Accenture Strategy / Valuation Research – Monetization Health / April 2026 / Release 1.0
The market is pricing more than your income statement.
Over the past decade, $27 trillion in enterprise value changed hands globally – and the pace doubled in the last two years, with acceleration driven by technology and AI. The companies gaining ground aren't just the ones growing faster.
By definition, EV is how much a company is worth to the market – and it's the product of three things: revenue, margin, and the multiple the market assigns to each dollar of profit. Revenue and margin are tangible: companies know what they are, how they're calculated, and what moves them. The multiple is not: it shifts without explanation and without a standard framework for understanding why. What if we tried?
This research introduces the Monetization Health Score – a framework for explaining why the market pays different multiples for financially similar companies – tests it across 19 large-cap MedTech companies, and applies it to Zimmer Biomet as an example.
FrameworkMonetization Health Score (MHS)
Release1.0 – MedTech subset
Companies analyzed21 total large-cap MedTech companies; 19 used in regression
Data as ofFeb 28, 2026 (CapIQ) · April 3, 2026 (AlphaSense)
In MedTech, companies with nearly identical financials are trading at multiples worlds apart
As we shared in The Great Value Migration, enterprise value is migrating and concentrating faster than ever before. Turning around investor sentiment – increasingly impatient and unforgiving – is easier said than done.
In MedTech specifically, the separation is already visible. One of the most common valuation multiples – and one we'll use throughout this analysis – is EV/EBIT. Across our 21-company peer set, this multiple ranges from 11x to 59x. That gap is not just a gap between winners and losers. It's also one between companies that look similar on paper but are being priced as if they're in completely different businesses.
The most striking evidence is in the pairs. Danaher and Drägerwerk grew at nearly identical rates last year: Danaher trades at 30.5x, Drägerwerk at 11.2x. Intuitive Surgical and Edwards Lifesciences have nearly identical EBIT margins: Intuitive Surgical trades at 58.8x, Edwards at 28.5x.
Same financials, but radically different valuations. Something else is doing the work – and it doesn't appear on an income statement.
Highest Multiple
58.8x
Intuitive Surgical
Lowest Multiple
11.2x
Drägerwerk
ZBH – Subject Co.
18.9x
Zimmer Biomet
A company's market value is three things multiplied: Revenue x Margin x Multiple.
Revenue and margin are tangible. The multiple is not. And right now, the multiple gap is widening fast.
02 – The Growth(Plus) Hypothesis
Surprise, Growth is only part of the story. Efficiency? Even less.
Our initial instinct (especially as Growth Strategists) is to start with growth. Plot annual revenue growth against EV/EBIT across our peer set and R² – which tells you how much of the story any given variable can explain – comes out at 0.53 or 53%. Not bad. But if we remove two high-growth outliers, Intuitive Surgical and Boston Scientific, this 53% collapses to 20%. Growth alone is insufficient in creating multiple expansion for the majority of the peer set. Understanding what else becomes our primary question.
0.525
R² = 0.525
R² shows correlation between modeled and observed multiples. ZBH highlighted in darker purple. ISRG and BSX enlarged for emphasis. n=19
Other common metrics around efficiency were looked at next. But operating margin explains even less (38% across all companies) than growth. FCF conversion explains almost nothing (3%).
Our hypothesis: The market isn't just pricing how fast you grow. It's pricing how you grow – and whether your revenue repeats, compounds, and becomes more valuable over time.
Growth drives investor confidence. They're also looking for predictability, durability, and consistency.
03 – The Monetization Health Score
Growth is a signal, but monetization health is the story.
Revenue is what you earn. Monetization is how you earn it. And we believe the "how" separates a 15x company from a 30x company – with monetization architecture as the primary valuation lever and markets reclassifying on economic structure.
Predictability
Is the business model creating repeatable revenue?
Structural recurrence, filed disclosure, and forward visibility
What the market can price rather than estimate
Revenue that shows up before the year begins
Durability
Are customer economics compounding?
Switching costs and revenue expansion (upsell, cross-sell) within existing accounts
Growth that retains and doesn't need to be re-won each year
Consistency
Are outcomes actually being delivered (vs. peers and over time)?
Relative growth: not just how fast, but how well, versus the field, over time
Market share: the scorecard of whether or not the monetization model works
Growth = ConfidenceThe external control variable – not scored within MHS. Growth is the market's primary confidence signal. MHS measures what explains multiples beyond it.
Introducing the Monetization Health Score
We broke down each anchor above into more specific dimensions – and used them to score every company in our peer set using public disclosures (annual reports, investor presentations, etc.). We then created a composite score – the MHS – based on how strongly each dimension correlates with the multiple the market actually assigns. Scores are out of 100%.
The Framework
Monetization Health Score Dimensions & WeightingWEIGHTS SUM TO 100%
Predictability · 35%
Revenue Architecture · 25%
Economic Mechanism ★
How recurring the underlying business model actually is, ranging from contractual to implicit
20%
Filing Disclosure
% recurring revenue formally disclosed as segments in financial reporting
Deprioritized due to insufficient multi-year data, but we'll look to add this back in one day
0%
Durability · 30%
Customer Expansion ★
The extent to which revenue per customer grows over time
20%
Switching Cost
Structural barriers to leaving, and how formal those are
10%
Consistency · 35%
Market Performance ★
Both consistency and magnitude of relative growth vs. industry benchmarks
20%
Market Position ★
Competitive standing in primary served market, whether quantified or asserted
15%
⚠ Disclaimers and disclosures
Your friendly reminder that correlation does not equal causation – and, this framework scores what companies disclose and communicate, based on publicly available data, which may be incomplete. A company with strong recurring infrastructure that doesn't report it will score lower than its economics warrant. But, if a tree falls in a forest, and nobody is around to hear it, did it really fall?
04 – What It Explains
So, does this thing work? Short answer: yes (longer answer: it highly correlates)
Earlier we told you that absolute growth alone only explains 53% of the variation of multiples in this peer set. Switch to our Monetization Health Score as the single variable, and that increases to 71%. Combine the two together – and you have a bivariate regression that explains 82%. Toggle the charts below to see.
As with all regressions, the question also comes down to: are you above the line or below the line? Above the line, and you’re actually doing better than the model predicts. Below the line, and the market is discounting you vs. your peers.
But an R²=0.71 signals a strong correlation between MHS and multiple. Translation: if you’re in this peer set, the market is probably not mispricing you based on your monetization health.
R² = 0.525
R² – Growth only (x = revenue growth %)
R² shows correlation between modeled and observed multiples. ZBH highlighted in darker purple. n=19
53%
of the variation in MedTech multiples correlates with revenue growth alone.
21 Companies (2 excluded). 7 Dimensions. Evidence for Every Number.
If Monetization Health correlates with EV/EBIT, who's winning?
Every score is grounded in specific public disclosure evidence. Click any company to see it.
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05 – The So What
More than a score: five things the data says this sector hasn't quite internalized
As consultants, nice data is interesting but we're constantly asking, "so what?" Our growth + MHS regression explains 82% of multiple variation; couple that with corresponding company-level insights and here are the five main trends we see:
Scroll to browse · Click to expand
01
The sector confuses replacement with commitment
Most MedTech companies disclose revenue that is likely to recur. The premium goes to revenue that is structurally governed. Those are not the same.
Consignment, procedural replenishment, and installed-base pull-through all generate repeat purchases – but none of them constitute a commitment. Zimmer Biomet’s 85% consignment infrastructure where implants are already on hospital shelves sounds like a recurring model until you recognize the hospital hasn’t committed to buy; it’s committed to store. Edwards Lifesciences scores near zero on recurring disclosure despite TAVR economics that are about as recurring as MedTech gets – and trades at 28.5x. But that’s a ceiling, not the top tier. The gap between 28.5x and 58.8x is the gap between “patients will need this” and “the revenue is contractually committed before the patient walks in.”
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02
Controlled revenue wins
The highest premiums go to companies that have decoupled revenue growth from procedure volume.
Intuitive Surgical’s instruments expire after 10–20 uses – the hospital reorders because the system won’t function without them. Danaher’s bioprocessing consumables are specified into FDA-regulated manufacturing processes, and switching suppliers requires regulatory re-validation that most customers won’t risk. Masimo’s equipment agreements contractually obligate sensor purchases for the life of the device. These companies aren’t just benefiting from repeat purchasing behavior – they are governing downstream economics by design. The companies at the bottom of the scoring – Zimmer Biomet, Smith & Nephew, and Drägerwerk – still depend on how many procedures hospitals choose to perform, which they don’t control. Consignment puts product on shelves. It doesn’t commit anyone to buy it.
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03
Robotics is not a business model
Every major MedTech company is investing hundreds of millions in robotic platforms. Most are assuming the multiple expansion will follow. The data says: not so fast.
Intuitive Surgical’s soft-tissue surgical robot, da Vinci, gives hospitals no choice but to reorder. Stryker’s orthopedic robot, Mako, only works with Stryker implants, and once the robot is installed, competitors are locked out. Zimmer Biomet’s orthopedic robot, ROSA, creates volume commitments at the account level, but the majority of what ZBH sells still moves through traditional channels ROSA doesn’t touch. Three “robotics” but three entirely different economic structures (and commercial models) underneath. The market prices the mechanism embedded in the platform, not the platform itself. If the robot makes surgery better but doesn’t change how revenue is earned and committed, it’s a clinical investment – not a valuation investment.
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04
Architecture matters most when growth weakens
Danaher trades at 30.5x on 2.9% growth. That number should stop any MedTech executive in their tracks.
Danaher’s growth didn’t slow because the business deteriorated – a post-COVID bioprocessing destocking cycle, where pharmaceutical customers over-ordered during the pandemic and have been burning through excess inventory since, took growth from 23% in 2021 to flat today. The market didn’t flinch, because the structural durability is visible in the filings: 82% recurring formally disclosed, FDA re-validation switching costs, formal minimum purchase commitments. Investors can see the floor, and they’re paying for it. Boston Scientific at 31x with zero recurring disclosure is the mirror image – 19.9% growth, dominant position in pulsed field ablation, nothing filed as recurring underneath. Both trade at roughly the same multiple today. The difference is what happens next. If Danaher’s growth recovers, the multiple expands from a protected base. If BSX’s growth decelerates, there’s no floor to catch the re-rating. Same price. Only one bet is hedged.
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05
Visibility without proof gets partial credit at best
The market requires a double proof: the architecture has to be legible, and it has to show up in benchmark-relative growth. Neither alone is enough.
STERIS files 79% recurring and has regulatory switching costs that make changing sterilization vendors prohibitively expensive. It trades at 24x. Becton Dickinson has $2.4B in minimum purchase commitments and pharmacy automation systems locked into hospital EMR and billing infrastructure. It trades at 19x. Both companies have architecture the market can clearly see – and the market still won’t pay a full premium because the growth record hasn’t validated it. Disclosure earns you the benefit of the doubt. The growth record determines whether the market extends it.
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What if the market isn't mispricing anyone?
Every multiple in this peer set is a rational response to the signals the market can see. But not everyone is starting from the same position.
06 – Four Positions. Four Plays.
Where you sit determines what you do next.
It's clear from our analysis that growth and monetization health are not one size fits all. And in addition to loving so-whats, consultants love segments.
Using the component scores from our Monetization Health Score composites, we created a bubble chart based on Predictability, Consistency, and Durability. We believe that Predictability drives Consistency, which is why it's on the X-axis (vs. the Y). Durability is weighted less heavily in our model, with many companies scoring in the same range, so it's used for bubble size.
In the purest definition of a 2x2, we end up with 4 segments. In reality, the majority of companies in this peer set live in the bottom half. This means the market is largely waiting for these companies to turn recurring architecture into consistent results, and for some of these companies to first build the recurring foundation. Click on each quadrant for segment-specific plays.
The takeaway: Companies don't move diagonally by accident. Top-left to top-right requires formalizing and disclosing the architecture beneath existing growth. Bottom-right to top-right requires proving the architecture through benchmark-relative outcomes. Bottom-left to anywhere else requires a real business model shift – not narrative work, not a better investor deck, but a structural change to how revenue is earned, committed, and governed.
07 – Bringing It to Life: Zimmer Biomet
The gap is measurable and the path is specific.
Zimmer Biomet scores 36% on the MHS – the second-lowest in its peer set. This is not an innovation problem. It is a revenue architecture and disclosure problem, exacerbated by five consecutive years of below-benchmark growth. The analysis considers ZBH structurally limited.
The diagnosis from the framework is precise: Predictability 29%, Durability 50%, Consistency 32%. ZBH has a broad technology portfolio across orthopedic robotics and digital health – ROSA, mymobility, ZBEdge, Monogram, AI Care Assistant. It also has the #1 global share in knee reconstruction (~31%). But the market is reading its 85% consignment infrastructure as volume-dependent, not recurring. ROSA robotic adoption is building procedural commitment but not yet priced as a structural economic change.
We’ve identified five specific moves to better monetize the base, evolve underlying business models, and deliver consistent results.
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Opportunity 01
Prerequisite
Realign the Commercial Model
This is the prerequisite. Stand up solutions selling, restructure incentives for ecosystem attach, and build a Customer Success function. Nothing else scales without this. Today, ZBH's field force is compensated on implant volume, which reinforces the transactional model the framework penalizes. Shifting incentives toward ecosystem adoption and account-level revenue expansion is what turns a sales organization into a recurring revenue engine.
Customer Expansion · Switching Cost
Opportunity 02
Parallel execution
Unify Data Into One Platform
Consolidate 15+ brands under ZBEdge, and sell clinical decision-support and real-world evidence as subscriptions. This creates the visibility and switching cost depth that the current fragmented structure does not. Right now, a hospital running ROSA, mymobility, and ZBEdge is using three systems that don't talk to each other. Unifying them creates the kind of workflow dependency that makes switching costly – the same dynamic that earns Philips, BDX, and ResMed their switching cost scores.
Price and disclose mymobility, ZBEdge, and AI Care Assistant as recurring subscription revenue. This is the filing and narrative change that makes visible what already partially exists. ZBH currently gives most of this away as part of the capital sale or bundles it without disaggregation. Pricing it separately and filing it as recurring moves two dimensions at once: economic mechanism and filing disclosure.
Create tiered bundles per hip, knee, and shoulder, and report attach rates and revenue per connected site quarterly. This is the move that creates a standing metric analysts can model forward – the same thing ISRG does with revenue per procedure. Today, ZBH has no per-account or per-procedure metric in its investor communications. Creating one is the single fastest way to give the market something to underwrite.
Design Monogram around per-procedure pricing with consumable pull-through, not as a one-time capital sale. This is the long-horizon catalyst that shifts the economic mechanism at the base level. Monogram's patient-specific implant design lends itself naturally to a model where the value is in the procedure, not the hardware – closer to ISRG's instrument economics than ZBH's current consignment model. If designed correctly, this is the move that takes ZBH from "structurally limited" to "not yet proven" and eventually into the top-right.
Begin disclosing digital services revenue as a standalone line item. Report ROSA ecosystem attach rates. Introduce recurring revenue language in investor communications. These are disclosure and narrative moves – they don’t require changing the underlying business model, just making visible what already exists.
At this score level, the peer set trades at multiples comparable to Smith & Nephew and Edwards Lifesciences – companies where the market sees partial architecture but is still waiting for growth validation.
Launch Monogram with per-procedure pricing and consumable pull-through – not as a capital sale. Unify data assets under ZBEdge and sell clinical decision-support as a subscription. Bundle robot + analytics + monitoring by anatomy and report revenue per connected site quarterly. Realign commercial incentives to sell the ecosystem, not individual products. Deliver above-benchmark organic growth for 3+ consecutive years.
At this score level, the peer set trades at multiples comparable to Stryker and Danaher – companies the market prices as structurally different businesses than ZBH today.
But wait, there's more
These estimates are based only on what the market would historically associate with a higher MHS score, and do not account for the incremental revenue from improved monetization or economics, the margin improvement from expanding into higher-margin software and data services, or the growth acceleration that new revenue streams would produce.
No one can predict a multiple. These scenarios show the art of the possible – not a forecast. The implied EV figures reflect what the market has historically associated with companies at these MHS score levels in this peer set. Actual re-rating depends on execution, market conditions, and investor perception.
08 – What's Next?
MedTech is the test case. The dynamic is not sector-specific.
MedTech – and this particular subset – is where we tested this thinking and the corresponding methodology. It's not where it ends. Despite the current "SaaSpocalypse" – Software & Platform companies are still the north star when it comes to market valuation, and every vertical is converging towards these business models – for good reason. The same dynamics – recurring economics that go undisclosed, growth momentum that masks absent architecture, market skepticism that disclosure alone can't fix – exist in every capital-intensive vertical where revenue model quality isn't yet measured.
The challenge (and the insight) will be doing this analysis at scale. While S&P companies' obsession with stock price has resulted in sector-specific metrics that are reported almost universally, this is not the case elsewhere. Recurring revenue is not the only business model, and revenue segments are reported differently at virtually every company analyzed. Scoring dimensions like Customer Economics are more difficult without metrics like NRR or NDR. Proxies exist – and that's what we did – and future iterations will likely need a combination of our current approach and methodology, more quantitative data sets, and primary research to hear from CXOs – including some of our clients, directly.
So on that note, let's talk about our approach – and some limits.
09 – How We Built This
Human-Led, AI-Powered
This framework was built over several weeks through a deliberate collaboration between human strategic judgment and AI-assisted analysis. As rapid improvements in AI completely transform the way we work, this study served to push both the thinking – and the limits of AI collaboration, shaping the art of the possible. The effort was primarily between 1 human and 1 LLM, with an additional couple of humans and LLMs assisting with data collection and pressure-testing.
What we directed
Everything that required judgment. We defined the central thesis: that valuation divergence in MedTech (and beyond) is driven by the quality of revenue and monetization architecture, not by innovation gap or operational performance or even absolute growth alone. We chose the peer set, the valuation metrics, and the analytical lens. We designed the framework for the composite score: the three anchors, the seven dimensions, the weighting logic, and the argument connecting them. This took several iterations until we felt confident in our model.
We made every ambiguous scoring call, caught and corrected errors throughout – FX adjustments, rubric inconsistencies, regression specification choices – and directed the narrative structure from first principles all the way through to what you're reading right now. We decided what the framework should and shouldn't claim. The intellectual architecture is ours (and so is most of this copy).
What AI did
Claude (Anthropic) did the analytical heavy lifting. Claude, in execution mode, ran all regression computations via Python/numpy, built the scoring rubric and methodology documentation, generated dimension-by-dimension scoring rationale for all 21 companies from AlphaSense evidence, and built this microsite. Claude also flagged analytical issues proactively throughout and served as primary thought partner, as the author is happy to admit this is not a vertical she has extensive experience in.
AlphaSense was the primary evidence retrieval platform for qualitative scoring. Earnings call transcripts, SEC filing passages, and investor presentations were retrieved company-by-company using structured prompts.
ChatGPT (GPT-4) ran as an independent blind scorer – same rubric, same evidence, without seeing Claude's scores. Disagreements were flagged for human review and reconciliation.
What We Know. What We Don't.
This is Release 1.0 of the Monetization Health Score. The analytical architecture is sound and the core finding – that revenue quality correlates with valuation multiples independently of growth – is robust. But several inputs require further refinement before this analysis is appropriate for broad publication or client delivery.
Data
AlphaSense evidence depth
Initial runs used 5–15 passages per company per dimension – insufficient to reliably distinguish "consistent across multiple venues" from "mentioned once" – and we trusted AlphaSense to truly pull the best passages across hundreds of filings, transcripts, and documents.
Data
FX corrections (Elekta, Getinge)
Elekta and Getinge CapIQ revenue, EBIT, FCF, and EV exported in SEK. A 9.15 SEK/USD rate (Feb 28, 2026) applied. EV/EBIT multiples are currency-neutral. Corrected USD figures should be verified against public sources before publication.
Data
Disclosure ≠ economic reality
MHS is better at measuring what a company communicates vs. the precision of what their business actually does. Most companies in this segment have some form of recurring revenue, but understanding what % of total that is is near-impossible for most. Future iterations suggest primary research (executive surveys) to get more approximations at scale.
Data
Regression sample is 19 companies
Sufficient for directional findings. Insufficient for high-confidence coefficient estimation. Three companies excluded for documented reasons (M&A distortion and restructuring FCF anomaly). Results are indicative.
LLM Limitation
Hallucination risk in qualitative scoring
Dimensions were scored using LLM analysis of AlphaSense passages. Two independent scoring runs (Claude + GPT-4) were used; disagreements were flagged and resolved through human judgment. All qualitative scores should be treated as directional, not definitive. The author would like to note that despite this, scoring is likely more consistent than what a human (or project team) could have done.
Human Limitation
Independent expert review required
The framework has not yet been reviewed by an independent party with both MedTech domain expertise and quantitative finance background. This is a stated prerequisite before presentation to client leadership or external publication.
Framework developed by Accenture Strategy. Analysis conducted April 2026. Financial data: CapIQ as of Feb 28, 2026. Qualitative scoring: AlphaSense (April 2026). Regression: Python/numpy OLS. Working draft – not for external distribution.
10 — Appendix
Full peer data
While we assessed 21 companies in this peer set, we excluded two due to multiple distortion from significant one-time events, rather than use an adjusted multiple.
⚠ Excluded
Hologic – COVID testing inflated revenue by ~$1.7B at peak, then collapsed by 2023. The EV/EBIT multiple is calculated against an EBIT base still normalizing. Scoring on today's multiple would penalize a strong recurring architecture for a cycle it didn't control.
⚠ Excluded
Teleflex – Mid-restructuring with planned separation of Urology Care. The multiple reflects transition uncertainty – elevated one-time costs, investor uncertainty about post-separation mix, and distorted FCF – rather than revenue architecture quality.
⚠ Debated
Masimo – Potential residual effects from a recent divestiture. We believe Masimo is closer to steady-state economics than the two above, and kept it in the regression.
Full Peer Table – sorted by EV/EBIT
Company
EV/EBIT ▼
MHS
Predict.
Durabil.
Consist.
Rev TTM
Rev Grw
EBIT Mgn
Score = weighted Monetization Health Score · n=19 · CapIQ Feb 28 2026 · click any column to sort · click any row to expand
