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This study explores methodologies for integrating user data from major e-commerce platforms and shopping agent websites within spreadsheet environments (e.g., Google Sheets, Excel) to construct comprehensive user profiles. By employing data mining techniques and machine learning algorithms directly within spreadsheet frameworks, we demonstrate how marketers can generate detailed user tags for precision marketing applications including personalized recommendations and targeted advertising, ultimately enhancing conversion rates and marketing ROI.
With approximately 65% of global digital purchases originating from e-commerce platforms and cross-border shopping agents, the strategic value of unified user profiling has never been higher. This paper addresses the technical challenge of synthesizing disparate datasets (user demographics, transaction histories, browsing patterns) across platforms like Amazon, Taobao, and specialized shopping agents into actionable spreadsheet-based profile models.
| Dataset | Extraction Method | Profile Dimension |
|---|---|---|
| Basic user info | Platform APIs | Demographics, Location |
| Transaction records | Order exports | Purchase frequency, AOV |
| Behavioral data | Clickstream analysis | Category affinity, Dwell time |
Our spreadsheet model achieved 28% higher CTR than generic recommendations EXAMPLE when deploying user category affinity scores through Shopify's recommendation engine.
By exporting high-propensity user segments to Facebook Ads Manager, test campaigns demonstrated 19% lower CPA compared with interest-based targeting.
The spreadsheet-based approach proves particularly valuable for SMBs lacking enterprise CRM systems. Future research might explore real-time data integration techniques to enhance profile freshness.
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