Contact-Level ABM: Targeting the Key Individuals Within Accounts 

Most ABM programs target accounts. The best ABM programs target people.

This distinction sounds subtle, but its commercial implications are significant. Account-level targeting — serving ads to all employees at a specified set of companies — is a meaningful improvement over broadcasting to the open market. But it still leaves a gap between the audience your budget is reaching and the audience that actually matters for a deal. Contact-level ABM closes that gap.

This post explains what contact-level ABM is, why it represents a genuine upgrade in precision over standard account-level targeting, and which tools are enabling it in B2B life sciences today.

The Limitation of Account-Level Targeting

When a standard ABM program runs programmatic display advertising to a target account list, the targeting mechanism works at the domain or IP address level. The platform identifies devices associated with the target company's network and serves ads to them. In practice, this means your ads are being seen by a broad cross-section of that organization — finance staff, HR, IT, facilities, legal — the vast majority of whom have no relevance to your solution.

Even layering in title or function targeting on LinkedIn, which significantly improves precision, is an approximation. Targeting "Senior Directors and above in Clinical Operations at your target account list" will reach a useful audience, but it will also include people adjacent to the buying committee, exclude buying committee members with non-standard titles, and provide no visibility into which specific individuals are engaging.

Therefore, if you know specific people to target at an account, using broad level account targeting is inefficient. You end up with significant ad spend reaching large numbers of people who will never influence a purchasing decision, while the specific individuals who will are reached only intermittently and without the individual-level visibility that would make outreach actionable.

Account-level targeting is not without value — it builds brand familiarity across an organization, which has genuine commercial benefit. But for the highest-priority accounts and personas, it limits the ability to prioritize getting in front of the right people.

What Contact-Level ABM Does Differently

Contact-level ABM operates from a fundamentally different starting point: instead of targeting a domain or an IP range, it targets a curated list of specific, named individuals.

Using email addresses, names, or device ID matching, contact-level ABM platforms serve ads directly to identified individuals — the exact people you have determined are most likely to be members of the buying committee at your target accounts. Your budget in contact-level ABM is spent almost entirely on those specific people, rather than on the broader organizational population.

The precision has two important consequences.

First, efficiency improves dramatically. When your advertising reaches a list of 200 named individuals across 40 target accounts - those most likely to be part of buying groups - every impression has much higher relevance. There is limited waste of the kind built into account-level programmatic targeting.

Second, and more importantly for the commercial team, the engagement data becomes individually actionable. In account-level ABM, you might know that three people at a target biotech have clicked on your ads this month — but you may not know who they are, what their roles are, or whether they may be part of an-market buying group. In contact-level ABM, you know that the VP of Translational Medicine clicked twice, the Clinical Program Director clicked four times and visited the content page, and the Head of Procurement opened the email sequence. That is a buying committee picture, not an account-level signal — and it significantly changes how the sales team is empowered to engage with a target account.

How Contact-Level Targeting Works

Contact-level ABM platforms work by matching the contacts on your list to real, observable individuals as they browse the web. When a contact on your list visits a property within the platform's publisher network, the platform recognizes them — through email address matching, device ID resolution, or identity graph lookups — and serves your ad to that specific person. The matching happens behind the scenes; from the contact's perspective, they simply see a relevant ad while going about their normal browsing behavior.

Because recognition and serving happen at the individual level, the engagement data the platform returns is correspondingly granular. Marketers and SDRs can see, for each specific contact, how many impressions they received, which ads they engaged with, what content they interacted with downstream, and how their engagement has trended over time. That individual-level record is what makes contact-level ABM commercially actionable in a way that account-level targeting cannot match.

The practical implication for the sales team is significant. Instead of receiving a notification that "someone at Acme Biotech has been active this week" and having to investigate who that might be, the SDR receives a ranked list of named individuals — with titles, content histories, and engagement frequencies — that tells them precisely who to call, in what order, and what to reference when they do. The quality of that first outreach conversation improves substantially when it can open with specific relevance rather than a generic pitch.

Contact-level platforms also integrate with CRM and marketing automation systems, meaning engagement signals flow automatically into the tools the commercial team is already using. A contact who crosses a defined engagement threshold can trigger an SDR task, a lead score update, or an account elevation — without requiring manual review of campaign data. The system surfaces the signal; the team acts on it.

Building the Right Contact List: Where the Real Work Happens

Contact-level targeting is only as powerful as the list it operates from. Precision targeting of the wrong people produces precise data about irrelevant engagement. The effort invested in constructing a high-quality contact list is what determines whether a contact-level ABM program generates genuine commercial intelligence or merely impressive-looking metrics.

In life sciences, building that list well requires assembling intelligence from several distinct sources and using each to inform the others.

1. Start with Life-Science-Specific Intent Signals to Identify the Right Accounts

Before identifying specific contacts, the first question is: which accounts are most likely to be in an active buying cycle right now? In life sciences, the answer comes not from keyword-based web intent data — the signal type that general-purpose ABM platforms are built around — but from events that are predictive of near-term purchasing need.

A new clinical trial registration indicates that a specific program is advancing and that buying groups are likely forming around the services and capabilities needed to execute it. An IND filing signals that a program is preparing for first-in-human work, with all the operational and scientific purchasing that entails. A Series C or D funding round almost always precedes acceleration of program investment and vendor engagement. A regulatory designation — Fast Track, Breakthrough Therapy, Orphan Drug — compresses timelines and creates urgent purchasing needs. A new partnership or licensing deal may bring capital and new program activity simultaneously.

These events are publicly available and systematically trackable through clinical trial registries, regulatory submission databases, SEC filings, and life-science-specific pipeline intelligence platforms. Organizations that build their target account lists from these signals — rather than from generic web intent data — are starting from a fundamentally stronger intelligence position. The accounts they target are not just companies that look like good fits on paper; they are companies with a demonstrable, time-stamped reason to be in-market for a relevant solution.

2. Map the Right Personas for Each Account Type

With a target account list grounded in life-science-specific intent, the next step is determining which individuals within those accounts belong on the contact list. This is where persona mapping does its most important work.

A well-constructed persona map for a given ICP segment documents the titles, functions, and seniority levels that typically appear on the buying committee for that type of purchase. It is built from commercial experience — the sales and BD team's accumulated knowledge of who is actually present when deals of a given type get evaluated and closed — and validated against known patterns in the target account type.

The persona map translates directly into search criteria: when using a contact database to find individuals at a target account, you are looking for the specific titles and functions identified in the persona map. A precise persona map produces a precise contact list. A vague one — "senior people in clinical" — produces a list that is too broad to be useful for contact-level targeting.

The persona map should also be differentiated by account type. The buying committee for a large pharma account and the buying committee for a Series B biotech may share some common roles but differ significantly in seniority, organizational structure, and the degree to which functions are centralized or distributed across programs.

3. Use Contact Databases to Find the Named Individuals

With target accounts identified through intent signals and target personas defined through the persona map, the final step in list construction is finding the specific named individuals who hold those roles at those accounts.

B2B contact databases — platforms that aggregate professional profile data including name, title, employer, email address, and seniority level — are the primary tool for this work. For life sciences, databases with strong coverage of the biotech and pharma sectors, regularly refreshed to account for the high rate of organizational change in this industry, are essential. A contact list built from data that is twelve months out of date in an environment where companies routinely restructure, merge, or reduce headcount is a list that will underperform.

The database search takes the persona map as its input and returns a set of matching individuals at each target account. That set is then cross-referenced with CRM contacts — elevating anyone the commercial team has already engaged — and supplemented with prior campaign engagement data and sales team intelligence from conferences and prior business development activity.

The resulting contact list is not a generic prospecting database. It is a curated set of individuals who work at accounts with documented reasons to be in-market, in roles that correspond to the buying committee for your specific solution type. That specificity is what makes contact-level ABM generate the kind of engagement data that actually moves pipeline.

The shift from lead-based marketing to account-based marketing was a meaningful evolution. Instead of chasing individual contacts and hoping the right person would eventually surface, ABM focused commercial resources on a defined set of high-value organizations, and measured success at the account level rather than the lead level.

But account-level thinking alone is not the complete picture. An account is not a buyer. A buying committee is.

The most sophisticated B2B marketing programs have taken a further step: targeting not just accounts, but the specific groups of individuals within those accounts who are collectively responsible for a purchase decision. Understanding who those people are, what each of them needs to see before they will advocate for your solution, and how to reach all of them simultaneously is what separates ABM programs that generate real pipeline from those that generate account-level engagement without commercial traction.

This post explains the buying committee reality in B2B life sciences, how ABM can be used to surface and engage every relevant stakeholder, and how the pattern of engagement across a buying group becomes one of the most valuable signals in the entire commercial program.

Why Targeting Accounts Is Necessary but Not Sufficient

When a B2B life science company runs an ABM campaign targeting a specific biotech account, it typically generates engagement from some individuals at that company. An ad gets clicked. A white paper gets downloaded. A webinar registration comes in. These are positive signals, but on their own they tell an incomplete story.

The critical question is not just whether someone at the account is engaging. It is whether the right people at the account are engaging — and whether enough of them are engaging simultaneously to indicate that a purchase decision is being considered.

In most B2B life science purchases, a single engaged contact is rarely sufficient to move a deal forward. The purchasing process involves multiple stakeholders, each with a distinct perspective and a distinct set of concerns. A scientist who loves your technology cannot unilaterally approve a contract. A procurement manager who sees your pricing as competitive cannot evaluate your technical capabilities. Each member of the buying committee holds a piece of the decision — and failing to engage all of them means the deal can stall or die even when the initial interest is genuine.

This is the gap that committee-level targeting is designed to close.

The Anatomy of a Life Science Buying Committee

Buying committees in B2B life sciences vary by organization size, outsourcing model, and the nature of what is being purchased. But most purchase decisions of meaningful size involve some combination of the following personas:

The Scientific End-User: the researcher, scientist, or clinical specialist who will actually use the solution. This person cares primarily about technical performance, methodological fit, and whether the solution will work for their specific application. Their endorsement is typically a prerequisite for any serious vendor evaluation.

The Program or Project Lead: the Clinical Program Director, Principal Investigator, or Head of Development for the specific program driving the purchasing need. This person cares about timelines, deliverables, and whether the vendor can reliably execute against the program milestones. They often function as the internal champion if the scientific case is strong.

The Procurement or Vendor Management Function: responsible for vendor qualification, contract terms, pricing, and compliance with the organization's purchasing policies. This persona is often invisible in early-stage engagement and becomes critical — and potentially obstructive — later in the process if not engaged proactively.

The Finance or Budget Holder: particularly relevant for larger contracts or early-stage biotechs where capital allocation decisions are made at a senior level. For mid-size and large biotech pharma, the budget approval process may be separated from the technical selection process.

The Legal and Regulatory Function: for purchases with regulatory or IP implications (which is a large proportion of life science vendor relationships), legal and regulatory affairs team members may need to review and approve vendor agreements.

The C-Suite or Senior Leadership: for strategic vendor relationships, preferred provider agreements, or contracts representing a significant portion of the organization's budget, executive-level buy-in is often required. Even when executives are not part of the day-to-day evaluation, their awareness and support is frequently necessary to close a deal.

The practical implication is that a marketing program focused exclusively on the scientific end-user — the most natural audience for technically-oriented content marketing — is engaging only one member of a committee that may include five or more distinct decision-makers. ABM creates the framework to reach all of them.

How ABM Surfaces the Buying Committee

Identifying who is on the buying committee at a specific target account requires both proactive list-building and reactive signal-reading.

Proactive persona mapping starts with your ICP and the buying committee profiles documented for each ICP segment. For a given account, the commercial team uses contact databases and LinkedIn to identify the individuals at that company who hold the titles and roles corresponding to each buying committee persona. These individuals become the named contacts in the contact-level targeting list for that account — the specific people to whom ads will be served, SDR outreach will be directed, and webinar invitations will be sent.

Reactive signal-reading is equally important. Not every buying committee member will be visible in advance. Some are identified only when they begin engaging with campaign content — a new title downloading a gated white paper, a different persona registering for a webinar, an unfamiliar name clicking through from a LinkedIn ad. Each new engagement from a previously unknown contact at a target account is a data point that refines and expands the picture of who the buying committee may include.

Contact-level ABM platforms — Influ2 and Propensity are platforms which enable contact-level targeting and individual-level visibility. Rather than knowing only that someone at a target account engaged, marketers can see precisely which person engaged, with what content, and how many times. When three individuals with different titles — a Biomarker Scientist, a Clinical Program Director, and a Procurement Manager — from the same mid-size biotech all interact with relevant content within a two-week window, this is a strong indication of possible  buying-committee level interest. That account should be elevated immediately in scoring and flagged for targeted sales outreach.

Deploying Content That Speaks to Each Persona

Identifying the buying committee is a key step, and one which is enabled by tailored content. Engaging each member of a buying committee effectively requires content and messaging calibrated to each persona's specific concerns — not a single piece of content served to everyone.

This is where the content matrix becomes an essential planning tool. A content matrix maps each buying committee persona to the content types and messages most likely to resonate with them at each stage of the funnel. What it produces in practice is a set of parallel tracks — each targeting a different member of the committee with content relevant to their role — that together build consensus across the full group.

For the scientific end-user, the most effective content is technically substantive: application notes, analytical validation data, peer-reviewed publications, detailed methodology comparisons. This audience wants to see evidence that the solution works and that the team behind it understands the science.

For the program lead, the relevant content shifts toward execution and outcomes: case studies documenting successful program delivery, data on timelines met and milestones achieved, testimonials from peers who have navigated similar program challenges. They need to be confident that the vendor will deliver.

For procurement and finance, the relevant content addresses risk management, vendor qualification, and commercial terms: quality system documentation, regulatory compliance credentials, contract flexibility, pricing transparency, and reference client information.

For senior leadership, the appropriate content is strategic: thought leadership on sector trends, evidence of the vendor's market position and reputation, and any data that positions the relationship as strategically valuable rather than merely transactional.

Running these parallel content tracks through ABM channels — LinkedIn ads targeted by title, contact-level ads to named individuals, email sequences to CRM contacts — means that every member of the buying committee is receiving relevant, role-specific content. This is how consensus is built before the formal vendor evaluation even begins.

A Content Matrix with Examples of Relevant Content by Persona and Engagement Stage

The Pattern of Engagement Is the Signal

The ultimate value of committee-level targeting in ABM is not just that it reaches more people at a target account. It is that the pattern of who is engaging, with what content, and over what timeframe, becomes a reliable indicator of purchase intent.

A single engaged contact might represent genuine interest — or it might represent a researcher doing background reading with no near-term purchasing intent. But when engagement begins to appear across multiple personas at the same account, spanning scientific, operational, and commercial functions, the probability that a buying group is actively evaluating shifts dramatically. The breadth of engagement is the signal.

This is why committee-level visibility changes the nature of the marketing and sales handoff. Instead of passing individual leads to the SDR team and leaving them to figure out whether a deal opportunity exists, a well-instrumented ABM program hands off a picture of the buying committee: which specific individuals have engaged, with which content, and how their engagement maps to the personas typically present when a deal closes. That intelligence turns a cold outreach sequence into a precisely targeted, contextually informed commercial conversation.

The Bottom Line

Targeting accounts is necessary. Targeting buying committees is what makes ABM commercially productive.

The investment required to get there — persona mapping, contact-level targeting infrastructure, parallel content tracks by persona, and the organizational discipline to act on engagement signals quickly — is not trivial. But it is what separates ABM programs that generate genuine pipeline from those that generate engagement data with no clear path to revenue.

In B2B life sciences, where purchasing decisions involve multiple stakeholders and carry real consequences for the programs that depend on them, the ability to surround the full buying committee with relevant, credible, role-specific content before the evaluation formally begins is one of the most durable competitive advantages available.

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Account-Based Marketing was not invented for the life sciences. It was developed primarily in the B2B technology sector, refined by SaaS companies selling software to enterprise buyers, and packaged into platforms built to serve that context. The intent data that powers most ABM platforms is calibrated to detect when a company is researching a new CRM, evaluating a cybersecurity vendor, or exploring cloud infrastructure options.

Life science companies that approach ABM by simply adopting the SaaS playbook — the same tools, the same intent signals, the same targeting logic — consistently find that the program underperforms. Not because ABM doesn't work in life sciences. Quite the opposite. ABM is arguably more naturally suited to life science commercial environments than to any other B2B sector. But realizing that potential requires understanding what makes life science buyers different, and adapting the approach accordingly.

This post makes the case for why ABM and life sciences are a particularly strong fit — and why the standard tools and playbooks built for tech markets need meaningful adaptation to work in this one.

The Life Science Buyer Is Unlike Any Other B2B Buyer

Before discussing what makes ABM effective in life sciences, it is worth being precise about what makes life science buyers distinctive. Two characteristics stand out above all others.

Scientific skepticism. Life science professionals — whether Principal Scientists, Clinical Program Directors, or VPs of Research — are trained to evaluate evidence. Their entire professional formation is built around the careful assessment of data quality, methodology, and reproducibility. When they encounter marketing, they apply the same analytical lens. Promotional claims without supporting evidence are dismissed. Vendor messaging that is generic, technically shallow, or obviously optimized for persuasion rather than information is tuned out.

This means that the content-driven approach central to ABM — deploying technically substantive, expertise-demonstrating content to engage target personas — is not just a nice-to-have in life sciences. It is the only category of marketing that this audience will meaningfully engage with. White papers grounded in real data, case studies documenting actual clinical or analytical outcomes, webinars led by genuine subject matter experts, technical guides that help scientists solve real problems — these are the currency of life science engagement. ABM provides the framework for deploying that content with precision to the right audience at the right time.

Purchasing conservatism. Life science purchase decisions are not made quickly, and they are not made lightly. The consequences of a poor vendor selection can be significant: a failed assay that delays a clinical trial, a manufacturing partner whose process fails to scale, a CRO whose regulatory expertise proves insufficient at the NDA stage. Vendors are evaluated not just on capability but on track record, regulatory rigor, quality systems, and the depth of the relationship they are willing to build.

This purchasing dynamic plays directly to ABM's structural strengths. ABM is designed for long sales cycles with multiple decision-makers. Its emphasis on nurturing every member of the buying committee over an extended period — building familiarity and credibility across the full group before a formal evaluation begins — aligns precisely with how life science buyers actually make decisions. The companies that are already known, trusted, and perceived as experts when a buying process formally begins have an enormous advantage. ABM is the systematic approach to building that position.

Life Science Accounts Often Contain Multiple Independent Buying Groups

Beyond buyer psychology, life science organizations have a structural characteristic that makes ABM particularly valuable: within a single account, there are often multiple distinct buying groups, each tied to a different program, trial, or research initiative, each with its own decision-makers, timeline, and purchasing needs.

A mid-size biopharmaceutical company running four clinical programs might have a buying group forming around a Phase 2 biomarker program, a separate group evaluating outsourced manufacturing for a Phase 3 asset, and a third group assessing clinical data management solutions for a new trial filing. These three groups share an employer but not a budget, not a decision-making process, and not a set of needs.

In a traditional lead-generation model, engagement from any employee at this company would be pooled into a single account record — and the commercial team would often have no clear picture of which program was generating interest, who the relevant stakeholders were, or which opportunity was most sales-ready. ABM — particularly contact-level ABM using platforms like Influ2 or Propensity — enables a different approach: identifying which individuals at the account are engaging, what content they are engaging with, and therefore which buying group is forming around a specific need. That granularity transforms an ambiguous account into a specific, actionable opportunity.

This reality also means that a single target account in life sciences can yield multiple distinct commercial opportunities over time. An ABM program that successfully engages the first buying group, delivers a strong outcome, and builds a genuine relationship within that account is positioned to identify and engage the next buying group as a new program advances. In life sciences, account depth — the degree to which a vendor is embedded across multiple programs and buying groups within a strategic account — is one of the most important drivers of long-term revenue. ABM is the framework for building it.

Why Standard ABM Tools Miss the Mark in Life Sciences

If ABM is so well-suited to life sciences, why do so many life science companies find that off-the-shelf ABM platforms underdeliver?

The answer lies primarily in intent data. Widely-used tools for determining "intent" and finding in-market accounts like 6Sense and Bombora build their intent models around web search behavior and content consumption patterns detected through publisher networks. These signals are effective at identifying when a company is actively researching a specific category of B2B software: an unusual volume of employees visiting competitor websites, searching industry-specific keywords, or reading relevant analyst reports.

In life sciences, this kind of keyword-based, web-behavior intent data captures only a fraction of the signals that actually predict purchasing intent. The most valuable in-market signals in this sector are explicit, publicly available events that standard ABM platforms are not designed to monitor:

A new funding round — a Series B, C, or D, an IPO, or a significant partnership or licensing deal — almost always triggers program advancement and the engagement of new or expanded vendor relationships. A biotech that just closed a $150M Series C to advance its lead oncology asset into Phase 2 is a highly actionable target. Standard ABM platforms will not surface this signal unless the company coincidentally happens to be searching relevant keywords at the same time.

A clinical trial filing — signals that a specific program is advancing and that a buying group is likely forming around the services and capabilities needed to execute it. These filings are public and structured, but they are not indexed by standard ABM intent platforms.

A regulatory milestone — a Fast Track designation, a Breakthrough Therapy designation, an NDA filing — compresses timelines and creates urgent purchasing needs around the capabilities required to reach or respond to that milestone. These events are highly predictable in their commercial implications, but invisible to standard intent platforms.

Similarly, grant awards in the academic and government research sector — NIH R01s, BARDA contracts, DoD funding — are among the clearest intent signals for vendors serving this segment, and are entirely absent from conventional ABM intent data.

Life science-specific data platforms that aggregate clinical trial registries, funding databases, regulatory filings, pipeline intelligence, and grant award data provide a categorically different quality of intent signal for this sector. Overlaying keyword-based web intent data on top of these signals creates a layered picture of in-market accounts that is far more actionable than either source alone. This is a critical adaptation — and one that requires deliberate attention when building or evaluating a life science ABM program.

The Takeaway

ABM works in life sciences not because it is a clever tactic, but because the structure of the approach maps almost perfectly onto the realities of the market: long sales cycles, conservative buyers, technically demanding audiences, complex multi-stakeholder purchasing processes, and accounts that contain multiple distinct commercial opportunities simultaneously.

What requires adaptation is not the strategic logic of ABM, but the specific tools and data sources used to execute it. Generic intent signals, tech-focused platforms, and SaaS-derived playbooks need to be replaced or supplemented with life-science-specific data intelligence, modality and pipeline-aware targeting, and content strategies calibrated for scientifically trained audiences.

Organizations that make that adaptation — and approach ABM as a life-science-native capability rather than a technology market import — consistently find that it outperforms any other commercial approach available to them.

Account-Based Marketing is not a single approach. It exists on a spectrum from highly bespoke, resource-intensive engagement with a handful of elite accounts, to scalable programmatic campaigns reaching hundreds of companies simultaneously. Especially in B2B life sciences marketing, understanding where your organization should sit on that spectrum, and for which accounts, is one of the most consequential decisions in building an effective ABM program.

The three primary ABM models — One-to-One, One-to-Few, and One-to-Many — each serve a distinct purpose, require a different level of resource investment, and produce different types of commercial outcomes. Most mature ABM programs deploy all three simultaneously, applying each to the appropriate tier of the account list. Getting that allocation right is what separates programs that generate strong ROI from those that spread resources too thin or concentrate them in the wrong places.

This post explains each model in detail, how to determine the right fit for your accounts, and how to architect a tiered program that applies the right approach to the right accounts.

Comparing ABM Deployment Frameworks

One-to-One ABM: The Strategic Account Model

One-to-One ABM treats each target account as its own individual market. Every element of the commercial engagement including the content, the advertising, the landing pages, and the BD outreach is customized specifically for that account. This is not personalization at the margins (using a company name in an email subject line). It is a fundamentally account-specific approach in which the marketing and sales investment is calibrated to match the revenue potential of winning that account.

This model is appropriate for a small number of accounts at the very top of your target list — organizations where a secured contract would represent a material impact on annual revenue, and where the complexity and duration of the sales process justify sustained, resource-intensive engagement. In practice, most organizations have between five and twenty accounts in this category, and rarely more than 5% of their total ICP account list.

What One-to-One ABM looks like in practice

For a Tier 1 account, the marketing team develops assets and digital destinations that address the target company specifically. Rather than a generic capabilities landing page, the account receives a purpose-built digital destination that references their known pipeline, their therapeutic or scientific focus, and exactly how your solution is positioned to help with the challenges they are navigating right now.

Content developed for One-to-One ABM might include a custom technical brief mapping your capabilities directly to the account's publicly announced development programs, a bespoke ROI analysis built around their organizational profile, or a thought leadership piece that addresses a specific regulatory or operational challenge the account is known to be facing.

BD and sales engagement runs in parallel: the commercial team maps the buying committee at the account, identifies the internal champion and key decision-makers, and executes highly personalized outreach informed by the specific content each individual has engaged with. Executive-level peer engagement — a meeting between your Chief Scientific Officer and their Head of Clinical Development, for example — is often part of the One-to-One playbook for the most strategic accounts.

The deal threshold that typically justifies One-to-One ABM varies by organization and industry, but in B2B life sciences, it's generally reserved for accounts with expected contract values in the millions of dollars over a multi-year engagement. Below that threshold, the math rarely works.

One-to-Few ABM: The Scale Model

One-to-Few ABM targets ICP segments or clusters of accounts that share meaningful characteristics — the same therapeutic area, the same development stage, the same outsourcing model, or the same organizational profile. Rather than customizing for each individual account, the commercial team creates messaging and content calibrated to the shared needs of the cluster. The result is a level of relevance and personalization that feels genuinely tailored to each account in the group, without the resource intensity of account-by-account customization.

This is the most versatile of the three ABM models, and for many B2B life science companies it is the most valuable entry point when building an ABM program for the first time. The typical cluster size ranges from ten to one hundred accounts, and the minimum deal threshold that makes the investment worthwhile is generally in the range of $50,000 to $100,000 — though this varies significantly depending on the nature of the offering and the length of the sales cycle.

Building effective clusters

The quality of a One-to-Few campaign depends heavily on how the cluster is constructed. The goal is to find a grouping of accounts with a shared challenge or shared context specific enough that you can speak directly to it in your messaging, and credibly claim genuine expertise in addressing it.

In life sciences, effective ways to segment your ICP to create a target cluster include criteria such as: companies developing assets in a specific modality and development stage (e.g., cell therapy programs in Phase 1/2 transition), companies with a specific funding profile and outsourcing history (e.g., Series B/C biotechs with a CRO-heavy operating model), or companies within a therapeutic area facing a common regulatory inflection point.

The more specific the shared characteristic, the more resonant the messaging can be. A campaign targeting "biotechs with oncology assets preparing for first-in-human trials" can deploy content that addresses the exact operational and scientific challenges that cohort is navigating. That level of specificity is impossible to achieve in a One-to-Many program and unnecessary for One-to-One. One-to-Few is exactly where it becomes commercially leverageable.

What One-to-Few ABM looks like in practice

A One-to-Few campaign typically includes a cluster-specific landing page — not customized by individual account, but built for the segment — along with content assets (white papers, webinars, case studies) directly addressing the cluster's shared challenges. LinkedIn ad campaigns target the relevant personas at the accounts in the cluster, with messaging aligned to each persona's specific role and concerns. SDR outreach sequences are informed by the cluster's shared context, persona-level concerns, and the individual engagement signals generated by the campaign.

The measurement framework for One-to-Few ABM focuses on account-level engagement within the cluster: which accounts are engaging with content, which personas within those accounts are showing up, and which accounts are generating enough engagement signals to warrant elevation to deeper One-to-One treatment or immediate BD outreach.

One-to-Many ABM: The Programmatic Model

One-to-Many ABM extends the program to the broadest segment of the target account universe — the accounts that meet your ICP baseline criteria but do not yet demonstrate the revenue potential or buying signals to warrant the investment of One-to-Few or One-to-One treatment. This group typically represents the majority of the total account list, often 65 to 80% of the ICP.

The purpose of One-to-Many ABM is twofold: build brand familiarity and category authority across a wide pool of potential future customers, and monitor that pool continuously for the engagement signals that indicate an account may be moving into an active buying cycle.

What One-to-Many ABM looks like in practice

At this tier, the commercial approach relies on programmatic advertising and content syndication at scale. Display ads serve consistent, high-level messaging to employees at all accounts on the list — not personalized by account or segment, but relevant to the broader audience defined by the ICP. Educational webinars, industry reports, and published thought leadership serve as broad awareness-building assets that establish credibility and create touchpoints across the account universe.

The intent data generated by One-to-Many campaigns is the primary commercial output. When an account in the One-to-Many pool begins showing elevated engagement — multiple employees visiting the website, specific content being downloaded, ad interaction rates rising — that account becomes a candidate for elevation to One-to-Few or, in some cases, directly to sales and BD outreach.

In this way, One-to-Many ABM functions as an intelligence-gathering layer as much as a marketing one. It keeps your brand visible across the full ICP, while surfacing the accounts that are beginning to move into an active buying cycle before your competitors have identified them.

The Blended Architecture: Running All Three Simultaneously

The most effective ABM programs do not choose one of these models. They run all three concurrently, applying each to the appropriate tier of the account list and allowing accounts to move between tiers as engagement signals and market conditions evolve.

In a well-architected blended program, Tier 1 accounts receive full One-to-One treatment. Tier 2 accounts are organized into One-to-Few clusters based on shared characteristics. The full ICP account universe receives One-to-Many programmatic engagement. The entire system is monitored for the signals — engagement data, intent data, life-science-specific triggers like funding events and clinical trial filings — that indicate an account should be elevated.

This dynamic movement between tiers is one of the defining features of a mature ABM program. The Tier 3 biotech that just closed a Series C and filed a new IND is not a Tier 3 account anymore. A Tier 2 cluster that is generating unusually strong engagement from a specific account may be telling you that account deserves One-to-One treatment. Keeping the system responsive to these signals is what keeps the program efficient over time.

Determining the right blend for your organization

The right distribution across the three models depends on several factors: the nature of your commercial offering, the size of your addressable market, your average deal value, and the maturity of your commercial infrastructure.

Organizations offering highly specialized services with large deal values and a narrow addressable market — a specialized CRO serving only late-phase oncology programs, for example — should weight their investment heavily toward One-to-One and One-to-Few. The market is too small for broad One-to-Many campaigns to generate meaningful ROI, and the revenue potential of each account in the ICP justifies deep investment.

Organizations offering services with broader applicability and moderate deal values such as standardized laboratory testing, research tools, or software platforms will find One-to-Many and One-to-Few doing more of the heavy lifting, with One-to-One reserved for only the largest and most strategically important accounts.

There is no universal ratio. What matters is that the investment at each tier is proportionate to the expected return — and that the intelligence generated at lower tiers is actively used to elevate the right accounts to higher ones.

The Organizing Logic of Tiered ABM

The underlying principle connecting all three models is straightforward: in any commercial program, resources are finite, and deploying them proportionally to expected return is the discipline that drives ROI.

One-to-One, One-to-Few, and One-to-Many are not just tactical variations. They are an expression of that principle and a framework for ensuring that the accounts with the greatest potential receive the investment their potential warrants, while the broader account universe remains engaged and monitored. Getting that balance right is the architecture of an ABM program that grows more effective, and more efficient, over time.

There's a tendency in discussions of account-based marketing to stay at a conceptual level — talking about aligning marketing and sales, or concentrating resources on high-value accounts, or engaging buying committees with relevant messaging. All of that is true and important. But at some point, the question becomes practical: what does an ABM program actually do? What are the specific mechanisms through which it reaches people, generates engagement, and produces the pipeline intelligence that makes it valuable?

This post answers those questions concretely. We'll walk through the ad types deployed in a typical ABM campaign, the destinations that campaign traffic is directed to, and the way engagement data accumulates over time into the account intelligence that drives sales action. By the end, you should have a clear picture of how an ABM campaign works from first impression to qualified account.

How ABM Reaches Target Accounts: The Ad Channels

ABM campaigns don't rely on a single channel. The most effective programs run multiple ad types simultaneously, each serving a different function within the overall campaign and reaching target accounts through different touchpoints. Here are the primary channels and what each contributes.

Programmatic display advertising is the backbone of most ABM programs. Programmatic platforms can match individuals to their employer using a combination of IP address data, device graphs, and cookie data, and serve display ads exclusively to employees of companies on your target account list. The result is that your advertising budget reaches only the companies you've defined as worth reaching — rather than being distributed across a broader audience that includes many people who will never be relevant buyers. Programmatic display builds awareness and familiarity over time, keeping your brand and messaging visible to target accounts even when those accounts aren't actively searching.

Paid social advertising  (particularly LinkedIn) has historically been the highest-precision channel in an ABM stack (now being replaced by contact-level advertising - see below). LinkedIn's targeting allows ads to be filtered simultaneously by company name or company size, job title, seniority level, function, skills, and industry. For ABM programs that need to reach specific personas within specific accounts, this level of specificity is valuable. A campaign targeting VP-level clinical development leaders at a defined list of clinical-stage biotech companies can be configured with enough precision that virtually every impression is served to someone who fits that profile exactly. The tradeoff is cost as LinkedIn CPMs are higher than most other channels. But for well-defined ABM audiences, the targeting precision justifies the premium.

Email campaigns to contacts already in your CRM add a direct, personal channel to the mix. Where programmatic and paid social reach people in their professional browsing and social media environments, email reaches them directly in their inbox with a message from a named sender. For accounts where you already have contact data, coordinated email sequences, timed to align with the broader campaign and referencing the same content themes, reinforce the message across an additional touchpoint and create opportunities for direct response.

Direct channel placements including newsletter sponsorships, podcast sponsorships, and display advertising purchased directly from industry publications and websites can reach target audiences in the professional content environments they already trust. In life sciences, this might mean placements in widely-read industry newsletters covering clinical development, regulatory affairs, or a specific therapeutic area. These placements combine the reach of programmatic with the contextual credibility of appearing in editorial environments the audience has actively chosen to engage with.

Content syndication extends the reach of your content assets — whitepapers, research reports, application notes, webinar recordings — through third-party platforms and publications that distribute content to defined professional audiences. Syndication is particularly effective for top-of-funnel awareness and lead capture: target accounts that haven't yet engaged with your owned channels encounter your content in a third-party context, and form fills on syndicated content generate new contact records that feed back into your ABM targeting.

Contact-level advertising is an important evolution in ABM targeting that goes beyond account-level reach. Traditional programmatic ABM targets at the account level and ads are served to anyone at a company on your target list, identified primarily through IP address matching. Contact-level advertising goes a step further, serving ads to specific named individuals by matching your contact database against device graphs, professional identity networks, and cookie data tied to known email addresses.

In practice, this means uploading a list of contacts — sourced from your CRM, from event registrations, from content downloads, or from purchased contact data — and running ad campaigns that reach those specific people rather than simply everyone at their employer. The result is a significant increase in targeting precision: instead of reaching the 400 employees of a target biotech company broadly, you're reaching the fifteen specific individuals in clinical development and procurement whose titles and roles match your persona definitions.

In B2B life sciences, contact-level advertising has a particularly valuable application in buying group discovery. By running contact-level campaigns against a carefully built title universe — covering every persona type likely to be involved in a buying decision for your solution — and tracking which individuals engage, you can begin to assemble a picture of who is paying attention at each target account. When a Director of Clinical Operations, a VP of Biostatistics, and a Head of Outsourcing at the same biotech company all engage with your content within a short window, that pattern is a strong signal that a buying group is forming around an active evaluation. Contact-level targeting makes that signal visible in a way that account-level programmatic alone cannot.

Where the Traffic Goes: Landing Pages and Content Destinations

Running well-targeted ads is necessary but not sufficient. Where you send the traffic those ads generate matters enormously — and this is one of the areas where ABM programs most commonly underperform.

The default approach is to send all campaign traffic to a generic homepage or a standard product page. The problem with this is obvious once you think about it: if you've built persona-specific ad creative designed to speak directly to the concerns of a VP of Clinical Operations, sending that person to a homepage that addresses everyone equally undermines the relevance you built in the ad. The experience of clicking feels like a bait-and-switch.

Effective ABM programs build persona-specific landing pages that continue the conversation the ad started. If the ad speaks to a clinical development audience about Phase II trial outsourcing, the landing page speaks to that audience about that topic using content, messaging, and calls to action calibrated to where that persona typically sits in the buying process. This continuity between ad and landing page is one of the simplest and most impactful improvements an ABM program can make.

Beyond persona-specific landing pages, ABM campaigns typically route traffic to several other types of destinations:

Gated content — whitepapers, technical guides, webinar recordings, application notes — serves two purposes simultaneously. It provides genuine value to the audience, which builds credibility and trust. And it captures a form fill: a named individual, an email address, a company association, and a signal about what topic that person cared enough about to exchange their contact information for. That form fill is one of the highest-value data points an ABM campaign generates.

Webinar and event registration pages function similarly to gated content in that they capture an active commitment from a named individual but also add a time dimension. A registrant for a webinar on a specific topic is signaling not just general interest but current, active attention to that subject area. When multiple individuals from the same target account register for the same event, that clustering is a meaningful buying group signal.

Direct response mechanisms — demo request forms, consultation booking pages, contact forms — are the highest-intent destinations in an ABM campaign. Traffic that reaches these pages represents accounts where interest has moved from awareness into active consideration.

How Engagement Accumulates Into Account Intelligence

The full value of an ABM campaign emerges not from any single interaction but from the aggregated pattern of interactions over time. This is where the account intelligence function of ABM becomes visible.

Every touchpoint an individual has with your campaign generates a data point: an ad impression, a click, a landing page visit, a content download, a webinar registration, a form fill. At the individual level, these data points feed contact scoring models that track how engaged a specific person is with your content and messaging. At the account level, they feed account scoring models that aggregate individual engagement across all the contacts associated with a company, building a composite picture of how active that account is overall.

As engagement accumulates, accounts progress through scoring thresholds. An account that has had multiple individuals engage with multiple pieces of content, visit key pages, and register for events over a period of weeks or months is behaving differently from an account that has seen a handful of ad impressions. The former is demonstrating the kind of sustained, multi-person engagement that characterizes an active evaluation — and that's the signal that should trigger a transition from marketing engagement to direct sales outreach.

That transition from marketing-engaged account to sales-qualified account is the moment ABM's account intelligence pays off. Sales doesn't receive a cold account name. They receive a company, a set of named and engaged individuals, a record of what each person has engaged with, and context for what messages have resonated. That's the difference between a warm call and a cold one, and it's the mechanism through which well-run ABM programs improve the efficiency and effectiveness of the entire commercial team.

The Role of ABM Software Platforms

Running ABM at any meaningful scale with multiple campaigns, multiple channels, multiple tiers of accounts requires technology infrastructure to manage what would otherwise be an unmanageable volume of data from disparate sources.

ABM-specific platforms such as 6Sense, Demandbase, Propensity, and AdRoll ABM were built to solve this problem. At a high level, they do three things that are difficult to replicate manually or with general-purpose marketing tools.

First, they centralize intent and engagement data. An ABM program generates signals from multiple sources simultaneously: programmatic ad interactions, LinkedIn clicks, website visits, email opens, content downloads, webinar registrations, CRM activity. Without a centralized platform, these signals sit in separate tools and need to be manually reconciled to form a picture of account-level engagement. ABM platforms ingest all of this data — combining first-party data from your CRM and website with third-party intent data from external providers — and unify it into a single account-level view.

Second, they enable programmatic ad delivery to target accounts. ABM platforms have native programmatic capabilities designed specifically for B2B account targeting. Rather than buying keywords on the open web, they use IP matching and B2B data networks to serve display ads to the devices operating within your specified target accounts. They can also dynamically adjust ad spend based on real-time account behavior — allocating more budget toward accounts showing rising intent signals and pulling back from accounts that have gone dormant.

Third, they automate account scoring and sales handoff. Rather than relying on manual review of engagement data to determine when an account is ready for outreach, ABM platforms apply scoring algorithms that aggregate engagement signals across the buying committee, weight them by intent level, and surface accounts that cross a defined threshold as Marketing Qualified Accounts (MQAs). When an account reaches that threshold, the platform can automatically alert the assigned SDR or BD representative in the CRM, along with a summary of which personas engaged and what content they consumed.

This last capability is significant. The quality of a sales outreach touchpoint improves dramatically when the rep knows that the VP of Clinical Operations at a target account downloaded a specific white paper twice and attended a webinar on the same topic last week. That context transforms a cold call into a relevant, informed conversation.

It is worth noting that ABM-specific platforms are not the only way to run an effective program. Organizations working with an experienced ABM partner can often leverage their partner's platform access as part of a managed service arrangement. But the capabilities these platforms provide including centralized data, automated scoring, and programmatic ad delivery represent genuine operational leverage that is difficult to replicate with general-purpose tools.

How This Changes in B2B Life Sciences

The mechanical framework described above of ad channels, landing page destinations, engagement accumulation, platform coordination applies across ABM programs regardless of industry. But in B2B life sciences, several aspects of how that framework is applied are meaningfully different, and getting those differences right determines whether a program actually fits the market it's selling into.

How accounts are targeted. In most B2B markets, account targeting starts with firmographic criteria: company size, industry code, revenue range, geography. In life sciences, the most relevant targeting criteria are often program-specific rather than firmographic. A vendor selling bioanalytical services doesn't simply want to target biotech companies — they want to target biotech companies with active clinical programs in a therapeutic area relevant to their capabilities, at a development stage where their service is likely to be needed. Those criteria come from clinical trial databases, pipeline tracking tools, and regulatory filing data, not from standard firmographic sources. The target account list in life sciences ABM is built from a fundamentally different data foundation than the one used in tech or professional services ABM.

What intent and engagement signals matter most. Standard ABM intent platforms track keyword search behavior and website activity — signals that are well-calibrated for technology buyers researching SaaS solutions. In life sciences, the most meaningful intent signals are often key events: a Series B funding close, a Phase I trial initiation, a regulatory submission, a pipeline expansion announcement. These events indicate that a company has entered a stage of development where specific outsourcing or technology decisions are imminent — and they're far more predictive of near-term purchasing activity than keyword search behavior alone. Layered on top of these structural signals, first-party engagement data (content downloads, webinar registrations, landing page visits) provides confirmation that specific individuals within a target account are actively paying attention.

How signals are used to find an in-market buying committee. In life sciences, the goal isn't simply to identify that a company is in-market, it's to identify which team within that company is in-market, and which individuals on that team are involved in the evaluation. A large pharma account might have dozens of active programs and hundreds of potential buyers. The structural and behavioral signals described above are most valuable when they're interpreted at the program and persona level: which therapeutic area is the engagement concentrated in? Which persona types — clinical operations, biostatistics, procurement — are showing up in the engagement data? As those patterns emerge, a picture of the relevant buying group takes shape, and that picture determines where sales attention should be directed and what message each person should receive.

How to coordinate across channels and attach meaning to different signals. Not all signals are equal, and in life sciences ABM, building a coherent picture of account and buying group status requires interpreting signals from different channels with appropriate weighting. A key event such as a trial initiation or funding announcement is a high-value but passive signal: it indicates potential need, not confirmed interest. A content download or webinar registration is a lower-volume but higher-intent signal: a specific person is actively engaging with your perspective on a topic relevant to their work. A demo request or direct inquiry is the highest-intent signal of all. Coordinating across channels means not just running multiple tactics simultaneously, but building a scoring framework that aggregates signals with appropriate weights — treating a cluster of mid-level signals from multiple personas as more significant than a single high-intent action from one person, and using the resulting account and buying group score to sequence sales outreach at the right moment, with the right context for each individual involved.

Every December, the business development team at a mid-size contract research organization would gather at a hotel for their annual planning meeting. Over three days, each BD rep would stand up and present their account priorities for the coming year — which companies they were going after, which they were treating as top-tier targets, and why. The problem: every rep had developed their own criteria. Some prioritized large pharma because of deal size. Others focused on mid-size biotechs because they were easier to get meetings with. Some tiered accounts based on geography. Others based it on relationships. The result was a fragmented, inconsistent commercial strategy that made it nearly impossible for marketing to support in any coordinated way.

When a structured ABM program was proposed, the first task wasn't setting up a platform or designing ads. It was building a shared, data-backed targeting framework — a real ICP — derived from analysis of which accounts had historically generated the most revenue, what their shared characteristics were, and how to translate those characteristics into consistent, actionable targeting criteria for the whole team.

That work transformed the annual planning process. It also provided the foundation that made everything else in the ABM program possible.

Why the ICP Is the Foundation of Everything in ABM

Account-based marketing lives or dies on targeting precision. Every subsequent decision in an ABM program — which accounts to include, what messaging to develop, which personas to reach, how to structure campaigns — flows from a clear, specific, commercially-grounded ICP.

Most B2B organizations have something they call an ICP. The problem, as the ABM Guide makes clear, is that those ICPs are often undocumented, outdated, or built more on assumptions and intuition than on actual business data. In life sciences especially, where market conditions shift with funding cycles, therapeutic pipeline evolution, and regulatory changes, an ICP that was accurate two years ago may no longer reflect the real profile of your best customers today.

The most important step in building a strong ICP is the one most often skipped: go back to your own revenue data and let it tell you who your best customers actually are.

Start With the 80/20 Analysis

In most B2B organizations, revenue distribution follows a consistent pattern: roughly 80% of revenue comes from approximately 20% of accounts. That top 20% — the accounts with the largest contracts, the best retention, the strongest expansion history — is the empirical starting point for your ICP.

Pull the data. Look at your highest-revenue accounts over the past two to three years. Look at average deal value by account, time to close, renewal rates, and expansion revenue. Identify the top tier — these are your Tier 1 accounts — and then look carefully at what they have in common.

Those commonalities are your ICP. Not what you think your best customers look like, not what your BD team has assumed over the years, but what your actual revenue data tells you is true.

From there, build out additional tiers. Tier 2 accounts are strong-fit companies that represent meaningful revenue potential but don't quite match the profile of your very best customers. Tier 3 accounts are worth reaching with broad, scalable marketing but don't warrant the level of investment required for more targeted ABM approaches. This tiered structure becomes the architecture of your entire ABM program: One-to-One treatment for Tier 1, One-to-Few for Tier 2, One-to-Many or broad demand gen for Tier 3.

What an ABM-Aligned ICP Looks Like for Pharma and Biotech Companies

Generic B2B ICP frameworks typically focus on firmographic criteria — company size, industry vertical, revenue range, geography. In life sciences, these firmographics are a starting point, but the most meaningful targeting criteria are considerably more specific. 

The following factors are commonly relevant when building ICP criteria for pharma and biotech targets:

Pipeline stage and asset maturity. For many life science service and technology providers, the most relevant signal is where a target company's assets sit in development. A company with three programs in early discovery is a very different prospect than one with a Phase 2 asset that just received FDA Fast Track designation. Depending on your offering, you may find that accounts with assets at a specific stage — late preclinical, Phase 1/2, or pre-NDA — are consistently your best customers. Documenting that stage specificity in your ICP sharpens targeting significantly.

Therapeutic area and modality. Not all pipelines are created equal from a targeting perspective. If your solution has particular depth in oncology biomarker work, a company developing cardiovascular small molecules may be a technically eligible but strategically poor fit. ICP segments based on therapeutic area (oncology, rare disease, neurology, immunology, etc.) and modality (small molecule, biologics, cell therapy, gene therapy, antibody-drug conjugates) allow you to match your specific expertise and capability to the accounts most likely to value it.

Company size and organizational maturity. Headcount and revenue are useful proxies, but for biotech specifically, pipeline size and funding stage are often more meaningful. A 200-person biotech with a single Phase 3 asset and a recent Series D is a different proposition than a 200-person biotech running four early-phase programs on a Series B runway. Commercial maturity matters too: a company with a dedicated clinical operations team and an established outsourcing function will have a faster path to vendor selection than one building those capabilities for the first time.

Funding status and financial signals. Recent funding events are one of the clearest intent signals in life science ABM. A Series C or D round, a successful IPO, or a significant partnership or licensing deal typically signals that capital has been allocated to advance programs — and that investment in supporting services and technologies is likely to follow. Companies without recent funding events, or with visible financial stress (layoffs, program cancellations, strategic reviews), are lower-priority targets regardless of how well they fit other ICP criteria.

Outsourcing orientation. Some companies build in-house capability wherever possible; others run lean and outsource heavily. If you are a CRO, a central lab, or a technology vendor, knowing whether a target company has a history of outsourcing work similar to yours is an important qualifying criterion. This can often be inferred from job postings, LinkedIn profiles of the commercial team, or known relationships with peer organizations.

Geography and regulatory context. Clinical trial activity, site networks, and regulatory strategy can all vary significantly by geography. If your solution has particular depth in EU regulatory pathways, or your service infrastructure is concentrated in North America, this should be reflected in your ICP rather than targeting globally and filtering retroactively.

Segmenting When Your Value Proposition Varies

The ABM Guide makes an important distinction that is frequently overlooked in ICP construction: if your organization offers multiple products or services with meaningfully different value propositions, a single monolithic ICP won't serve you well. You need segmented ICPs — one for each major audience group — with distinct messaging frameworks for each.

A contract service organization offering both early-phase bioanalytical services and late-phase central lab services, for example, is effectively selling to two different audiences with different stages of need, different buying committee compositions, and different primary concerns. Treating those as one audience produces messaging that is generic to both and compelling to neither.

Each segmented ICP should document: the firmographic and life sciences-specific profile of the segment, the typical buying committee composition, the primary business challenges driving a need for your solution, and the core value propositions most relevant to that audience. This segmentation becomes the blueprint for everything downstream — persona targeting, content development, ad messaging, and landing page design.

Account Tiering: Not All ICP Accounts Are Equal

Once you have a working ICP — and any relevant segments — the next step is to recognize that even among accounts that all meet your criteria, there is a wide range in terms of potential deal value, strategic importance, and sales readiness. Treating every qualifying account with the same level of marketing investment is a recipe for poor ROI. This is where account tiering comes in.

Tiering divides your ICP account list into groups that warrant different levels of commercial resource — and directly determines which ABM approach (One-to-One, One-to-Few, or One-to-Many) is applied to each group.

A common and effective framework uses three tiers:

Tier 1 — Strategic. These are your highest-value targets: accounts with the potential for exceptionally large, multi-year engagements that would represent a material impact on annual revenue. They represent a perfect match with your strongest service or product capabilities, at exactly the right stage of development. This group is typically small — often no more than 5 to 10 accounts, and rarely more than 5% of your total ICP list. Because the revenue potential justifies it, these accounts receive a fully bespoke One-to-One ABM treatment: customized landing pages, account-specific content, and executive-level BD engagement.

Tier 2 — Scale. The next 20 to 30% of your ICP list falls here. These accounts have strong clinical and scientific fit, a high likelihood of closing, and represent healthy deal values with good operational alignment. They may not individually justify the resource intensity of Tier 1, but they are by no means generic targets. The appropriate approach is One-to-Few ABM: grouping accounts by a shared characteristic — the same therapeutic area, the same development phase, the same outsourcing model — and deploying messaging and content tailored to the needs of that cluster.

Tier 3 — Programmatic. The remaining accounts that meet your baseline ICP criteria sit in Tier 3. They may not yet demonstrate the revenue potential or buying signals to warrant deeper investment, but they are worth keeping in view. One-to-Many ABM — broad programmatic advertising, content syndication, educational webinars — is the right approach here. The goal is dual: build brand awareness within this pool, and monitor for signals (a new funding round, a regulatory filing, a pipeline advancement) that suggest an account is ready to be elevated to Tier 2.

It is worth noting that tiering is not a static assignment. Accounts should move between tiers as market conditions change and new information becomes available. A Tier 3 biotech that closes a Series C and announces a new Phase 2 program is no longer a Tier 3 account. Your commercial team needs a defined process — typically tied to the life-science-specific intent signals discussed in later posts in this series — for identifying when an account should be elevated and what happens next when it is.

The practical value of tiering extends beyond resource allocation. It also creates internal clarity and accountability. Marketing knows which accounts to invest in most deeply. The SDR team knows which qualified accounts are highest priority for outreach. BD knows which opportunities to prioritize for executive engagement. When tiers are agreed upon and documented, the commercial team stops debating priorities and starts executing against them.

Add Persona Targeting to Complete the Picture

An ICP defines which companies to target. Persona targeting defines which people within those companies to reach. Both are required for ABM.

With your ICP segments in place, the next step is to document the buying committee for each segment — the typical roles involved in a purchase decision, and the titles that align to each role. Sales and BD input is essential here: they know who is actually in the room when a deal is being decided, whose concerns can stall a deal, and who tends to be the internal champion.

The output is a matrix of ICP segments and persona types, each with associated title keywords for targeting. This becomes the foundation for all persona-level ad targeting, content development, and outreach sequencing in your ABM campaigns.

Treat the ICP as a Living Document

A well-designed ICP is not a one-time exercise. Markets shift, pipelines move, your own offerings evolve, and the profile of your best-fit customer changes accordingly. Building in an annual ICP review — with input from marketing, sales, BD, and ideally a commercial leadership sponsor — ensures that your ABM program continues to target the right accounts as your business grows.

The companies that get the most from ABM are not always those with the most sophisticated technology or the largest campaign budgets. They are the ones with the clearest, most commercially aligned picture of who they are trying to win — and the discipline to keep that picture current.