Introduction
In today's data-driven e-commerce landscape, understanding user behavior is crucial for business success. CSSBuy, as a leading proxy shopping service, accumulates vast amounts of user behavior data including browsing history, search keywords, and purchase records. This article explores how to leverage Spreadsheets for data mining of these behavioral patterns and apply machine learning algorithms to enable precision marketing strategies that boost conversion rates.
Data Collection and Organization
CSSBuy's user behavior data can be systematically organized in Spreadsheets with the following structure:
| User ID | Browsing Duration | Clicked Products | Search Keywords | Purchase History |
|---|---|---|---|---|
| 10001 | 12:05:34 | Air Jordans, Supreme T-shirt | limited sneakers | 3 items |
| 10002 | 08:12:09 | Japanese skincare | authentic Japanese cosmetics | 0 items |
Data Analysis Techniques
Using Spreadsheets functions and add-ons, we can perform sophisticated analysis:
- Keyword Frequency Analysis:=COUNTIF()
- Purchase Probability Modeling:
- RFM Analysis:
- Purchase Probability Modeling:
Machine Learning Integration
External machine learning libraries can connect with Spreadsheets to supercharge analysis:
- Using Python scripts via Google Apps Script to run predictive models
- Implementing collaborative filtering for product recommendations
- Training classification models to predict high-value customers
Targeted Marketing Applications
The analyzed data enables powerful marketing executions:
1. Personalized Product Suggestions:
2. Timely Discount Offers:
3. Remarketing Campaigns:
Performance Measurement
Key metrics to track using Spreadsheet dashboards:
- Click-through rate (CTR) of recommended products
- Conversion lift from targeted campaigns
- Customer lifetime value improvement