Customer Analytics
Cluster analysis and dimensionality reduction to help to segment the customers.
- We used both hierarchical and flat clustering techniques, ultimately focusing on the K-means algorithm. We also combined it with Principal Components Analysis (PCA) to reach an even better insight about our customers.
- Customer Analytics Segmentation.ipynb
- segmentation data.csv
Descriptive statistics and exploratory data analysis.
- Once segmented, customers’ behavior requires some interpretation. We obtained the descriptive statistics by brand and by segment and visualizing the findings.
- Through the descriptive analysis, we formed our hypotheses about our segments, thus ultimately setting the ground for the subsequent modeling.
- Purchase analytics descriptive analysis
- purchase data.csv
Elasticity modeling for purchase probability, brand choice, and purchase quantity
- We calculated purchase probability elasticity, brand choice own price elasticity, brand choice cross-price elasticity, and purchase quantity elasticity.
- We used linear regressions and logistic regressions.
- Purchase analytics predictive analysis
- purchase data.csv
Deep Learning to predict future behavior
- Result: reached 90%+ accuracy in our predictions about the future behavior of our customers.
- Deep Leaning Processing, Modeling and Predicting
- Audiobooks_data.csv
Packages - NumPy, SciPy, pickle TensorFlow, and scikit-learn.