1. Current State Assessment
🔧 Algorithm Types Used
Check what methods power personalization:
- ✅ Rule-based logic (e.g., “If user is a jobseeker → show jobs”)
- ✅ Content-based filtering (e.g., recommend similar courses based on user’s history)
- ✅ Collaborative filtering (e.g., “users like you also viewed…”)
- ✅ Hybrid models (blending above)
✅ SayPro’s effectiveness depends on hybrid use due to diverse services (Jobs, Courses, Donate, Classified, Fundraising).
📊 2. User Data Inputs Audit
🔍 Key Signals Used:
| Data Type | Current Use | Quality | Recommendation |
|---|---|---|---|
| Browsing history | Partial (pages viewed) | Moderate | Add scroll-depth, time-on-page |
| Search queries | Used for live suggestions | High | Add NLP clustering of intent |
| Registrations/applications | Logged | High | Leverage for intent segmentation |
| Demographics (age, location) | Stored at signup | Good | Use for geo-personalization |
| Behavioral tags (e.g., ‘frequent donor’) | Inconsistent | Low | Automate tagging logic |
🧠 3. Model Performance Review
🔬 Evaluate Models Using:
- Precision & Recall on recommendations (e.g., were top 5 job/course recs clicked/applied?)
- Click-Through Rate (CTR) on personalized sections
- Conversion Rates from recommendations
- Engagement Lift vs control (A/B testing group)
🔴 If users are ignoring personalized rows, you have an issue with either model accuracy or UX placement.
🎯 4. Personalization Depth by Service
| SayPro Service | Personalization Level | Recommendations |
|---|---|---|
| Courses | Medium | Add interest-based dynamic homepage |
| Jobs | High | Leverage job alerts + skill-based matching |
| Donate | Low | Use cause-preference tagging and geo relevance |
| Events | Low | Match events to user interests + past attendance |
| Classifieds | Medium | Suggest similar ads or saved searches |
🧰 5. Optimization Opportunities
✅ Fixes to Consider:
- Cold Start Problem: Use onboarding quizzes to infer user interests quickly
- Context-Awareness: Adjust recommendations by device/time/location
- Content Richness: Enrich course/job listings with topic embeddings (via NLP)
- Explainability: Show “Recommended because you liked X” for transparency
🔐 6. Ethical + Compliance Review
- Are consent-based data practices in place? (GDPR/POPIA compliant)
- Can users opt out of personalization?
- Is sensitive data (e.g. race, religion) being unintentionally used?
📎 7. Scoring Snapshot
| Area | Score (1–5) | Notes |
|---|---|---|
| Data Coverage | 3.5 | Needs behavioral tag automation |
| Model Accuracy | 4.0 | Good, but no explainability |
| User Control | 2.5 | No opt-out or preference dashboard |
| Multi-service Adaptability | 4.0 | Hybrid architecture works |
| Business Impact | 3.5 | Promising, under-leveraged |
✅ Next Steps
- Run model performance report by platform section (Jobs, Courses, Donations, etc.)
- Design onboarding flow to gather better personalization inputs (interests, goals)
- Audit and clean data for behavioral tagging consistency
- Launch A/B test with new recommendation logic + UX placement
- Build a Personalization Settings Panel for user transparency and control
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