Summary of A Machine Learning and Empirical Bayesian Approach For Predictive Buying in B2b E-commerce, by Tuhin Subhra De and Pranjal Singh and Alok Patel
A Machine learning and Empirical Bayesian Approach for Predictive Buying in B2B E-commerce
by Tuhin Subhra De, Pranjal Singh, Alok Patel
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Udaan, India’s largest B2B e-commerce platform, relies on telecallers to build buyer relationships and streamline orders. Anticipating buyer behavior is crucial for growth, competitiveness, and efficiency. To address this challenge, an ensemble approach combining XGBoost and a modified Poisson Gamma model was employed to predict customer order patterns with precision. This paper explores the strategic fusion of machine learning and empirical Bayesian approaches, selecting relevant features judiciously. The innovative method yielded a 3-fold increase in customer order rates, showcasing its potential for transformative impact in e-commerce. Key concepts include XGBoost, Poisson Gamma model, machine learning, empirical Bayesian approach, B2B e-commerce, and telecallers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Udaan is an online platform that helps businesses buy and sell things in India. It relies on people to talk to customers and make sure they get what they need. To help these workers do their jobs better, a special computer model was created. This model uses two different techniques – XGBoost and Poisson Gamma – to predict what customers will want to buy next. By using this model, Udaan saw a big increase in sales. This is important because it can help businesses grow and be more successful. |
Keywords
* Artificial intelligence * Machine learning * Precision * Xgboost
