Part 3 and final part of the IDR Medical Conjoint Insights Series
Conjoint analysis is a cornerstone of medical device pricing research. It quantifies how clinicians, procurement teams, and administrators make trade-offs between performance, ease of use, service levels, and, of course, price.
This article, “From Conjoint to Commercial Reality: Turning Pricing Insight into Revenue Impact in MedTech,” is the third and final installment of our IDR Medical Conjoint Insights Series. Across this series, we explore how to transform the rich insights from conjoint analysis into actionable strategies that drive real commercial results for MedTech manufacturers.Conjoint analysis is a powerful tool. It reveals how clinicians, procurement teams, and administrators make trade-offs between price, performance, and service. But understanding preference is only the first step. To maximise revenue and strategic impact, pricing insight must be translated into market-anchored, financially sound decisions. In this final article, we show how to do just that by connecting customer preference data to revenue models, P&L simulations, and strategic pricing decisions.
Catch up on the earlier parts of the series:
Part 1: Conjoint Preference Shares Are Not Market Shares – Understanding why preference doesn’t automatically translate into market share.
Part 2: From Preference to Price: Using Conjoint Data to Build a Winning Pricing Strategy – Converting preference insights into practical pricing guidance.
Stay tuned for our upcoming series whitepaper, which will distill all three articles into a practical guide for MedTech teams seeking to get more value from their conjoint research
1. Conjoint as the Core Engine
Every model starts with the utility data from conjoint. These utilities quantify how each attribute and price shapes customer choice. From them, we generate share-of-preference curves that show how demand shifts as price changes, effectively a behavioural demand curve specific to your product.
These curves remain the analytical engine of the model. All subsequent layers simply contextualise them within real-world conditions, ensuring the model reflects how customers say they would choose and how the market will allow them to choose.
2. Anchoring to Market Dynamics
In Med-Tech, adoption rarely mirrors pure consumer behavior. Procurement processes, replacement cycles and access to tenders all influence whether customers act on their stated preferences.
To ground the conjoint data in operational reality, we anchor it to market dynamics such such as:
- Target market size (e.g. accounts, beds, units, or patients)
- Replacement or replenishment frequency (e.g., device lifecycle, consumable pull-through)
- Tender access or framework listing share
- Adoption trajectories by segment (early adopters, mainstream, laggards)
With these parameters in place, the model scales customer preference into realistic, evidence-based market potential.
3. Integrating Commercial Levers
Next, we incorporate the financial mechanics that determine commercial outcomes in Med-Tech markets. These elements don’t change what customers value; they determine how that value converts into revenue and profitability.
Typical commercial levers include:
- List vs. net selling price after framework or volume discounts
- Contract duration and renewal likelihood
- Channel mix including distributor margins
- Service, maintenance and training costs
- Regional reimbursement or budget differences
Integrating these factors ensures the model reflects both sides of the pricing equation: market share dynamics and financial realisation.
4. Extending to P&L Modelling
For clients requiring a full financial perspective, we extend the revenue model into a P&L simulation. This involves incorporating:
- Cost of Goods Sold (COGS), including manufacturing and distribution costs
- Sales, marketing, and service spend by channel or region
- Overheads or R&D amortisation
This enhancement turns the model into a profitability tool, showing not only which price point maximises adoption, but which one delivers sustainable margin. It allows teams to evaluate pricing strategies in the context of portfolio goals, growth targets, and investment priorities.
5. Supporting Strategic Decision-Making
The purpose of this approach is not to replace conjoint analysis, but to extend it. Conjoint remains the foundation that quantifies customer value. The revenue or P&L model provides the bridge to commercial reality – enabling leaders to evaluate pricing decisions with both market and financial clarity.
For our Med-Tech clients, this combined approach supports evidence-based decisions on:
- Launch pricing and positioning
- Value-based pricing justification for internal or external stakeholders
- Investment prioritisation for new product introductions
- Scenario planning across segments, regions, or tender environments
In Summary
Conjoint analysis doesn’t just measure preference - it monetises it.
By converting stated preferences into financial insights, it provides a defensible, data-driven roadmap to pricing success for Med-Tech manufacturers.
Whether assessing price elasticity, modelling willingness-to-pay, or simulating alternative pricing scenarios, conjoint helps ensure pricing decisions are robust in theory and effective in the market.
Start transforming your conjoint insights into profitable pricing strategies with IDR Medical. Let’s talk.
This article is the third and final part of IDR Medical’s Conjoint Insights Series:
- Part 1: Conjoint Preference Shares Are Not Market Shares
- Part 2: From Preference to Price: Using Conjoint Data to Build a Winning Pricing Strategy
- Part 3: From Conjoint to Commercial Reality