Introduction

In today's competitive e-commerce landscape, understanding customer behavior and preferences is crucial for staying ahead. For Superbuy, a leading purchasing agent platform, leveraging user demand data—such as product categories, brand preferences, and budget ranges—can unlock valuable opportunities for personalized service delivery. By applying clustering analysis in Spreadsheets, we can segment users into distinct groups and tailor strategies to enhance satisfaction and loyalty.

Data Collection and Preparation

The foundation of this analysis is the extraction of user demand data from Superbuy's database, focusing on variables like:

• Product categories (e.g., electronics, fashion, health products)
• Brand preferences (e.g., luxury, budget-friendly, niche)
• Budget ranges (low, mid, high)
• Purchase frequency and seasonal trends

This data is then cleaned, formatted, and imported into a spreadsheet (e.g., Google Sheets or Microsoft Excel) for structured clustering analysis.

Clustering Analysis in Spreadsheets

Using built-in spreadsheet functions (e.g., data sorting, conditional formatting) or add-ons like XLSTAT, we implement k-means clustering or hierarchical clustering to identify patterns. Key steps include:

1. Feature Scaling:
2. Algorithm Selection:
3. Cluster Validation: