Data-Driven Market Segmentation and Expansion Strategy for Whole Foods
Conducted a comprehensive segmentation analysis of 88 regions across seven European countries to identify markets that align with Whole Foods' core strengths in service quality. The study revealed two key market segments, with 69.5% of regions showing high compatibility. By targeting these segments, Whole Foods is projected to achieve a 5.8% increase in store image and optimized market penetration in Germany, the Netherlands, and Portugal.
This project focused on optimizing Whole Foods’ market expansion strategy by identifying high-potential market segments across Europe. Our team analyzed survey data from 88 regions spanning seven European countries to evaluate how service quality, price perception, and store atmosphere influenced store image.
Using SAS, we developed a Generalized Linear Mixed Model (GLMM) to examine the relationship between these store image factors and market segmentation. The dependent variable was store image, while the independent variables included service quality, price perception, and store atmosphere. The analysis revealed two key market segments, with Segment 1 accounting for 69.5% of regions and demonstrating a strong alignment with Whole Foods' core strength in service quality.
Our findings projected a 5.8% increase in store image in key regions if Whole Foods targeted this segment. We recommended a phased market entry strategy focused on high-potential areas to maximize market penetration and minimize risk.
Skills and Tools Utilized:
Software: SAS, Excel
Techniques: Generalized Linear Mixed Model (GLMM), Predictive Analytics, Market Segmentation
Analytical Skills: Statistical modeling, data interpretation, segmentation analysis, strategic planning
Power in Numbers
Stores Analyzed
Optimal Price($)
% sales drop for a $1 price increase without promotions