Tuesday I broke down how PE-backed platforms beat the bankers by building sourcing pipelines before a process ever starts. Thursday I covered the real cost of building that capability through a full-time hire: $250K-$410K fully loaded in Year 1, $3M-$4M in enterprise value impact at exit multiples.
The question that follows both pieces is the practical one: what do you actually build, and in what order?
Most platforms get this wrong. They hire first and build second. The BD professional shows up on Day 1, opens their laptop, and starts from scratch. Three to six months later they've built a target list, started making calls, and maybe generated a few conversations. Meanwhile the platform paid full freight for a half-year ramp and the pipeline didn't move.
The platforms that move fastest flip the sequence. They build the infrastructure first and put a person behind it second. When the hire starts, they inherit a functioning system, not a blank CRM.
Here's what the first 90 days of that build looks like.
Days 1-30: Build the Target Universe
Before anyone picks up a phone, you need to know who to call and why.
Bain and Sutton Place Strategies found that the average PE firm only sees 18% of intermediated deals relevant to their strategy. Sutton Place's 2024 origination benchmark puts the industry median at 17.6% pipeline coverage. For every 10 relevant deals, you're seeing fewer than 2.
The fix isn't more banker relationships. It's building the target map before anyone else is looking.
Week 1-2: Define the buy-box with hard parameters. Not "we like urgent care in the Southeast." That's a preference. A buy-box that drives sourcing has specifics: specialty and sub-specialty, geography by MSA or county, size thresholds (provider count, location count, estimated revenue range), ownership type, and payer mix signals. The sharper the criteria, the smaller and more actionable the universe.
Week 2-4: Build and enrich the target list. State licensing databases, NPI registries, payer directories, professional association lists, CMS provider data. These are public sources that most teams underutilize. The goal is volume first: 200+ names for a state-level screen, then filter against the buy-box. AI compresses what used to take an analyst 2-3 weeks into days. I covered the data sources and approach in an earlier piece on building acquisition pipelines.
The output at Day 30: a scored and ranked target universe with priority tiers, provider counts, location data, ownership indicators, and succession signals.
Days 31-60: Build the Competitive Map and Monitoring Layer
Your target list doesn't exist in a vacuum.
Week 5-6: Map the competitive landscape. For every geography you're evaluating: which groups are already PE-backed, who's the sponsor, when did they acquire, are they in buy-and-build mode or approaching exit. A market where three PE-backed platforms are actively acquiring is a different dynamic than one with a single incumbent. This is also where you identify white space: MSAs with no PE presence, founder-owned groups with no succession plan. The absence of competition is a data point.
Week 7-8: Build the monitoring system. This is what separates a static list from a living pipeline. You need alerts that flag when a founder approaches retirement age, when a competitor closes a deal in an adjacent market, when a group's website drops a partner from the About page, when a practice posts a job listing that signals expansion or distress. Most of this can be automated. The teams that build monitoring infrastructure catch timing signals that cold outreach misses entirely.
The output at Day 60: a competitive landscape map showing every relevant PE-backed platform by geography, a white space analysis, and a monitoring layer that surfaces timing signals without someone manually checking.
Days 61-90: Build the Outreach System and Sequencing
Not every target gets the same approach at the same time.
Week 9-10: Sequence the pipeline. Beachhead targets first: the 1-2 acquisitions that give you immediate credibility in a market. When a 10-provider group hears you acquired the market leader, the next conversation gets easier. Add-ons second, once the beachhead is established. Watch-list third: targets that aren't ready today but will be in 12-18 months. The goal is that when they're ready, they call you instead of hiring a banker.
Week 11-12: Build outreach infrastructure. CRM configured to track relationship status and touchpoints across the full target universe. Outreach templates personalized by target profile, not generic. A contact cadence that accounts for the reality of physician practice M&A: these are 6-18 month relationship arcs with founders deciding whether to sell something they built over 20 years. From my personal experience, the outreach that works is peer-to-peer, direct, and references specific details about the practice. AI handles the research and personalization at scale. The human handles the conversation.
Sutton Place's data shows PE firms typically close deals approximately 7 months after sourcing, and only 30-40% of deals that firms log end up closing. Proprietary deals close at a lower rate than intermediated ones because sellers aren't always fully committed, but they trade at a meaningful discount to auction pricing. The infrastructure you build in these 90 days determines whether you're running a system or just making calls.
The output at Day 90: a sequenced outreach playbook with beachhead targets, add-on opportunities, and watch-list names. A CRM with the full target universe loaded, scored, and tracked. An outreach cadence running.
What This Changes
When a BD hire walks into this system on Day 91, they're executing, not researching. They open their laptop and see 15 priority targets with contact paths, competitive context, and a recommended approach sequence already built.
That's the difference between a $300K bet and a $300K investment.
The platforms that win are not the ones that spend the most on BD headcount. They're the ones that build the pipeline infrastructure first and put a person behind it second.
If you're building a healthcare platform and thinking through what this system looks like for your specific market and thesis, I'd be happy to talk through it.
If you want to see how we're applying this for specific specialties and geographies on the buy-side and sell-side, reply to this email. I'll make time for the right conversations.
-Shawn