Abstract

This study explores the integration of user data from major e-commerce platforms and purchasing agent websites within spreadsheets to construct comprehensive user profiles. By employing data mining and machine learning algorithms, we generate detailed user tags and apply them in precision marketing strategies such as personalized recommendations and targeted advertising, ultimately enhancing marketing effectiveness and user conversion rates.

1. Introduction

The rapid growth of e-commerce and cross-border shopping has generated vast amounts of user data. This research focuses on organizing structured and unstructured user data within spreadsheet environments (e.g., GooglSheets, Excel) to create multidimensional customer profiles for marketers.

2. Methodology

2.1 Data Collection Framework

• Basic Demographics: Age, gender, location from platform registration
• Behavioral Data: Purchase history, browsing patterns, cart abandonment rates
• Preference Indicators: Product reviews, wish lists, socialmedia interactions

2.2 Spreadsheet Architecture

3. Portrait Modeling Techniques

We implement machine learning through spreadsheet add-ons like:

1. XLMiner
2. Analyse-it
3. Custom Python scripts via Google Apps Script