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.