Clustering Analysis of Superbuy Shopping Agent User Data in Spreadsheets and Personalized Service Strategy Development-OK Spreadsheet-The best and new spreadsheet

Introduction

Understanding user preferences is crucial for e-commerce platforms like Superbuy to enhance customer satisfaction and loyalty. By analyzing user demand data—including product categories, brand preferences, and budget ranges—retailers can tailor their services to meet specific needs. This article explores the application of clustering analysis in spreadsheets to segment Superbuy users into distinct demand groups and develop personalized recommendations.

Data Collection and Preparation

Superbuy regularly collects user behavior data, including:

Product categories
Brand preferences
Budget ranges
Frequency of purchases

This data is organized in spreadsheets for structured analysis. Before clustering, preprocessing steps—such as handling missing values, normalizing numerical data, and encoding categorical variables—are essential to ensure reliable results.

Clustering Analysis Using Spreadsheet Tools

Spreadsheet tools like Google Sheets or Excel support clustering via statistical functions or VBA
1. K-means Clustering

A distance-based algorithm categorizes users into predefined groups
2. Hierarchical Clustering

Produces visual dendrograms to show natural groupings based on preference hierarchies—useful for discovering subgroup trends.

Segment Key Characteristics

Group A High-end tech products, Apple/Samsung loyalists

Group B Fashion-conscious mid-budget buyers

Personalized Service Strategies

Segements inform differentiated recommendations:

For High-Budget Users

Priority customer support

Exclusive previews of luxury items

For Bargain Hunters

Promotions featuring affordable alternatives

Price-drop notifications

For Niche Collectors

Curated rare-item alerts

Implementing these strategies can increase conversion rates and long-term engagement.

Conclusion

Segment	Key Characteristics
Group A	High-end tech products, Apple/Samsung loyalists
Group B	Fashion-conscious mid-budget buyers