Maximizing Profits Via Price Optimization and Sales Analytics for Tropicana
Achieved a 68.4% profit increase by optimizing the pricing and promotional strategy for 64 oz. Tropicana orange juice. Analyzed 2 years of sales data across 15 store locations, uncovering that prices ending in 9 increased sales by 20%, while promotions reduced price sensitivity, enabling more profitable price increases.
This project aimed to solve a key business challenge for Nick’s, a regional grocery retailer: How to maximize profits for 64 oz. Tropicana orange juice through optimal pricing and promotional strategies. We conducted a comprehensive analysis of two years of sales data from 15 store locations, evaluating how price changes, seasonality, store-specific factors, and promotional activities influenced sales and profitability.
Using SAS, we tested multiple regression models (linear, log-log, and semi-log). The semi-log regression model was identified as the best fit, capturing non-linear price and sales relationships. Key variables included price, promotions, quarter and holiday seasonality, and psychological pricing.
Skills and Tools Utilized:
Software: SAS, Excel
Techniques: Semi-Log Regression, Sales Analytics, Predictive Modeling
Analytical Skills: Statistical modeling, price sensitivity analysis, profit optimization, data interpretation
Power in Numbers
Stores Analyzed
Optimal Price($)
% sales drop for a $1 price increase without promotions