https://github.com/ansh420/mcdonald_case-study
It is basically depend on the market Segment Analysis. It is a case study of mcDonald.
https://github.com/ansh420/mcdonald_case-study
algorithms-implemented data-analysis python3 segmentation
Last synced: 3 months ago
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It is basically depend on the market Segment Analysis. It is a case study of mcDonald.
- Host: GitHub
- URL: https://github.com/ansh420/mcdonald_case-study
- Owner: Ansh420
- Created: 2023-04-25T02:24:51.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-03-06T11:47:46.000Z (4 months ago)
- Last Synced: 2025-03-06T12:32:42.401Z (4 months ago)
- Topics: algorithms-implemented, data-analysis, python3, segmentation
- Language: Jupyter Notebook
- Homepage:
- Size: 1.06 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# mcDonald_Case-study
This project aims to analyze **McDonald's market segments using data analysis techniques**. By examining various factors, such as customer demographics, preferences, and behaviors, we aim to identify distinct groups of customers and understand their unique needs.
# Data Analysis Methodology
## Data Collection:
Gather relevant data from McDonald's internal databases, customer surveys, and external sources. Data points may include:
- Customer demographics (age, gender, income, location).
- Purchase history (frequency, product preferences, spending patterns).
- Customer feedback (surveys, social media comments).
- Market trends and competitor analysis.
## Data Cleaning and Preparation:
- Handle missing values, outliers, and **inconsistencies** in the data.
- Normalize and standardize numerical data to ensure comparability.
- Convert categorical data into **numerical formats** if necessary.
## Exploratory Data Analysis (EDA):
- Explore the data to gain insights into its distribution, relationships, and patterns.
- Use visualizations (histograms, scatter plots, box plots) to understand the data visually.
- Calculate **summary statistics** (mean, median, mode, standard deviation) to quantify the data.
## Segmentation Techniques:
- **Cluster Analysis**: Group customers based on similarities in their characteristics and behaviors. Common algorithms include **K-means clustering, hierarchical clustering, and DBSCAN**.
- **RFM Analysis**: Segment customers based on Recency (time since last purchase), Frequency (number of purchases), and Monetary Value (total spending).
- **Demographic Segmentation**: Divide customers based on **demographic factors** like age, gender, income, and location.
## Segment Profiling:
### Describe each identified segment in detail, including:
- Demographic characteristics.
- Purchase behavior.
- Preferences and needs
- Psychographic attributes (lifestyle, values).
## Segment Prioritization:
- Evaluate the potential value and profitability of each segment.
- **Prioritize segments** based on factors like market size, growth potential, and customer loyalty.
## Expected Outcomes
- Identification of distinct customer segments within McDonald's market.
- Understanding of the unique needs, preferences, and behaviors of each segment.
- Development of targeted marketing strategies and product offerings tailored to specific segments.
- Improved customer satisfaction and loyalty.