Measure swarm · Per-Location Rollup + Executive Reporting Agent · Drivers analysis skill · Build pillar · Published September 8, 2026

How to build revenue drivers analysis for multi-location operators

When monthly revenue moves +12 percent across 200 locations, the board asks why. A CFO who answers “same-store-sales were strong” gets a follow-up “but why?” The drivers- analysis skill decomposes that delta into contribution shares across 30+ standing drivers (customer count, AOV, frequency, conversion rate, channel mix, product mix, promotional lift, pricing, cannibalization, FX, seasonality, cohort shift, iOS 14 ATT, Google Privacy Sandbox, supply chain, labor). The Decompose + Estimate + Gate + Audit skill bundle on the rollup-reporting agent sits above your existing Anaplan + Pigment + Causal + Cube + Mosaic + Workday Adaptive + Oracle Hyperion + OneStream + Vena FP&A platforms and writes a confidence-weighted per-driver decomposition with named regulatory anchors preserved in every audit record — SEC Regulation G non-GAAP reconciliation, Sarbanes-Oxley Section 302/404/906, FASB ASC 606/842/326, AICPA, PCAOB AS 2410, multiple-comparisons correction, Simpson-paradox detection, FDD Item 19, GDPR Article 22, EU AI Act Article 50, NIST AI RMF.

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The 4-skill bundle on the rollup-reporting agent

Decompose

Shapley-value decomposition of the period-over-period delta across the standing driver set (Shapley 1953 + Aumann-Shapley + Banzhaf + Owen coalitional Shapley + SHAP Lundberg-Lee 2017). Monte-Carlo permutation sampling or KernelSHAP / TreeSHAP / DeepSHAP / GradientSHAP / LinearSHAP approximations apply where the driver count makes exact Shapley infeasible, with explicit error bounds. Per-driver explainability via SHAP + LIME + anchor explanations + counterfactual explanations + Integrated Gradients + DeepLIFT + Shapley-Lorenz to surface heterogeneity across the per-location cohort.

Estimate

Five coordinated causal-inference subsystems: Granger causality (statsmodels VAR + Toda-Yamamoto + Diks-Panchenko + multivariate Granger + block-exogeneity Wald) with lag-order + stationarity + cointegration testing; counter-factual estimation under the Rubin causal model + Pearl do-calculus (ATE + ATT + ATC + CATE via T-learner + S-learner + X-learner + DR-learner using CausalML + DoubleML + EconML + Causal Forest + BART + Bayesian causal inference via PyMC + Stan + NumPyro); difference-in- differences (DiD + event study + Callaway-Sant’Anna + de Chaisemartin-D’Haultfoeuille + Goodman-Bacon); synthetic control (Abadie-Gardeazabal + Doudchenko-Imbens + Ferman- Pinto); RDD + propensity-score matching + IPW. Confidence intervals via bootstrap (percentile + BCa + Studentized) + Bayesian credible intervals + asymptotic normal. LLM-augmented tie-breakers (OpenAI + Anthropic under per-vendor zero- retention) flag conflicts for operator review.

Gate

Five anchors before publish: SEC Regulation G 17 CFR 244 non- GAAP reconciliation + SEC C&DI Q100/101/102 + SEC Reg S-K Item 303 MD&A + SEC Reg S-X; Sarbanes-Oxley Section 302/404/906; FASB ASC 606/842/326/805/740 + AICPA non-GAAP + PCAOB AS 2410 + AICPA SSAE; multiple-comparisons correction (Bonferroni + Holm + Benjamini-Hochberg FDR + Benjamini- Yekutieli + Tukey HSD + Dunnett + Scheffe) + Simpson paradox + collider bias + Berkson paradox + confounding-variable detection + replication-crisis pre-registration (AsPredicted + OSF); FDD Item 19 per FTC Franchise Rule 16 CFR 436 + NASAA Commentary + 15-state franchise registration; GDPR Article 22 + CCPA right to opt out of automated decision-making + EU AI Act Article 22/26/50 + NIST AI RMF + ISO 42001 + per-vendor LLM zero-retention.

Audit

Per-driver WORM record: per-driver ID + per-banner pointer + per-period pointer + Decompose Shapley snapshot + Estimate snapshot (Granger + counter-factual + DiD + synthetic control + RDD + propensity matching + IPW) + confidence-interval snapshot + LLM tie-breaker snapshot + per-anchor Gate decision with evidence + non-GAAP reconciliation evidence + multiple- comparisons correction record + Simpson-paradox detection + FDD Item 19 substantiation + sibling-handoff pointers. Storage on AWS S3 Object Lock + Azure Blob immutable + Google Cloud Storage Bucket Lock + Wasabi WORM. Retention stacks: 7-year SOX Section 802 + 7-year IRS tax + 7-year FTC substantiation + 6-year SEC + 5-year PCAOB + 3-year FDD Item 19 + 7-year state franchise registration. End-to-end replay rewinds every stage.

The real vendor ecosystem this sits above

FP&A platforms

Anaplan, Pigment, Causal, Cube, Mosaic, Workday Adaptive Planning, Oracle Hyperion, OneStream, Vena Solutions, Planful, Prophix, IBM Planning Analytics, SAP BPC, BlackLine, FloQast, Workiva, Vareto, Datarails, Jirav, NetSuite Planning, Sage Intacct remain the substrate. The decomposition reads canonical FP&A data; the driver tree authored in the FP&A platform remains the system of record for the mathematical identity.

Causal inference + explainability + numerical

CausalML + DoubleML + EconML + Causal Forest + BART for CATE + heterogeneous treatment effects; PyMC + Stan + NumPyro + bambi + brms-R + RStan + Edward2 + Pyro Bayesian causal; statsmodels VAR + tslearn time-series + scikit-learn + PyTorch + JAX numerical; SHAP + LIME + Captum + DALEX + InterpretML explainability; OpenAI + Anthropic LLM tie-breakers under per-vendor zero-retention; LangSmith + Weights & Biases + Arize + WhyLabs + Helicone + Langfuse + PromptLayer + Galileo observability.

Policy-as-code + disclosure + WORM

OPA Rego + AWS Cedar + Casbin + Cerbos + Oso + Styra DAS + Permit.io policy-as-code expresses the SEC Reg G, Sarbanes- Oxley, FASB ASC, multiple-comparisons, and Simpson-paradox gates. Workiva + BlackLine + FloQast + Trintech + AuditBoard + Hyperproof + Drata + Vanta disclosure-management remain where final XBRL tagging happens. AWS S3 Object Lock + Azure Blob immutable + Google Cloud Storage Bucket Lock + Wasabi compliance WORM holds the per-driver audit substrate.

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.

Decompose coverage. Per-driver Shapley completeness across the 30+ standing driver set; approximation method choice (exact / KernelSHAP / TreeSHAP / DeepSHAP / GradientSHAP / LinearSHAP) with error bound; per-driver explainability trace.
Estimate quality. Granger / counter-factual / DiD / synthetic control / RDD / propensity matching subsystem coverage; per-driver confidence-interval coverage; per-method cross-validation; LLM tie-breaker escalation rate.
Gate quality. Per-anchor evaluation completeness (SEC Reg G + Sarbanes-Oxley + Reg S-K + FASB ASC + AICPA + PCAOB + multiple-comparisons + Simpson paradox + FDD Item 19 + GDPR Article 22 + CCPA opt-out + EU AI Act); per-anchor pass / fail / route-to-counsel distribution; remediation-loop turnaround.
Audit quality. Per-driver WORM record completeness; retention-window coverage per record (longest of 7-year SOX 802 + 7-year IRS + 7-year FTC + 6-year SEC + 5-year PCAOB + 3-year FDD Item 19 + 7-year state franchise registration); end-to-end replay success rate.
Compliance posture. SEC Reg G non-GAAP reconciliation completeness across Adjusted EBITDA + same-store- sales + comp-sales + 4-wall EBITDA + unit economics + FCF + EBITDAR + ARR + cohort-LTV; Sarbanes-Oxley Section 906 certification readiness; FDD Item 19 substantiation file completeness; multiple-comparisons correction adherence; pre- registration coverage; EU AI Act Article 50 disclosure coverage.
Audit-trail completeness. Per-anchor regulatory citation completeness; sibling-handoff pointer completeness into the rollup-reporting bundle (monthly executive summary drafting — drivers feed the variance commentary; quarterly board deck generation; PE sponsor LP letter drafting; cohort-framed benchmark reports; cohort-framed per-location KPI rollup; per-franchisee accountability views).

Frequently asked questions

What is revenue drivers analysis for multi-location operators — what does the board-asks-why-and-the-CFO-has-no-answer problem look like?

When monthly revenue moves +12 percent year-over-year across 200 locations, the board asks why. A CFO who answers "same-store-sales were strong" gets a follow-up "but why?" The drivers-analysis skill decomposes that 12 percent into contribution shares across 30+ standing drivers: customer count + AOV + frequency + conversion rate + channel mix + product mix + promotional lift + pricing change + cannibalization + FX + weather + seasonality + new-location opening + comp-store vs non-comp + cohort tenure shift + loyalty tier mix shift + new-customer acquisition + reactivation rate + churn rate + AOV mix shift + promotional cannibalization + cross-banner cannibalization + organic-vs-paid mix + multi-touch attribution shift + iOS 14 ATT impact + Google Privacy Sandbox impact + supply-chain disruption + labor shortage. The four-skill bundle on the rollup-reporting agent — Decompose, Estimate, Gate, Audit — sits above your existing Anaplan + Pigment + Causal + Cube + Mosaic + Workday Adaptive + Oracle Hyperion + OneStream + Vena FP&A platforms and writes a confidence-weighted per-driver decomposition with named regulatory citations preserved in the audit trail.

Why do Anaplan + Pigment + Causal + Cube + Mosaic + Workday Adaptive + Oracle Hyperion + OneStream + Vena break at multi-location revenue-drivers scale?

Anaplan, Pigment, Causal, Cube, Mosaic, Workday Adaptive Planning, Oracle Hyperion, OneStream, Vena Solutions, Planful, Prophix, IBM Planning Analytics, and SAP BPC ship per-tenant flat driver-tree primitives — the planner manually authors a formula tree (Revenue = Customer Count × AOV × Frequency) and the system computes mathematical-identity variance. None resolves true causal decomposition. None produces counter-factual estimates ("what would revenue have been without the promotion?"). None attaches confidence intervals to attribution. None applies multiple-comparisons correction when reporting many drivers. None detects Simpson paradox, collider bias, or Berkson paradox at the cross-banner aggregation step. None enforces SEC Regulation G non-GAAP reconciliation before the driver tree reaches investors. The four-skill bundle Decompose + Estimate + Gate + Audit sits above the FP&A vendor primitives — it does not replace them. Decompose computes Shapley-based contribution shares grounded in the FP&A canonical data. Estimate runs Granger causality + counter-factual + difference-in-differences + synthetic control + propensity-score matching. Gate enforces SEC Reg G + Sarbanes-Oxley + SEC Reg S-K Item 303 + FASB ASC + AICPA + PCAOB AS 2410 + multiple-comparisons correction + Simpson-paradox detection + FDD Item 19 (when franchisor) + GDPR Article 22 + EU AI Act Article 50 disclosure. Audit writes a per-driver WORM record.

What does Decompose do — Shapley + cooperative-game-theory + SHAP and per-driver explainability?

Decompose runs cooperative-game-theory decomposition of the period-over-period revenue delta across the standing driver set. Shapley-value computation (Shapley 1953 cooperative-game-theory + Aumann-Shapley + Banzhaf + Owen coalitional Shapley + SHAP Lundberg-Lee 2017) assigns each driver its marginal contribution averaged over all permutations of the driver coalition. Where the driver count makes exact Shapley infeasible, Monte-Carlo permutation sampling or KernelSHAP / TreeSHAP / DeepSHAP / GradientSHAP / LinearSHAP approximations apply with explicit error bounds. Per-driver explainability layers the Shapley result with SHAP + LIME + anchor explanations + counterfactual explanations + Integrated Gradients + DeepLIFT + Shapley-Lorenz curves to surface heterogeneity across the per-location cohort. Per-driver confidence tier and explainability trace write to Audit at every run.

What does Estimate do — Granger causality + counter-factual + DiD + synthetic control + propensity-score matching?

Estimate runs five coordinated causal-inference subsystems on the driver candidate set. Granger causality (statsmodels VAR + Toda-Yamamoto + Diks-Panchenko nonparametric + multivariate Granger + block-exogeneity Wald) with lag-order selection (AIC + BIC + HQC + FPE) and stationarity testing (ADF + KPSS + PP + DF-GLS) and cointegration testing (Engle-Granger + Johansen + Johansen-Juselius + Phillips-Ouliaris) plus impulse-response and variance-decomposition. Counter-factual estimation under the Rubin causal model and Pearl do-calculus framework: ATE + ATT + ATC + CATE conditional average treatment effects via T-learner + S-learner + X-learner + DR-learner using CausalML + DoubleML + EconML + Causal Forest + Bayesian Additive Regression Trees (BART), with Bayesian causal inference via PyMC + Stan + NumPyro. Difference-in-differences (DiD + event study + Callaway-Sant’Anna staggered treatment + de Chaisemartin-D’Haultfoeuille + Goodman-Bacon decomposition). Synthetic control (Abadie-Gardeazabal + Doudchenko-Imbens + Ferman-Pinto). Regression discontinuity design + propensity-score matching (nearest neighbor + kernel + stratified + radius) + IPW (inverse propensity weighting + augmented IPW + targeted MLE). Confidence intervals attach via bootstrap (percentile + BCa + Studentized) + Bayesian credible intervals + asymptotic normal + Wald + Wilson + Agresti-Coull + Clopper-Pearson + Jeffreys. Each estimate carries a confidence tier and explainability trace. Conflicting estimates route to LLM-augmented tie-breakers (OpenAI + Anthropic under per-vendor zero-retention) for operator review rather than auto-resolve.

What does Gate do — SEC Reg G + Sarbanes-Oxley + SEC Reg S-K + FASB ASC + AICPA + multiple-comparisons + Simpson paradox + FDD Item 19 + EU AI Act?

Gate evaluates five operationally distinctive regulatory and statistical anchors before any driver decomposition is delivered. Anchor 1 (the most operationally distinctive): SEC Regulation G 17 CFR 244 — when public-company drivers analysis output (Adjusted EBITDA, same-store-sales, comp-sales, 4-wall EBITDA, unit economics, FCF, EBITDAR, ARR, cohort LTV) reaches investors, every non-GAAP measure must reconcile to the most directly comparable GAAP measure with equal-or-greater prominence and purpose disclosure; SEC C&DI Questions 100, 101, and 102 enforcement; SEC Reg S-K Item 303 MD&A; SEC Reg S-X Article 3, 5, 8, 11. Anchor 2: Sarbanes-Oxley Section 302 (CEO + CFO certification of disclosure controls, quarterly + annual), Section 404 (ICFR management assertion + auditor attestation per PCAOB AS 2201), Section 906 (criminal certification — $1M and ten years for knowing false certification; $5M and twenty years for willful false certification). Anchor 3: FASB ASC 606 revenue recognition 5-step model (drivers analysis output must align with revenue-recognition timing across multi-element arrangements, variable consideration, and significant financing component); FASB ASC 842 leases; FASB ASC 326 CECL; FASB ASC 805 business combinations; FASB ASC 740 income taxes; AICPA non-GAAP financial measures; PCAOB AS 2410 related parties + significant unusual transactions; AICPA SSAE attestation; forensic accounting + fraud detection. Anchor 4 (statistical discipline): multiple-comparisons correction (Bonferroni + Holm-Bonferroni + Benjamini-Hochberg FDR + Benjamini-Yekutieli + Tukey HSD + Dunnett + Scheffe) when reporting many drivers — without correction, the family-wise error rate inflates and the replication crisis applies; Simpson paradox detection at cross-banner aggregation + Yule-Simpson + aggregation-bias + base-rate-fallacy + collider-bias + Berkson paradox + confounding-variable detection + stratification + marginalization; pre-registration via AsPredicted or OSF when the analysis is hypothesis-confirming. Anchor 5: FDD Item 19 financial performance representations per FTC Franchise Rule 16 CFR 436 when the franchisor reports driver-decomposed outlet performance (voluntary, but if included must have reasonable basis + written substantiation + actual outlet data not projections + per-class disclosure + statistical-significance disclosures where applicable); NASAA Commentary on FPRs; 15-state franchise registration; GDPR Article 22 automated decisions + CCPA right to opt out of automated decision-making when driver decomposition drives downstream automated action; EU AI Act Article 22 + 26 + 50 transparency for AI-generated content; NIST AI RMF Govern + Map + Measure + Manage; ISO 42001 AI Management System; per-vendor LLM zero-retention verified per call.

What does Audit do — per-driver WORM record + end-to-end replay across the rollup-reporting bundle?

Audit writes a per-driver WORM record at every publish: per-driver ID + per-banner pointer + per-period pointer + per-canonical-driver-class snapshot + Decompose Shapley decomposition snapshot (with method version + cooperative-game-theory variant + Monte-Carlo permutation count or KernelSHAP / TreeSHAP / DeepSHAP / GradientSHAP / LinearSHAP variant) + Estimate snapshot (Granger lag-order + stationarity tests + cointegration tests + impulse-response + counter-factual T/S/X/DR-learner + CausalML / DoubleML / EconML / Causal-Forest / BART + DiD + synthetic control + RDD + propensity-score matching + IPW) + confidence-interval snapshot + LLM tie-breaker snapshot (per-vendor zero-retention verification) + Gate per-anchor decision with evidence + SEC Reg G non-GAAP reconciliation evidence + Sarbanes-Oxley 302/404/906 readiness + AICPA + PCAOB AS 2410 + FDD Item 19 substantiation + multiple-comparisons correction record + Simpson-paradox detection record + EU AI Act Article 50 disclosure + 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 tax + 7-year FTC substantiation + 6-year SEC + 5-year PCAOB + 3-year FDD Item 19 + 7-year state franchise registration. End-to-end replay rewinds canonical data + Decompose + Estimate + Gate + Audit with confidence tier and explainability at every stage. Sibling handoffs flow into monthly executive summary drafting (sibling skill on the same rollup-reporting agent — drivers feed the variance commentary), quarterly board deck generation, PE sponsor LP letter drafting, cohort-framed benchmark reports, cohort-framed per-location KPI rollup, and per-franchisee accountability views.

Engage Completions on the rollup-reporting bundle

The Decompose + Estimate + Gate + Audit four-skill bundle ships as the orchestration layer above your existing FP&A platform + causal-inference + explainability + disclosure-management stack. SEC Regulation G non-GAAP reconciliation + Sarbanes-Oxley + FASB ASC + multiple-comparisons correction + Simpson-paradox detection + FDD Item 19 + GDPR Article 22 + EU AI Act + NIST AI RMF anchors are preserved in every per-driver 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.

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