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Research on E-Commerce Database Marketing Based on Machine Learning Algorithm.

Nie Chen1

  • 1Department of Electronic Commerce, Zhejiang Business Technology Institute, Ningbo 315012, China.

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This study integrates machine learning algorithms with database marketing in e-commerce. It enhances marketing strategies by improving target audience selection and preventing potential customer loss.

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Area of Science:

  • * E-commerce and Marketing Science
  • * Data Science and Machine Learning Applications

Background:

  • * Database marketing is cost-effective but often lacks predictive power in unpredictable markets.
  • * Combining database marketing with machine learning algorithms presents an under-explored area in marketing.
  • * E-commerce transactions have grown complex, necessitating advanced marketing techniques.

Purpose of the Study:

  • * To research database marketing in e-commerce using machine learning algorithms.
  • * To analyze the current state of e-commerce marketing and identify areas for improvement.
  • * To develop and apply machine learning models for enhanced database marketing.

Main Methods:

  • * Theoretical preparation including database marketing principles and four machine learning algorithms (logistic regression, random forest, support vector machine, GBDT).
  • * Analysis of e-commerce marketing object distribution, channel proportions, and method composition.
  • * Model construction involving data acquisition, processing, sample setting, and feature combination from consumer, store, and relationship perspectives.
  • * Model testing and application by predicting target customer scores.

Main Results:

  • * Machine learning algorithms can address uneven marketing object distribution in e-commerce.
  • * Predictive modeling effectively prevents the loss of potential consumers.
  • * A specific target crowd (predicted score 80-99) was identified for market application.

Conclusions:

  • * E-commerce should prioritize database marketing methods based on model prediction.
  • * Machine learning integration optimizes marketing object distribution and reduces customer attrition.
  • * Strategies for optimizing other database marketing methods and assisting model prediction are proposed to boost marketing effectiveness.