Home > Cluster Analysis of Superbuy User Demand Data and Personalized Service Strategy

Cluster Analysis of Superbuy User Demand Data and Personalized Service Strategy

2025-04-25
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Introduction

As an international shopping agent platform, Superbuy aims to provide seamless purchasing experiences for global consumers. To enhance user satisfaction and loyalty, this study conducts cluster analysisGoogle Sheets/Excel, categorizing users into distinct groups based on preferences. Subsequently, personalized service strategies

Data Preparation

The dataset includes key user behavior metrics:

  • Product categories
  • Brand preferences
  • Budget ranges
  • Purchase frequency

Data is cleaned and normalized in spreadsheets using functions (FILTER, UNIQUE) for analysis.

Cluster Analysis Methodology

Using spreadsheet tools (XLMinerk-means

  1. Feature scaling: Normalize numerical values (budget, frequency) to 0-1 range.
  2. Elbow method: Determine optimal cluster count (typically 3-5 groups).
  3. Cluster labeling: Profile groups like "Luxury brand enthusiasts" or "Budget-conscious casual buyers."

Sample Output: Key User Clusters

Cluster ID Profile Average Budget Top Categories
1 High-end tech shoppers $500+ Smartphones, Laptops
2 Frequent beauty buyers $100-$300 Skincare, Makeup
3 Occasional gift shoppers $50-$200 Accessories, Home Decor

Personalized Service Strategies

Cluster 1 - Premium Tech Buyers
  • Priority logistics for fragile items
  • Bundled accessory recommendations
  • Exclusive early-bird discounts on new releases
Cluster 2 - Beauty Regulars
  • Korean/Japanese brand restock alerts
  • Loyalty program with free skincare samples
  • How-to videos for trending products
Cluster 3 - Seasonal Shoppers
  • Holiday-themed gift guides 2 months in advance
  • Budget-friendly combo deals
  • Simplified gift wrapping service

Implementation & Outcome Measurement

Strategies are deployed via:

  • Automated emails: Triggered based on cluster tags
  • Dynamic homepage widgets: Show cluster-matched recommendations

Key performance indicators (KPIs) to track:

  • 15% increase in repeat purchase rate
  • 10% higher average order value (AOV) for targeted clusters
  • Improved NPS scores in post-purchase surveys

Conclusion

By leveraging spreadsheet-based cluster analysis, Superbuy can transform raw user data into actionable segments. Tailored strategies

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