Working out exactly when a biotech becomes ready to buy — then targeting that moment

The problem

You can sift the open ocean for treasure and never find a sliver of iron ore. Or you can do the research and find the one small cave in northern California worth digging in. In B2B technology sales, casting a wide net is a reliable way to get nowhere.

The B2B SaaS sales cycle for products from $150K to over a million dollars is long and punishing. Whatever process you use to move prospects through qualification has to hand your sales team the best possible candidates for that arduous cycle — because every unqualified one costs months.

My challenge: market a patient master data management solution to an industry that had not widely, or even narrowly, adopted MDM as a technical necessity.

Research

I started with standard market research, studying the life sciences industry as it relates to actual patients. We believed the solution would help life sciences and biotech organizations market to and manage their patients, so I had to learn how these organizations reach prospects and how patients move through the onboarding-to-in-therapy lifecycle.

Once I understood the mechanisms and technologies they used, some useful patterns appeared. Past a certain size, an organization was unlikely to choose us — we were a startup competing against billion-dollar incumbents. But the consultative part of our implementation was genuinely valuable to biotechs just beginning to build their marketing and patient management infrastructure.

That is not enough on its own, though. A biotech with under $50 million in the bank might be about to take off, or might be failing, or might be stagnant. We needed other signals.

The equation

I built a prospecting equation to narrow toward emerging biotechs that were about to need us:

• Eliminate the big players by capping revenue at $500 million.
• Keep smaller organizations that had just received large funding rounds.
• Mine FDA clinical trial data to establish where each organization sat in the trial process and when phase 3 was expected to complete.
• Flag anyone completing phase 3 within six months — because they were about to have to prepare to go to market.
• Confirm the theory against LinkedIn Sales Navigator hiring data: a sharp jump in business development, sales, and marketing hiring, or a major executive newly hired into one of those functions.
• Search job listings for biotechs hiring change agents, marketing directors, and business development or operations executives — and note whether the words "patient data management" or "MDM" appeared in the requirements, which revealed their technology stack in a way that is otherwise almost impossible to discover.

I turned all of it into a defined equation and a repeatable monthly process that marketing coordinators or research assistants could run without me, so every qualified candidate entered the funnel on schedule.

Results

With the list substantially narrowed, traditional research became viable again — I could research 50 prospects thoroughly instead of 5,000 poorly. We dropped extremely qualified prospects into funnels tailored to their situation, and our spend on ads, content, and ABM went from casting a wide net to something closer to a surgical instrument.