Self Learn Project: Identify Customer Segments

This is one of the Udacity Data Scientist Nanodegree Project. This project aims to identify segments of the population from the core customer base for a mail-order sales company in Germany. Therefore, these segments can then be used to direct marketing campaigns towards audiences with the highest expected rate of returns.

The techniques I used in this project include:

1. Data cleaning
2. Encoding and processing mixed-type feature
3. Feature Scaling and Dimensionality Reduction
4. Clustering
5. Performance improvement with OpenBLA

Data

The data files associated with this project (not included in this repository):

• Udacity_AZDIAS_Subset.csv: Demographics data for the general population of Germany; 891,211 persons (rows) x 85 features (columns).
• Udacity_CUSTOMERS_Subset.csv: Demographics data for customers of a mail-order company; 191,652 persons (rows) x 85 features (columns).
• Data_Dictionary.md: Detailed information file about the features in the provided datasets.
• AZDIAS_Feature_Summary.csv: Summary of feature attributes for demographics data; 85 features (rows) x 4 columns

Analysis Structure

1. Data exploration and data cleaning (85% of the analysis)
2. Feature Engineering (One Hot, Scaling, and PCA)
3. Clustering with k-means

Conclusion

Cluster 2 is overrepresented, and cluster 3 is underrepresented. Compare two groups of diagrams, we can find that there are many differences in demographics between overrepresented and underrepresented people.

• Membership in Green Avantgarde: In cluster 2, more people are the member of Green Avantgarde
• Personality - Dutiful: People in cluster 3 have a higher affinity
• Number of adults in household: In cluster 2, most households have 1 to 4 adults.
• Distance from building to the point of sale: People in cluster 3 are closer to Pos than people in cluster 2.
• Wealth / Life Stage Typology: More people in cluster 2 are upper class
• Type of Building: Most builds in cluster 2 are residential buildings.
• Social status: Most people in cluster 2 are top earners, and most people in cluster 3 are house owners.
• Age: Most people in cluster 3 from the 40s' and most people in cluster 3 from the 50s'.

Licensing and Acknowledgements

Udacity Data Scientist provided the starting code for this project. Udacity partners at Bertelsmann Arvato Analytics provided the data.