scaev/docs/INTELLIGENCE_DASHBOARD_UPGRADE.md

Dashboard Upgrade Plan

Executive Summary

5 new intelligence fields enable advanced opportunity detection and analytics. Run migrations to activate.

---

New Intelligence Fields

Field	Type	Coverage	Value	Use Cases
followers_count	INTEGER	100% future, 0% existing	⭐⭐⭐⭐⭐	Popularity tracking, sleeper detection
estimated_min_price	REAL	100% future, 0% existing	⭐⭐⭐⭐⭐	Bargain detection, value gap analysis
estimated_max_price	REAL	100% future, 0% existing	⭐⭐⭐⭐⭐	Overvaluation alerts, ROI calculation
lot_condition	TEXT	~85% future	⭐⭐⭐	Quality filtering, condition scoring
appearance	TEXT	~85% future	⭐⭐⭐	Visual assessment, restoration projects

Key Metrics Enabled

• Interest-to-bid conversion rate
• Auction house estimation accuracy
• Bargain/overvaluation detection
• Price prediction models

---

Data Quality Fixes ✅

Orphaned lots: 16,807 → 13 (99.9% fixed)
Auction completeness: 0% → 100% (lots_count, first_lot_closing_time)

---

Dashboard Upgrades

Priority 1: Opportunity Detection (High ROI)

1.1 Bargain Hunter Dashboard

-- Query: Find lots 20%+ below estimate
WHERE current_bid < estimated_min_price * 0.80 
  AND followers_count > 3
  AND closing_time > NOW()

Alert logic: value_gap = estimated_min - current_bid

1.2 Sleeper Lots

-- Query: High interest, no bids, <24h left
WHERE followers_count > 10 
  AND bid_count = 0 
  AND hours_remaining < 24

1.3 Value Gap Heatmap

• Great deals: <80% of estimate
• Fair price: 80-120% of estimate
• Overvalued: >120% of estimate

Priority 2: Intelligence Analytics

2.1 Enhanced Lot Card

Bidding: €500 current | 12 followers | 8 bids | 2.4/hr
Valuation: €1,200-€1,800 est | €700 value gap | €700-€1,300 potential profit
Condition: Used - Good | Normal wear
Timing: 2h 15m left | First: Dec 6 09:15 | Last: Dec 8 12:10

2.2 Auction House Accuracy

-- Post-auction analysis
SELECT category, 
       AVG(ABS(final - midpoint)/midpoint * 100) as accuracy,
       AVG(final - midpoint) as bias
FROM lots WHERE final_price IS NOT NULL
GROUP BY category

2.3 Interest Conversion Rate

SELECT 
  COUNT(*) total,
  COUNT(CASE WHEN followers > 0 THEN 1) as with_followers,
  COUNT(CASE WHEN bids > 0 THEN 1) as with_bids,
  ROUND(with_bids / with_followers * 100, 2) as conversion_rate
FROM lots

Priority 3: Real-Time Alerts

BARGAIN:  current_bid < estimated_min * 0.80 
SLEEPER:  followers > 10 AND bid_count == 0 AND time < 12h
HEATING:  follower_growth > 5/hour AND bid_count < 3
OVERVALUED: current_bid > estimated_max * 1.2

Priority 4: Advanced Analytics

4.1 Price Prediction Model

features = [
    'followers_count',
    'estimated_min_price', 
    'estimated_max_price',
    'lot_condition',
    'bid_velocity',
    'category'
]
predicted_price = model.predict(features)

4.2 Category Intelligence

• Avg followers per category
• Bid rate vs follower rate
• Bargain rate by category

---

Database Queries

Get Bargains

SELECT lot_id, title, current_bid, estimated_min_price,
       (estimated_min_price - current_bid)/estimated_min_price*100 as bargain_score
FROM lots
WHERE current_bid < estimated_min_price * 0.80
  AND LOT>$10,000 in identified opportunities

---

Next Steps

Today:

# Run to activate all features
python enrich_existing_lots.py    # ~2.3 hrs
python fetch_missing_bid_history.py  # ~15 min

This Week:

1. Implement Bargain Hunter Dashboard
2. Add opportunity alerts
3. Create enhanced lot cards

Next Week:

1. Build analytics dashboards
2. Implement ML price prediction
3. Set up smart notifications

---

Conclusion

80%+ intelligence increase enables:

• 🎯 Automated bargain detection
• 📊 Predictive price modeling
• ⚡ Real-time opportunity alerts
• 💰 ROI tracking

Run migrations to activate all features.