Case Study: Retail
Price Optimization has business relevance across cost and revenue stream
Very simply put, what approach should an enterprise take to Optimize Price.
Development of a decentralized data driven price recommendation engine way the foundation of optimizing price. This engine provided price recommendation based on elasticity of demand (Product Price Elasticity) and Organizational objectives of revenue maximization vs profit maximization metric based on business constraints.
Next step was to segment all products within the sub-category on price, brand and off-take rates. Product objective was set at a tier level and price elasticity of demand for each item was computed by our AI engine based on various factors such as geography, past sales and variance in demand was attributed to pricing and non-pricing factors.
Demand factors were based on a complex calculation by our AI engine after determining Price, Store attributes, Demographic attributes, Promotions and competition. The client could develop reports using Power BI which helped the optimization exercise.
Decentralized price recommendation engine helped store directors in setting the right price while maintaining full pricing autonomy for store directors, gaining an 18% increase in profit over the previous year.