Hospital decision makers who reply.

Who

Lifen, French publisher of secure medical messaging for hospitals and clinics. The sales team targets facilities with hospital nursing managers (CDS) as the pivot persona for operational adoption.

Problem

No market source is exhaustive or up-to-date on nursing managers (CDS): high turnover in these roles, job titles vary across facilities, RPPS often incomplete. Purchased databases are outdated in 30-50% of cases.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 professional career path analysis
• Background verification (qualifications, past mobility)
• Validation of current role and department affiliation
• Deliverable: named list with department + facility context, ready for Lifen SDRs to engage

Extension - suivi temps réel

Lifen extended the collaboration to integrate day-by-day tracking of appointments and role changes across all 7,458 CDS. Each detected movement (appointment, internal mobility, facility change) becomes an ultra-hot inbound signal: a new CDS taking up their role = open purchase window, ideal moment to reach out with a relevant angle.

"The database we received became our primary source. The SDR team stopped buying lists. Real-time appointment tracking is the add-on that changes everything - we call at the right moment, with the right angle." - VP Growth, Lifen

Results

Who

Curecall (groupe Lifen depuis 2024), éditeur d'une solution de suivi patient par SMS certifiée DM par la HAS - rappels pré-op, consignes, suivi post-op, recueil de satisfaction. Cible : directions MCO et cadres chirurgie ambulatoire des hôpitaux et cliniques français.

Problem

Le produit fait bouger des indicateurs HAS très précis, mais ça n'est pas lisible dans une séquence outbound générique. L'enjeu : trouver les établissements qui ont objectivement le problème que Curecall résout, et leur parler sur cette douleur exacte - pas "bonjour, voici notre 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, 30-day unplanned readmission rate, discharge letter sending delay (DEC)
• Ciblage des établissements en dégradation simultanée sur les 3 indicateurs - signal fort que le parcours patient est en tension
• Personalised message using the recipient's public scores (e.g. "your e-Satis dropped from X to Y over 18 months while your DEC deteriorated by Z days") - no generic sequence
• Deliberately minimal outbound volume: 42 accounts instead of 1,000+

"We had CFOs writing to us 'how do you know that about our e-Satis?' - it's public data, but nobody uses it like this. The message was so relevant that we closed €60k without needing to run a large campaign." - Curecall Team

Results

Context

Le client, éditeur d'une suite IA de lecture scanner thoracique, préparait l'ouverture du marché DACH en amont du nouveau programme national allemand de remboursement du dépistage du cancer du poumon (cadre G-BA, entrée en vigueur 2026). Objectif : ouvrir 10 hôpitaux universitaires allemands en 2025. Leur approche jusque-là : présence au Deutscher Röntgenkongress, quelques démos spontanées, 0 process outbound. Les radiologues répondaient aux démos, mais rien n'avançait côté achats - personne ne savait à qui parler côté budget.

What we did

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

"We closed 3 university hospitals in 4 months, including one that had been in our prospecting deck for 2 years. The difference: we were no longer only talking to the radiologist, we were talking to the biomed at the same time. That's what unlocked everything." - Dir. commercial, éditeur IA imagerie thoracique

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 blasted 4,000 emails for nothing".

What we did

On a jeté leur liste et on a reconstruit depuis la donnée publique : IFAQ chirurgie, autorisations d'activité bloc, volumes SAE sur les DRG visés. Chaque email commençait par un fait vérifiable sur l'établissement (ex. "votre IFAQ sur la filière chirurgie ambulatoire est passée de 8 à 6 sur 2 ans - c'est cohérent avec ce qu'on voit sur 40 autres sites"), pas par "j'espère que votre début de semaine se passe bien".

"68% open and 19% reply on a cold campaign - I thought it was fake when I saw the stats. The typical reply was actually 'how do you know that about my OR?'. That's when we understood we had a message problem, not a volume problem." - CEO, fabricant DM bloc opératoire

Results

Context

The client had been spending €10k/month on LinkedIn Ads targeting neuroradiologists for 18 months. Average result: 3-4 MQLs per month. No scalability in sight. We proposed running a Medify Sprint in parallel for one month, on a tighter budget (€7k).

What we did

Identification of all 180 stroke centres (AVC/UNV) in France, scored by thrombolysis volume + IFAQ. Cross-targeted: referring neuroradiologist + head of emergency department + biomedical engineer. Message built around a precise insight: "your centre handles X thrombolyses/year, Y% arrive outside the time window - here is why this is a workflow problem our tool solves".

"LinkedIn Ads: €10k → 7 MQLs over 4 weeks. Medify Sprint: €7k → 143 MQLs and 22 demos booked. Let's be honest, we cut the Ads the following week." - Head of Marketing, éditeur IA imagerie cérébrale

Results