Hospital decision makers who reply.

Who

Lifen, French publisher of secure medical messaging for hospitals and clinics. Its sales force hunts inside healthcare facilities with hospital nursing managers (CDS) as the pivotal persona for operational adoption.

Problem

No market source is exhaustive or up to date on CDS: high turnover on these positions, job titles vary from one facility to another, RPPS is often incomplete. Purchased databases are out of date in 30-50% of cases.

What we did

• Exhaustive identification of 7,458 nursing managers practicing in France (cross-referencing RPPS + Hospimedia + public sources)
• Profile screening and career path analysis
• Background verification (degrees, past mobility)
• Validation of current position and reporting unit
• Deliverable: named list with service + facility context, ready to be worked by Lifen SDRs

Extension · real-time tracking

Lifen extended the collaboration to integrate day-by-day tracking of nominations and job changes across all 7,458 CDS. Each detected move (appointment, internal mobility, change of facility) becomes an ultra-hot inbound signal: a new CDS taking up duties = open buying window, ideal moment to make contact with a relevant topic.

Results

Who

Curecall (part of the Lifen group since 2024), publisher of a patient follow-up SMS solution certified as a medical device by HAS · pre-op reminders, instructions, post-op follow-up, satisfaction collection. Target: MCO directorates and ambulatory surgery managers of French hospitals and clinics.

Problem

The product moves very specific HAS indicators, but this is not legible in a generic outbound sequence. The challenge: find facilities that objectively have the problem Curecall solves, and speak to that exact pain · not "hello, here is our solution".

What we did

• Deep dive into HAS public data (Scope Santé + e-Satis)
• Isolated the 3 exact indicators that Curecall mechanically moves: e-Satis +48h MCO, unscheduled 30-day readmission rate, hospital discharge letter delay (DEC)
• Targeting of facilities in simultaneous degradation on all 3 indicators · strong signal that the patient journey is under tension
• Personalised message with the recipient's public scores (e.g. "your e-Satis went from X to Y over 18 months while your DEC degraded by Z days") · no generic sequence
• Deliberately tiny outbound volume: 42 accounts instead of 1,000+

Results

Context

The client, publisher of an AI thoracic CT reading suite, was preparing to open the DACH market ahead of the new German national lung cancer screening reimbursement programme (G-BA framework, effective 2026). Target: open 10 German university hospitals in 2025. Previous approach: presence at Deutscher Röntgenkongress, a few spontaneous demos, zero outbound process. Radiologists responded to demos, but nothing moved on the procurement side · no one knew who to talk to on the budget side.

What we did

Scoring of all DACH Universitätskliniken + top hospitals with heavy imaging platforms aligned with the criteria of the future screening pathway. Cross-referencing G-BA Qualitätsberichte (thoracic CT volumes), thoracic radiology accreditations, and CT equipment renewal signals. Dual targeting: radiology department head (user) + biomedical engineer or procurement director (buyer). Parallel email + LinkedIn sequences, message calibrated by role.

Results

Context

The client, manufacturer of a premium-margin OR consumable, had tested a "classic" Apollo + Lemlist campaign 6 months before talking to us: reply rate < 2%, and the few who replied were never the right contact. The founder: "we sprayed 4,000 emails for nothing".

What we did

We threw out their list and rebuilt from public data: surgery IFAQ, OR activity authorizations, SAE volumes on the targeted DRGs. Each email started with a verifiable fact about the facility (e.g. "your IFAQ on the ambulatory surgery pathway went from 8 to 6 over 2 years · consistent with what we see on 40 other sites"), not with "I hope your week is off to a good start".

Results

Context

The client had been spending €10K/month on LinkedIn Ads on the neuroradiologist target for 18 months. Average result: 3-4 MQLs per month. No scalability in sight. We proposed taking one month to run a Medify Sprint in parallel, on a tighter budget (€7K).

What we did

Identification of 180 stroke / UNV centres in France, scoring by thrombolysis activity volume + IFAQ. Cross targeting referring neuroradiologist + emergency department head + biomedical engineer. Message built around a specific insight: "your centre treats X thrombolyses/year, Y% arrive outside the window · here is why this is a workflow problem our tool solves".

Results