A global diagnostics company facing profitability and revenue growth challenges to optimizes their pricing strategy using machine learning (ML)
Client Objective
The company was facing increasing competitive pricing pressure and needed a strategy to drive profitability and revenue while considering the unique requirements of the diagnostics industry.
Our Approach
Phronesis adopted a multi-modal approach to ensure comprehensive coverage:
Data integration
- Integrated weekly sales data, product and sales hierarchy, competitor pricing information, and other relevant SKU (stock keeping unit) metadata into the company’s data infrastructure
- Utilized the company’s existing systems, including its SAP database, to ensure seamless data integration and availability for analysis
Machine Learning (ML) modeling and optimization
- Developed ML models using Python and Alteryx to capture the demand-price response and predict sales, volume, and gross profit for each SKU and price point
- Incorporated pricing rules and guardrails defined at the enterprise, category, member, and location levels to ensure compliance and adherence to specific guidelines
- Considered member objectives, balancing revenue and profit goals, in selecting the optimal pricing strategy
Dynamic pricing execution
- Implemented a daily optimization process that recalculated optimal prices based on the defined rules and member objectives
- Shared the optimized prices with the company’s pricing team for evaluation and approval
- Leveraged Power BI to visualize high-level summaries of revenue, profitability, opportunity curves, and the cost of rules for effective decision-making
Outcome
Phronesis Partners’ study helped the diagnostics company achieve daily price optimization for each of its lab locations, product categories, and SKU families.
Within the first three months of deployment, the company had successfully met and/or maintained revenue and gross profit targets at 80% of its locations. This improved its pricing competitiveness within the market and enhanced overall profitability and revenue generation.
