Clustering Analysis of Superbuy Purchase Agent User Needs in Spreadsheets and Personalized Service Strategy Development

2025-04-27
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In today's competitive e-commerce market, understanding user preferences and tailoring services accordingly is crucial for logistics agents like Superbuy. This article explores how clustering analysis of user data in spreadsheets can reveal distinct customer segments, enabling personalized service strategies that boost satisfaction and loyalty.

I. Methodology: Clustering User Demand Data

By analyzing structured spreadsheets containing:

  • Product Categories
  • Brand Affinity
  • Budget Allocation
  • Purchase Frequency

We employ K-means clustering algorithms (directly executable via Google Sheets' kmeans() 3D scatter plot of clustered users

II. Key User Segments Identified

Cluster Characteristics % of Users
Luxury Trendfollowers High-end fashion, >¥5000 budget, brand-sensitive 12%
Value Procurers Deal-focused, ¥500(g -ax getElementsByMethod ) <= ParentDocument -    parser_method reference

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