A complete measurement infrastructure — from independently verified signal collection through AI-powered modeling to analyst-grade insight delivery. Built on Ground Signal™, our proprietary data quality foundation, and built exclusively for complex, omni-channel advertising programs.
01 — Data Collection
Accurate attribution starts with complete, high-quality data. C3 Metrics deploys three complementary collection methods — each purpose-selected based on channel type, publisher relationship, and data quality — so nothing in your media mix goes unmeasured.
No cookies. No fingerprinting. No PII ingested at any layer. The Attribution Data Cloud was designed from the ground up for a privacy-compliant world — structurally ready for whatever regulatory changes come next, not patched to comply after the fact.
Most measurement platforms treat offline channels as outside the attribution model, or handle them only as MMM inputs. C3 Metrics converts offline exposure events — linear TV airings, direct mail drops, and others — into measurable digital signals by detecting correlated lift in Branded, Organic, and Search (BOS) traffic in the period following an exposure. This allows offline channels to participate fully in MTA alongside digital, as individual attributed touchpoints — not statistical proxies.
Client-specific, cookie-less tags deployed as the primary digital collection mechanism — built for accuracy, cross-device coverage, and privacy compliance without third-party cookies or fingerprinting.
Where certified measurement programs or publisher-supplied reporting is required or preferred, C3 Metrics integrates those streams as first-class inputs — normalized alongside tag data for unified modeling.
C3 Metrics maintains active data integrations across all 20+ channel categories — including linear TV, display/programmatic, CTV, social, audio, search, OOH, email, direct mail, in-app, call center, mobile messaging, and AI-driven placements — plus leading ad platforms including Google Campaign Manager, DV360, Meta, Adobe Campaign, and Ads Data Hub. Connected to all of them, commercially tied to none.
Direct server-level connections to publishers and platforms — providing the cleanest, most reliable signal stream entirely independent of browser-based collection and its growing limitations.
Ground Signal™ — The Data Quality Layer
The distance between what your tags collect and what your models can trust is where most attribution falls apart. Ground Signal™ is C3's proprietary monitoring and verification layer — it sits between collection and modeling, continuously validating data quality, detecting gaps, reconciling cross-channel discrepancies, and documenting everything.
When something breaks — a tag fires wrong, a platform shifts its reporting, a server-side feed goes stale — Ground Signal™ catches it before it corrupts your attribution output. The result is attribution built on data you can actually defend to a CFO or an auditor.
02 — AI & ML Pipeline
C3 Metrics runs two sequential machine learning systems — the first to maximize signal quality, the second to model consumer journeys and channel interactions. Together they produce attribution that reflects how consumers actually behave, not how a rules-based model assumes they do.
Most platforms apply a single model to whatever data arrives. C3 Metrics runs a deliberate two-stage process: first cleaning and structuring incoming data to maximize signal quality, then applying supervised learning to that high-integrity input.
Each stage has a distinct purpose — and together they produce attribution that is more accurate, more stable, and more explainable than single-pass approaches.
An unsupervised learning model processes all incoming data to maximize signal-to-noise ratio — identifying patterns, structuring data, and filtering out invalid or misleading signals before anything reaches the modeling layer.
A supervised learning model — using Bayes model scoring — processes the clean, structured data to model how consumers move through the funnel and how channels interact. This stage produces the attribution outcomes and predictions that drive client decisions.
Standard funnel frameworks like AIDA are too coarse for attribution modeling — they don't distinguish between the specific roles that different touchpoints play in driving a conversion. C3 Metrics uses ORAC: a four-position taxonomy that classifies every touchpoint by its functional role in the consumer journey. This enables more precise fractional credit assignment than position-agnostic models, and surfaces channels that assist and originate conversions rather than just closing them.
03 — Measurement Suite
No single approach answers every marketing question. C3 Metrics integrates MTA, MMM, and Incrementality Testing — each with a defined role, together delivering complete measurement coverage for complex advertising programs.
Multi-Touch Attribution assigns fractional credit to every touchpoint across the consumer journey using C3's dual-model AI pipeline and ORAC funnel taxonomy. It is the core of ongoing media optimization — providing channel-level clarity on what's actually driving conversions, right now.
Every touchpoint receives a fractional attribution score — digital and offline, trackable and non-trackable. Linear TV via BOS, display/programmatic, CTV, social, audio, search, OOH, email, direct mail, in-app, call center, mobile messaging, AI-driven placements, and more. No gaps in the consumer journey.
Each touchpoint is classified by its functional funnel role. Credit reflects actual contribution — surfacing channels that converter systematically under-credits, particularly upper-funnel and offline.
Attribution models run separately for new and returning consumers — because the channels that drive acquisition behave differently from those that drive repeat purchase.
e-Commerce transactions, digital KPIs (form fills, downloads, appointments, product configurations), offline conversions (phone calls, dealer visits, application submissions, account openings), CRM and POS data — all modeled simultaneously. Plus AI agentic conversions, as they emerge. The model reflects your actual business outcomes, not just what's easy to track.
Marketing Mix Modeling uses statistical modeling across longer time horizons to evaluate investment efficiency, competitive dynamics, and macro factors. It complements MTA by answering questions that individual-level journey data cannot — and by providing the strategic context for budget allocation decisions.
Seasonality, competitive spend, pricing changes, economic conditions, and promotional periods are modeled as inputs — so results reflect true media contribution, not macro correlation.
MMM identifies where additional spend stops generating proportional returns — enabling smarter reallocation before efficiency drops, not after.
MMM findings validate and calibrate MTA results over longer time horizons — creating a feedback loop between short-term attribution and long-term strategic measurement.
For channels where user-level tracking isn't available or appropriate, MMM provides the strategic view — ensuring no major investment is evaluated by guesswork alone.
C3 Metrics offers two distinct approaches to incrementality — each answering a different question, at a different speed, with a different cost.
In-Mix Incrementality™ answers the question your media buyer is actually asking — is this channel pulling its weight in the system? — without designing a holdout experiment, suppressing ad spend, or waiting weeks for results.
The mechanism: run the MTA model with a channel included, then re-run without it. The delta in attributed conversions is that channel's System Contribution Score (SCS) — a continuous, model-native measure of how much the system depends on that channel to explain what it observes.
Unlike holdout testing, In-Mix Incrementality™ requires no control group, no ad suppression, and no episodic test design. It runs inside the MTA model — continuously, as part of every attribution cycle. Requires MTA.
The output of In-Mix Incrementality™ — the delta in attributed conversions when a channel is removed from the model. Continuous, comparable across channels, and requires no holdout design.
Social platforms report their own contribution metrics with obvious incentives to over-report. Holdout incrementality provides an independent, causal view of what social campaigns actually drove.
Both In-Mix™ and holdout results feed back into MTA and MMM as calibration inputs — strengthening model accuracy and informing channel inclusion and weighting decisions over time.
04 — Data Isolation & Model Integrity
C3 Metrics runs on shared enterprise infrastructure — but every client's data environment is fully isolated. Models are built, structured, and run individually for each client. There is no pooling, no cross-client data sharing, no shared model weights.
Each client's raw data, processed data, and model inputs are stored and processed in a fully separate environment. No data crosses the client boundary.
Each client's attribution model is structured individually — reflecting their specific channels, conversion types, consumer journeys, and business context. No generic shared model is applied and adjusted. Your model is yours.
For clients in competitive categories — automotive, financial services, healthcare — isolation is not just a preference, it is guaranteed by architecture, not policy.
05 — Outputs & Delivery
Attribution data is only as valuable as its accessibility. C3 Metrics delivers outputs in every format that matters — from live dashboards to raw data feeds to analyst-ready reports — designed to fit how media buyers and analytics teams actually work.
Live, always-on reporting interface with channel-level attribution, campaign performance, efficiency trends, and ORAC funnel analysis.
Structured attribution data delivered directly to your data warehouse or BI environment for custom analysis and integration.
Formatted reports designed for media agency workflows — actionable channel-level insights on the timelines that match campaign decisions.
Structured Excel outputs and scheduled data exports for analyst teams who work outside the dashboard environment.
Programmatic access to attribution data for integration with internal tools, custom dashboards, and automated reporting workflows.
Beyond data delivery — C3 Metrics account teams provide ongoing ad hoc analysis, proactive insight, and direct question answering on the timelines your media buyers need.
06 — For Your Team
The Attribution Data Cloud is designed so executive stakeholders and technical teams each get exactly what they need — without one group having to translate for the other.
Clear channel-level efficiency view — no black box, no jargon. What each dollar is doing and what it should be doing instead.
Budget reallocation scenarios with projected impact — quantified, actionable, and tied to business outcomes.
Confidence in independence — no publisher relationships, no platform incentives, no reason to shade results in anyone's favor.
Proof points for internal budget conversations — attribution findings with the methodological rigor to hold up to scrutiny.
Two-stage AI/ML pipeline — unsupervised learning for signal quality, supervised Bayesian modeling for consumer journey attribution. Explainable, auditable outputs at every stage.
ORAC funnel taxonomy provides position-based attribution with full methodology documentation — richer than standard converter or linear models.
BOS offline signal methodology, collinearity handling, and fraud filtering all documented for technical review. Nothing is a black box.
Raw data feeds, API access, and warehouse delivery — work with the underlying data in your own environment and tools, not just the dashboard.
Walk through the platform with our team — from data collection through AI modeling to the outputs your media buyers will actually use. No generic demo. Your channels, your program.