Your CTV Reporting Only Shows 35% of Conversions. We Find The Other 65%.
Standard attribution (UID2, device graphs) captures 25-35% of cross-device CTV conversions. The rest disappear into a black hole—even when your ads directly drove them. AdHedge uses ML-powered probabilistic matching to recover what deterministic tracking misses.
See Your Hidden Conversions
The Hidden Value We Find
Real client example:
BEFORE ADHEDGE
  • 12,450 CTV conversions tracked
  • $2.1M in conversion value
  • ROAS: 2.8x
  • Decision: Cut CTV budget 30%
AFTER ADHEDGE
  • 20,750 total conversions found (+66%)
  • $3.5M in true conversion value
  • True ROAS: 4.7x
  • Decision: Increase CTV budget 40%
What changed? We found 8,300 conversions their UID2 tracking completely missed. Same campaigns. Same data. Better math.
The CTV Attribution Challenge
The Reality of Cross-Device Conversion
Someone watches your CTV ad on their living room TV. Hours later, they pull out their phone and convert. Your attribution platform searches for a match and finds nothing. This happens 30-50% of the time—even when the ad directly drove the conversion.
The fundamental issue: viewers use different devices with different identifiers, and standard deterministic matching requires identical logins across both touchpoints. When emails don't match or users aren't logged in, the connection becomes invisible.
Why Standard Tracking Fails
Different Devices
📺 Living room TV
📱 Personal phone
💻 Work laptop
Different Identifiers
User@gmail.com on CTV
User@work.com on mobile
Or no login at all
No Connection
Standard attribution requires same identifier across both devices to establish match
Result: Your best-performing campaigns look mediocre in reports. Your budgets get cut based on incomplete data. Your clients question CTV ROI and threaten to shift spend elsewhere.
What 65% Missing Conversions Actually Means
For a brand spending $2M annually on CTV with a 50% attribution gap:
  • ❌ $1M in unattributed conversion value is lost, obscuring true ROI.
  • ❌ "Worst performing" campaigns are actually your best-performing, leading to misinformed decisions.
  • ❌ Budgets are unnecessarily cut on campaigns driving 2x reported ROAS.
  • ❌ Your CFO questions CTV effectiveness based on fundamentally incomplete data.
  • ❌ Spend shifts to "trackable" channels that may ultimately perform worse for your brand.
THE OPPORTUNITY:
Find the hidden 65% of conversions. Reallocate budgets to what actually works. Defend CTV spend with complete data. Stop leaving $1M on the table.

The Concrete Financial Impact
Consider a client spending $2M annually on CTV:
  • If 30% of conversions are hidden, that’s $600K in unattributed value. This directly impacts reported campaign effectiveness and budget justification.
  • If 50% of conversions are hidden, that means $1M in unattributed value. This significant gap distorts performance metrics and leads to suboptimal strategic investments.
Optimizing campaigns and making strategic decisions with fundamentally incomplete data means every budget allocation, creative test, and audience strategy is based on partial truth, leading to missed growth opportunities and inefficient spending.
Why Cross-Device Attribution Is So Difficult
Cross-device attribution requires matching impressions to conversions when devices differ and identifiers don't align—a problem that has stumped the industry for years. The challenge isn't just technical; it's fundamental to how digital identity works.
Deterministic Matching (Standard Approach)
  • ✅ User logged in with same email
  • ✅ UID2 identifier on both devices
  • ✅ Direct identifier match possible
  • ✅ 99% confidence when found
Coverage: 20-35% of conversions
What Actually Happens
(Reality)
  • ❌ User has multiple email addresses
  • ❌ UID2 only available on one device
  • ❌ No shared identifier exists
  • ❌ Connection impossible
Coverage: 40-60% COMPLETELY MISSED
Existing Solutions Fall Short
Device Graphs
LiveRamp, Neustar provide household-level linking using third-party data
Gap: Still miss 30-40% of conversions
Multi-Touch Attribution
Excellent for tracking within-device customer journey and touchpoint analysis
Gap: Doesn't solve cross-device problem at all
Marketing Mix Modeling
Shows overall channel performance at aggregate level over time
Gap: No impression-level attribution or optimization insights
Until now, there's been no affordable, accurate solution that specifically solves the cross-device CTV attribution problem. Advertisers have been forced to choose between incomplete data or expensive supplements that still leave gaps.
How We Find Hidden Conversions
Instead of requiring perfect identifier matches, we use ML to analyze behavioral patterns that reveal true causation:
Four Categories of Attribution Signals
  • IP address proximity (same household network)
  • Timing patterns (hours between impression & conversion)
  • Geographic consistency (same DMA, zip code)
  • Campaign exposure frequency (how many times they saw your ad)
  • Contextual signals (content genre, supply source, viewing time)
When these signals align in specific patterns, we can predict attribution with 73% accuracy—even when no identifier match exists.
VALIDATION:
  • Tested on millions of impressions using industry-standard methodology
  • 87% agreement with UID2 ground truth where it exists
  • 2-3x more conversions attributed vs deterministic matching alone
The AdHedge Methodology
01
Learn from known data
Train ML models on millions of impressions with ground truth to establish baseline patterns.
02
Identify signal combinations
Our models learn which signal combinations indicate true attribution, revealing relationships invisible to rules-based systems.
03
Apply to missing cases
Apply these learned patterns to score previously unattributable impressions with confidence levels.
04
Deliver reliable attributions
Output confidence-scored attributions for every impression, providing transparency you can trust.

What Makes AdHedge Different
  • ✅ Platform-agnostic: Works with Trade Desk, DV360, Amazon DSP, and custom DSP formats
  • ✅ Confidence-calibrated: Transparent scoring on every attribution—you decide which predictions to use
  • ✅ Built for the gap: Purpose-designed to recover cross-device conversions standard tracking misses
  • ✅ ML-powered signals: Uses IP proximity, timing, geography, and behavioral patterns deterministic matching can't leverage
Why AdHedge vs Alternatives
VS. DOING NOTHING
Them: Accept 65% attribution gap
Us: Find the missing conversions, $400K-$1.2M in hidden value
VS. LIVERAMAP/NEUSTAR DEVICE GRAPH
Them: $50-150K+ annually, still miss 30-40% of conversions
Us: $180K/year, specialized for CTV cross-device gap
VS. ROCKERBOX/NORTHBEAM
Them: Great for web/mobile, weak on CTV cross-device
Us: Purpose-built for CTV → mobile/desktop attribution
VS. MARKETING MIX MODELING
Them: Aggregate channel performance, no campaign optimization
Us: Impression-level attribution + ongoing optimization consulting
WHAT YOU GET:
  • ✓ 2-3x more conversions attributed than UID2 alone
  • ✓ Confidence scores on every attribution (high/medium/low)
  • ✓ Campaign-level optimization insights
  • ✓ Works with Trade Desk, DV360, Amazon DSP, any event-level data
  • ✓ Consulting + ongoing optimization, not just software
Data Requirements
We work with event-level data from any major DSP:
  • Trade Desk REDS (most common)
  • Google DV360 data transfer
  • Amazon DSP log-level data
  • Xandr / Microsoft Advertising log files
Minimum Required Fields:
  • Impression and conversion timestamps
  • IP addresses (can be hashed or truncated for privacy)
  • Campaign and creative identifiers
  • Basic geographic signals (DMA, zip)
  • Available identity signals (UID2, email hashes if present)
  • Device type indicators
DON'T HAVE ACCESS YET?
We provide a template request you send to your DSP account manager. Takes 30 minutes to request, 1-2 weeks to receive.
PRIVACY & SECURITY:
All data processed in secure environment. No data retention after engagement ends. GDPR/CCPA compliant.
Who This Is For
✓ Best Fit: Spending $500K-$3M+ annually on CTV advertising
✓ Best Fit: Using Trade Desk, DV360, Amazon DSP, or Xandr
✓ Best Fit: Trackable conversions (e-commerce, leads, app installs)
✓ Best Fit: Frustrated with current attribution accuracy
✓ Best Fit: Need to justify CTV spend to CFO/leadership
Industries
E-commerce brands, subscription services, online education, mobile apps, fintech, health & wellness, home goods
Proof & Validation
INDEPENDENT VALIDATION
  • ✓ Tested on millions of impression dataset
  • ✓ Industry-standard attribution methodology
  • ✓ Validated approach before client work
REAL CLIENT DATA
  • ✓ 87% agreement with UID2 deterministic matches
  • ✓ On cases where ground truth exists, we match it
  • ✓ Then apply same patterns to find hidden conversions
AVERAGE RESULTS
  • ✓ 65% more conversions found vs UID2 alone
  • ✓ $400K-$1.2M in hidden monthly conversion value
  • ✓ 2-3x improvement in campaign visibility
What Clients Actually Get
DIAGNOSTIC PHASE (4 weeks, $10K)
  • Complete analysis of 60 days Trade Desk/DV360/Amazon DSP data
  • Find 2-3x more conversions than your current tracking
  • See which campaigns truly drive results (not just which track well)
  • Quantified hidden value: typically $50-200K per month
  • 15-20 page report + executive presentation
  • Raw attribution data (CSV export)
OPTIMIZATION RETAINER ($15K/month, 6-month minimum)
  • Monthly attribution updates with new campaign data
  • Budget reallocation recommendations (which campaigns to fund/cut)
  • Creative and targeting optimization based on true performance
  • Campaign ROI tracking (reported vs actual)
  • Monthly strategy calls + quarterly business reviews
  • Ongoing model refinement as your campaigns evolve
TYPICAL OUTCOMES
  • Discover $400K-$1.2M in previously unattributed conversion value
  • Identify campaigns driving 2-3x reported ROAS
  • Shift 20-30% of budgets to higher-performing campaigns
  • Defend CTV investment to CFO with complete data
  • Stop cutting budgets on campaigns that actually work

MONEY-BACK GUARANTEE: If our diagnostic doesn't find significant hidden conversion value (minimum 40% increase vs baseline), we refund your $10K.
The Process
FREE CONSULTATION (30 minutes)
We review your current CTV attribution coverage and identify the likely gap. No obligation.
DIAGNOSTIC PHASE ($10K, 4 weeks)
You export 60 days of event-level data from your DSP. We analyze, train models, find hidden conversions. You get comprehensive report + presentation.
DECISION POINT
Convert to monthly optimization retainer ($15K/month), or Take findings and optimize internally. Your choice.
ONGOING OPTIMIZATION (Most clients choose this)
Monthly attribution analysis + budget recommendations. Your campaigns get smarter every month.