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Sandra Blevins
February 10, 2016
Business
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25
Branch8 consulting work
Actionable insights and retention prediction modeling from e-commerce seller profiles
Sandra Blevins
February 10, 2016
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Transcript
Sandra Blevins Insight Data Science Insights in Action: Building e-commerce
seller profiles to predict retention
? eBay Amazon
Goal Keep sellers active and happy! Build seller profiles, predict
retention/churn
Export MongoDB Clean Python regex, NumPy, pandas, JSON Database schema
Seller profiles Predictive modeling Python scikit-learn
Seller profiles Active Churn ? 7 days
Seller profiles Active 7 days Churn ?
Seller profiles Active 7 days Churn ?
Seller profiles Active 7 days Churn ?
Seller profiles Active 7 days Churn ?
Number of sellers Active sellers in Shanghai, Hong Kong, and
Singapore
300+ Number of orders Active sellers have more orders
300+ Number of orders Active sellers have more orders Active!
Active sellers Inactive sellers
Sellers of electronics with active Malaysian customer base Deliverable: actionable
insights on seller profiles
sklearn.ensemble.RandomForestClassifier 10-fold cross validation Accuracy = 93%, AUC = 71%
n-orders is best predictor of activity n orders n platforms mean price
Organize database Codes and flowsheets Customer profiles New feature recommendations
for modeling
Sandra Blevins Education: Physics Ph.D. 2015, The Catholic University of
America, Washington, DC Dissertation: The Distribution of Volatiles in Protoplanetary Disks Love: travel, nature, art, yoga