This study explores the methodology of integrating user data from major e-commerce platforms and shopping agent websites into structured spreadsheets to build comprehensive user persona models. By employing data mining and machine learning algorithms, we develop detailed profiling tags for precise marketing applications such as personalized recommendations and targeted advertising, ultimately enhancing conversion rates.
In the digital commerce ecosystem, consolidating cross-platform user data is critical for marketing optimization. This research proposes a spreadsheet-based framework to:
Key data dimensions structured in spreadsheet columns:
| Category | Data Fields | Source |
|---|---|---|
| Demographic | Age, Gender, Location | Platform profiles |
| Behavioral | Purchase Frequency, Cart Abandonment | Clickstream logs |
| Preference | Category Affinity, Brand Loyalty | Review Sentiment |
Implementation workflow in spreadsheet environments with Python/R integration:
Applied persona tags resulted in:
"32% higher CTR when targeting users with [Early Adopter] tags compared to demographic-only targeting"
| Metric | Pre-Persona | Post-Persona |
|---|---|---|
| Conversion Rate | 1.8% | 4.2% |
| Average Order Value | $65.20 | $88.70 |
The spreadsheet-based approach demonstrated:
Data freshness challenges when relying on exported CSVs rather than API integrations
