Measure swarm · Per-Location Rollup + Executive Reporting Agent · Forward-looking-recommendations skill · Build pillar · Published July 12, 2026

How to build a per-location MMM-driven budget recommendation engine

Multi-banner franchise systems running 50-500 locations need budget recommendations that respect the per-location grain. Robyn (Meta), LightweightMMM (Google), PyMC-Marketing, Recast, Marketing Evolution, Analytic Edge, Nielsen MTA, Neustar MarketShare, IRI MMM, Fospha, and Ascential Edge ship per-account rolled-up MMM outputs — one rolled-up coefficient set and one rolled-up budget recommendation. That fails the moment the regional manager asks why Tampa differs from Austin or why 30 banners should differ. The Simulate + Optimize + Gate + Audit skill bundle on the per-location-rollup-reporting agent sits above the MMM vendor surface and writes a per-location counterfactual + per-location Lagrangian budget optimization + per-location confidence interval + per-location explainability with named regulatory anchors preserved in every audit record: FDD Item 19 FPR + NASAA + 30-state FRR + FTC substantiation + Lanham + Robinson-Patman + replication-crisis statistical discipline + Sarbanes-Oxley + SEC Reg G non-GAAP + ECOA + EU AI Act Annex III + NIST AI RMF.

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The 4-skill bundle on the per-location-rollup-reporting agent

Simulate

Consumes baseline MMM from the per-location MMM sibling skill. Per-location counterfactual scenarios across 8-perturbation × 25-channel grid (up-10 / up-25 / up-50 / up-100 + down-10 / down-25 / down-50 / down-100). Per-location saturation / adstock / halo / cannibalization prediction across banners + cross-DMA halo + cross-banner halo. Monte Carlo posterior predictive + bootstrap + Bayesian credible interval + quantile-regression interval via Stan + PyMC + NumPyro + bambi + brms-R + RStan + Edward2 + Pyro Bayesian probabilistic programming. Causal-inference overlay (CausalML + DoubleML + EconML + Causal Forest + BART) anchors counterfactual claims where geo-lift and matched-market experiments exist. Per- scenario confidence tier + explainability written to Audit.

Optimize

Per-location Lagrangian objective (maximize revenue + margin + conversions + LTV + incrementality) subject to overall budget + per-banner / per-location / per-channel / per-DMA floor + ceiling constraints. Marginal ROAS honoring decreasing-marginal-returns at per-location saturation. Multi- objective optimization via Pareto frontier + NSGA-II (DEAP + pymoo) + CMA-ES + Bayesian optimization (Optuna + Hyperopt + Ax + BoTorch + scikit-optimize) + simulated annealing (scipy.optimize) surfaces multiple non-dominated allocations. Recommendation tier (high / medium / low / experimental) anchored to Bayesian posterior + bootstrap + Monte Carlo + rolling-window OOS validation + walk-forward validation + domain-expert review. Recommendation-rejection threshold + human-in-the-loop approval gate publish. LLM-augmented tie- breakers under per-vendor zero-retention flag conflicts for operator review.

Gate

Five anchors before publish. FDD Item 19 FPR substantiation per FTC Franchise Rule 16 CFR 436 + NASAA Commentary + 15- state franchise registration + 7-additional-state disclosure + 30-state franchise relationship laws + FDD Item 12 + 17 + FTC Section 5 + Pfizer 1972 + FTC Endorsement Guides 16 CFR Part 255. Lanham + per-state UDAP + Robinson-Patman + state business-opportunity + state investment disclosure + CFPB UDAAP. Replication-crisis statistical discipline (rolling- window + walk-forward + Brier + ECE + reliability diagram + Rosenbaum bounds + E-value + tipping-point + prior-sensitivity + multiple-comparisons correction + pre-registration AsPredicted + OSF). Sarbanes-Oxley 302 / 404 / 906 + PCAOB AS 2201 + SEC Regulation G + SEC C&DI Q100 / 101 / 102 + SEC Reg S-K Item 303 + FASB ASC 606 + AICPA non-GAAP + PCAOB AS 2410. ECOA Regulation B + Fair Housing + FCRA + GDPR Article 22 + CCPA / CPRA opt-out + EU AI Act Article 22 + 26 + 50 + Annex III + Article 9 + 13 + 14 + 15 + NIST AI RMF + ISO 42001 + per-vendor LLM zero-retention. Policy-as-code via OPA Rego + AWS Cedar + Casbin + Cerbos + Oso + Styra DAS + Permit.io.

Audit

Per-location WORM record + FBC feedback loop. Storage: AWS S3 Object Lock + Azure Blob immutable + Google Cloud Storage Bucket Lock + Wasabi WORM. Retention stacks (longest applicable): 7-year SOX 802 + 7-year IRS + 7-year FTC + 7- year FDD + per-state franchise + 6-year SEC + 5-year PCAOB + GDPR Article 30 + EU AI Act Article 12 + SOC 2 CC7 / CC8. FBC feedback captures realized vs projected revenue + margin + conversions + LTV + incrementality and recalibrates the counterfactual scenario grid + MMM prediction + saturation / adstock + halo / cannibalization + Lagrangian objective + constraint stack + Pareto frontier + NSGA-II / CMA-ES / Bayesian optimization + recommendation confidence tier + rejection threshold + emerging-channel + emerging-budget- constraint detection. End-to-end replay rewinds every stage.

The real vendor ecosystem this sits above

MMM platforms + numerical

Open-source MMM (Robyn (Meta) + LightweightMMM (Google) + PyMC-Marketing). Vendor MMM (Recast + Marketing Evolution + Analytic Edge + Nielsen MTA + Neustar MarketShare + IRI MMM + Fospha + Ascential Edge + AnalyticEdge + HelloIntelligent + Bayesx + Numerator MMM). Bayesian probabilistic programming (Stan + PyMC + NumPyro + bambi + brms-R + RStan + Edward2 + Pyro). Numerical (scikit-learn + PyTorch + JAX + statsmodels + tslearn).

Optimization + experiment tracking + explainability

Bayesian optimization (Optuna + Hyperopt + Ax + BoTorch + scikit-optimize). Multi-objective optimization (DEAP + pymoo NSGA-II + CMA-ES + scipy.optimize). Causal inference (CausalML + DoubleML + EconML + Causal Forest + BART). Experiment tracking (MLflow + Weights & Biases + CometML + Neptune.ai + ClearML). Explainability (SHAP + LIME + Captum + DALEX + InterpretML). OpenAI + Anthropic + Google + Mistral + Cohere LLM tie-breakers under per-vendor zero-retention. LangSmith + Arize + WhyLabs + Helicone + Langfuse + PromptLayer + Galileo observability.

Policy-as-code + WORM + sibling skills

OPA Rego + AWS Cedar + Casbin + Cerbos + Oso + Styra DAS + Permit.io policy-as-code expresses every Gate rule. AWS S3 Object Lock + Azure Blob immutable + Google Cloud Storage Bucket Lock + Wasabi compliance WORM holds the per-location audit substrate. Sibling skills: per-market-budget- recommendations (parent commercial); per-location MMM (upstream baseline); per-location cross-channel attribution rollup; compliance-first budget-creative pairing; per- location PPC bid + budget management; per-location competitive- density mapping; per-location metric ingestion; multi-vendor receipt-joining + offline attribution; marketing-stack integration-health; routing-audit-trail + versioned-history regulatory-defense; revenue drivers analysis sister skill on rollup-reporting agent; per-unit per-driver EBITDA causal decomposition sibling skill on territory-analysis-market- scoring agent.

The 6-workstream reporting cycle

Numeric uplift commitments are not made up-front. The engagement ships a pre-engagement baseline across six workstreams; the cycle tracks delta against that baseline. Reporting is the substrate, not the promise.

Simulate coverage. Counterfactual scenario grid completeness (8-perturbation × 25-channel); per-location saturation + adstock + halo + cannibalization prediction coverage; Monte Carlo + Bayesian posterior diagnostic (R-hat + effective sample size + divergent transitions); causal- inference layer coverage when geo-lift / matched-market experiments exist.
Optimize quality. Per-location Lagrangian objective + constraint adherence; marginal ROAS curve steepness; Pareto frontier coverage; NSGA-II / CMA-ES / Bayesian optimization convergence; recommendation tier distribution; rejection-threshold adherence; LLM tie-breaker escalation rate.
Gate quality. Per-anchor evaluation completeness (FDD Item 19 FPR + 30-state FRR + FTC substantiation + Lanham + Robinson-Patman + replication-crisis correction + SOX + Reg G + ECOA + EU AI Act Annex III); per- anchor pass / fail / route-to-counsel distribution; FPR substantiation document completeness; multiple-comparisons correction adherence.
Audit quality. Per-location WORM record completeness; retention-window coverage (longest of 7-year SOX 802 + 7-year IRS + 7-year FTC + 7-year FDD + per-state franchise + 6-year SEC + 5-year PCAOB + GDPR Article 30 + EU AI Act Article 12 + SOC 2 CC7 / CC8); end-to-end replay success rate; FBC feedback-loop recalibration cadence.
Compliance posture. FDD Item 19 FPR substantiation file completeness; SEC Reg G reconciliation completeness when ROAS + Adjusted EBITDA + LTV-to-CAC + same- store-sales reach public-co or PE-sponsor disclosure; Robinson- Patman per-franchisee allocation audit; ECOA disparate-impact audit cadence; EU AI Act Annex III + Article 50 disclosure coverage.
Audit-trail completeness. Per-anchor regulatory citation completeness; sibling-handoff pointer completeness into the per-location-rollup-reporting bundle (per-market- budget-recommendations + per-location MMM upstream + per- location cross-channel attribution rollup + compliance-first budget-creative pairing + per-location PPC bid + budget management + per-location competitive-density mapping + per- location metric ingestion + multi-vendor receipt-joining + integration-health + routing-audit-trail + versioned-history regulatory-defense) and into revenue drivers analysis sister skill + per-unit per-driver EBITDA causal decomposition sibling skill.

Frequently asked questions

What is a per-location MMM-driven budget recommendation engine — and why is per-account rolled-up MMM not enough at 50-500-location scale?

Multi-banner franchise systems running 50-500 locations need budget recommendations that respect the per-location grain — per-location revenue, per-location margin, per-location LTV, per-location channel mix, per-location saturation curves, per-location halo and cannibalization. Robyn (Meta), LightweightMMM (Google), PyMC-Marketing, Recast, Marketing Evolution, Analytic Edge, Nielsen MTA, Neustar MarketShare, IRI MMM, Fospha, Ascential Edge, and AnalyticEdge ship per-account rolled-up MMM outputs — one MMM run rolls up the entire portfolio into a single set of channel coefficients and a single budget recommendation. That works for the consolidated planner; it fails the moment the regional manager asks why the recommendation for Tampa differs from Austin or why the recommendation should differ across the 30 banners. The four-skill bundle on the per-location-rollup-reporting agent — Simulate, Optimize, Gate, Audit — sits above the MMM vendor surface and writes a per-location counterfactual + per-location Lagrangian budget optimization + per-location confidence interval + per-location explainability trace with named regulatory anchors preserved in every audit record.

Why do Robyn + LightweightMMM + PyMC-Marketing + Recast + Marketing Evolution + Analytic Edge + Nielsen MTA + Neustar MarketShare + IRI MMM + Fospha break at 50-500-location multi-banner scale?

Each MMM vendor ships a per-account rolled-up flat-recommendation primitive — one rolled-up coefficient set + one rolled-up budget recommendation. None generates per-location counterfactual scenarios across the 8-perturbation × 25-channel grid. None runs the per-location Lagrangian budget optimization with constraints across overall budget + per-banner floor + per-banner ceiling + per-location floor + per-location ceiling + per-channel floor + per-channel ceiling + per-DMA floor + per-DMA ceiling. None attaches confidence intervals (Bayesian posterior + bootstrap + Monte Carlo + quantile regression). None enforces the regulatory anchors when the budget recommendation reaches franchisee-facing FPR or franchise-marketing-fund allocation. None writes a per-location WORM record that retains the recommendation for the longest applicable retention window across FDD + SOX + SEC + FTC. The four-skill bundle Simulate + Optimize + Gate + Audit sits above the MMM vendor surface — it does not replace it. Simulate generates counterfactuals. Optimize runs the Lagrangian + Pareto + NSGA-II + CMA-ES + Bayesian optimization stack. Gate enforces the regulatory anchors. Audit writes the per-location WORM record + FBC feedback loop.

What does Simulate do — per-location counterfactual scenario generation + saturation / adstock / halo / cannibalization + Bayesian posterior + Monte Carlo?

Simulate consumes the baseline MMM from the per-location MMM sibling skill (per-location MMM for multi-store marketing budget allocation) and generates per-location counterfactual scenarios across an 8-perturbation × 25-channel grid (per-up-10 percent + per-up-25 + per-up-50 + per-up-100 + per-down-10 + per-down-25 + per-down-50 + per-down-100). For each scenario it runs counterfactual MMM prediction + per-location saturation / adstock prediction + per-location halo / cannibalization prediction across banners + cross-DMA halo + cross-banner halo. Monte Carlo simulation produces a posterior predictive distribution + bootstrap + Bayesian credible interval + quantile-regression interval. Bayesian probabilistic programming via Stan + PyMC + NumPyro + bambi + brms-R + RStan + Edward2 + Pyro. Causal-inference layer (CausalML + DoubleML + EconML + Causal Forest + BART) overlays where geo-lift + matched-market experiments anchor counterfactual claims. Each scenario carries a per-scenario confidence tier and explainability trace written into Audit.

What does Optimize do — Lagrangian objective + constraint stack + marginal ROAS + Pareto frontier + NSGA-II + CMA-ES + Bayesian optimization?

Optimize composes the per-location budget recommendation from the counterfactual posterior under a Lagrangian objective function (maximize revenue + maximize margin + maximize conversions + maximize LTV + maximize incrementality) subject to per-overall-budget + per-banner floor + per-banner ceiling + per-location floor + per-location ceiling + per-channel floor + per-channel ceiling + per-DMA floor + per-DMA ceiling. Marginal ROAS computation honors decreasing-marginal-returns at per-location saturation. Multi-objective optimization via Pareto frontier + NSGA-II genetic algorithm (DEAP + pymoo) + CMA-ES evolution strategy + Bayesian optimization (Optuna + Hyperopt + Ax + BoTorch + scikit-optimize) + simulated annealing (scipy.optimize) explores the constraint manifold and surfaces multiple non-dominated allocations. Recommendation tier (high-confidence + medium-confidence + low-confidence + experimental) attaches to each allocation based on per-Bayesian-posterior + per-bootstrap + per-Monte-Carlo + rolling-window out-of-sample validation + walk-forward validation + business-validation handoff to domain-expert review. Recommendation-rejection threshold and human-in-the-loop approval gate the publish step. LLM-augmented tie-breakers (OpenAI + Anthropic under per-vendor zero-retention) flag conflicts for operator review rather than auto-resolve. Per-location optimization confidence tier + explainability written into Audit.

What does Gate do — FDD Item 19 FPR + FTC substantiation + Lanham + Robinson-Patman + replication-crisis + Sarbanes-Oxley + Reg G + ECOA + EU AI Act Annex III?

Gate evaluates five operationally distinctive anchors before any per-location budget recommendation is delivered. Anchor 1 (the most operationally distinctive — distinctive to franchise-system budget recommendations): FDD Item 19 financial performance representations per FTC Franchise Rule 16 CFR 436 + NASAA Commentary on FPRs — when MMM-driven budget recommendations reach franchisee-facing FPR or franchise-marketing-fund allocation, the recommendation must have reasonable basis + written substantiation + actual outlet data not projections + per-class disclosure + statistical-significance disclosures where applicable + FPR bucket + time window + cohort inclusion criteria + cohort exclusion criteria + substantiation document hash + source-of-record; FTC Section 5 + FTC substantiation doctrine (Pfizer 1972 reasonable-basis); FTC Endorsement Guides 16 CFR Part 255 (2023 AI-content) when AI-driven recommendations are shared with franchisees; 15-state franchise registration + 7-additional-state franchise disclosure + 30-state franchise relationship laws + FDD Item 12 + FDD Item 17. Anchor 2 (anti-trust + competition law): Lanham Act 15 USC 1125(a) false-advertising risk when per-location recommendations reference competitive positions; per-state UDAP + per-state Deceptive Trade Practices Act; Robinson-Patman Act when budget allocation produces per-franchisee discrimination (e.g., systematically allocating marketing-fund dollars to favored franchisees); state business-opportunity disclosure; state investment disclosure; CFPB UDAAP. Anchor 3 (replication-crisis statistical discipline): rolling-window out-of-sample validation + walk-forward validation + Brier score + expected calibration error (ECE) + reliability diagram + Rosenbaum bounds + E-value + tipping-point + prior-sensitivity for unmeasured-confounder exposure; multiple-comparisons correction (Bonferroni + Holm-Bonferroni + Benjamini-Hochberg FDR + Benjamini-Yekutieli + Tukey HSD + Dunnett + Scheffe) when reporting many per-location recommendations; pre-registration (AsPredicted + OSF) when hypothesis-confirming. Anchor 4 (accounting + investor disclosure): Sarbanes-Oxley Section 302 + 404 + 906 + PCAOB AS 2201 + SEC Regulation G 17 CFR 244 non-GAAP financial measures reconciliation + SEC C&DI Question 100 / 101 / 102 (Adjusted EBITDA + ROAS + LTV-to-CAC + same-store-sales subject to Reg G when reaching public-co or PE-sponsor) + SEC Reg S-K Item 303 MD&A + FASB ASC 606 revenue recognition + FASB ASC 842 leases + AICPA non-GAAP + PCAOB AS 2410. Anchor 5 (anti-discrimination + AI-governance): ECOA Regulation B disparate-impact + Fair Housing Act when per-location budget recommendation correlates with protected class proxies via ZIP code + demographic correlate; FCRA when budget recommendation affects credit decisioning; GDPR Article 22 + CCPA + CPRA right to opt out of automated decision-making; EU AI Act Annex III high-risk classification when AI-driven budget recommendation drives capital deployment + franchise-marketing-fund allocation + termination decisions + Article 9 risk-management system + Article 13 + 14 + 15 + 22 + 26 + 50; NIST AI Risk Management Framework; ISO 42001 AI Management System; per-vendor LLM zero-retention verified per call. Policy-as-code expression via OPA Rego + AWS Cedar + Casbin + Cerbos + Oso + Styra DAS + Permit.io.

What does Audit do — per-location WORM record + FBC realized-vs-projected feedback + end-to-end replay?

Audit writes a per-location WORM record at every recommendation run + every counterfactual scenario + every Lagrangian optimization run: per-recommendation-run ID + per-banner pointer + per-location pointer + baseline MMM handoff snapshot + counterfactual scenario grid snapshot + counterfactual MMM prediction snapshot + saturation / adstock snapshot + halo / cannibalization snapshot + Monte Carlo simulation snapshot + Bayesian posterior snapshot + counterfactual confidence interval snapshot + Lagrangian objective function snapshot + Lagrangian constraint snapshot + marginal ROAS snapshot + decreasing-marginal-returns snapshot + OPA Cedar Casbin Cerbos Oso budget policy snapshot + Pareto frontier snapshot + NSGA-II / CMA-ES / Bayesian optimization snapshot + optimization confidence tier + optimization explainability + recommendation tier snapshot + recommendation rejection threshold + human-in-the-loop approval snapshot + per-anchor Gate decision with evidence (FDD Item 19 FPR substantiation document hash + Lanham + Robinson-Patman + replication-crisis correction record + Rosenbaum E-value + multiple-comparisons correction + pre-registration + SOX 302/404/906 + Reg G non-GAAP reconciliation + FASB ASC 606 + ECOA disparate-impact + EU AI Act Annex III + Article 50 disclosure) + per-vendor LLM zero-retention verification + FBC feedback loop record (per-realized revenue vs projected + per-realized margin vs projected + per-realized conversions vs projected + per-realized LTV vs projected + per-realized incrementality vs projected + per-counterfactual spend scenario recalibration + per-MMM prediction recalibration + per-saturation/adstock recalibration + per-halo/cannibalization recalibration + per-Lagrangian objective recalibration + per-Lagrangian constraint recalibration + per-Pareto frontier recalibration + per-NSGA-II/CMA-ES/Bayesian optimization recalibration + per-recommendation confidence tier recalibration + per-recommendation rejection threshold recalibration + pattern learning + emerging-channel detection + emerging-budget-constraint detection) + sibling-handoff pointers. Storage on AWS S3 Object Lock + Azure Blob immutable + Google Cloud Storage Bucket Lock + Wasabi compliance WORM. Retention stacks (longest applicable wins): 7-year SOX Section 802 + 7-year IRS + 7-year FTC substantiation + 7-year FDD record + per-state franchise registration + 6-year SEC + 5-year PCAOB + GDPR Article 30 + EU AI Act Article 12 + SOC 2 CC7 / CC8. End-to-end replay rewinds Simulate + Optimize + Gate + FBC feedback with confidence tier and explainability at every stage. Sibling handoffs flow into the per-market-budget-recommendations parent commercial pillar, the per-location MMM sibling build-pillar (upstream baseline MMM), the per-location cross-channel attribution rollup sibling build-pillar, the compliance-first budget-creative pairing sibling build-pillar, the per-location PPC bid + budget management sibling build-pillar, the per-location competitive-density mapping sibling build-pillar, the per-location metric ingestion sibling build-pillar, the multi-vendor receipt-joining + offline attribution sibling build-pillar, the marketing-stack integration-health sibling build-pillar, the routing-audit-trail + versioned-history regulatory-defense sibling build-pillars, and the revenue drivers analysis sister skill on the rollup-reporting agent + per-unit per-driver EBITDA causal decomposition sibling skill on the territory-analysis-market-scoring agent.

Engage Completions on the rollup-reporting bundle

The Simulate + Optimize + Gate + Audit four-skill bundle ships as the orchestration layer above your existing MMM + Bayesian + optimization + experiment-tracking surface. FDD Item 19 FPR substantiation + 30-state franchise relationship laws + FTC substantiation + Lanham + Robinson-Patman + replication-crisis statistical discipline + Sarbanes-Oxley + SEC Reg G non-GAAP + ECOA disparate-impact + EU AI Act Annex III + NIST AI RMF anchors are preserved in every per-location audit record. Tier 1 AI Readiness Assessment scopes the bundle in two to three weeks; Tier 3 Fractional CMO with AI Swarm operates the bundle end-to-end.

Tier 1 AI Readiness Assessment Tier 3 Fractional CMO with AI Swarm Take the 3-question quiz