The challenge

In the pharmaceutical industry, effective price setting is crucial for maximizing both medicine accessibility and profitability, relying on complex analyses of large datasets. To ease the decision-making burden of pricing personas and enable improved decisions, advanced analytics, simulations, and AI play a pivotal role. Intellishore assisted a global pharma company in reshaping the analytics landscape to directly enable decision-making in market access pricing processes. This case study highlights our collaborative journey to define and describe analytics solutions that directly empower end-user processes, focusing on three core themes:

• Shifting from source- to outcome-oriented analytics
• Utilizing advanced simulation tools and AI
• Embedding analytics within decision-making processes

(I) From Source- to Outcome-Oriented Analytics

Initially, the company’s pricing analytics solutions were directly tied to their source systems, resulting in fragmented and inefficient data retrieval processes for pricing decisions. For instance, financial data translated directly into financial reports with limited connectivity to other data sources, forcing users to manually gather data from multiple systems and solutions. This led to poor utilization of developed solutions and a reliance on personal solutions in Excel.

To address this, we started from a clean slate, identifying key pricing processes and breaking them down into tangible steps with constituent analyses through interviews with pricing leads. The analyses were grouped and translated into use cases that would support pricing decision needs. Based on these use cases, process-specific user journeys were drafted and used as the foundation for designing analytics solutions at workshops with the pricing leads. This resulted in an individual catalogue of mock-ups for each pricing process – constituting the content of the new large-scale pricing and market access analytics program.

This shift from source-oriented to outcome-oriented analytics ensures that pricing personas will have seamless access to the information they need, significantly improving utilization and decision support.

(II) Utilizing Advanced Simulation Tools and AI

Given the complexity of pricing decisions in a global pharmaceutical company, there is immense potential in utilizing advanced simulation tools and AI. As part of the project, we defined the need for simulation tools in the analytics suite, enabling pricing leads to optimize price for access, identify optimal launch sequences, and set efficient prices across markets. For example, pricing leads will be able to calculate specific reference price implications of changing prices in certain countries or evaluate the impact of current price changes on pipeline products using machine learning models.

Additionally, we identified a use case for a GPT-based interface containing qualitative information on pricing data, which will substantially speed up and improve the process of searching for relevant pricing documents and strategies during pricing processes.

This integration of advanced AI tools is designed to enhance decision-making and increase the efficiency and accuracy of pricing strategies once implemented.

(III) Embedding Analytics within Decision-Making Processes

To further streamline analytics usage and to avoid pricing personas having to navigate between multiple different solutions and tools, the developed analytics will be embedded directly into the operational systems used in pricing processes or onto a centralized web interface. The web interface is structured around the key processes, allowing users to select the process they are undertaking to then gain an overview of the analytics solutions relevant for the process in the order in which they are needed. This approach represents a significant shift in user engagement with analytics, providing a seamless and intuitive user experience.

Project Outcome and Operationalization

The project concluded with an actionable roadmap detailing initiatives to support pricing processes through streamlined analytics. The outcome of the roadmap will result in the following key benefits for the company’s pricing function:

• Increased Analytics Utilization: Tailored solutions and embedded analytics ensure higher usage.
• Enhanced Decision-Making: Automation of advanced analytics, simulations, and information retrieval with AI and ML.
• Improved Decision Accuracy: Enhanced data quality from new data management practices introduced to develop solutions.

Intended users also anticipate a significant potential revenue impact, with some projecting up to a 100% value increase for pipeline products.

To bring these solutions to live, we collaboratively defined the scope of the newly developed analytics program, established the core team, described roles and responsibilities, and anchored a new agile operating model to ensure quick and efficient development.