Done-for-you offer · Fractional CMO with AI Swarm · walk-in-phone-attribution 4-skill bundle · walk-in- phone-attribution agent
Per-market Marketing Mix Modeling for DTC ecommerce, multi- location retail, multi-unit franchise, and multi-location service brand operators — Ingest + Fit + Validate + Recommend 4-skill bundle on the walk-in-phone-attribution agent, under a 5-anchor compliance overlay anchored on causal-inference methodology + uncertainty quantification, FTC Pfizer 1972 reasonable-basis substantiation, privacy + per-platform data- use, SEC Reg G + SOX + ASC 280, and EU AI Act
iOS App Tracking Transparency (April 2021), the multi-year browser tracking-prevention trajectory (Apple WebKit ITP + Firefox ETP + Safari + Google Chrome Privacy Sandbox under the July 2024 user-choice approach), and state and EU privacy laws have made person-level multi-touch attribution increasingly unreliable. Marketing Mix Modeling — aggregate-data, privacy- friendly, causal-inference-based — has had a corresponding resurgence. Meta open-sourced Robyn; Google open-sourced Meridian in January 2024 alongside the earlier LightweightMMM library; Uber maintains Orbit; PyMC-Marketing extends PyMC for MMM; commercial vendors (Recast, LiftLab, Mass Effect, Nielsen, Kantar) ship managed MMM platforms. Geo-experiments (GeoLift, Recast, Google MMM Audit) provide the causal-validation gold standard. For 10-100 markets across 50-1,500 locations, methodology matters: priors, saturation curves, adstock decay, identifiability constraints, holdout validation, geo-experiment lift validation, multi-model triangulation, and uncertainty quantification with credible/confidence intervals (not point estimates) are operator-data-science-team-and-counsel- approved. When MMM outputs flow into marketing claims (ROAS, incrementality, payback), FTC Pfizer 1972 reasonable-basis substantiation + Lanham 15 USC 1125(a) + state UDAP attach. When MMM outputs feed non-GAAP measures or revenue forecasts or segment reporting for public registrants, SEC Reg G + Item 10(e) Reg S-K + SOX 302/404 + ASC 280 Operating Segments + Reg S-K Item 303 MD&A attach. Per-platform data-use terms govern how Meta CAPI/AEM/Limited Data Use, Google Enhanced Conversions/RDP, LiveRamp DPAs, and Snowflake Data Marketplace dataset licenses may be combined in modeling. CCPA cross- context-behavioral-advertising opt-out + GDPR Articles 6/22/26 + Article 35 DPIA + state-comprehensive-privacy apply when modeling joins to personal data. EU AI Act Articles 9/13/14/26 apply when MMM informs high-stakes decisions. The open-source MMM, commercial MMM, geo-experiment, attribution, warehouse, per-platform data-source, BI, and consent-management vendors below ship strong primitives. The orchestration above them — methodology specification + uncertainty surfacing + multi- model triangulation + holdout + geo-experiment validation + FTC substantiation chain preservation + per-platform data-use enforcement + CCPA cross-context propagation + GDPR Article 22 + DPIA workflow + SEC Reg G + SOX + ASC 280 + MD&A coordination + EU AI Act applicability evaluation — is operator-side architecture. You keep the MMM stack, the methodology specification, the FTC substantiation library, the per-platform data-use policy, the CCPA cross-context opt- out records, the GDPR DPIA records, the SEC Reg G reconciliation, the WORM audit trail, and the policy-as-code policies. You keep the ability to in-house at any time.
Published September 24, 2026
The real ecosystem this sits above
Open-source MMM + commercial MMM
Open-source: Meta Robyn, Google Meridian (open-sourced January 2024), Uber Orbit, Google LightweightMMM, PyMC- Marketing, Stan/PyMC, R MMM packages. Commercial: Recast, LiftLab, Mass Effect, AnalyticEdge, Nielsen MMM, Kantar Analytics. Each ships strong modeling primitives. Multi- model triangulation + operator-data-science-team-and- counsel-approved methodology specification above them is operator-side architecture.
Geo-experiment + attribution
Geo-experiment: GeoLift (Meta), Recast geo-experiment, Google MMM Audit. Attribution: Northbeam, Hyros, Polar Analytics, Triple Whale, Rockerbox, ChannelMix, Funnel.io. Each ships strong lift + attribution primitives. Holdout + geo-experiment validation pipeline above them is operator-side architecture.
Data warehouse + per-platform data sources
Warehouse: Snowflake, Databricks, BigQuery, Redshift, ClickHouse, dbt, Hightouch, Census. Per-platform sources: Meta Conversions API + Aggregated Event Measurement + Limited Data Use, Google Enhanced Conversions + Restricted Data Processing, Floodlight, Search Ads 360, LiveRamp clean rooms, Snowflake Data Marketplace. Each ships strong storage + data-source primitives. Per-platform data-use enforcement + CCPA cross-context propagation above them is operator-side architecture.
BI + consent management
BI: Looker, Tableau, Power BI, Sigma, Hex, Mode, ThoughtSpot. Consent: OneTrust, TrustArc, Ketch, Securiti, BigID. Each ships strong primitives. Uncertainty surfacing + FTC Pfizer substantiation chain preservation above them is operator-side architecture.
Policy-as-code + WORM + legal research
Policy-as-code: OPA Rego, AWS Cedar, Casbin, Cerbos, Oso. WORM: AWS S3 Object Lock, GCS retention, Azure Blob immutable, Snowflake Time Travel. Legal research: Westlaw, Lexis+, Bloomberg Law, Practical Law, Compliance.ai. Each ships strong primitives. The 5-anchor compliance gate that maps methodology + FTC + privacy + SEC/SOX + per- platform data-use + EU AI Act onto an operator-counsel- approved policy bundle is operator-side architecture.
Frequently asked
What does per-market Marketing Mix Modeling actually deliver, and how does the 4-skill bundle decompose?
An orchestration layer that sits above the operator open-source-MMM + commercial-MMM + geo-experiment + attribution + warehouse + per-platform-data-source + BI + consent-management + policy-as-code + WORM-storage stack and runs per-market MMM at the cadence the operator marketing and finance teams need, with uncertainty quantification surfaced explicitly. The skill is a four-skill bundle on the walk-in-phone-attribution agent. Skill 1 — Ingest: pull per-market per-channel per-tactic spend, impressions, clicks, and conversions from the operator data warehouse (Snowflake, Databricks, BigQuery, Redshift, ClickHouse — operator chooses) populated through the operator-chosen ETL/reverse-ETL stack (dbt, Hightouch, Census, Fivetran). Pull aggregated platform signals from Meta Conversions API + Aggregated Event Measurement (Meta Limited Data Use for opted-out users), Google Enhanced Conversions (Restricted Data Processing for opted-out users), Floodlight, Search Ads 360, LiveRamp data clean rooms, Snowflake Data Marketplace shared datasets. Pull per-market outcome data (revenue, transactions, AOV, walk-ins for multi-location operators). Pull per-market context variables (macroeconomic, weather, competitive intensity, seasonality, promotional calendar, distribution events). Pull geo-experiment results when GeoLift (Meta) or operator-run geo-experiments are available. Pull operator marketing-team-set business constraints (channel minimums, channel caps, vertical-specific spend restrictions). Skill 2 — Fit: fit the MMM through the operator-chosen modeling tool (Meta Robyn, Google Meridian open-sourced January 2024, Uber Orbit, Google LightweightMMM, PyMC-Marketing, Stan/PyMC Bayesian, R MMM packages, Recast, LiftLab, Mass Effect, AnalyticEdge, Nielsen MMM, Kantar Analytics — operator chooses; many operators run multiple models for triangulation). Fit applies operator-data-science-and-counsel-approved priors, saturation curves (Hill, S-curve), adstock decay (Koyck, geometric, Weibull), and identifiability constraints. Fit produces coefficient estimates with credible intervals (Bayesian) or confidence intervals (frequentist), not point estimates. Skill 3 — Validate: validate model fit against holdout data, against geo-experiment lift when geo-experiments are available (geo-experiments are the strongest validation since they produce causal incrementality estimates the MMM should reproduce), against per-platform conversion uplift studies, and against operator-data-science-team posterior predictive checks. Validate emits a per-model per-market fit quality score, identifies coefficient confidence intervals that span economically meaningful ranges (a channel with a credible interval that includes zero is not a defensible recommendation candidate), and flags model assumptions that may be violated. Skill 4 — Recommend: emit operator-marketing-team-targeted recommendations with uncertainty intervals attached. Recommendations frame as ranges rather than point estimates. Substantiation chains are tagged so when MMM outputs are quoted in marketing claims (ROAS, payback, incrementality), the FTC Pfizer 1972 reasonable-basis substantiation chain is preserved. Recommendations honor operator counsel + paid-media team approved per-platform data-use restrictions and per-jurisdiction CCPA cross-context-behavioral-advertising opt-out propagation. The open-source MMM, commercial MMM, geo-experiment, attribution, warehouse, per-platform data-source, BI, consent-management vendors below ship strong primitives. The orchestration above them — operator-data-science-and-counsel-approved methodology + uncertainty quantification + holdout + geo-experiment validation + FTC substantiation chain preservation + privacy posture + SEC/SOX disclosure coordination when MMM outputs feed financial reporting + per-platform data-use enforcement, audit trail — is operator-side architecture.
Where does single-vendor attribution stop compounding, and why is MMM having a resurgence for DTC + multi-location operators?
Single-vendor attribution is solved. Northbeam, Hyros, Polar Analytics, Triple Whale, Rockerbox, ChannelMix, Funnel.io ship strong attribution. Meta and Google ship strong native platform attribution. The compound case the walk-in-phone-attribution agent has to handle is the one where (a) iOS 14.5 App Tracking Transparency (April 2021) reduced person-level signal across iOS users and eliminated reliable cross-app deterministic identifier matching, with Meta and other ad platforms shifting to Aggregated Event Measurement and modeled conversions, (b) third-party cookie deprecation in browsers — Apple WebKit Intelligent Tracking Prevention has been progressively limiting third-party cookies and storage since 2017 and applied further restrictions through 2024; Firefox Enhanced Tracking Protection similarly; Google’s long-promised Chrome third-party-cookie deprecation has been repeatedly delayed and in July 2024 Google announced a new approach offering users a choice rather than unilateral deprecation (the announcement does not eliminate the broader signal-loss trajectory), (c) state privacy laws (CCPA + state-comprehensive-privacy patchwork) and EU GDPR + ePrivacy continue to limit person-level tracking via cookie consent + opt-out requirements + sensitive-PI restrictions, (d) per-platform privacy-preserving APIs (Meta Conversions API + Aggregated Event Measurement + Google Enhanced Conversions + Privacy Sandbox APIs + Apple SKAdNetwork + Apple AdAttributionKit) replace person-level tracking with aggregated and modeled signal, (e) the cumulative effect is that person-level multi-touch attribution increasingly under-represents the true incremental contribution of paid media channels, and operators need a privacy-friendly aggregate-data measurement approach to make multi-million-dollar budget decisions. MMM is the privacy-friendly aggregate-data measurement approach — it uses aggregate spend + aggregate outcome data + macroeconomic context to estimate per-channel response curves and incremental contribution, without requiring person-level tracking. Google open-sourced Meridian in January 2024 as a modern Bayesian MMM framework alongside the LightweightMMM library Google open-sourced earlier; Meta open-sourced Robyn. Commercial vendors (Recast, LiftLab, Mass Effect, AnalyticEdge, Nielsen) ship managed MMM platforms. Geo-experiments (GeoLift from Meta, Recast geo-experiments, Google geo-experiment audits) provide the causal validation that allows MMM coefficient estimates to be compared against geographic-holdout lift. The compound case the agent has to handle: per-market MMM across 10-100 markets where per-market response curves vary materially, where seasonality varies materially, where competitive intensity varies materially, where the operator marketing team needs recommendations that account for per-platform data-use compliance + privacy compliance + FTC substantiation when outputs are quoted in marketing claims, where SEC Reg G + SOX + ASC 280 + Reg S-K Item 303 MD&A enter scope when MMM outputs feed non-GAAP measures or revenue forecasts or segment reporting for public registrants. Without an orchestration layer above the MMM + geo-experiment + attribution + warehouse + per-platform data-source + consent vendors, methodology drifts (the data team picks a single model + treats point estimates as ground truth), uncertainty disappears (recommendations are quoted as point ROAS without credible intervals), holdout and geo-experiment validation gets skipped, FTC substantiation chains are not preserved when outputs flow into marketing claims, per-platform data-use restrictions get violated when Meta CAPI / AEM data is used outside its terms, CCPA cross-context opt-out propagation breaks when modeling joins to behavioral data, SEC Reg G reconciliation gets missed when MMM outputs feed non-GAAP measures, and the audit trail of "which model + which priors + which validation status + which counsel-policy-version drove which recommendation" fragments across consoles. The orchestration above the vendors is what holds the cross-model + cross-validation + cross-jurisdiction + cross-platform-data-use invariants.
How does Skill 2 Fit and Skill 3 Validate keep methodology defensible across multiple models, geo-experiments, and holdout?
Multiple-model triangulation + geo-experiment validation + holdout validation are the operator-data-science-team-and-counsel-approved pattern. Step 1 — operator-data-science-team-approved methodology specification. The operator data science team specifies allowable model families (Robyn, Meridian, Orbit, LightweightMMM, PyMC-Marketing, commercial vendor model), prior choices (informative versus weakly informative versus non-informative), saturation curves (Hill, S-curve), adstock decay (Koyck, geometric, Weibull), and identifiability constraints (sign constraints on coefficients, hierarchical structure for per-market hierarchical Bayesian, constraint on saturation half-saturation point). Operator counsel signs off on the methodology pack for substantiation purposes when MMM outputs may drive marketing claims. Step 2 — multi-model fit. The Fit skill runs at least two and ideally three model specifications in parallel (e.g., Meridian + Robyn + a commercial model). Cross-model triangulation surfaces when models agree (high confidence in the estimate) versus disagree (the operator data science team investigates why). Cross-model disagreement on a channel coefficient is a flag, not a deal-breaker — the operator data science team explores priors, data range, and identifiability. Step 3 — holdout validation. Fit the model on the operator-data-science-team-approved training window; validate predictions against a held-out future window. Holdout fit (Mean Absolute Percentage Error, R-squared, or Bayesian posterior predictive checks) becomes the model fit quality signal. Step 4 — geo-experiment validation. When the operator runs geo-experiments (lift studies where treatment markets receive a media intervention and control markets do not), the experimental incrementality estimate is the gold-standard causal validation. The Validate skill compares MMM coefficient-derived incrementality predictions against geo-experiment incrementality estimates. When MMM and geo-experiment agree within credible intervals, confidence is high. When they disagree, the operator data science team investigates (model specification, attribution-window mismatch, geo-experiment design issues, contamination across treatment-control). GeoLift (Meta) ships strong synthetic-control-based geo-experiment methodology. Recast ships geo-experiment-integrated MMM. Google Meridian incorporates geo-experiment priors. Step 5 — per-platform conversion uplift studies. When Meta or Google provides conversion uplift studies for the operator’s campaigns, compare MMM estimates against the platform study with the caveat that platform uplift studies have their own methodology limitations. Step 6 — posterior predictive checks. For Bayesian MMM, posterior predictive checks (simulating outcomes from the posterior and comparing to observed) catch model misspecification. Step 7 — uncertainty surfacing. Validate emits credible/confidence intervals alongside point estimates. The Recommend skill never strips uncertainty intervals before downstream consumption. Step 8 — audit. The audit trail records per-model methodology specification + prior choices + holdout fit + geo-experiment validation results + posterior predictive checks + counsel-policy-version under which the methodology was approved. The audit trail supports defense in FTC substantiation inquiries when MMM outputs flow to marketing claims.
How does the orchestration handle per-platform data-use compliance when modeling joins Meta Conversions API, Google Enhanced Conversions, and LiveRamp data?
Per-platform data-source compliance is operator-counsel-approved and per-platform-specific. Meta Conversions API (CAPI) terms govern how operator-side server-emitted conversion data may be used; Meta Aggregated Event Measurement (AEM) is the privacy-preserving aggregated framework that handles iOS opted-out users; Meta Limited Data Use restricts data use for users who exercise California or other state opt-out rights. Google Enhanced Conversions terms govern how operator-side conversion data may be hashed and shared with Google for measurement; Google Restricted Data Processing handles opted-out users. LiveRamp Data Processing Agreements govern how identity-resolved data may be used in clean rooms with strict data-use restrictions. Snowflake Data Marketplace shared datasets carry per-dataset license terms. The orchestration enforces operator-counsel-approved per-platform data-use restrictions before any modeling: (a) data from Meta CAPI / AEM is used within the Meta-approved data-use scope and not commingled with restricted-use data from other sources, (b) Google Enhanced Conversions data is used within the Google-approved data-use scope, (c) LiveRamp clean room data is used within the operator-LiveRamp-DPA-approved scope, (d) operator-side CDP joins respect CCPA Section 1798.120 opt-out, Section 1798.121 sensitive PI opt-out, Section 1798.140(ae) cross-context-behavioral-advertising opt-out, and per-state opt-out propagation, (e) GDPR Articles 6 (lawful basis), 9 (special-category restriction), 22 (solely automated decisionmaking when relevant), 26 (joint-controller analysis when applicable), 30 (records of processing), and Article 35 DPIA when high-risk processing apply for EU-user data, (f) UK GDPR + UK PECR apply for UK-user data, (g) state-comprehensive-privacy patchwork (Texas DPSA + Virginia CDPA + Connecticut CTDPA + Colorado CPA + Utah CPA + Oregon CPA + Tennessee IPA + Maryland Online Data Privacy Act + Washington My Health My Data Act + Florida DBR + Delaware PDPA + Indiana CDPA + Iowa CDPA + Montana CDPA) imposes per-state opt-out and consent-class management. The orchestration emits per-data-source per-modeling-input attestation (which dataset, under what license/DPA/platform-terms version, with what aggregation level, under what consent class) to the WORM audit trail. The orchestration never autonomously expands data-use scope; operator counsel approves data-use boundaries. When per-platform terms change (Meta updates CAPI terms, Google updates Enhanced Conversions terms, LiveRamp updates clean-room data-use), operator counsel reviews and updates the per-platform data-use policy; the orchestration enforces the updated policy.
What compliance does the orchestration enforce, and how does it map to methodology + FTC substantiation + privacy + SEC/SOX + per-platform data-use + EU AI Act?
Five anchors. Anchor 1 — Causal-inference methodology + uncertainty quantification + identifiability. Bayesian MMM frameworks (Meta Robyn, Google Meridian open-sourced January 2024, Uber Orbit, Google LightweightMMM, PyMC-Marketing, Stan/PyMC) and frequentist frameworks (commercial vendor models) with operator-data-science-team-approved methodology specification — prior choices, saturation curves (Hill, S-curve), adstock decay (Koyck, geometric, Weibull), identifiability constraints (sign constraints, hierarchical structure, half-saturation constraints), holdout validation, posterior predictive checks, geo-experiment lift validation (GeoLift Meta, Recast geo-experiment, Google MMM Audit), per-platform conversion uplift study comparison, credible-interval/confidence-interval reporting (never point estimates without uncertainty). Multi-model triangulation pattern. Anchor 2 — FTC Section 5 + FTC Pfizer (1972) reasonable-basis substantiation doctrine + Lanham Act 15 USC 1125(a) false advertising + state UDAP + FTC Endorsement Guides 2024 + FTC Fake Review Rule 16 CFR Part 465 (effective October 2024) when MMM outputs drive marketing claims (ROAS, incrementality, payback, attribution) the operator quotes in pitch decks, case studies, public statements, paid media, or franchise sales discussions. The substantiation chain must exist in operator possession before claim runs and be retained through FTC limitations period (5-year typical) plus operator-counsel-set tail. Per-row methodology + validation + counsel-policy-version + uncertainty interval supporting the claim must be producible. Anchor 3 — Privacy. CCPA Section 1798.120 + Section 1798.121 sensitive PI + Section 1798.140(ae) cross-context-behavioral-advertising opt-out + state-comprehensive-privacy patchwork (Texas DPSA + Virginia CDPA + Connecticut CTDPA + Colorado CPA + Utah CPA + Oregon CPA + Tennessee IPA + Maryland Online Data Privacy Act + Washington MHMDA + Florida DBR + Delaware PDPA + Indiana CDPA + Iowa CDPA + Montana CDPA). GDPR Articles 6 (lawful basis) + 9 (special-category) + 22 (solely automated decisionmaking) + 26 (joint controller) + 30 (records of processing) + Article 35 DPIA + ePrivacy Directive 2002/58/EC. UK GDPR + UK PECR. State health-data laws when MMM joins to health-context data. Anchor 4 — SEC Regulation G + Item 10(e) of Regulation S-K non-GAAP measures + Regulation S-K Item 303 MD&A + SOX Section 302 CEO/CFO certification + Section 404 internal control over financial reporting + ASC 280 Operating Segments + Statement on Auditing Standards 99 (now AU-C 240) on fraud considerations. When MMM outputs feed non-GAAP financial measures (adjusted ROAS, adjusted contribution margin), Reg G reconciliation to the closest GAAP measure applies. When MMM outputs feed revenue forecasts, internal control over financial reporting under SOX applies. When MMM outputs inform per-segment reporting, ASC 280 applies. When MMM informs MD&A, Reg S-K Item 303 applies. Reg S-K Item 1.05 (effective December 18, 2023) applies if a cybersecurity incident affects MMM data integrity at materiality threshold. Anchor 5 — Per-platform data-use compliance + EU AI Act. Meta Conversions API + Meta Aggregated Event Measurement + Meta Limited Data Use + Google Enhanced Conversions + Google Restricted Data Processing + LiveRamp Data Processing Agreements + Snowflake Data Marketplace dataset licenses + per-platform Terms of Service govern how per-platform data may be used in modeling. EU AI Act (Regulation 2024/1689) Articles 9 (risk management) + 13 (transparency) + 14 (human oversight) + 26 (deployer obligations) when MMM informs high-stakes decisions affecting individuals (employment-adjacent marketing, credit-adjacent marketing, housing-adjacent marketing, healthcare-adjacent marketing); when MMM operates as part of an AI system that produces consequential decisions under Annex III high-risk classification, the full EU AI Act high-risk framework applies. Broader gate also enforced: COPPA + California AADC + DSA Article 28 child protection when MMM informs ads reaching minors + per-vertical regulator (FDA OPDP + DEA + DISCUS + -regulator + state insurance + state real-estate) when MMM outputs flow into vertical-specific marketing claims + ADA Title III + WCAG 2.2 AA for output dashboards via policy-as-code (OPA Rego + AWS Cedar + Casbin + Cerbos + Oso). WORM audit trail (AWS S3 Object Lock + GCS retention + Azure Blob immutable + Snowflake Time Travel) with per-statute retention (FTC 7yr + state-AG variable + GDPR 6yr + CCPA 3yr + SOX 7yr + SEC Reg G/S-K 5yr + IRS 7yr + EU AI Act 10yr) per operator counsel policy.
What does the engagement look like across Tier 1 → Tier 2 → Tier 3, and what does the Tier 3 reporting cycle commit to?
Tier 1 AI Readiness Assessment (2-3 weeks, diagnostic): audits the operator current MMM posture against the 4-skill bundle + 5-anchor compliance overlay + per-vendor open-source MMM + commercial MMM + geo-experiment + attribution + warehouse + per-platform data-source + BI + consent state; deliverable is a gap-pack report identifying which markets lack MMM coverage, whether methodology specification is operator-data-science-team and counsel-approved, whether holdout + geo-experiment validation is wired, whether multi-model triangulation is run, whether uncertainty intervals are surfaced to downstream consumers, whether FTC Pfizer substantiation chains are preserved when MMM outputs flow into marketing claims, whether per-platform data-use restrictions (Meta CAPI + AEM + Limited Data Use + Google Enhanced Conversions + Restricted Data Processing + LiveRamp DPA) are enforced, whether CCPA Section 1798.140(ae) cross-context-behavioral-advertising opt-out propagates from CDP through modeling, whether GDPR Article 22 + Article 35 DPIA workflow is wired for EU-user data, whether SEC Reg G + Item 10(e) reconciliation is wired when MMM outputs feed non-GAAP measures for public registrants, whether SOX 302/404 + ASC 280 + Reg S-K Item 303 MD&A coordination is wired with operator finance + disclosure committee, whether EU AI Act applies when MMM informs high-stakes decisions, and a recommended remediation sequence for Tier 2. Tier 2 AI Swarm Setup Sprint (4-8 weeks): builds the 4-skill bundle on the walk-in-phone-attribution agent, wires open-source MMM + commercial MMM + geo-experiment + attribution + warehouse + per-platform data-source + BI + consent + policy-as-code + WORM-storage vendors (operator-chosen subset), configures the operator-data-science-team-and-counsel-approved methodology specification + multi-model triangulation + holdout + geo-experiment validation + uncertainty surfacing + FTC substantiation chain preservation + per-platform data-use enforcement + privacy posture (CCPA + GDPR + state-comprehensive-privacy) + SEC Reg G + SOX + ASC 280 + MD&A coordination + EU AI Act when applicable, runs 30-day shadow + canary period before flipping to enforce-mode. Tier 3 Fractional CMO with AI Swarm (6-month minimum, 1-2 days/wk embedded): continues operating with weekly/monthly model refit + geo-experiment lift study cadence + quarterly methodology review + quarterly per-platform data-use policy review against platform terms updates + quarterly compliance evidence packages. Tier 3 reporting is a 6-workstream pre-engagement-baseline reporting cycle (per-market MMM coverage trend + methodology defensibility against operator-data-science-team-and-counsel-approved spec + holdout + geo-experiment validation coverage + uncertainty interval surfacing + per-platform data-use compliance + WORM audit-trail completeness) measured against the operator’s pre-engagement baseline. Each workstream surfaces trend direction and the gap to operator-defined targets. Reporting carries explicit caveats: open-source MMM + commercial MMM + geo-experiment + attribution + warehouse + per-platform data-source + BI + consent vendor SLA + Meta CAPI + AEM + Limited Data Use term updates + Google Enhanced Conversions + RDP term updates + LiveRamp DPA updates + Snowflake Data Marketplace license updates + iOS App Tracking Transparency evolution + browser third-party-cookie roadmap (Chrome Privacy Sandbox + Apple WebKit ITP + Firefox ETP + Safari) + Google MMM/Meridian community updates + Meta Robyn community updates + FTC Endorsement Guides + Fake Review Rule + Pfizer doctrine interpretive guidance + SEC interpretive guidance + GDPR + ePrivacy + CCPA + state-comprehensive-privacy implementing rules + EU AI Act implementing acts sit outside Completions control. Attorney-client privilege preservation across operator-data-science-and-counsel-approved methodology + FTC substantiation chain library + per-platform data-use policy + CCPA cross-context opt-out records + GDPR DPIA records + SEC Reg G reconciliation records + EU AI Act records is maintained per operator counsel policy.
Who owns the MMM stack, the methodology specification, the FTC substantiation library, the SEC reconciliation, and the audit trail?
Operator owns every artifact. The open-source MMM frameworks (Meta Robyn, Google Meridian open-sourced January 2024, Uber Orbit, Google LightweightMMM, PyMC-Marketing, Stan/PyMC) run on operator-controlled compute. The commercial MMM subscription (Recast, LiftLab, Mass Effect, AnalyticEdge, Nielsen MMM, Kantar Analytics — operator chooses) runs under operator billing. The geo-experiment tooling (GeoLift Meta open-source, Recast geo-experiment, Google MMM Audit — operator chooses) runs on operator compute or under operator billing. The attribution + MMM-hybrid subscription (Northbeam, Hyros, Polar Analytics, Triple Whale, Rockerbox, ChannelMix, Funnel.io — operator chooses) runs under operator billing. The data warehouse (Snowflake, Databricks, BigQuery, Redshift, ClickHouse — operator chooses) runs under operator cloud account. The per-platform data-source integrations (Meta CAPI/AEM/Limited Data Use, Google Enhanced Conversions/RDP, LiveRamp clean rooms, Snowflake Data Marketplace — operator chooses) run under operator credentials. The BI tooling (Looker, Tableau, Power BI, Sigma, Hex, Mode, ThoughtSpot — operator chooses) runs under operator billing. The consent-management vendor (OneTrust, TrustArc, Ketch, Securiti, BigID — operator chooses) runs under operator account. The operator-data-science-team-and-counsel-approved methodology specification + multi-model triangulation policy + holdout protocol + geo-experiment validation protocol + FTC Pfizer substantiation chain library + per-platform data-use policy + CCPA Section 1798.140(ae) cross-context-behavioral-advertising opt-out propagation records + GDPR Article 22 + Article 35 DPIA records + SEC Reg G + Item 10(e) non-GAAP reconciliation library + SOX 302/404 + ASC 280 + Reg S-K Item 303 MD&A coordination workflow + EU AI Act compliance records all live in operator data-science + counsel + finance repo. The Ingest + Fit + Validate + Recommend skill code lives in operator code repo. The WORM audit trail lives on operator-controlled cloud storage (AWS S3 Object Lock + GCS retention + Azure Blob immutable + Snowflake Time Travel) with per-statute retention enforcement. The policy-as-code policies (OPA Rego + AWS Cedar + Casbin + Cerbos + Oso) live in operator code repo, counsel-aligned. The FTC + SEC + SOX + ASC + GDPR + CCPA + state-comprehensive-privacy + per-platform data-use + EU AI Act compliance evidence records are operator-counsel-and-CFO-maintained. Completions owns the orchestration knowledge — how to design the per-market methodology specification against the operator’s actual channel mix and seasonality, how to wire multi-model triangulation, how to wire holdout + geo-experiment validation, how to surface uncertainty intervals without losing recommendation actionability, how to preserve FTC Pfizer substantiation chains when MMM outputs flow into marketing claims, how to enforce per-platform data-use restrictions across Meta + Google + LiveRamp + Snowflake Data Marketplace, how to propagate CCPA cross-context opt-out through modeling, how to wire GDPR Article 22 + DPIA, how to wire SEC Reg G + SOX + ASC 280 + MD&A coordination with operator finance + disclosure committee, how to evaluate EU AI Act applicability — and that knowledge transfers under the Tier 3 transition path (30-60 days at engagement end with full hand-off of the methodology playbook, multi-model triangulation runbook, holdout + geo-experiment validation runbook, FTC substantiation chain library, per-platform data-use enforcement playbook, CCPA cross-context propagation playbook, GDPR Article 22 + DPIA playbook, SEC Reg G + SOX + ASC 280 + MD&A coordination playbook, EU AI Act applicability playbook, and the compliance evidence-package generation playbook). Completions credentials revoke on engagement-end.
Engage Completions
Start with the AI Readiness Assessment (Tier 1, 2-3 weeks): audit of operator current MMM posture against the 4- skill bundle + 5-anchor compliance overlay + per-vendor open-source MMM + commercial MMM + geo-experiment + attribution + warehouse + per-platform data-source + BI + consent state. Hand off to Tier 2 AI Swarm Setup Sprint (4-8 weeks): build the 4-skill bundle on the walk- in-phone-attribution agent, wire open-source MMM + commercial MMM + geo-experiment + attribution + warehouse + per-platform data-source + BI + consent-management + policy- as-code + WORM-storage, configure operator-data-science- team-and-counsel-approved methodology spec + multi-model triangulation + holdout + geo-experiment validation + uncertainty surfacing + FTC Pfizer substantiation chain + per-platform data-use enforcement + CCPA cross-context propagation + GDPR Article 22 + DPIA + SEC Reg G + SOX + ASC 280 + MD&A coordination + EU AI Act applicability evaluation, run 30-day shadow + canary before flipping to enforce-mode. Continue under Tier 3 Fractional CMO with AI Swarm (6-month minimum, 1-2 days/wk embedded).