Introduction

In the competitive landscape of cross-border e-commerce, Superbuy purchasing agency service can gain significant advantages by implementing data-driven decision making. This article explores how cluster analysis of user demand data in spreadsheets (including product categories, brand preferences, and budget ranges) can be leveraged to create customized service strategies for distinct user segments.

Data Collection and Preparation

The analysis begins with compiling the following key data points from Superbuy users:

• Product categories (electronics, fashion, cosmetics, etc.)
• Brand preferences (luxury, boutique, mass-market)
• Frequency of purchases (weekly, monthly, seasonal)
• Average budget per transaction
• Preferred shipping methods and speed

This data is cleaned and standardized in spreadsheets for accurate analysis.

Cluster Analysis Methodology

Using spreadsheet tools like Google Sheets or Excel with plugin capabilities, we employ k-means clustering to group users by similarity:

1. Normalize all numerical values (e.g., budget ranges)
2. Convert categorical data using one-hot encoding
3. Calculate Euclidean distances between data points

The Silhouette Coefficient helps determine the optimal number of clusters (typically 4-5 identifiable segments).

Identified User Segments

Personalized Strategy Implementation