Dominick Data Analysis

The Dominick Data Analysis project aimed to examine the correlation between demand (logmove) and various factors such as price, brand, feat, and demographic variables across different store locations, using Dominick’s orange juice sales database. The dataset included 5000 cases and attributes such as storeweek, UPC, store, move, logmove, quantity, price, logprice, profit, and feat were selected for the analysis.

The project used pivot tables to select two low-priced brands (HH and TREE FRESH) and one high-priced brand (TROPICANA PURE PREM) based on their average prices, and regression models and hypothesis tests were conducted to answer research questions. The results of the analysis suggested that price elasticity of demand varies across different customer segments, indicating that businesses should adopt different pricing strategies for different customer groups to maximize revenue. The lack of significant difference in demand between spring and summer suggested that businesses may not need to make significant adjustments to their operations during this time.

The project’s findings also suggested that businesses should use seasonality analysis to optimize inventory and staffing levels to meet demand during peak periods and minimize waste during slow periods. Furthermore, by dropping demographic variables with higher p-values, businesses can focus on the most relevant factors that impact demand. Knowing the price elasticity of demand for different customer segments can help businesses set optimal prices for each group, maximizing revenue. The significant relationships between demand and certain demographic variables (EDUC, SINGLE, WORKWOM, AGE60) can also help businesses better target their marketing efforts and tailor their products/services to meet the needs and preferences of specific customer segments.