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This report provides a customer segmentation analysis for Arvato Financial Services, a mail-order sales company. The goal of this analysis is to identify customer segments that are most likely to respond to a marketing campaign and become customers.

The dataset used for this analysis contains demographic and socioeconomic data for a sample of the general population in Germany, as well as a corresponding dataset of customers of Arvato Financial Services. The datasets were provided by Arvato Financial Services and are not publicly available.

The analysis was performed in several stages:

1. Data Preprocessing: The data was preprocessed to handle missing values, feature engineering, and feature scaling.

2. Dimensionality Reduction: Principal Component Analysis (PCA) was used to reduce the dimensionality of the data.

3. Clustering: K-means clustering was used to cluster the customers into segments.

4. Analysis: The segments were analyzed to identify their characteristics and differences.

5. Validation: The segments were validated by comparing them to the general population dataset.

The analysis identified seven(7) customer segments, each with unique characteristics and differences. The segments were validated by comparing them to the general population dataset, which showed that some segments were overrepresented while others were underrepresented.

Based on the results of the analysis, we recommend that Arvato Financial Services target marketing campaigns to the segments that are overrepresented in their customer base, while also considering strategies to attract customers from underrepresented segments.

Future work could include refining the segmentation analysis by incorporating additional data sources or applying other clustering algorithms. Additionally, further analysis could be performed to identify specific marketing strategies that are most effective for each customer segment.

• Python 3.6 or later
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn

• To reproduce the results of the analysis, follow these steps:
• Clone the repository to your local machine.
• Install the dependencies listed above.
• Open the Jupyter notebooks in the notebooks folder and run the cells in order (provided you have the data, execution would be seamless).

This analysis was performed by Seun for Arvato Financial Services as a part of Udacity Data Science NanoDegree. The data used in this analysis was provided by Arvato Financial Services.

Check out my Medium post about the project here