Mike Carter

Churn wasn't the problem. It was the symptom. A PE-backed digital health company was approaching exit. Six-month revenue churn had climbed from 6.7% to 14.2% across 6,000+ SMB customers. Leadership assumed onboarding was the issue. Not entirely true. The real problem: one engagement model applied to every customer, regardless of value. Lower-value accounts consumed disproportionate effort. Higher-value accounts couldn't reach value fast enough. Margins eroded. Churn followed. We didn't just address churn. We redesigned the model. → Onboarding: 90 days → 30  → Team: 16 FTEs → 8 on scaled operations  → ~90% adoption at graduation  → $1.5M in-year efficiency gains  → Retention improved 2%+ before the valuation event For PE-backed companies, elevated churn isn't an ops issue. It's a valuation risk. Full case study → https://lnkd.in/gxEPTFr3

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Back to margin improvement: Front-loaded investment disrupted JDL's margin improvement in 2025, but we see a clear return to margin expansion in 2026-27 with moderated investment and improved efficiency. We expect JDL's net margin to improve from 3.6% in 2025 to 3.7% in 2026 and 3.8% in 2027, with identifiable drivers including earnings turnaround at Deppon, consolidation of on-demand delivery business and loss reduction in overseas business. We also see room for JDL to expand margins over the mid- to long term given its relatively higher cost vs. peers and dedication to tech investments.

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💥 this one's for the operators.. special edition of the ai market memo just dropped PE or VC, which actually creates more wealth for operators? this one’s special because I’m in these conversations weekly with GTM execs, VC partners, and PE sponsors. and most operators are making a huge financial decision of their career using wrong napkin math. It’s cap table math. and the math decides whether “0.2% equity” becomes $0 or millions. so in this special edition, we broke it down in parts: 1. the real equation wrong napkin math and the actual payout formula that determines outcomes. 2. the two wealth engines broke down the two operator wealth models: - VC = power law (home run derby outcomes) - PE = manufacturing (timelines + transaction probability)  Milad Alucozai gave some great advice on participating preferred. 3. cap table terms before we touched the math, we got the vocabulary straight: common vs preferred, pref stack, dilution, vesting, acceleration, Polina Karachentseva explains single-trigger vs. double-trigger acceleration really well using the windsurf deal as an example.  4. the ‘right’ math. why pressure-testing these four variables: dilution, preference stack, timing and eligibility / acceleration are critical. 5. scenarios then we ran real scenarios both VC and PE including the good and the brutal: - the home run - the “decent exit” - common gets smoked - the acqui-hire trap (headline exit, $0 payout) - the PE leaver clause haircut 6. due diligence checklist finally, the questions operators should take note on. and some wise words from Colin Lernell's stuff. link in the comments.

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Reposting for PE teams evaluating platforms in manufacturing software. Our Market Ontology gives deal teams a clean, defensible framework for positioning, adjacencies, and M&A strategy.

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10 Key Factors for HealthTech and MedTech founders to consider in today's M&A market environment (Part 2) 6. Alignment with Value-Based Care (VBC) 🤝 Healthcare is shifting from fee-for-service to VBC. Companies that enable this transition (e.g., remote patient monitoring, population health, chronic disease management) are strategically valuable to payers and providers. Founder Focus: Frame your solution in terms of how it reduces costs and improves patient outcomes, directly supporting a VBC model. 7. Scalable Processes and Management Team Depth 👥 Buyers assess "key person risk." Over-reliance on the founder for key client relationships or daily operations raises concerns about post-acquisition sustainability. Founder Focus: Build a strong second-tier management team and document repeatable Standard Operating Procedures (SOPs). Prove the company can run and scale without the founders' constant involvement. 8. Interoperability and Data Monetization Strategy 🔗 The ability to seamlessly integrate with Electronic Health Records (EHRs) and other systems (interoperability) is crucial. Companies with clean, actionable data that can be ethically leveraged (monetization) command higher multiples. Founder Focus: Prioritise API development and integration capabilities. Demonstrate a clear, compliant strategy for how your data creates additional value. 9. Customer Concentration and Acquisition Costs (CAC) 🎯 High customer concentration (revenue dependent on one or two clients) signals a major risk to a buyer. Low Customer Acquisition Costs (CACs) are a key profitability driver. Founder Focus: Diversify your customer base to reduce concentration risk. Optimize your sales and marketing process to prove an efficient, scalable, and low CAC model. 10. Understanding Alternative Deal Structures 💰 To bridge valuation gaps between buyers and sellers, alternative structures are becoming common. These include earn-outs (additional payments based on future performance) and royalty/licensing agreements. Founder Focus: Be prepared to negotiate a portion of your deal value into an earn-out structure, especially if a buyer is cautious about future revenue projections. This shows confidence in your team and product's future performance. To discuss how Nelson Advisors can help your HealthTech or MedTech company, please email lloyd@nelsonadvisors.co.uk

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Most deals don't fail at the table. They fail in the data room. If you're running M&A due diligence or a fundraising round, the platform at https://vdr.ai was built specifically for the pressure those processes create. Investors and acquirers move fast. They request hundreds of documents. They need instant access, version control, and a clear audit trail showing who viewed what and when. A generic file-sharing tool doesn't cut it when the stakes are this high. VDR.ai uses AI to organize and index documents automatically, so your team spends less time filing and more time closing. Deals that used to take weeks of back-and-forth can move in days. Security is where most platforms cut corners. The https://lnkd.in/gcVYs6ne page lays out exactly how VDR.ai protects sensitive documents, covering enterprise-grade encryption, granular access controls, and permission settings that let you decide precisely who sees what and for how long. That level of transparency matters when you're sharing cap tables, financial models, or IP documentation with outside parties. Some context on why this is worth paying attention to: global M&A volume exceeded $3 trillion in 2023, and the majority of those deals involved a virtual data room at some stage. The quality of that room shapes how buyers perceive your organization. A disorganized data room signals a disorganized business. If you've been through a deal process recently, what was the biggest friction point in managing documents and access for your counterparty? #MergersAndAcquisitions #DueDiligence #VirtualDataRoom #DealManagement #DocumentSecurity #MAtech

Some quick and candid / not just bullish observations on AI in digital health, especially given the enormous level of noise at #JPM2026. Last (5th) post in the 5-on-5 series, opening 2026 with 5 different pieces with 5 observations each on the market for digital health founders. We are passed the hype phase. I actually think 2026 will be the year when saying 'AI' or 'Agentic' will raise more questions than applause. Too many pilots are not scaling, too many promises are yet to be fulfilled. When building a product takes less time, the path to commoditization also takes less time. Scribes, chart summarizations, etc. which leads IMO to NRR being much more important than ARR (show you are resilient to price pressure and churn) Most real impact of AI is on the back end of new companies. This is massive and hence any budget that looks similar to 2023 / 2024 or even 2025 budgets should render a very serious question As a byproduct, and unlike 2021, increased head count becomes a negative signal if the company is indeed AI-centered (proxy to inefficiency) And another key difference from 2021: raising a lot of money (with a big valuation) while the public market is at best open for high 9-digit revenue, is really playing very aggressive poker. For additional color, see this interview with CTech by Calcalist (first comment)

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