Ground Truth Data

Ground Truth is PriveTag’s unique advantage - real, verified user behavior data that proves what users actually do, not just what they click or book.

What is Ground Truth?

Ground Truth = Data verified by real-world action (NFC scan or QR verification at venue)Unlike web analytics that track clicks, Ground Truth captures when a user actually visited a venue.

The Problem with Traditional Data

Most recommendation systems rely on:

Data Type	Weakness
Clicks	Users browse but don’t visit
Bookings	Users book but don’t show up
Reviews	Selection bias, fake reviews
Surveys	Recall bias, social desirability

Result: Recommendation Drift

This creates a feedback loop of assumptions, not reality.

PriveTag’s Ground Truth Approach

How It Works

The Verification Moment

When a user visits a venue:

NFC Tap - User taps their hotel NFC card at venue
QR Scan - Venue scans voucher QR code
Check-in - User checks in at venue reception

Each verification creates a Ground Truth record:

{
  "id": "gt_xyz789",
  "booking_id": "bkg_abc123",
  "context_log_id": "ctx_def456",
  "activity_id": "act_safari",
  "verified_at": "2025-12-15T10:30:00Z",
  "verification_method": "nfc_tap",
  "user_profile": {
    "travel_type": "family",
    "nationality": "KR"
  }
}

Feedback Loop

Ground Truth creates a virtuous cycle:

Real Example

Profile	Without Ground Truth	With Ground Truth
Korean Family in Bangkok	Safari World (popular online)	Safari World (85% verified visits)
	Dream World (high click rate)	SEA LIFE (78% verified visits)
	Night Market (cheap)	❌ Night Market (12% verified visits)

The Night Market appears in traditional recommendations because it’s cheap and gets clicks, but families rarely actually visit. Ground Truth deprioritizes it.

Data Quality

Verification Rate

Metric	Value
Bookings with Ground Truth	85%+
NFC Verification	60%
QR Verification	35%
Manual Check-in	5%

Why High Verification?

Hotel Integration: NFC cards issued to all guests
Incentive Structure: Venues earn commission on verification
Seamless UX: Tap or scan takes < 3 seconds

Using Ground Truth in Your App

Recommendation Quality

Higher Ground Truth coverage = better recommendations:

{
  "recommendations": [
    {
      "id": "act_safari",
      "relevance_score": 95,
      "ground_truth_score": 92,
      "verified_visits": 1250,
      "similar_profile_visits": 340
    }
  ]
}

Webhook Events

Receive Ground Truth events via webhook:

{
  "event": "qr_verified",
  "data": {
    "booking_id": "bkg_abc123",
    "context_log_id": "ctx_def456",
    "verified_at": "2025-12-15T10:30:00Z"
  }
}

Analytics

Track your recommendation effectiveness:

# Your AI agent's performance
GET /api/analytics/ground-truth?api_key=pk_xxx

# Response
{
  "period": "last_30_days",
  "recommendations_made": 5420,
  "bookings_created": 892,
  "ground_truth_verified": 758,
  "verification_rate": 85%,
  "recommendation_accuracy": 78%
}

Ground Truth Score

Activities are scored based on Ground Truth:

Calculation

GT_Score = (verified_visits / recommended_count) * profile_similarity_weight

Factors

Factor	Description
Verified Visits	How many times users actually visited
Recommended Count	How many times activity was recommended
Profile Similarity	How similar visiting users’ profiles are to current user
Recency	More recent visits weighted higher

Example

Safari World for Korean Families:

Recommended 1000 times to Korean families
850 bookings created
720 verified visits (Ground Truth)
GT Score: 72% (720/1000)

Night Market for Korean Families:

Recommended 500 times to Korean families
180 bookings created
60 verified visits (Ground Truth)
GT Score: 12% (60/500)

Privacy & Data Handling

Privacy First: We collect Ground Truth data with user consent and anonymize for aggregate analysis.

What We Store

Data	Purpose	Retention
Visit timestamp	Aggregate patterns	24 months
Profile type	Segment recommendations	24 months
Activity visited	Improve scoring	Permanent

What We DON’T Store

Personal identification details
Exact location tracking
Session recordings
Financial information

Anonymization

Individual records are anonymized for ML training:

// Raw record
{
  "user_id": "usr_abc123",
  "name": "John Doe",
  "activity": "Safari World"
}

// Anonymized for ML
{
  "profile_hash": "a1b2c3",
  "profile_type": "family_kr_mid",
  "activity_id": "act_safari"
}

Improving Your Ground Truth

Best Practices

Always pass context_log_id

When booking, include the context_log_id from recommendations. This links the recommendation to the eventual visit.

{
  "activity_id": "act_safari",
  "context_log_id": "ctx_abc123"  // Don't forget this!
}

Enable webhooks for qr_verified

Track when your recommendations lead to actual visits:

{
  "webhook_url": "https://your-server.com/webhooks",
  "webhook_events": ["qr_verified"]
}

Encourage voucher usage

Remind users to use their voucher at the venue. Higher verification = better future recommendations.

Metrics Dashboard

Track Ground Truth metrics for your integration:

Metric	Description	Good Target
Verification Rate	% of bookings verified	> 80%
Recommendation Accuracy	% of verified visits that matched top 3 recs	> 60%
Profile Match Rate	How well visits match profile predictions	> 70%
Time to Visit	Days between booking and visit	< 3 days

B2A Platform

Ground Truth

Ground Truth Data

What is Ground Truth?

The Problem with Traditional Data

Result: Recommendation Drift

PriveTag’s Ground Truth Approach

How It Works

The Verification Moment

Feedback Loop

Real Example

Data Quality

Verification Rate

Why High Verification?

Using Ground Truth in Your App

Recommendation Quality

Webhook Events

Analytics

Ground Truth Score

Calculation

Factors

Example

Privacy & Data Handling

What We Store

What We DON’T Store

Anonymization

Improving Your Ground Truth

Best Practices

Metrics Dashboard

Next Steps

Webhooks

Context Pipeline

​Ground Truth Data

​What is Ground Truth?

​The Problem with Traditional Data

​Result: Recommendation Drift

​PriveTag’s Ground Truth Approach

​How It Works

​The Verification Moment

​Feedback Loop

​Real Example

​Data Quality

​Verification Rate

​Why High Verification?

​Using Ground Truth in Your App

​Recommendation Quality

​Webhook Events

​Analytics

​Ground Truth Score

​Calculation

​Factors

​Example

​Privacy & Data Handling

​What We Store

​What We DON’T Store

​Anonymization

​Improving Your Ground Truth

​Best Practices

​Metrics Dashboard

​Next Steps

Webhooks

Context Pipeline

Ground Truth Data

What is Ground Truth?

The Problem with Traditional Data

Result: Recommendation Drift

PriveTag’s Ground Truth Approach

How It Works

The Verification Moment

Feedback Loop

Real Example

Data Quality

Verification Rate

Why High Verification?

Using Ground Truth in Your App

Recommendation Quality

Webhook Events

Analytics

Ground Truth Score

Calculation

Factors

Example

Privacy & Data Handling

What We Store

What We DON’T Store

Anonymization

Improving Your Ground Truth

Best Practices

Metrics Dashboard

Next Steps