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Building an ICP as a CRO, CDMO, or Life Science Services Seller

An ICP for a life science services or tools company needs two layers, not one: a company profile (therapeutic area, modality, development stage, size) and a buyer profile (which function and seniority actually owns the decision). Most teams only write down the first layer, then wonder why "good-fit" accounts never turn into conversations.

8 min readUpdated Jul 8, 2026

Key takeaways

  • An ICP needs two layers: company fit (therapeutic area, modality, stage, size) and buyer fit (which function and seniority actually owns the decision).
  • Your existing best customers are usually the fastest, most reliable starting point for a first-draft ICP.
  • Buyer titles in life science are inconsistent across companies, so a buyer-fit model needs to reason about the role, not just match exact title strings.
  • An ICP should be revisited against real conversion data as your product, customers, and best-fit segment change.

A lot of ICP advice is written for generic B2B software and translates poorly to a CRO, CDMO, or life science tools and services company. "Company size" and "industry" are not enough: two biotech companies of identical size and headcount can be in completely different buying postures depending on development stage, modality, and how their program mix maps to what you actually sell.

Why generic ICP frameworks fall short in life science

The attributes that predict whether a life science company will buy from a services or tools vendor are domain-specific: therapeutic area and modality, where a company sits in the development pipeline, and what kind of operational complexity they are dealing with right now. A generic firmographic filter (industry, headcount, revenue) misses almost all of that nuance, which is why "good-fit on paper" accounts so often turn out to be a poor match once a rep actually talks to them.

Why so many CROs and CDMOs end up reactive instead of proactive

Without a working ICP, the default motion becomes reactive: wait for inbound interest, work whatever account happens to be in the pipeline, and rely on existing relationships and referrals for new business. That is a reasonable way to fill capacity when demand is strong, but it means missing the emerging and newly funded biotechs that have not discovered you yet, exactly the accounts that are cheapest to win early and hardest to win once a competitor has already locked in the relationship. A clear ICP is what turns "find clients" into a repeatable, proactive search instead of waiting for the phone to ring.

Layer 1: company fit

A working company profile for a life science seller usually needs to define:

  • Therapeutic area(s). Which indications overlap with your team's expertise or your existing customer base, and which are explicitly out of scope.
  • Modality. Small molecule, biologics, cell and gene therapy, and other modalities require different capabilities from a services partner. This often matters more than therapeutic area for a CDMO or CRO specifically.
  • Development stage. Preclinical, Phase 1 through 3, or commercial-stage companies have very different needs and buying cycles. A CDMO selling process development work has a different sweet spot than one selling commercial-scale manufacturing.
  • Company size and funding stage. A rough proxy for budget and how formalized the company's vendor evaluation process is likely to be.
  • Overlap with your best existing customers. The single most reliable starting point: look at the accounts you already win and keep, and reverse-engineer what they have in common before writing down anything else.

Layer 2: buyer fit

Buyer fit is who inside the account actually owns the decision, which company fit alone does not tell you. Getting the buyer wrong is one of the most common reasons a "good-fit" account list underperforms.

  • Function. The department that actually owns the pain you solve, whether that is manufacturing and CMC, clinical operations, regulatory affairs, quality, or business development, depending on what you sell.
  • Seniority, adjusted for company size. A Director at a 20-person biotech can carry the budget authority of a VP at a larger organization. Scoring buyer fit purely on title level, without adjusting for company size, systematically undervalues small biotechs.
  • Title variability. Life science titles are inconsistent across companies. Chief Scientific Officer, Head of Scientific Affairs, and VP of R&D can all describe the same science leadership function; VP of External Partnerships, Head of Alliance Management, and Director of Business Development can all describe the same partnerships function. A buyer-fit model needs to reason about the role, not just match exact title strings.

A practical way to build the first draft

Start from your best current customers, not a blank page. Pull the last 10-20 accounts you won and are happy to have as customers, and look for what they share on both layers: similar modality or therapeutic focus, a similar development-stage range, and a similar buyer function and seniority pattern among the people who actually drove the deal. Write that pattern down as your first-draft ICP, narrow enough that it functions as a real filter. An ICP that matches half your addressable market is not doing its job.

Keeping the ICP alive

An ICP is a living definition, not a document you write once at company founding and forget. As your product or service offering expands, as you win customers in a new modality or stage, or as your best-fit segment shifts, the ICP should be revisited, ideally against real data on which accounts converted and stuck, not just intuition.

Where Arcova fits: Arcova builds this two-layer model directly. It starts by learning your company, your competitors, and your best existing customers, then scores the accounts and contacts you bring in, whether from your CRM or a data provider you already use, on company fit and buyer fit against that profile, so the ranked list a rep sees reflects both layers, not just a firmographic filter. From there, readiness signals decide when to act inside the accounts that clear the ICP bar.
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Frequently asked questions

How many ICPs should we run at once?

Most CROs, CDMOs, and services sellers do well with a small number of distinct ICPs, one per meaningfully different offering or buyer, rather than one broad definition or dozens of narrow ones. If your company sells process development to preclinical biotechs and separately sells commercial manufacturing to late-stage companies, those are two different company profiles and often two different buyer profiles, and treating them as one ICP will blur both.

What if our target market spans multiple therapeutic areas?

That is normal and does not mean you need a separate ICP per therapeutic area. Therapeutic area is usually one filter within a single ICP rather than the organizing principle: what usually matters more for fit is modality, development stage, and company size, with therapeutic area used to include or exclude specific accounts rather than to define entirely separate customer profiles.

Should a CRO, a CDMO, and a software or tools vendor build their ICP the same way?

The two-layer structure (company fit, then buyer fit) applies to all three, but the specific attributes differ. A CDMO cares heavily about development stage and manufacturing complexity; a CRO cares about trial design, phase, and therapeutic area; a tools or software vendor may care more about company size and technical infrastructure than clinical stage. Build the framework once, then fill in attributes specific to what you actually sell.

What is the difference between an ICP and a buyer persona?

An ICP describes the company: the therapeutic area, modality, development stage, and size that make an account worth pursuing. A buyer persona describes the person inside that company: their function, seniority, and what they care about. Both matter, but they answer different questions, which is exactly why this framework splits them into company fit and buyer fit rather than treating "our ICP" as a single blended description.

How do we validate an ICP once we have written it down?

Score your last 50 to 100 closed deals, won and lost, against the draft ICP as if you were seeing them for the first time. If accounts that scored as strong fits closed at meaningfully higher rates than weak fits, the definition is doing real work. If fit score barely correlates with outcome, the attributes are probably wrong, too broad, or missing the buyer-fit layer entirely, and are worth revisiting before you build outreach around them.

What if we do not have enough closed deals yet to build an ICP from historical data?

Use the best proxy you have: the accounts you most want to look like, not the accounts you have already won. Founders and early sales hires usually have a strong intuitive sense of which company and buyer profile the product was actually built for. Write that down as a first-draft ICP, narrow enough to function as a real filter, and plan to revisit it against real conversion data once you have enough closed and lost deals to make the comparison meaningful.

Related reading

Guides

Prioritizing accounts: fit and readiness

A practical account prioritization framework for life science sales teams: gate on fit first, then use buying-readiness signals to decide when to act inside your best-fit list.

Reference

GTM signals glossary

Plain-language definitions of the fit, readiness, and buying-signal terms used in life science sales: from fit score and ICP to phase transitions, FDA milestones, and conference signals.

Guides

Biotech and pharma buying signals

A working framework for life science buying signals: which events point to new budget or new work to outsource (funding, clinical and regulatory milestones, hiring, expansion), and which fill in the picture around them.

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Bring in the data you already work with, from your CRM or the providers you already use, and Arcova scores it against your ICP, watches for the life science signals that matter, and tells you who is ready for outreach and why.

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