A Machine learning and Empirical Bayesian Approach for Predictive Buying in B2B E-commerce

• URL: http://arxiv.org/abs/2403.07843v1
• Date: Tue, 12 Mar 2024 17:32:52 GMT
• Title: A Machine learning and Empirical Bayesian Approach for Predictive Buying in B2B E-commerce
• Authors: Tuhin Subhra De and Pranjal Singh and Alok Patel
• Abstract summary: We have employed an ensemble approach comprising XGBoost and a modified version of Poisson Gamma model to predict customer order patterns with precision. This paper provides an in-depth exploration of the strategic fusion of machine learning and an empirical Bayesian approach, bolstered by the judicious selection of pertinent features. This innovative approach has yielded a remarkable 3 times increase in customer order rates, show casing its potential for transformative impact in the ecommerce industry.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the context of developing nations like India, traditional business to business (B2B) commerce heavily relies on the establishment of robust relationships, trust, and credit arrangements between buyers and sellers. Consequently, ecommerce enterprises frequently. Established in 2016 with a vision to revolutionize trade in India through technology, Udaan is the countrys largest business to business ecommerce platform. Udaan operates across diverse product categories, including lifestyle, electronics, home and employ telecallers to cultivate buyer relationships, streamline order placement procedures, and promote special promotions. The accurate anticipation of buyer order placement behavior emerges as a pivotal factor for attaining sustainable growth, heightening competitiveness, and optimizing the efficiency of these telecallers. To address this challenge, we have employed an ensemble approach comprising XGBoost and a modified version of Poisson Gamma model to predict customer order patterns with precision. This paper provides an in-depth exploration of the strategic fusion of machine learning and an empirical Bayesian approach, bolstered by the judicious selection of pertinent features. This innovative approach has yielded a remarkable 3 times increase in customer order rates, show casing its potential for transformative impact in the ecommerce industry.

Related papers

• Information Discovery in e-Commerce [97.71958017283593]
  Information retrieval has a natural role to play in e-commerce, especially in connecting people to goods and services. The rise in popularity of e-commerce sites has made research on information discovery in e-commerce an increasingly active research area. Methods for information discovery in e-commerce largely focus on improving the effectiveness of e-commerce search and recommender systems.
  arXiv  Detail & Related papers  (2024-10-08T07:41:01Z)
• Towards Lifelong Learning Embeddings: An Algorithmic Approach to Dynamically Extend Embeddings [4.680296118462097]
  This paper introduces a modular algorithm that extends embedding input size while preserving learned knowledge. The proposed algorithm also incorporates strategies to mitigate the cold start problem associated with new products.
  arXiv  Detail & Related papers  (2024-08-26T09:06:35Z)
• Valuing an Engagement Surface using a Large Scale Dynamic Causal Model [2.079231388824219]
  AI-powered Engagement Surfaces (ES) have become ubiquitous across retail services. We develop a causal model at scale to disentangle value attributable to an ES, and to assess its effectiveness.
  arXiv  Detail & Related papers  (2024-08-21T19:42:45Z)
• MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding [67.26334044239161]
  MIND is a framework that infers purchase intentions from multimodal product metadata and prioritizes human-centric ones. Using Amazon Review data, we create a multimodal intention knowledge base, which contains 1,264,441 million intentions. Our obtained intentions significantly enhance large language models in two intention comprehension tasks.
  arXiv  Detail & Related papers  (2024-06-15T17:56:09Z)
• Finding Lookalike Customers for E-Commerce Marketing [5.2300714255564795]
  We present a scalable and efficient system to expand targeted audience of marketing campaigns. We use a deep learning based embedding model to represent customers and an approximate nearest neighbor search method to quickly find lookalike customers of interest.
  arXiv  Detail & Related papers  (2023-01-09T02:18:58Z)
• Automatic Controllable Product Copywriting for E-Commerce [58.97059802658354]
  We deploy an E-commerce Prefix-based Controllable Copywriting Generation into the JD.com e-commerce recommendation platform. We conduct experiments to validate the effectiveness of the proposed EPCCG. We introduce the deployed architecture which cooperates with the EPCCG into the real-time JD.com e-commerce recommendation platform.
  arXiv  Detail & Related papers  (2022-06-21T04:18:52Z)
• ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest [60.841761065439414]
  At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases. This approach has led to significant improvements in engagement and conversion metrics, while reducing both infrastructure and maintenance cost.
  arXiv  Detail & Related papers  (2022-05-24T02:28:58Z)
• Towards Revenue Maximization with Popular and Profitable Products [69.21810902381009]
  A common goal for companies marketing is to maximize the return revenue/profit by utilizing the various effective marketing strategies. Finding credible and reliable information on products' profitability is difficult since most products tends to peak at certain times. This paper proposes a general profit-oriented framework to address the problem of revenue based on economic behavior, and conducting the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing.
  arXiv  Detail & Related papers  (2022-02-26T02:07:25Z)
• PreSizE: Predicting Size in E-Commerce using Transformers [76.33790223551074]
  PreSizE is a novel deep learning framework which utilizes Transformers for accurate size prediction. We demonstrate that PreSizE is capable of achieving superior prediction performance compared to previous state-of-the-art baselines. As a proof of concept, we demonstrate that size predictions made by PreSizE can be effectively integrated into an existing production recommender system.
  arXiv  Detail & Related papers  (2021-05-04T15:23:59Z)
• Audience Creation for Consumables -- Simple and Scalable Precision Merchandising for a Growing Marketplace [1.8667240717298954]
  We present the design and implementation of a precision merchandising system at Supermart, one of the largest online grocery stores in India. We employ temporal point process to model the latent periodicity and mutual-excitation in the purchase dynamics of consumables. We develop a likelihood-free estimation procedure that is robust against data sparsity, censure and noise typical of a growing marketplace.
  arXiv  Detail & Related papers  (2020-11-17T11:46:38Z)
• AliCoCo: Alibaba E-commerce Cognitive Concept Net [21.08037780019654]
  We propose to construct a large-scale e-commerce cognitive net named "AliCoCo" We formally define user needs in e-commerce, then conceptualize them as nodes in the net. We present details on how AliCo is constructed semi-automatically and its successful, ongoing applications in e-commerce.
  arXiv  Detail & Related papers  (2020-03-30T05:42:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.