

The buying group maps the people within those accounts. It identifies who influences the decision, what each role evaluates, and how the demand generation system must engage multiple stakeholders simultaneously, rather than treating leads as isolated individuals who happen to share an email domain.

The anti-target sharpens the boundary by exclusion. It removes segments where the economics collapse or automated outreach would create more brand damage than commercial opportunity. It also prevents structurally unsuitable data that will never convert from attracting downstream conversion costs of five to ten times the acquisition spend.

The sequence matters: ICP first, buying group second, anti-target third. Complete all three before execution begins.

Completing that targeting sequence separates account-based marketing programmes that produce pipeline from those that produce activity reports. ABM without a complete targeting layer is expensive broadcasting aimed at a named account list.

With only 10% of the total addressable market in-market at any given time and buying groups arriving with preferences already formed before first contact (6sense 2025), the targeting layer is not optional infrastructure. It is the foundation on which everything else (content, campaigns, scoring, nurturing, sales engagement) either works or does not.

Targeting directs the concurrent work of creation, influence and capture. The targeting layer underpins the demand generation system.

What comes next

The targeting layer is a prerequisite; it does not create demand on its own. In our view, activity without targeting precision is the single most expensive mistake in B2B demand generation. Use the completed definitions to plan content, campaigns, nurturing and sales engagement. Each activity should have an account-fit requirement, a buying-group role to serve and explicit exclusions. The difference between organisations that build this foundation and those that skip it shows up in pipeline conversion rates, cost per opportunity, and the relationship between marketing spend and revenue.