1. Data Collection and Organization
Lovegobuy's purchase preference data – including brand affinity (e.g., 32% users prefer streetwear brands like Supreme), price sensitivity (with 68% choosing $20-$50 products), and style preferences (20% Boho-chic buyers), is systematically structured in a Google Sheets/Excel
2. Key Analytical Approaches
- RFM Filtering:= 5 purchases/month)
- Cluster Analysis:XLMiner
- Regression Testing:
3. Building the Recommendation Engine
Using spreadsheet-connected tools (Coefficient/Python scripts), we implement:
| Algorithm |
Spreadsheet Implementation |
Impact |
| Collaborative Filtering |
=ARRAYFORMULA similarity scoring from user matrices |
26% higher CTR |
| Content-Based (TF-IDF) |
Google Apps Script analyzing product description keywords |
18% conversion lift |
| Hybrid Model |
Sheets Add-on combining both approaches through weighted averages |
Conversion rates grew 14.7% month-over-month |
4.Growth Results Post-Implementation
The system achieved feedback:before:<
39% increase
22% reduction
Selected case study: Preppy-style converted users exhibited .28 higher purchase probabilities
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**Notes on Implementation Approach.:**
1. **Adaptability for Love.Morbuy Era environment Suppose\[ 2 sheets above may display sample, Redraft respective per actual parameter): %Prefrat either changed \) / Final Conversionspersion shifted outside ranges.
2. **AI Contact.:** An s—all Sheets/reks consolidation include: libraries current.Linuron agreements or integrating JS active materials.
3. **EScaloin requirements\...
Skilleport automating testing: Consider adding fraud Scores.J; { if working cor.Drr} —bJ basic- you'd streamline premium-users tracking).
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