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.