Editorial Note:

This article features a more recent rewrite.

Profits are steadily eroding in the pharmaceutical industry – thanks to rising costs across labour and raw materials, alongside persistent price pressure. Getting drug pricing right is therefore more important than ever.

Traditional methods to set and negotiate drug pricing over the lifetime of a drug fail to leverage the wealth of available data and therefore limit profitability. It also poses the risk of costly mistakes: just consider the embarrassing climb-down by Aduhelm maker Biogen, for example.

Vamstar addresses these challenges by harnessing AI combined with a meticulously curated dataset to streamline and automate the pricing and negotiation process.

From assembling and refining relevant data to applying sophisticated pattern recognition and predictive analytics, read on to see how Vamstar’s Polaris platform helps suppliers set optimal prices in a tough market.

Manually setting prices is inherently risky

In life sciences, optimal pricing maximises value, minimises risk, and ultimately improves patient outcomes. Nonetheless, suppliers often rely on outdated, error-prone methods to set their prices – errors that can be very costly across multi-year contracts. The source of the problem looks roughly like this:

• The Excel trap: Excel was never suited to the complexities of pharmaceutical pricing. Modelling dynamic market factors, competitor strategies, and intricate cost structures using a spreadsheet is cumbersome and risk prone. Formulas get misaligned, data becomes outdated, and costly errors inevitably creep in.
• Oversimplified models: Simple cost-plus pricing functions as a baseline, but it fails to offer any modelling depth in a market that is nuanced and complex. Simplified models won’t reflect the true potential of a product in comparison to the competition, nor the willingness (and ability) of payers to improve their offers.
• Revenue management falls short: Past attempts using revenue-management platforms did improve pricing management, but revenue management platforms are not built with the specific goal of price optimisation in mind and cannot model drug prices.
• Untapped data goldmine: A treasure trove of valuable pricing data is going unutilised. Market share information, historical pricing trends, and competitor intelligence are all available in public data sets, but these data sets are scattered and disconnected. Current pricing methods offer no way to integrate and make sense of this vast pool of insights.

The net result is missed profit opportunities, vulnerability to competitor moves, and less-than-ideal value delivered to payers and to patients, risking suboptimal patient care.

Complex negotiations require extensive analytics

Setting prices for pharmaceuticals is far from a one-and-done task. It’s an intricate dance involving internal calculations, external market dynamics, and ultimately, a value proposition that convinces payers.

Comprehensive and up-to-date data, deep and broad analytics, and a good measure of automation lead to a more accurate and more defensible process of setting initial prices. It means that suppliers can include market trends, comparable products, and the unique benefits a drug offers in their pricing decisions.

Furthermore, these insights help manufacturers optimise pricing through the lifecycle of the drug – providing much-needed data during complex negotiations.

Payers will naturally seek to minimise costs, often armed with internal datasets. Here, concrete pricing data provides the firepower to counter objections and negotiate from a position of strength.

Detailed market analysis, insights into competitor pricing, and even historical trends on discounts – all provide leverage. Extensive analytics and data also offer supporting evidence in a world of evolving regulations and heightened scrutiny around drug costs.

Data, AI, and automation from Vamstar

To secure the value of innovation, drug manufacturers must find a way to collect and analyse the data that support pricing decisions – but that’s easier said than done. At Vamstar, we developed a pricing product that utilises a deep pool of data, carefully applied AI, and purpose-built automation to deliver unprecedented control over the pricing process. It works like this:

Step 1: Assembling and refining a Pharma dataset

The first obstacle to setting pricing is the fragmented nature of relevant data. Public datasets on pricing trends, competitor analysis, historical wins, and losses, and countless other factors exist, but these data sets are scattered, messy, and often designed for other industries.

Vamstar begins the journey here as our expert data scientists meticulously gather these scattered datasets. We collect data on market share, penetration rates, and product adoption rates; cleaning and reshaping applying life science-specific knowledge to ensure the data brings value.

We then connect this data to produce a highly curated proprietary data set that acts as an engine for pharmaceutical intelligence.

Step 2: Harness the pattern-seeking power of AI

With the foundations laid, Vamstar’s AI-driven tools take centre stage. Using proven AI models tweaked for life science use, Vamstar builds ultra-sophisticated pattern recognition and prediction engines, trained specifically on the complexities of life science pricing.

We combine your internal data with our custom dataset to reveal hidden connections between discount levels, competitor strategies, market dynamics, and a product’s unique value proposition.

AI also monitors market changes across the lifecycle of the drug, using rigorous analysis to spot pricing opportunities. These patterns wouldn’t be visible to the naked eye, but form the basis of intelligent, profitable pricing decisions including negotiating different discounts and higher reimbursement rates.

Step 3: Automation that transforms insight into action

Data and analysis are meaningless without action, and taking the steps to price correctly is often beyond the abilities of time-pressured life sciences companies. Vamstar delivers a streamlined platform that eliminates the tedious, error-prone aspects of pricing.

Vamstar Polaris delivers data-driven pricing recommendations, scenario modelling tools, and a centralised hub for tracking pricing performance. It’s the difference between fumbling with spreadsheets and having an expert co-pilot guiding every decision.

Automation goes beyond internal efficiency. When payer negotiations begin, a supplier is already armed with hard evidence. Suppliers can confidently justify pricing not with vague promises, but with clear data demonstrating a product’s superior value.

Empowered pricing across the drug lifecycle

The impact of Vamstar’s informed, automated, AI-powered pricing platform extends far beyond a single launch or negotiation. It’s about ensuring optimal pricing at every stage of a drug’s lifecycle:
Strong foundations: Pricing is never a shot in the dark, always backed with data-driven insights that ensure initial offers are aligned with the state of the market.

• Stop costly mistakes: Prevent excel errors and miscalculations that can cost millions thanks to the safety net of set processes and extensive automation.
• Maximise pricing over the lifecycle: Vamstar ensures that manufacturers continue to drive profits even after loss of exclusivity, providing pricing data to ensure optimal price setting and price calibration through the lifecycle of the drug.
• Right offer, right customer: Compensate for shifting market conditions through AI analytics that monitor trends and buyer behaviour – and tailor offers for specific segments to prevent leakage caused by overly generalised pricing.
• Negotiation advantage: At every step, never guess what the payer might accept – work with concrete data analytics including historical trends and market and competitor intelligence.
• Profit optimisation within constraints: Find the sweet spot even within heavily regulated frameworks, by modelling different scenarios and understanding the impact of discounts.

AI-driven pricing also supports industry efforts to maximise patient outcomes by indirectly (or directly) outcomes through an improved understanding of the true value drivers for each and every stakeholder.

Dynamic pricing for a changing landscape

As profit margins remain under pressure, more market changes are on the way. It is only the suppliers that invest in getting pricing right that will manage to avoid ongoing price erosion, and squeezed profits over the lifetime of the drug.

Suppliers that harness Vamstar’s Polaris can develop a pricing strategy that’s as dynamic as the market itself, minimising missed opportunities and maximising the value a life sciences company extracts from every innovation it brings into the world.

The required data and analytics tools are out there – the time to act is now. Get in touch with Vamstar now to see how Polaris can help your organisation improve the pricing process – and your organisation’s bottom line.

In the complex and fast-evolving landscape of global markets, the strategic importance of pricing can hardly be overstated. It’s the linchpin that not only affects revenue and margins but also determines market competitiveness.

This is where Artificial Intelligence (AI) steps in, revolutionising the way industries approach pricing strategies. In particular, the implementation of AI in tender and RFP (Request for Proposal) pricing across Italy, Spain, France, the Nordics, and other EU & ME markets has been nothing short of transformative.

The AI-Driven Pricing Revolution

AI technology has opened new avenues for analysing historical data, recognising patterns of wins and losses, and applying these insights to future tenders and RFPs. This analytical prowess has empowered businesses with predictions and scenarios rooted in real-life outcomes, leading to substantial revenue growth — ranging from 12% to 25% — and enhanced margins by 17% to 25% across diverse markets and assets.

Our Three-Phased Approach to Pricing

Our journey towards pricing is meticulously structured into three phases, each designed to build upon the insights and foundations laid in the preceding steps.

Phase 1: Data Discovery, Cleansing, and Enrichment

The first step in the process is to meticulously curate and enhance the dataset, ensuring its integrity and richness. This involves a thorough examination of the data to identify any inconsistencies, errors, or missing information that could potentially undermine the accuracy of the predictive models. Once these issues are detected, the data undergoes a rigorous cleansing process to correct the invalid entries and ensure the dataset’s overall quality.

However, the preparation phase goes beyond mere data cleaning. To truly unlock the potential of the predictive models, it is essential to enrich the dataset with valuable market insights. This enrichment process involves integrating relevant external data sources, such as industry trends, competitive intelligence, and regulatory information, to provide a more comprehensive and contextual understanding of the market dynamics.

By combining the internal data with these external insights, the dataset becomes a powerful asset that can drive more accurate and actionable predictions. This solid foundation of clean, enriched data sets the stage for the development of robust and reliable predictive models in the subsequent phases of the project.

Phase 2: Model Building

In this phase, the focus is on developing sophisticated predictive models that incorporate a vast array of variables. These models are designed to tackle complex challenges, such as forecasting prices at the molecular level and identifying the most likely winning bids for individual stock-keeping units (SKUs).

The algorithms take into account a wide range of factors that influence the pricing of drugs or medical products throughout their entire lifecycle, from initial launch to post-patent expiry scenarios. By considering the impact of various market dynamics, regulatory changes, and competitive landscapes, these models provide valuable insights into pricing strategies and help organisations navigate the complexities of the pharmaceutical and healthcare industries. The ultimate goal is to empower local teams with data-driven recommendations that optimise revenue, maximise profitability, and ensure sustainable growth in an increasingly competitive market.

Phase 3: Iterative optimisation through A/B testing and reinforcement learning

In the final phase of the project, a two-pronged approach will be used to refine and validate the effectiveness of our pricing pilot. First, extensive A/B testing will be conducted, comparing the performance of our AI-driven pricing strategies with traditional methods. This rigorous benchmarking process will allow us to quantify the concrete improvements and added value brought by the new solution. By measuring key metrics such as revenue growth, margin expansion, and market share gains, the model simulates real-world scenarios.

However, for a continuous learning process, we harness the power of reinforcement learning to create a self-optimising feedback loop. Because Pricing Co-Pilot is deployed under real market conditions, it actively learns from the results of its decisions. By analysing real-world results, the machine learning algorithms identify patterns, correlations, and causal relationships between different factors and their impact on pricing effectiveness. This ongoing learning process allows the models to adapt and refine their predictions over time, becoming increasingly accurate and responsive to changing market dynamics.

One of the key benefits of this iterative optimisation approach is the ability to simulate a variety of scenarios. Leveraging the advanced models, teams can explore different pricing strategies, campaigns, and competitive responses in a virtual environment. This allows them to evaluate the potential outcomes and risks associated with each scenario and empowers them to make informed decisions based on data-driven insights.

By combining A/B testing and reinforcement learning, the Pricing Co-Pilot aims to achieve continuous evolution and adaptation to the ever-changing landscape of the pharmaceutical and medical device industries. This phase serves as the foundation for the project, delivering a robust, reliable, and continuously improved pricing solution that drives sustainable growth and profitability.

The Vamstar Difference

The drive for greater commercial efficiency has become increasingly urgent against a backdrop of inflation, shortages, and the shift towards value-based healthcare. Vamstar distinguishes itself by leveraging AI to orchestrate, analyse, and provide intelligence on MedTech and Pharmaceutical data. This approach not only enhances market visibility but also optimises pricing strategies, thereby simplifying and automating commercial workflows to achieve sales excellence.

The Impact

Adopting AI in pricing does more than just improve financial metrics; it represents a paradigm shift in how businesses approach the market. By providing a granular view of demand and supply dynamics, and facilitating informed decision-making, AI technologies like those offered by Pricing Co-Pilot are setting new standards for efficiency and competitiveness in the healthcare sector.

Conclusion

The integration of AI into pricing strategies marks a significant leap forward for industries striving to navigate the complexities of modern markets. With its proven track record of enhancing revenues and margins, AI offers a promising path to not just survive but thrive in the competitive landscape. As we continue to explore and refine these technologies, the possibilities for innovation and improvement in pricing strategies are boundless.

Leading pharmaceutical companies are increasingly focusing on global health initiatives, which has led to a rise in procurement through tenders and long-term agreements (LTAs) by Intergovernmental Organizations (IGOs) and Non-Governmental Organizations (NGOs).

In 2023, the total value of pharmaceuticals and medical supplies distributed by these entities is estimated at US$ 21.3 billion, with a significant portion allocated to vaccines, medical supplies, and diagnostics. Pharmaceuticals alone account for about US$ 4.7 billion of this total.

The Covid-19 pandemic caused a noticeable surge in healthcare spending by NGOs and IGOs in 2021, particularly in diagnostics and vaccines. Although the pharmaceutical sector experienced a decline during this period, it has been witnessing a notable upward trend since 2022, with an average year-over-year growth of over 11%.

This trend underscores the growing significance of global health teams within pharmaceutical companies.

These teams are becoming increasingly important due to various factors:

1. Shifting Disease Landscape: There’s a growing prevalence of neglected tropical diseases and infectious diseases in low- and middle-income countries (LMICs), requiring innovative approaches and partnerships for drug development.

2. Equity and Access: Heightened awareness of healthcare inequities in LMICs is pressuring pharmaceutical companies to improve access to essential medicines. Global health teams are pivotal in addressing these challenges by developing sustainable distribution models and navigating complex healthcare systems.

3. Emerging Markets: LMICs are emerging as significant markets for pharmaceuticals. Global health teams are key in helping companies understand and penetrate these markets.

4. Pandemic Preparedness: The COVID-19 pandemic highlighted the need for improved outbreak preparedness. Teams with expertise in public health and vaccine development are valuable for future pandemic response strategies.

5. Regulatory Landscape: As governments and international bodies implement policies to enhance medicine accessibility in LMICs, global health teams are crucial for ensuring compliance and navigating these regulations.

Companies are recognising that strong global health programs not only fulfill social responsibilities but also attract consumers and investors. These programs are focused on making medicines more affordable and accessible in LMICs through various initiatives.

Industry Response Includes:

• Establishing dedicated global health units.
• Engaging in public-private partnerships.
• Exploring innovative financing models.
• Investing in local capacity building.
• Developing medicines for neglected tropical diseases (NTDs).

Challenges and Opportunities:

Despite their importance, global health teams face challenges such as limited resources and the need to balance commercial objectives with social impact. However, the opportunities for significant social impact, market access, and long-term value creation are substantial. These teams play a crucial role in strengthening healthcare systems, advocating for policy change, and demonstrating the value of global health initiatives.

One major challenge is the lack of transparency and market intelligence in the sector, where a large portion of the market remains non-tender based and procurement is often local and in partnership with governments. Vamstar is addressing this challenge by combining AI-based data scraping with subject matter expertise to provide ATC class-level market insights, which are vital for shaping global health strategies in the pharmaceutical industry.