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Product pricing solutions using hybrid machine learning algorithm.

Anupama Namburu1, Prabha Selvaraj1, M Varsha2

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This study introduces X-NGBoost, a hybrid algorithm for competitive e-commerce product pricing. It accurately predicts prices using product reviews and features, outperforming existing models.

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Area of Science:

  • E-commerce analytics
  • Machine learning applications
  • Computational economics

Background:

  • E-commerce has grown significantly, especially post-COVID-19, increasing the complexity of product pricing.
  • Traditional pricing strategies struggle with vast product numbers, seasonal trends (apparel), and fluctuating specifications (electronics).

Purpose of the Study:

  • To develop a competitive product pricing strategy for e-commerce businesses.
  • To leverage product reviews, statistical, and categorical features for accurate price prediction.
  • To introduce and evaluate a novel hybrid machine learning algorithm for e-commerce pricing.

Main Methods:

  • A hybrid algorithm, X-NGBoost, combining extreme gradient boost (XGBoost) and natural gradient boost (NGBoost), was developed.
  • The model utilizes product reviews, statistical, and categorical data for price prediction.
  • Performance was benchmarked against ensemble models: XGBoost, LightGBM, and CatBoost.

Main Results:

  • The proposed X-NGBoost model demonstrated superior performance compared to existing ensemble boosting algorithms.
  • The hybrid approach effectively captures complex pricing dynamics influenced by product attributes and customer feedback.
  • Accurate price prediction was achieved, aiding competitive market positioning.

Conclusions:

  • X-NGBoost offers a robust solution for dynamic e-commerce product pricing.
  • The model provides valuable insights for businesses aiming to optimize pricing strategies in competitive online markets.
  • Leveraging machine learning enhances pricing accuracy and competitiveness in the e-commerce landscape.