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Application of business intelligence based on big data in E-commerce data evaluation.

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This study integrates Big Data with business intelligence to analyze e-commerce beverage sales. Findings reveal growth potential and identify consumer segments for targeted marketing strategies.

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Area of Science:

  • E-commerce analytics
  • Business intelligence applications
  • Big Data in marketing

Background:

  • E-commerce growth necessitates advanced data analysis beyond traditional business intelligence.
  • Big Data technologies offer enhanced capabilities for complex e-commerce datasets.
  • Optimizing e-commerce strategies requires deeper consumer insights.

Purpose of the Study:

  • To propose and evaluate the application of Big Data-enhanced business intelligence for e-commerce data analysis.
  • To develop a Days-Times-Money (DTM) model for analyzing beverage consumption patterns.
  • To classify consumers and provide actionable marketing insights for brand growth.

Main Methods:

  • Integration of Big Data technologies with business intelligence systems.
  • Development and application of the Days-Times-Money (DTM) model.
  • Utilizing data mining for consumer segmentation based on consumption attributes.

Main Results:

  • Consumption density analysis indicated significant room for growth across consumption days, times, and amounts (all below 70%).
  • Consumers were segmented into four distinct groups based on their consumption patterns.
  • The DTM model provided data-driven insights for marketing strategies and brand value enhancement.

Conclusions:

  • Big Data-powered business intelligence is crucial for effective e-commerce data analysis.
  • Consumer segmentation offers a strategic approach to targeted marketing and brand development.
  • The study provides a practical framework for e-commerce enterprises to leverage data for growth.