Pricing Strategy
Optimization

Pricing Decisions Were Based on Gut Feel

The company's pricing strategy had evolved organically over the years — a mix of competitor matching, cost-plus margins, and "what feels right." There was no systematic understanding of how price changes affected demand for different product categories.

Leadership wanted to increase revenue but feared that raising prices would drive away customers. Without data on price sensitivity, every pricing decision was a gamble.

Elasticity-Based Price Segmentation

I built a structured analysis framework to quantify price sensitivity across the product catalog:

• Historical Analysis: Collected 2 years of sales data including price changes, promotional periods, and volume fluctuations using Python.
• Elasticity Modeling: Calculated price elasticity of demand for each product category, measuring how volume responded to past price changes.
• Product Segmentation: Grouped products into three tiers — inelastic (can sustain increases), moderately elastic (requires careful adjustment), and highly elastic (price-sensitive).
• Recommendation Engine: Built an Excel model that simulated revenue impact of various pricing scenarios, allowing leadership to compare options before committing.

35% of Products Were Significantly Underpriced

The analysis revealed that over a third of the product catalog had very low price elasticity — meaning customers would continue buying at higher prices. These products had been priced conservatively despite strong demand.

Conversely, a smaller segment of highly elastic products was being priced aggressively, causing unnecessary volume loss. The optimal strategy wasn't uniform price increases — it was targeted adjustments based on each product's sensitivity profile.