Done-for-you offer · Fractional CMO with AI Swarm · forward-looking-recommendations skill
Completions builds the per-location Bayesian MMM + quarterly recommendation engine for your multi-location budget
You have 50-1,500 locations and a marketing budget allocated by gut + last-click attribution that mis-allocates 30-50% of spend. Your CFO demands per-channel ROAS proof. Your CMO demands per-location budget recommendations. Your board demands SEC Reg FD + Reg G + Item 7 MD&A + FINRA + SOX + GAAP ASC 606 + IFRS 15 compliance on every recommendation surfacing in public filings or franchisee-shared dashboards. Completions builds the forward-looking-recommendations skill on the per-location-rollup-reporting agent end-to-end with per-location Bayesian MMM + per-channel saturation curve fitting + per-cohort lift attribution + quarterly budget recommendation engine + per-vertical compliance overlay. You keep every artifact. You keep the spend + revenue data + MMM model. You keep the ability to in-house at any time.
Published September 24, 2026
What we operate every quarter
Per-location per-channel MMM ingestion across 10+ channels (organic + paid search + paid social + paid video + paid programmatic + email + SMS + push + direct mail + OOH + radio + TV + sponsorships + referral + partnerships) with per-channel per-week spend + impressions + clicks + conversions + revenue + per-location attribution.
Bayesian Mixed-Media Modeling via Stan + PyMC + NumPyro + bambi + brms-R Bayesian-inference frameworks with per -channel adstock + per-channel saturation curve + per -channel carryover + per-channel seasonality + per-channel trend + per-channel synergy + per-channel cross-channel interaction. Per-location per-channel saturation curve fitting across 5 curve types (Hill function + Michaelis -Menten + Gompertz + logistic + Weibull) with diminishing -returns inflection-point detection.
Per-cohort lift attribution via causal-uplift CATE meta -learner ensemble (T-learner + S-learner + X-learner + DR -learner + CausalML + DoubleML + EconML + Bayesian -treatment-effect + counterfactual-prediction + causal -forest) for per-location per-channel incrementality measurement.
Quarterly budget recommendation engine emits per-location per-channel per-quarter spend recommendation with confidence-tier + explainability + scenario-analysis (current-budget + +10% + +25% + -10% + -25% + optimal -allocation). Per-vertical compliance overlay. Per -quarterly SEC Reg FD + Reg G + Item 7 MD&A + FINRA Rule 2210 + SOX Section 404 + GAAP ASC 606 + IFRS 15 disclosure compliance.
Why in-house breaks at multi-location budget scale
Per-location per-channel MMM ingestion across 50-1,500 locations × 10+ channels × per-week × 2-3-year history = 52k-2.3M location-channel-week ingestion cells requires production data infrastructure. Bayesian Mixed-Media Modeling at per-location grain requires data-science capacity with Bayesian-inference expertise. Per-location per-channel saturation curve fitting requires production ML infrastructure. Per-cohort lift attribution requires data-science capacity. Quarterly budget recommendation engine requires production recommendation infrastructure. Per-vertical compliance overlay requires legal-engineering capacity. Per-quarterly disclosure compliance requires legal-vendor coordination.
Completions absorbs all seven axes under one Tier 3 Fractional CMO with AI Swarm engagement. The embedded executive (1-2 days/wk) coordinates the MMM + recommendation engine across the per-location-rollup-reporting agent + location-benchmarking + compliance-overlay-manager + master-record-canonicalization siblings.
How the engagement progresses
Tier 1 AI Readiness Assessment ($10k, 2-3 weeks, diagnostic). Completions audits the operator current per-location MMM operation across seven axes — per -location per-channel MMM ingestion coverage + Bayesian Mixed-Media Modeling maturity + per-location per-channel saturation curve fitting + per-cohort lift attribution + quarterly budget recommendation engine + per-vertical compliance overlay + per-quarterly disclosure compliance. Deliverable: gap-pack report.
Tier 2 AI Swarm Setup Sprint ($25-50k, 4-8 weeks, build with 30-day operating tail). Completions builds the per-location Bayesian MMM + quarterly recommendation engine on operator infrastructure — forward -looking-recommendations + per-location-mmm-driven-budget -recommendation-engine + bayesian-mixed-media-modeling + per-channel-saturation-curve-fitting + per-cohort-lift -attribution on per-location-rollup-reporting agent + peer -cohort-computation on location-benchmarking + per -jurisdiction-overlay-config on compliance-overlay-manager + change-event-emission on master-record-canonicalization.
Tier 3 Fractional CMO with AI Swarm ($15-25k/ month, 6-month minimum, 1-2 days/wk embedded). Completions continues operating the MMM + recommendation engine with weekly per-location MMM update + monthly per-channel saturation curve refresh + quarterly budget recommendation delivery + per-event compliance overlay refresh + cross -agent swarm coordination.
Frequently asked
What does "Completions builds the per-location Bayesian MMM + quarterly recommendation engine for your multi-location budget" actually deliver?
Completions builds and operates per-location Bayesian mixed-media modeling + quarterly budget recommendation engine + per-cohort lift attribution + per-channel saturation curves across the operator multi-location budget. Per-location per-channel MMM ingestion across 10+ channels (organic + paid search + paid social + paid video + paid programmatic + email + SMS + push + direct mail + OOH + radio + TV + sponsorships + referral + partnerships) with per-channel per-week spend + per-channel per-week impressions + per-channel per-week clicks + per-channel per-week conversions + per-channel per-week revenue + per-channel per-week per-location attribution. Bayesian Mixed-Media Modeling via Stan + PyMC + NumPyro + bambi + brms-R Bayesian-inference frameworks with per-channel adstock + per-channel saturation curve + per-channel carryover + per-channel seasonality + per-channel trend + per-channel synergy + per-channel cross-channel interaction. Per-location per-channel saturation curve fitting (Hill function + Michaelis-Menten + Gompertz + logistic + Weibull) with diminishing-returns inflection-point detection. Per-cohort lift attribution via causal-uplift CATE meta-learner ensemble (T/S/X/DR-learner + CausalML + DoubleML + EconML + Bayesian-treatment-effect + counterfactual-prediction + causal-forest) for per-location per-channel incrementality measurement. Quarterly budget recommendation engine emits per-location per-channel per-quarter spend recommendation with confidence-tier + explainability + scenario-analysis (current-budget + +10% + +25% + -10% + -25% + optimal-allocation). Per-vertical compliance overlay. Per-quarterly SEC Reg FD + Reg G + Item 7 MD&A + FINRA Rule 2210 + SOX Section 404 + GAAP ASC 606 + IFRS 15 disclosure when recommendations surface in public filings or franchisee-shared dashboards. Operator team owns the spend data + revenue data + per-channel attribution + brand spec + compliance overlay. Completions owns the swarm orchestration on the per-location-rollup-reporting agent.
Why does in-house per-location Bayesian MMM break at multi-location budget scale?
In-house operation at multi-location budget scale fails on seven axes: (1) per-location per-channel MMM ingestion across 50-1,500 locations × 10+ channels × per-week cadence × 2-3-year history = 52k-2.3M location-channel-week ingestion cells requires production data infrastructure unstaffable by internal teams; (2) Bayesian Mixed-Media Modeling via Stan + PyMC + NumPyro + bambi + brms-R at per-location grain requires data-science capacity with Bayesian-inference expertise; (3) per-location per-channel saturation curve fitting across 5 saturation curve types requires production ML infrastructure with hyperparameter tuning; (4) per-cohort lift attribution via causal-uplift CATE meta-learner ensemble requires data-science capacity with holdout-control infrastructure; (5) quarterly budget recommendation engine with confidence-tier + explainability + scenario-analysis requires production recommendation infrastructure; (6) per-vertical compliance overlay across SEC Reg FD + Reg G + Item 7 MD&A + FINRA Rule 2210 + SOX Section 404 + GAAP ASC 606 + IFRS 15 requires legal-engineering capacity; (7) per-quarterly disclosure compliance when recommendations surface in public filings or franchisee-shared dashboards requires legal-vendor coordination. Completions absorbs all seven axes under one Tier 3 Fractional CMO with AI Swarm engagement.
What does the engagement look like across Tier 1 → Tier 2 → Tier 3?
Tier 1 AI Readiness Assessment ($10k, 2-3 weeks, diagnostic): Completions audits the operator current per-location MMM operation across seven axes — per-location per-channel MMM ingestion coverage + Bayesian Mixed-Media Modeling maturity + per-location per-channel saturation curve fitting + per-cohort lift attribution + quarterly budget recommendation engine + per-vertical compliance overlay + per-quarterly disclosure compliance. Deliverable: gap-pack report. Tier 2 AI Swarm Setup Sprint ($25-50k, 4-8 weeks, build with 30-day operating tail): Completions builds the per-location Bayesian MMM + quarterly recommendation engine on operator infrastructure — forward-looking-recommendations + per-location-mmm-driven-budget-recommendation-engine + bayesian-mixed-media-modeling + per-channel-saturation-curve-fitting + per-cohort-lift-attribution on per-location-rollup-reporting agent + peer-cohort-computation on location-benchmarking + per-jurisdiction-overlay-config on compliance-overlay-manager + change-event-emission on master-record-canonicalization. Tier 3 Fractional CMO with AI Swarm ($15-25k/month, 6-month minimum, 1-2 days/wk embedded): Completions continues operating the MMM + recommendation engine with weekly per-location MMM update + monthly per-channel saturation curve refresh + quarterly budget recommendation delivery + per-event compliance overlay refresh + cross-agent swarm coordination.
Who owns the spend data, revenue data, MMM model, and audit trail?
Operator owns 100% of every artifact: spend data + revenue data + per-channel attribution data (in operator data infrastructure — Snowflake + Databricks + BigQuery + Redshift + Postgres operator data warehouse), per-channel data source credentials (Google Search Console + Google Ads + Meta Business Manager + Microsoft Ads + TikTok Ads + LinkedIn Ads + Klaviyo + Twilio + NetSuite + SAP + Salesforce + HubSpot under operator billing + operator credentials), Bayesian MMM model code (in operator repo with operator-controlled deploy pipeline), per-location per-channel saturation curve registry (operator-owned), per-cohort lift attribution methodology (operator-owned + operator-finance-team-aligned + operator-counsel-aligned), quarterly budget recommendation engine code (in operator repo), per-vertical compliance overlay (rule library in operator repo with attorney-approved updates), SEC Reg FD + Reg G + Item 7 MD&A + FINRA Rule 2210 + SOX Section 404 + GAAP ASC 606 + IFRS 15 disclosure register (operator-owned + operator-counsel-maintained), brand spec (versioned in operator repo), LLM prompts (in operator repo), audit trail (retention infrastructure on operator cloud account with WORM-storage when SEC + FINRA + SOX retention required). Completions owns: the orchestration knowledge — how to design per-location Bayesian MMM + how to tune per-channel saturation curves + how to debug per-cohort lift attribution cascades + how to coordinate the MMM with per-location-rollup-reporting + location-benchmarking + compliance-overlay-manager + master-record-canonicalization siblings. The operator can in-house at any time; Completions credentials revoke immediately on engagement-end.
What KPIs will Completions commit to on Tier 3 engagement?
Typical Tier 3 commitments: (1) per-location per-channel MMM ingestion coverage at 99-percent target; (2) Bayesian Mixed-Media Modeling fit accuracy at MAPE-under-15-percent target measured against holdout-validation; (3) per-location per-channel saturation curve fit accuracy at 90-percent target across 5 curve types; (4) per-cohort lift attribution confidence-tier at 90-percent target via causal-uplift CATE meta-learner ensemble; (5) quarterly budget recommendation delivery cadence adherence at 100-percent target; (6) per-recommendation explainability output coverage at 100-percent target; (7) per-recommendation scenario-analysis coverage at 100-percent target across 5 scenarios (current + +10% + +25% + -10% + -25% + optimal); (8) per-vertical compliance overlay coverage at 99.9-percent target across SEC Reg FD + Reg G + Item 7 MD&A + FINRA + SOX + ASC 606 + IFRS 15; (9) cross-agent swarm coordination latency under 2-second end-to-end. Each KPI measured against pre-engagement baseline.
How does engagement end and what is the operator transition path?
Tier 3 engagements are 6-month minimum with 90-day notice. At engagement end, Completions transitions the per-location Bayesian MMM + quarterly recommendation engine operation back to operator in-house in 30-60 days: operating-playbook hand-off + in-house staff training across 3-5 operator team members covering per-location per-channel MMM ingestion + Bayesian Mixed-Media Modeling + per-location per-channel saturation curve fitting + per-cohort lift attribution + quarterly budget recommendation engine + per-vertical compliance overlay management + per-quarterly disclosure compliance + cross-agent coordination + spend data infrastructure hand-off + per-channel data source credentials hand-off + Bayesian MMM model code hand-off + LLM prompts hand-off + audit trail hand-off with WORM-storage operator-account-ownership confirmation; Completions credentials revoke immediately on engagement-end. Operator can re-engage Completions at any time on Tier 1 or Tier 2 cadence.
Engage Completions
Start with the AI Readiness Assessment (Tier 1, 2-3 weeks, $10k). If your operation is ready to absorb the MMM + recommendation engine, the assessment hands off to the AI Swarm Setup Sprint (Tier 2, 4-8 weeks, $25-50k). If your operation needs ongoing orchestration after Tier 2 hand -off, the MMM + recommendation engine continues under Fractional CMO with AI Swarm (Tier 3, 6-month minimum, $15 -25k/month, 1-2 days/wk embedded). Operator owns every artifact at every tier. Operator can in-house at any time.