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Developed customer retention algorithm which monitors customer behavior to offer flash sales and coupons, resulting in 8% annual revenue increase potential. Performed attribution modeling to identify key revenue drivers.
Sunishchal/Integral-Studio-Analytics
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-INTEGRAL STUDIO ANALYTICS- Optimizing E-Commerce Conversions for Hip Hop Merchandisers By: Sunishchal Dev Within this repository is the Jupyter Notebook I used to model a classifier for predicting "on the fence" customers. This model calculates the conversion probability for customers as they navigate through a e-commerce site. While this information has many valuable uses, I implement it as a tool to offer flash sales to customers who may be leaving the store soon without checking out. This technique has the potential to drive revenues by making purchases possible for customers who may not be able to afford full price merchandise. An ROI analysis is performed at the end of the notebook, although most of the information has been resricted to protect the client's confidential information. Feature engineering notebooks have also been hidden to protect customer PII. Please direct any specific questions to [email protected]. Link to blog post: https://medium.com/@sunishchaldev/optimizing-kendrick-lamars-e-commerce-portal-using-data-science-e81853e1059e Link to presentation slides: https://drive.google.com/file/d/0B5ccLwUNt8PzZkFnTTJXekQzdTQ/view?usp=sharing
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Developed customer retention algorithm which monitors customer behavior to offer flash sales and coupons, resulting in 8% annual revenue increase potential. Performed attribution modeling to identify key revenue drivers.
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