{"id":6011,"date":"2026-04-06T02:12:31","date_gmt":"2026-04-06T00:12:31","guid":{"rendered":"https:\/\/nuna.digital\/?p=6011"},"modified":"2026-04-06T22:41:42","modified_gmt":"2026-04-06T20:41:42","slug":"abm-in-life-sciences-whats-actually-possible-now","status":"publish","type":"post","link":"https:\/\/nuna.digital\/de\/articles\/abm-in-life-sciences-whats-actually-possible-now\/","title":{"rendered":"Account Based Marketing in Life Sciences: What’s Actually Possible in 2026"},"content":{"rendered":"

AI didn’t change the fundamentals of B2B targeting. It changed who can actually execute at scale \u2014 and how fast.<\/p>\n\n\n\n

Account-based marketing has been talked about in life sciences for years. The concept is simple: instead of casting a wide net and hoping something sticks, you define a precise target, build a verified list, and reach out with something relevant to that specific person at that specific company.<\/p>\n\n\n\n

In theory, everyone agrees this is the right approach for niche B2B \u2014 especially in pharma services, biotech tools, and contract research, where your total addressable market might be 300 companies in Europe and your buyer is a very specific person with a very specific job. In practice, most companies are doing broadcast marketing with a spreadsheet bolted on the side and calling it ABM.<\/p>\n\n\n\n

What’s changed in the last 18 months isn’t the strategy. It’s the execution layer. And if you’re not aware of what’s now possible \u2014 and accessible without an enterprise budget \u2014 you’re working with tools from a different era.<\/p>\n\n\n\n

Start with the ICP<\/h2>\n\n\n\n

Before any tool, before any database, before the first search query \u2014 you need a clear ideal customer profile. This sounds obvious. It isn’t, based on how most life sciences companies actually approach this.<\/p>\n\n\n\n

An ICP is not “pharma companies in Germany.” It’s something like: early-stage biotech companies with small molecule pipelines that are entering or approaching preclinical development, operating in Western Europe, without in-house analytical capabilities. Two sentences. Completely different targeting exercise.<\/p>\n\n\n\n

When you’re working with a highly specialized service provider \u2014 a niche CRO, a contract lab, an analytical services company \u2014 the target universe is genuinely small. That’s the whole point of ABM: it makes sense precisely because the market is this concentrated. And when the market is this small, precision isn’t optional. It’s the entire game.<\/p>\n\n\n\n

The ICP should be specific enough to produce a disqualification list, not just a target list. If you can’t explain clearly why a company doesn’t belong, your criteria aren’t tight enough yet.<\/p>\n\n\n\n

Disqualifiers matter just as much as qualifiers. Depending on the service, that might mean filtering out biologics-only pipelines, companies with established in-house infrastructure, CROs that are actually competitors, or companies at a stage where all vendor decisions are already locked. Getting this wrong costs time and credibility in outreach. A cold call to a company that has no reason to ever use your service is worse than no call at all.<\/p>\n\n\n\n

The data problem in life sciences \u2014 and why it’s actually solvable now<\/h2>\n\n\n\n

Life sciences target lists have a specific problem: the right sources are technical and scattered, and the wrong sources are everywhere. LinkedIn alone is not a sourcing strategy. Neither is buying a generic “pharma contacts DACH” list from a data vendor and assuming it maps to your actual ICP.<\/p>\n\n\n\n

Good sources exist \u2014 they’re just not obvious if you haven’t done this kind of work before. Clinical trial registries, industry association directories, university spin-off portfolios, national biotech association sites, investor portfolio lists \u2014 these are underused precisely because they require real research to interpret. Cross-referencing a company’s pipeline against their public communications to check therapy area, development stage, and outsourcing posture: that used to take hours per company.<\/p>\n\n\n\n

That’s the part AI has transformed. Not the sourcing logic \u2014 you still need to know where to look and what you’re looking for. But the research layer sitting on top: screening dozens of companies for fit against specific criteria, verifying whether a startup is still active, checking whether a CRO actually offers a given service \u2014 this batch qualification work that previously required a full-time analyst for weeks can now be structured and executed dramatically faster. We’re talking a 4\u20136x reduction in time for this layer, at comparable or better accuracy, when the workflow is set up correctly.<\/p>\n\n\n\n

The sourcing logic still requires expertise. The execution layer \u2014 batch research, qualification, enrichment \u2014 is where AI changes the math entirely.<\/p>\n\n\n\n

The 2026 stack \u2014 what’s actually worth using<\/h2>\n\n\n\n

There’s no shortage of tools claiming to solve prospecting. Most are either expensive enterprise platforms with limited European data, or enrichment tools that return low-confidence results for smaller companies that don’t have significant web presence. Here’s what actually works for life sciences ABM at this level:<\/p>\n\n\n\n