Pharmaceuticals
Smarter pricing through AI
Turning pricing strategy into a data-driven advantage
Pharmaceuticals
Smarter pricing through AI
Turning pricing strategy into a data-driven advantage
Together with our partner, we faced this challenge. Their global pricing team was relying mainly on expert intuition and static analysis to guide decisions. With more product variants and increasing pricing complexity, they needed a way to base choices on evidence and data - not just experience.
Together with our partner, we faced this challenge. Their global pricing team was relying mainly on expert intuition and static analysis to guide decisions. With more product variants and increasing pricing complexity, they needed a way to base choices on evidence and data - not just experience.
Together with our partner, we faced this challenge. Their global pricing team was relying mainly on expert intuition and static analysis to guide decisions. With more product variants and increasing pricing complexity, they needed a way to base choices on evidence and data - not just experience.
How do you ensure smarter pricing decisions when market dynamics get more complex and traditional methods no longer scale?
Together with our partner, we faced this challenge. Their global pricing team was relying mainly on expert intuition and static analysis to guide decisions. With more product variants and increasing pricing complexity, they needed a way to base choices on evidence and data - not just experience.
How do you ensure smarter pricing decisions when market dynamics get more complex and traditional methods no longer scale?
Together with our partner, we faced this challenge. Their global pricing team was relying mainly on expert intuition and static analysis to guide decisions. With more product variants and increasing pricing complexity, they needed a way to base choices on evidence and data - not just experience.
Working side by side with the commercial data and AI strategy team, we co-developed a new data-driven solution. The journey started with a 6-week proof of concept to test whether sales volume could be predicted solely from IQVIA data.
Together, we explored historical data across markets, assessed data quality, and tested different machine learning models. As the value became clear, we continued in agile sprints - scaling scope, aligning with users, and shaping a minimum viable product that matched real workflows. Staying close to stakeholders ensured that technical design and business needs evolved in step.
Working side by side with the commercial data and AI strategy team, we co-developed a new data-driven solution. The journey started with a 6-week proof of concept to test whether sales volume could be predicted solely from IQVIA data.
Together, we explored historical data across markets, assessed data quality, and tested different machine learning models. As the value became clear, we continued in agile sprints - scaling scope, aligning with users, and shaping a minimum viable product that matched real workflows. Staying close to stakeholders ensured that technical design and business needs evolved in step.
Working side by side with the commercial data and AI strategy team, we co-developed a new data-driven solution. The journey started with a 6-week proof of concept to test whether sales volume could be predicted solely from IQVIA data.
Together, we explored historical data across markets, assessed data quality, and tested different machine learning models. As the value became clear, we continued in agile sprints - scaling scope, aligning with users, and shaping a minimum viable product that matched real workflows. Staying close to stakeholders ensured that technical design and business needs evolved in step.
Co-Creating Data Driven Impact
Working side by side with the commercial data and AI strategy team, we co-developed a new data-driven solution. The journey started with a 6-week proof of concept to test whether sales volume could be predicted solely from IQVIA data.
Together, we explored historical data across markets, assessed data quality, and tested different machine learning models. As the value became clear, we continued in agile sprints - scaling scope, aligning with users, and shaping a minimum viable product that matched real workflows. Staying close to stakeholders ensured that technical design and business needs evolved in step.
Co-Creating Data Driven Impact
Working side by side with the commercial data and AI strategy team, we co-developed a new data-driven solution. The journey started with a 6-week proof of concept to test whether sales volume could be predicted solely from IQVIA data.
Together, we explored historical data across markets, assessed data quality, and tested different machine learning models. As the value became clear, we continued in agile sprints - scaling scope, aligning with users, and shaping a minimum viable product that matched real workflows. Staying close to stakeholders ensured that technical design and business needs evolved in step.
"The main value is not to get the exact price, the intention is to understand how can we utilize a model like this to say something clever also in the future. I really like the simplicity of the model."
The case in numbers
The final solution was a machine learning model using XGBoost to predict market share within therapeutic areas. It combined inputs such as price, competitor data, product specifics, and lossof exclusivity.
Deployed on our partner’s Snowflake platform with MLOps pipelines, the solution was made accessible through a simple Streamlit interface. This allowed pricing teams to explore different scenarios, analyze historical data, and test strategies in a way that felt natural and intuitive.
The final solution was a machine learning model using XGBoost to predict market share within therapeutic areas. It combined inputs such as price, competitor data, product specifics, and lossof exclusivity.
Deployed on our partner’s Snowflake platform with MLOps pipelines, the solution was made accessible through a simple Streamlit interface. This allowed pricing teams to explore different scenarios, analyze historical data, and test strategies in a way that felt natural and intuitive.
The final solution was a machine learning model using XGBoost to predict market share within therapeutic areas. It combined inputs such as price, competitor data, product specifics, and lossof exclusivity.
Deployed on our partner’s Snowflake platform with MLOps pipelines, the solution was made accessible through a simple Streamlit interface. This allowed pricing teams to explore different scenarios, analyze historical data, and test strategies in a way that felt natural and intuitive.
From Data to Scenario‑Driven Pricing: Our ML Build
The final solution was a machine learning model using XGBoost to predict market share within therapeutic areas. It combined inputs such as price, competitor data, product specifics, and lossof exclusivity.
Deployed on our partner’s Snowflake platform with MLOps pipelines, the solution was made accessible through a simple Streamlit interface. This allowed pricing teams to explore different scenarios, analyze historical data, and test strategies in a way that felt natural and intuitive.
From Data to Scenario‑Driven Pricing: Our ML Build
The final solution was a machine learning model using XGBoost to predict market share within therapeutic areas. It combined inputs such as price, competitor data, product specifics, and lossof exclusivity.
Deployed on our partner’s Snowflake platform with MLOps pipelines, the solution was made accessible through a simple Streamlit interface. This allowed pricing teams to explore different scenarios, analyze historical data, and test strategies in a way that felt natural and intuitive.
From Data to Scenario‑Driven Pricing: Our ML Build
The global pricing team now has a working model embedded in their daily platform - giving them evidence-based guidance on how pricing impacts market share. Teams can test assumptions, compare strategies, and create consistency across regions.
The global pricing team now has a working model embedded in their daily platform - giving them evidence-based guidance on how pricing impacts market share. Teams can test assumptions, compare strategies, and create consistency across regions.
The global pricing team now has a working model embedded in their daily platform - giving them evidence-based guidance on how pricing impacts market share. Teams can test assumptions, compare strategies, and create consistency across regions.
From Assumptions to Evidence: Smarter Pricing at Scale
The global pricing team now has a working model embedded in their daily platform - giving them evidence-based guidance on how pricing impacts market share. Teams can test assumptions, compare strategies, and create consistency across regions.
From Assumptions to Evidence: Smarter Pricing at Scale
The global pricing team now has a working model embedded in their daily platform - giving them evidence-based guidance on how pricing impacts market share. Teams can test assumptions, compare strategies, and create consistency across regions.
From Assumptions to Evidence: Smarter Pricing at Scale
More than just a tool, the project marked a shift in how pricing is approached. The global team gained a stronger voice in regional discussions and built a shared foundation for negotiation. Over time, this capability helps pharma companies adapt faster to market shifts, improve patient access through affordability, and align pricing decisions with both business strategy and health outcomes.
More than just a tool, the project marked a shift in how pricing is approached. The global team gained a stronger voice in regional discussions and built a shared foundation for negotiation. Over time, this capability helps pharma companies adapt faster to market shifts, improve patient access through affordability, and align pricing decisions with both business strategy and health outcomes.
More than just a tool, the project marked a shift in how pricing is approached. The global team gained a stronger voice in regional discussions and built a shared foundation for negotiation. Over time, this capability helps pharma companies adapt faster to market shifts, improve patient access through affordability, and align pricing decisions with both business strategy and health outcomes.
Global Consistency. Local Flexibility. Better Outcomes.
More than just a tool, the project marked a shift in how pricing is approached. The global team gained a stronger voice in regional discussions and built a shared foundation for negotiation. Over time, this capability helps pharma companies adapt faster to market shifts, improve patient access through affordability, and align pricing decisions with both business strategy and health outcomes.
Global Consistency. Local Flexibility. Better Outcomes.
More than just a tool, the project marked a shift in how pricing is approached. The global team gained a stronger voice in regional discussions and built a shared foundation for negotiation. Over time, this capability helps pharma companies adapt faster to market shifts, improve patient access through affordability, and align pricing decisions with both business strategy and health outcomes.
Global Consistency. Local Flexibility. Better Outcomes.
What we learned
Focusing on predicting market share rather than absolute volume improved performance and interpretability.
Ongoing input from pricing experts kept the model aligned with real workflows.
Trust and simplicity drove adoption across global and local teams.
Want to build something similar? Let's talk.
Feel free to reach out to one of our team members with expertise in this area. Whether you're facing a similar challenge or exploring where to start, we’re ready to help you turn ideas into action.
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