Retail Pharma Price Optimization
- A US-based mid-sized pharma company wanted to compete with bigger retail giants; particularly in terms of pricing
- The client faced intense competition from larger retail chains with sophisticated pricing strategies and needed to optimize their pricing to stay competitive
- Help create an ML model for price-demand response and optimize item prices to maximize profit or revenue
- Execute price optimizations on a weekly basis through automation
- Designed jobs to integrate weekly sales, product and sales hierarchy, competitor pricing, and other SKU metadata for SKU and locations into Databricks
- Created ML jobs in Databricks to model demand-price response and calculate predicted sales, volume, and gross profit for each SKU and price-point
- Selected the optimal price based on:
- – Defined price rules and guardrails
- – Member targets of balancing revenue and profit goals
- Triggered and executed optimization jobs on a daily basis to recompute optimal pricing and share with the client pricing team for approval
- High-level summaries of revenue, profitability, opportunity curve, and cost of rules were visualized in Power BI
- Prices were now optimized for each store location, product category, and SKU family on a daily basis
- The pharma retailer was able to meet revenue and gross profit targets for 80% of its stores within the first 6-months of deployment
