Six Considerations to Evolve Your Capital Equipment Pricing Strategy

Six Considerations tEvolve Your Capital Equipment Pricing Strategy  

Capital Equipment Pricing Models  

As the world of capital equipment changes rapidly, manufacturers must find innovative ways to stay competitive. With new technology and enhancements, such as software upgrades and digital and connectivity solutions, as a crucial part of the value proposition of capital equipment, manufacturers can offer alternative pricing solutions to provide greater customer customization. Flexible pricing is especially pertinent for highervalue equipment on the market, such as AI-assisted imaging equipment, roboticallyassisted surgical equipment, and advanced monitoring equipment. With the shift in procedures from hospitals to smaller office-based labs and ambulatory surgical centers, manufacturers can also capture new business opportunities with these alternative models.  

New Pricing Models for Capital Equipment  

The traditional pricing models offered three options: an upfront purchase with each component sold separately, a consumable upcharge model, or a leasing model. A combination of new technology and more customized pricing improves a manufacturer’s value proposition. With new pricing models, manufacturers can boost revenues, increase penetration, and build long-term relationships with clients by offering greater flexibility.  

These new models include:   

  • Risk-based or outcomes-based model: Risk-sharing pricing strategies factor in a cost savings component from any operational efficiencies gained and clinical outcomes achieved in this model 
  • Recurring revenue stream model: Instead of the traditional transfer of ownership model, manufacturers offer a subscription with a recurring fee. The purchaser or user is granted access to a set number of capital equipment devices for the subscription period. This fee may or may not include unlimited usage in the number of consumables 
  • Patient usage pricing model: Based on patient usage, manufacturers can offer a per-patient fee for the equipment over a period of time with this model 
  • Enterpriselevel model: This model bundles the customers capital equipment needs across entire care units and multiple hospitals within the network. The bundle would normally include both equipment and services components. Payment schedules could be yearly or bi-yearly

Six Considerations for Identifying the Right Pricing Model  

With so many pricing models emerging, it can be challenging to identify the right one for your product offerings. As your organization moves towards a new pricing model, like the ones above, there are six key considerations to keep in mind to identify the most effective model for your team: 

  1. Set clear objectives: With an identified goal, your organization can better evaluate the potential models. Objectives can include increasing revenue, expanding the adoption rate, smoothing revenue, maximizing profit, and expanding account use of a suite of products and services. For example, if a manufacturer’s goal is to increase the use of a full suite of products, the recurring revenue stream model may be a better fit for their goal 
  2. Identify the value drivers of your product:  Drivers, such as clinical efficacy, payment model, ROI, and the strength of existing relationships, impact pricing and the manufacturer’s ability to customize the pricing model  
  3. Determine the decision-maker: Sometimes the end-user who makes the purchase decision is different from the purchaser who chooses the pricing model. Broadly, there are three types of capital equipment buyers: economic, clinical, and operational. The economic buyer’s sole focus is to improve their organization’s profit. In contrast, the clinical buyer is more focused on the clinical value of the product offerings, like patient outcomes or improved patient experienceOperational buyers typically focus on other factors such as department workflow, device integration with key clinical systems, or maintenance and uptime. Knowing the levels of influence and priorities for each buyer type will help determines which pricing model to offerIf the buying decision is more committee-basedthe pricing model will need to consider all stakeholder needs  
  4. Consider the impact on clients’ budgets: Pricing structure might impact where a client categorizes a purchase (i.e., capital expenses or operational expenses). Often capital expenses will fall under the hospital’s capital budget while operational expenses fall under the department’s budget, which may sway some clinicians towards a capital expense model  
  5. Understand clients’ financial health: Innovative recurring pricing models might be a better fit for clients working under capital restraints. This consideration is crucial overseas, especially in countries where COVID-19 has ravaged hospitals, which would seek to reduce the upfront cost of purchasing capital equipment  
  6. Establish benchmarking metrics: This consideration is the most important for risk-based or outcomes-based pricing, as identifying the equipment’s impact on patient outcomes or operational efficiencies dictates price. These metrics could include hospital readmission rates or the number of adverse events related to the equipment. When determining these benchmarks manufacturers must ensure they are measurable and that clients have the right infrastructure system in place to measure them 

How Kx Can Help You  

With our expertise in pricing strategy, Kx Advisors can guide your team through developing an optimized capital equipment business model. Our team of healthcare experts will help you evaluate your base, identify your customer segments, effectively appeal to your ideal customer, and position your organization for long-term success.  

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Using Your Product Launch Forecast as a Strategic Tool

Using Your Product Launch Forecast as a Strategic Tool 

Forecasting as a Decision-Making Tool

Too often, commercial planning teams approach revenue forecasting as a required financial exercise rather than a strategic planning activity. The highest performing teams, however, develop revenue models to drive decision-making and reinforce strategy. The forecasting process – designing the model, understanding the demand funnel, gathering insights, aligning on assumptions, and analyzing results – can drive decisions and deliver more value than the output itself. Elevating a revenue forecast to a powerful decision-making tool requires careful planning and thoughtful design considerations.

Crafting Your Product Launch Forecast to Inform Strategy

While revenue models are commonly designed to inform resource allocation, investor communications, or inventory planning, the most robust and accurate models are also designed to inform commercial strategy.  With the correct design considerations, your commercial planning team can learn more about the patient segments driving forecast value, identify opportunities in the patient journey to drive product adoption, and pinpoint the investments needed to drive share. Further, the most effective models consider multiple scenarios to plan for key unknowns.

Unfortunately, many project teams jump into forecasting with unclear objectives, insufficient data sources, or too many scenarios, and ultimately fail to develop a useful decision-making tool. Your team can avoid common modeling pitfalls by following best practices in three critical planning steps:

  1. Create the decision-making framework  
  2. Find the applicable data for the model   
  3. Define the scenarios    

Create the Decision-making Framework

The first step of any model should be aligning on the end goals (i.e., defining the decisions model will inform).  An end goal could be, for example, determining the focus and magnitude of commercial investments, evaluating strategic options in the face of a new market force, or determining supply need for a quarterly production plan. After identifying the overarching question(s), your team can determine how to approach the model.  

Your team can use the end goal to decide on the type of model. The type of model, from market share model to launch planning to production demand, determines the model’s specificity. Analyzing the structure before gathering data to ensure the model aligns with the end goal will save your team time and effort.  For example, is an annual model sufficient to calculate net revenue, or does the organization need monthly/weekly sales granularity to inform production planning? Is there a need (and reliable data) to support international country-level forecasts, or would a regional forecast be more accurate and equally actionable? Once you understand the key questions the model is answering, the desired output, and the aligned model type, your team will have the clarity to move to the next step: finding the data.     

Find the Applicable Data for the Model    

First, your team should identify and classify relevant data sources about your product and market, such as epidemiology studies, claims data sets, qualitative interviews, quantitative surveys, historical product sales, and competitor sales, among others, for input. Forecasting teams should take a “best source” approach –evaluating every assumption individually for the highest confidence source. Without pinpointing potential knowledge gaps, models can provide incorrect or incomplete information, impacting the outputs of the model, and ultimately, the launch’s success.  

If the data does not exist in the public domain or within research resources, it can often be collected. Kx specializes in designing and executing market research with key stakeholders (e.g., providers, patients, and payers) to inform quantitative forecasts.   When designing primary market research, it is important to start with the model structure and work backward to design the research to fit the model. Designing for the model’s purpose will lead to a more accurate forecast. Finally, research should be designed to enable a “living, breathing” forecast. Research approaches such as choice-based conjoint surveys can allow forecasters to simulate new market conditions as they arise and update key assumptions without conducting additional market research. 

Define the Scenarios  

After outlining the criteria for the data, the next step is to determine which scenarios need to be analyzed. Often teams will initially attempt to investigate and list all possible options, but if the model becomes too complex, it will lose its effectiveness. Instead, the best practice is to define a base case or most likely scenario. A base case typically uses a consensus estimate or confidence-based weighting along key assumptions to drive forecast outputs. Once your team identifies the most likely scenario, define the parameters you want to test. Identifying priorities and areas of uncertainty will help determine which scenarios to test. For example, breadth of market access or coverage, varying price points, the impact of future clinical study outcomes on adoption, and changes to competitor mix are some of the most frequently explored scenarios.  

How Kx Can Help With Your Product Launch Forecast

Over the last four decades, our team has developed proven modeling and forecasting approaches that produce accurate, insightful outputs and drive strategic decision making. Our team acts as strategic partners to help guide you through the entire process, including developing the product launch forecast, gathering the underlying data and insights, aligning internal stakeholders, and ultimately preparing the model for you to run.  

 

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How Understanding Cognitive Bias Can Drive Patient Volumes

How Understanding Cognitive Bias Can Drive Patient Volumes

Understanding Patient and Healthcare Provider Behavior 

We all strive to make rational choices. But as humans, we are prone to bias and misjudgment. In the medical field, cognitive biases can have a profound impact on both patients and healthcare providers. Kx frequently conducts studies to uncover cognitive biases in referral pathways, including specialist referrals for more advanced therapies or interventions.

By pinpointing these biases, our team helps specialty drug and medical device clients focus their marketing and education efforts and increase market penetration within their eligible patient population. 

Common Dilemmas

Often specialists do not refer patients for treatment quickly enough or at all. These delays in or lack of treatment not only allow the patient’s condition to deteriorate, but also prevent the drug and device companies from fully reaching their addressable patient population. In our recent studies, Kx found that cognitive biases exist at each stage of the referral process.

Cognitive bias map of specialist referral pathway
Click to expand

 

The Kx Solution

When guiding healthcare organizations by improving their specialist referral pathway, the Kx team runs an in-depth qualitative analysis speaking with specialists to understand critical attitudes, behaviors, and beliefs across the population of relevant doctors. Key differences within demographic segments (e.g., age, specialty, practice type) and behavioral segments (e.g., willingness to refer, referral choice) help identify drivers of attitudes and potential solutions for changing these behaviors. 

Uncovering Cognitive Bias to Reach the Addressable Patient Population  

Kx developed the following key findings to drive the patients through the specialist referral pathway:

  1. Awareness that symptom recognition is often the most significant barrier in the referral process and education to combat the issue. Early in the referral process, during the diagnosis phase, cognitive biases result in the specialist not probing consistently, missing symptoms by not asking the right questions, or simply not asking enough clarifying questions. Incorrectly identifying patients’ health status based on outward appearance or insufficiently probing symptoms can result in critical underdiagnoses or undertreatment.
  2. A clear path to referral, particularly one with a singular point of contact, can help referring physicians feel more at ease. Successful drug and device companies reduce friction in referral pathways by helping referring physicians establish clear points of contact across hospitals, specialists, and surgical teams. Elucidating a single point of contact cuts down on ambiguity and removes an obstacle for referring providers.
  3. Direct relationships between the referring specialists and the treatment teams (surgery team) build comfort and encourage referrals.
  4. When creating tools for doctors, simplicity and ease of use are key factors. Biases exist among doctors to simplify complex thought processes. Though tools, like decision guides for complex cases, can be extremely beneficial, they must be simple and easy to understand and use to overcome biases and help physicians better identify which patients need further treatment.

How Kx Can Help

Our healthcare experts can guide you by adjusting various aspects of our corporate strategy, including your referral pathway, with insights from market research. Cognitive bias is built into our research methodology, enabling your team to overcome any we find and fulfill more referrals. As data-driven decision-makers, we design research using both traditional factors and behavioral science to pinpoint process improvement and qualitative analysis opportunities. 

 

Contact Our Team Today