Forward to: Analytics Team

Measurement &
Attribution Workflows

Ten agent workflows for Criteo's analytics team — multi-touch attribution enrichment, incrementality measurement, media mix modeling inputs, cross-channel conversion intelligence, advertiser benchmarking, campaign lift measurement, audience quality scoring for attribution, viewability-to-conversion analysis, geo-attribution enhancement, and predictive ROAS modeling across 100M+ domains.

1Multi-Touch Attribution Domain Enrichment

AI agent enriches Criteo's multi-touch attribution model by adding domain-level context to every touchpoint — classifying each publisher impression by IAB Category, PageRank quality tier, and Persona type to understand which types of publisher interactions drive the most conversions.

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Enrich Attribution Touchpoints with Domain Signals
/about /blog IAB Categories OpenPageRank Personas
ATTRIBUTION ENRICHMENT — TOUCHPOINT ANALYSIS ══════════════════════════════════════════════ CONVERSIONS ANALYZED: 34.5M attributed conversions this quarter TOUCHPOINTS ENRICHED: 234M impressions with domain-level signals AVG TOUCHPOINTS PER CONVERSION: 6.8 enriched touchpoints ATTRIBUTION VALUE BY PUBLISHER CONTEXT: Review Sites (IAB: Shopping > Reviews): Avg position in path: 3rd (mid-funnel) Attribution credit: 28% of conversion value Key insight: Review site touchpoints increase CVR by 67% News Sites (IAB: News, PageRank >7): Avg position in path: 1st (upper-funnel awareness) Attribution credit: 12% of conversion value Key insight: Premium news builds awareness for later conversion Social/Entertainment (IAB: Entertainment): Avg position in path: 2nd (consideration) Attribution credit: 18% of conversion value Key insight: Entertainment context drives aspirational interest Retailer Sites (IAB: Shopping, high intent): Avg position in path: Last (conversion) Attribution credit: 42% of conversion value Key insight: Final touchpoint on retailer domains closes the sale
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Optimize Budget Allocation Based on Attribution
Agent translates enriched attribution insights into budget allocation recommendations — shifting spend toward publisher contexts that contribute most to conversion paths while maintaining upper-funnel awareness on premium publishers.
Attribution Insight
Review Site Impact — Review site touchpoints (wirecutter.com, rtings.com, pcmag.com) appear in 67% of high-value conversion paths. Attribution credit: 28% of total. These mid-funnel touchpoints are undervalued in last-click attribution but critical for conversion. Recommendation: Increase review site CPM bids by 35%.
INCREASE BID +35% — Review sites undervalued in attribution model
Attribution Insight
Premium News Awareness — High-PageRank news sites (nytimes.com, bbc.com) appear as first touchpoint in 45% of conversion paths. Low direct attribution credit (12%) but removing them decreases overall conversion volume by 23%. Recommendation: Maintain awareness spend but optimize frequency.
MAINTAIN SPEND — Awareness touchpoints critical for conversion funnel
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Output: Attribution-Optimized Budget Allocation

MTA ENRICHMENT — BUDGET OPTIMIZATION

ATTRIBUTION-OPTIMIZED ALLOCATION: ══════════════════════════════════════════════ Review sites: +35% budget (undervalued mid-funnel impact) Premium news: Maintain (awareness critical for conversion paths) Entertainment: -10% budget (high CPA, low attribution credit) Retailer sites: Maintain (highest last-touch conversion) Revenue impact: +$12.3M/quarter from attribution-optimized spend ROAS improvement: +28% Enrichment: IAB Categories, PageRank, Personas, domain path analysis

2Incrementality Measurement Engine

AI agent measures true incrementality of Criteo campaigns by analyzing domain intelligence to create control groups — matching exposed and unexposed users by their domain visit profiles (IAB Categories, Personas) rather than random assignment, improving measurement accuracy.

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Build Domain-Matched Control Groups
/products IAB Categories Personas Countries
INCREMENTALITY — DOMAIN-MATCHED CONTROL GROUPS ══════════════════════════════════════════════ CONTROL GROUP METHODOLOGY: Traditional (random): Control accuracy 72% (unmatched profiles) Domain-matched: Control accuracy 94% (matched by domain profile) Matching criteria: IAB Category distribution: Within 5% match Persona mix: Within 8% match Geographic distribution (Countries): Within 3% match PageRank tier distribution: Within 5% match INCREMENTALITY RESULTS (Nike Q1 2026 campaign): Exposed group CVR: 4.2% (234K conversions) Control group CVR: 2.8% (156K conversions) True incremental lift: +50% (78K incremental conversions) Incremental ROAS: 5.4x Traditional method would have shown: +72% lift (inflated by 44%) Domain-matched method: +50% true lift (accurate measurement)
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Output: Incrementality Dashboard

INCREMENTALITY — MEASUREMENT REPORT

INCREMENTALITY MEASUREMENT RESULTS: ══════════════════════════════════════════════ Campaigns measured: 4,567 this quarter Avg true incremental lift: +38% (vs inflated +62% from random controls) Control group accuracy: 94% (domain-matched) Advertiser confidence: +56% in Criteo measurement reports Revenue from measurement-driven retention: $23.4M/year

3Media Mix Modeling Domain Inputs

AI agent generates media mix modeling inputs by classifying Criteo campaign impressions by publisher domain characteristics — providing advertisers with channel-level performance data segmented by content type, quality tier, and audience composition for MMM analysis.

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Generate MMM-Ready Domain-Level Data
/about IAB Categories OpenPageRank Personas Countries
MEDIA MIX MODEL INPUTS — DOMAIN SEGMENTATION ══════════════════════════════════════════════ CRITEO IMPRESSION BREAKDOWN FOR MMM: Premium Publishers (PageRank >7): 28% of impressions | ROAS: 6.8x Mid-Tier Publishers (PageRank 4-7): 45% of impressions | ROAS: 4.2x Long Tail (PageRank <4): 27% of impressions | ROAS: 2.1x By IAB Category: News: 23% | ROAS: 4.8x (brand awareness contribution) Shopping: 34% | ROAS: 7.2x (high intent) Entertainment: 18% | ROAS: 3.1x Tech: 12% | ROAS: 5.4x Other: 13% | ROAS: 2.8x MMM EXPORT FORMAT: Weekly aggregates by PageRank tier, IAB, Country
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Output: MMM Data Quality Report

MMM INPUTS — DATA QUALITY REPORT

MMM INPUT DATA QUALITY: ══════════════════════════════════════════════ Impressions classified: 98.4% (with domain-level signals) Granularity: Weekly by PageRank tier, IAB category, Country Historical depth: 8 quarters of enriched data Advertiser MMM satisfaction: +45% vs non-enriched data MMM-attributable Criteo value: +23% higher than pre-enrichment

4Cross-Channel Conversion Intelligence

AI agent tracks cross-channel conversion paths by monitoring advertiser domain activity — /products page visits, /login activity, and /pricing page engagement — to understand how Criteo campaigns interact with other marketing channels in driving purchases.

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Map Cross-Channel Conversion Journeys
/products /login /pricing IAB Categories Personas
CROSS-CHANNEL CONVERSION PATHS ══════════════════════════════════════════════ MOST COMMON CONVERSION PATHS (with Criteo touchpoint): Path 1 (23%): Google Search → Criteo Retarget → Purchase Criteo contribution: Last touch, conversion driver Path 2 (18%): Social (Meta) → Criteo Retarget → Review Site → Purchase Criteo contribution: Mid-funnel, kept user in consideration Path 3 (15%): Criteo Prospecting → Google Search → Purchase Criteo contribution: First touch, drove awareness Path 4 (12%): Email → Criteo Commerce Ad → Purchase Criteo contribution: Reinforcement, aided conversion CROSS-CHANNEL SYNERGY: Criteo + Google: +34% combined ROAS vs either alone Criteo + Social: +28% combined ROAS vs either alone Criteo + Email: +45% combined ROAS vs either alone
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Output: Cross-Channel Value Report

CROSS-CHANNEL — CONVERSION VALUE

CRITEO CROSS-CHANNEL CONTRIBUTION: ══════════════════════════════════════════════ Conversions with Criteo in path: 34.5M (67% of total) Avg Criteo position in path: 2.3 (mid-funnel critical role) Cross-channel ROAS synergy: +36% avg lift when Criteo present Standalone attribution: 42% of conversions Assisted attribution: 25% additional (under-counted without MTA) Signals: /products, /login, /pricing domain visits, IAB, Personas

5Advertiser Benchmarking Intelligence

AI agent generates advertiser performance benchmarks by analyzing domain intelligence across IAB Categories — providing advertisers with context on how their Criteo campaigns perform relative to their vertical, allowing for data-driven optimization recommendations.

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Build Category Benchmarks from Domain Data
/products IAB Categories OpenPageRank Countries
ADVERTISER BENCHMARKING — BY IAB CATEGORY ══════════════════════════════════════════════ CATEGORY BENCHMARKS (Q1 2026): Category Avg CTR Avg CPA Avg ROAS Top Quartile Electronics 2.1% $12.40 6.8x 9.2x Fashion 1.8% $8.90 5.4x 7.8x Home & Garden 1.4% $15.20 4.2x 6.1x Beauty 2.4% $6.80 7.1x 10.2x Sporting Goods 1.6% $11.50 4.8x 6.9x Pet Supplies 1.9% $9.20 5.6x 8.1x Grocery 2.8% $4.50 8.4x 12.1x METHODOLOGY: Based on 47,234 advertisers segmented by IAB Category
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Output: Benchmarking Report

ADVERTISER BENCHMARKS — QUARTERLY REPORT

BENCHMARKING IMPACT: ══════════════════════════════════════════════ Advertisers benchmarked: 47,234 across 400+ IAB categories Below-benchmark advertisers identified: 12,345 (optimization opportunity) Optimization recommendations generated: 8,901 ROAS lift from benchmark-driven optimization: +23% Advertiser retention improvement: +18% (data-driven confidence)

6Campaign Lift Measurement

AI agent measures campaign lift by comparing advertiser domain activity before and after Criteo campaign exposure — tracking /products page visits, /pricing page engagement, and /login activity as proxy metrics for brand lift and purchase intent.

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Measure Domain Activity Lift Post-Exposure
/products /pricing /login OpenPageRank
CAMPAIGN LIFT — DOMAIN ACTIVITY MEASUREMENT ══════════════════════════════════════════════ NIKE Q1 2026 CAMPAIGN LIFT RESULTS: /products page visits (exposed vs control): Exposed: +34% more visits to nike.com/products Control: baseline (no change) Lift: +34% product page engagement /pricing page visits: Exposed: +28% more visits to nike.com pricing pages Control: baseline Lift: +28% purchase intent /login activity: Exposed: +18% more account logins Control: baseline Lift: +18% account engagement COMPOSITE BRAND LIFT SCORE: +26.7% (weighted average)
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Output: Campaign Lift Report

CAMPAIGN LIFT — MEASUREMENT REPORT

CAMPAIGN LIFT MEASUREMENT: ══════════════════════════════════════════════ Campaigns measured for lift: 2,345 this quarter Avg product page lift: +29% Avg pricing page lift: +22% Avg login activity lift: +15% Composite brand lift: +22% average across campaigns Advertiser confidence in Criteo: +34% improvement

7Audience Quality Scoring for Attribution

AI agent scores audience quality within attribution paths by analyzing the domain profiles of converting users — determining which audience segments (by Personas and IAB affinity) generate the highest-value conversions for more accurate budget allocation.

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Score Audience Quality by Conversion Value
/products Personas IAB Categories OpenPageRank
AUDIENCE QUALITY — CONVERSION VALUE ANALYSIS ══════════════════════════════════════════════ AUDIENCE QUALITY TIERS (by conversion value): Premium Buyers (Persona: Luxury, PageRank >7 sites): Avg order value: $289 | CVR: 3.8% | LTV: $1,234 Active Shoppers (Persona: Active Buyer, Review sites): Avg order value: $134 | CVR: 4.2% | LTV: $567 Casual Browsers (Persona: Browser, Entertainment sites): Avg order value: $67 | CVR: 1.2% | LTV: $189 Deal Seekers (Persona: Value Seeker, Coupon sites): Avg order value: $45 | CVR: 5.6% | LTV: $123 INSIGHT: Premium Buyers are 10x more valuable by LTV than Deal Seekers
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Output: Quality-Adjusted Attribution

AUDIENCE QUALITY — ATTRIBUTION ADJUSTMENT

QUALITY-ADJUSTED ATTRIBUTION: ══════════════════════════════════════════════ Revenue from Premium Buyer audiences: $89M/quarter (38% of total) Revenue from Active Shoppers: $67M/quarter (29% of total) Revenue from Casual Browsers: $45M/quarter (19% of total) Revenue from Deal Seekers: $34M/quarter (14% of total) Quality-adjusted ROAS: +23% higher when weighting by LTV Budget reallocation: Shift 15% from Deal Seekers to Premium Buyers

8Viewability-to-Conversion Analysis

AI agent correlates publisher domain viewability metrics with conversion rates — analyzing how PageRank, IAB Category, and domain quality impact the relationship between ad viewability and actual purchase behavior to optimize Criteo's viewability standards.

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Analyze Viewability-Conversion Correlation
/about OpenPageRank IAB Categories Domain Ages
VIEWABILITY-CONVERSION CORRELATION ══════════════════════════════════════════════ VIEWABILITY IMPACT BY DOMAIN QUALITY: Premium domains (PageRank >7): Viewability: 78% | CVR: 3.8% | Correlation: 0.82 Mid-tier domains (PageRank 4-7): Viewability: 58% | CVR: 2.1% | Correlation: 0.67 Long tail (PageRank <4): Viewability: 34% | CVR: 0.8% | Correlation: 0.43 KEY INSIGHT: Viewability threshold of 50% is optimal cost-efficiency point Below 50%: CVR drops sharply (-67%) Above 70%: Diminishing returns on CVR improvement PageRank is strongest predictor of both viewability AND conversion
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Output: Viewability Standards Report

VIEWABILITY — CONVERSION ANALYSIS

VIEWABILITY STANDARD RECOMMENDATION: ══════════════════════════════════════════════ Optimal viewability threshold: 50% (cost-efficient) Revenue recovered from low-viewability filtering: $5.6M/quarter CVR improvement from viewability standards: +34% PageRank correlation with viewability: 0.82 (strongest predictor) Signals: PageRank, Domain Age, IAB Category, content quality

9Geographic Attribution Enhancement

AI agent enhances geographic attribution by using Countries enrichment data — determining which geographic audiences respond best to Criteo campaigns and optimizing ad spend allocation across markets based on domain-level geographic conversion intelligence.

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Analyze Conversion Performance by Geography
/products Countries IAB Categories Personas
GEOGRAPHIC ATTRIBUTION — CONVERSION BY MARKET ══════════════════════════════════════════════ CRITEO CAMPAIGN PERFORMANCE BY MARKET: US: CVR: 2.8% | ROAS: 6.8x | AOV: $134 | Budget share: 45% France: CVR: 2.3% | ROAS: 5.4x | AOV: $112 | Budget share: 15% UK: CVR: 2.4% | ROAS: 5.8x | AOV: $128 | Budget share: 12% Germany:CVR: 2.1% | ROAS: 4.8x | AOV: $118 | Budget share: 10% Japan: CVR: 1.9% | ROAS: 4.2x | AOV: $98 | Budget share: 8% Brazil: CVR: 1.4% | ROAS: 3.1x | AOV: $67 | Budget share: 5% GEO OPTIMIZATION RECOMMENDATION: Increase: UK (+3% budget share, under-invested vs ROAS) Decrease: Brazil (-2% budget share, low ROAS) Maintain: US, France (optimal allocation)
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Output: Geographic Attribution Report

GEO ATTRIBUTION — OPTIMIZATION REPORT

GEOGRAPHIC OPTIMIZATION RESULTS: ══════════════════════════════════════════════ Markets analyzed: 34 countries Budget reallocation: UK +3%, Brazil -2%, others adjusted Revenue from geo optimization: +$8.9M/quarter ROAS improvement: +18% from geo-optimized allocation Signals: Countries, IAB Categories, Personas, advertiser domain geo

10Predictive ROAS Modeling

AI agent predicts campaign ROAS before launch by analyzing the target publisher domain mix and advertiser domain characteristics — using historical correlations between domain intelligence features and campaign outcomes to forecast performance with 87% accuracy.

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Build ROAS Prediction from Domain Features
/products IAB Categories OpenPageRank Personas Countries Domain Ages
PREDICTIVE ROAS MODEL — DOMAIN FEATURES ══════════════════════════════════════════════ MODEL FEATURES (predicting campaign ROAS): Publisher PageRank distribution: Weight: 0.22 ■■■■■■■■■■■ IAB Category match score: Weight: 0.20 ■■■■■■■■■■ Persona alignment: Weight: 0.18 ■■■■■■■■■ Advertiser catalog depth (/products): Weight: 0.14 ■■■■■■■ Advertiser PageRank: Weight: 0.10 ■■■■■ Geographic match (Countries): Weight: 0.08 ■■■■ Domain Age (advertiser): Weight: 0.08 ■■■■ PREDICTION EXAMPLES: Nike on sports publishers: Predicted ROAS 7.8x (actual: 7.2x) Samsung on tech review sites: Predicted ROAS 6.4x (actual: 6.8x) Sephora on beauty/lifestyle: Predicted ROAS 8.2x (actual: 7.9x) MODEL ACCURACY: R² = 0.87 | Avg prediction error: 8.4%
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Output: Predictive ROAS Dashboard

PREDICTIVE ROAS — FORECASTING REPORT

PREDICTIVE ROAS MODEL RESULTS: ══════════════════════════════════════════════ Campaigns predicted: 4,567 this quarter Model accuracy: R² = 0.87 (87% of variance explained) Avg prediction error: 8.4% (within acceptable range) Low-ROAS campaigns prevented: 1,234 ($8.9M waste avoided) Revenue from prediction-optimized launches: +$12.3M/quarter Top features: PageRank, IAB match, Personas, catalog depth
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