Skip to content

This project was developed during a 3-month internship program at Feynn Labs. The project replicates a McDonald's case study using Python. It involves data analysis, PCA for dimensionality reduction, KMeans clustering, segment profiling, and decision tree classification for marketing segmentation,

License

Notifications You must be signed in to change notification settings

Shreyaprasad21/Feynn-Market-Segmentation-McDonalds

Repository files navigation

Market Segmentation: Unveiling Consumer Insights for McDonald's

Introduction

During my internship at Feynn Labs, I had the chance to analyze a detailed dataset from McDonald's, focusing on market segmentation and uncovering valuable consumer insights. Market segmentation enables businesses to divide their target market into distinct groups based on shared characteristics, preferences, and behaviors. By understanding these segments, companies can refine their marketing strategies to effectively engage with different consumer groups, thus improving overall brand performance.

Market segmentation is vital for McDonald's to ensure ongoing success. Here are some key insights:

Demographic Segmentation: By analyzing age, gender, income, and family size, we identified a diverse consumer base, allowing McDonald's to customize menus and campaigns.

Psychographic Segmentation: Examining lifestyle choices and values helped uncover unique segments. Health-conscious consumers prefer lighter menu options, while those seeking convenience value quick service and accessible locations.

Behavioral Segmentation: By understanding consumer behaviors and purchase patterns, McDonald's can optimize operations and target promotions to specific time frames or menu items.

Market segmentation empowers McDonald's to provide personalized experiences to their varied customer base, fostering meaningful connections.

I am thankful to the entire Feynn Labs team for this remarkable opportunity. This internship has been an invaluable learning experience, enabling me to contribute to practical marketing research. Thank you, Feynn Labs, for supporting my growth and allowing me to explore market segmentation. The insights gained will benefit McDonald's and support their continued success.

About

This project was developed during a 3-month internship program at Feynn Labs. The project replicates a McDonald's case study using Python. It involves data analysis, PCA for dimensionality reduction, KMeans clustering, segment profiling, and decision tree classification for marketing segmentation,

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published