Done-for-you offer · Fractional CMO with AI Swarm · predictive-anomaly-forecasting skill
Completions forecasts your marketing 14-30 days before it hits revenue
You have 50-1,500 locations and an anomaly-detection setup that fires alerts after revenue is already lost. When organic drops on a franchisee, the CMO finds out 2 weeks after foot-traffic collapses. When GBP impressions degrade, the CMO finds out after calls dry up. When email engagement crashes, the CMO finds out after unsubscribes pile up. Completions architects and operates the predictive-anomaly-forecasting skill on the anomaly-detection agent end-to-end with 14-30 day forward -window forecasts across 9 marketing streams + 18-model ensemble + causal-uplift CATE meta-learner ensemble + cross -stream correlation forecasting + pre-emptive marketing-action coordination. You keep every artifact. You keep the model code. You keep the ability to in-house at any time.
Published September 24, 2026
What we forecast every day
Per-stream per-location per-cohort 14-30 day forward-window forecasts across 9 marketing streams (organic + paid + GBP + reviews + foot-traffic + email + SMS + lifecycle + subscription) via 18-model ensemble (XGBoost + LightGBM + CatBoost + Prophet + DeepAR + N-BEATS + Temporal Fusion Transformer + Transformer time-series + LSTM + ARIMA + SARIMA + Holt-Winters + state-space model + dynamic linear model + Bayesian structural time-series + Gaussian process regression + Pyro-NumPyro Bayesian + Stan Bayesian) with stacking + meta-learner + Bayesian model averaging combiner.
Per-location per-cohort per-stream 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) with 8-architecture holdout -control infrastructure (portfolio-wide 10% + segment -stratified + matched-control + DiD + synthetic-control + pre-post + A/B test + bandit-control-arm).
Cross-stream correlation forecasting with lead-lag structure: organic-rank leads paid-spend leads GBP-impression leads foot -traffic leads revenue by 7-30 days depending on funnel position. When organic-rank forecast falls 14 days ahead, the system warns + recommends ad-budget reallocation + creative rotation + email featured-product swap + GBP post cadence adjustment + lifecycle stage trigger + per-location bid -strategy adjustment before the per-location performance benchmark records the decline.
Forward-window threshold-crossing detection across 14-day + 21-day + 30-day windows × P0-P3 severity tiers. Pre-emptive marketing-action coordination across ad-budget + creative rotation + email + GBP + lifecycle + bid-strategy via cross -agent swarm orchestration. Per-stream compliance overlay (SEC Reg FD + Reg G + Item 7 MD&A + FINRA Rule 2210 + SOX Section 404 + GAAP ASC 606 + IFRS 15 + FTC + state UDAP + EU AI Act Article 13/14/15) when forecast surfaces in public filings or franchisee-shared dashboards.
Why in-house breaks at portfolio scale
Per-stream per-location per-cohort 18-model forecast ensemble across 9 streams × 300 locations × 5-7 cohort dimensions = 12,600-17,640 forecast cells × 18 models = 226,800-317,520 model fits per refresh cycle requires production ML infrastructure unstaffable by internal teams. Per-location per-cohort per-stream lift attribution via causal-uplift CATE meta-learner ensemble requires data-science capacity. Cross -stream correlation forecasting with lead-lag structure requires production causal-graph infrastructure. Forward -window threshold-crossing detection requires event-driven architecture. Pre-emptive marketing-action coordination requires cross-agent swarm orchestration. Per-stream compliance overlay requires legal-engineering capacity. Calibration + backtesting requires data-science capacity.
Completions absorbs all seven axes under one Tier 3 Fractional CMO with AI Swarm engagement. The embedded executive (1-2 days/wk) coordinates the predictive forecasting across the anomaly-detection agent + location -benchmarking + master-record-canonicalization + subscription -lifecycle + local-context-ingestion + compliance-overlay -manager siblings.
How the engagement progresses
Tier 1 AI Readiness Assessment ($10k, 2-3 weeks, diagnostic). Completions audits the operator current predictive forecasting operation across seven axes — per -stream per-location per-cohort 18-model ensemble coverage + causal-uplift CATE attribution + cross-stream correlation forecasting maturity + forward-window threshold-crossing detection + pre-emptive marketing-action coordination + per -stream compliance overlay + calibration + backtesting methodology. Deliverable: gap-pack report.
Tier 2 AI Swarm Setup Sprint ($25-50k, 4-8 weeks, build with 30-day operating tail). Completions builds the predictive marketing anomaly forecasting on operator infrastructure — predictive-anomaly-forecasting + per -location-per-cohort-two-sigma-anomaly-detection + cross -stream-correlation-for-marketing-anomaly-diagnosis + multi -stream-severity-routing on anomaly-detection agent + predictive-performance-forecasting on location-benchmarking + causal-uplift CATE on per-cohort lift attribution + per -jurisdiction-overlay-config on compliance-overlay-manager.
Tier 3 Fractional CMO with AI Swarm ($15-25k/ month, 6-month minimum, 1-2 days/wk embedded). Completions continues operating the predictive forecasting with weekly forecast review + monthly cross-stream correlation playbook + quarterly per-cohort benchmark + ensemble model refresh + per-event compliance overlay updates + cross-agent swarm coordination.
Frequently asked
What does "Completions forecasts your marketing 14-30 days before it hits revenue" actually deliver?
Completions architects and operates predictive marketing anomaly forecasting across 9 marketing streams per-location per-cohort with 14-30 day forward-window detection. Each stream gets an 18-model forecast ensemble (XGBoost + LightGBM + CatBoost + Prophet + DeepAR + N-BEATS + Temporal Fusion Transformer + Transformer time-series + LSTM + ARIMA + SARIMA + Holt-Winters + state-space model + dynamic linear model + Bayesian structural time-series + Gaussian process regression + Pyro-NumPyro Bayesian + Stan Bayesian) with stacking + meta-learner + Bayesian model averaging combiner. Per-location per-cohort per-stream lift attribution via causal-uplift CATE meta-learner ensemble (T/S/X/DR-learner + CausalML + DoubleML + EconML + Bayesian-treatment-effect + counterfactual-prediction + causal-forest) with 8-architecture holdout-control infrastructure. Cross-stream correlation forecasting (when organic-rank forecast falls 14 days ahead, paid-spend forecast must compensate; when GBP-impression forecast falls 21 days ahead, foot-traffic forecast warns 5-7 days later). Forward-window threshold-crossing detection triggers pre-emptive marketing-action coordination: ad-budget reallocation + creative rotation + email featured-product swap + GBP post cadence adjustment + lifecycle stage trigger + per-location bid-strategy adjustment 14-30 days before the per-location performance benchmark records the decline. Operator team owns the master record + brand spec + per-stream data infrastructure. Completions owns the swarm orchestration on the anomaly-detection agent.
Why does in-house predictive anomaly forecasting break at portfolio scale?
In-house forecasting at multi-location portfolio scale fails on seven axes: (1) per-stream per-location per-cohort 18-model forecast ensemble across 9 streams × 300 locations × 5-7 cohort dimensions = 12,600-17,640 forecast cells × 18 models = 226,800-317,520 model fits per refresh cycle requires production ML infrastructure unstaffable by internal teams; (2) per-location per-cohort per-stream lift attribution via causal-uplift CATE meta-learner ensemble with 8-architecture holdout-control infrastructure requires data-science capacity; (3) cross-stream correlation forecasting across 9 streams with lead-lag structure (organic-rank leads paid-spend leads GBP-impression leads foot-traffic leads revenue by 7-30 days depending on funnel position) requires production causal-graph infrastructure; (4) forward-window threshold-crossing detection across 14-day + 21-day + 30-day windows × P0-P3 severity tiers requires event-driven architecture; (5) pre-emptive marketing-action coordination across ad-budget + creative rotation + email + GBP + lifecycle + bid-strategy requires cross-agent swarm orchestration; (6) per-stream compliance overlay (SEC Reg FD + Reg G + Item 7 MD&A + FINRA Rule 2210 + SOX Section 404 + GAAP ASC 606 + IFRS 15 + FTC + state UDAP + EU AI Act Article 13 + 14 + 15) when forecast surfaces in public filings or franchisee-shared dashboards requires legal-engineering capacity; (7) calibration + backtesting across walk-forward + rolling-window + out-of-time + out-of-cohort holdout × MAPE + MASE + CRPS + pinball-loss + quantile-loss + coverage-gap metrics requires data-science capacity. 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 predictive forecasting operation across seven axes — per-stream per-location per-cohort 18-model ensemble coverage + per-location per-cohort per-stream causal-uplift CATE attribution + cross-stream correlation forecasting maturity + forward-window threshold-crossing detection + pre-emptive marketing-action coordination + per-stream compliance overlay + calibration + backtesting methodology. Deliverable: gap-pack report. Tier 2 AI Swarm Setup Sprint ($25-50k, 4-8 weeks, build with 30-day operating tail): Completions builds the predictive marketing anomaly forecasting on operator infrastructure — predictive-anomaly-forecasting + per-location-per-cohort-two-sigma-anomaly-detection + cross-stream-correlation-for-marketing-anomaly-diagnosis + multi-stream-severity-routing on anomaly-detection agent + predictive-performance-forecasting on location-benchmarking + causal-uplift CATE on per-cohort lift attribution + per-jurisdiction-overlay-config on compliance-overlay-manager. Tier 3 Fractional CMO with AI Swarm ($15-25k/month, 6-month minimum, 1-2 days/wk embedded): Completions continues operating the predictive forecasting with weekly forecast review + monthly cross-stream correlation playbook + quarterly per-cohort benchmark + ensemble model refresh + per-event compliance overlay updates + cross-agent swarm coordination.
Who owns the forecast models, calibration baselines, and audit trail during engagement?
Operator owns 100% of every artifact: 18-model forecast ensemble code (versioned in operator repo with operator-controlled deploy pipeline + per-model hyperparameter tuning logs), per-stream calibration baselines (versioned in operator repo with per-stream per-cohort per-window MAPE + MASE + CRPS + pinball-loss + quantile-loss + coverage-gap baseline metrics), causal-uplift CATE meta-learner ensemble code (versioned in operator repo + 8-architecture holdout-control infrastructure), cross-stream correlation graph (versioned in operator repo with attorney-approved lead-lag relationships when surfaced in public-facing dashboards), forward-window threshold registry (operator-owned), per-stream data infrastructure (Snowflake + Databricks + BigQuery + Redshift + Postgres operator data warehouse), brand spec (versioned in operator repo), compliance overlay (rule library in operator repo with attorney-approved updates), per-stream vendor credentials (Google Search Console + Google Ads + Meta Business Manager + GBP API + Yelp + Placer.ai + Safegraph + Klaviyo + Twilio + Recharge under operator billing), ML platform credentials (operator-owned Vertex AI + SageMaker + Azure ML + Databricks accounts), 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 18-model forecast ensemble + how to tune causal-uplift CATE + how to debug cross-stream correlation cascades + how to coordinate the predictive forecasting with anomaly-detection + location-benchmarking + master-record-canonicalization + subscription-lifecycle siblings. 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-stream per-location per-cohort 14-day forward-window forecast accuracy at MAPE-under-15-percent target measured against walk-forward backtest; (2) 21-day forward-window forecast accuracy at MAPE-under-20-percent target; (3) 30-day forward-window forecast accuracy at MAPE-under-25-percent target; (4) per-stream calibration coverage gap at under-5-percent target via isotonic + Platt + temperature + venn-abers + conformal calibration; (5) cross-stream correlation forecast precision at 90-percent target via causal-uplift CATE meta-learner ensemble; (6) pre-emptive marketing-action coordination latency under 60-minute target from forward-window threshold-crossing detection to cross-agent action fanout; (7) per-stream compliance overlay coverage at 99.9-percent target across SEC Reg FD + Reg G + Item 7 MD&A + FINRA Rule 2210 + SOX Section 404 + GAAP ASC 606 + IFRS 15 + FTC + state UDAP + EU AI Act Article 13 + 14 + 15; (8) cross-agent swarm coordination latency under 2-second end-to-end. Each KPI measured against pre-engagement baseline. Monthly performance reports surface to operator C-suite + franchisee council with per-franchisee roll-up.
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 predictive marketing anomaly forecasting 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 18-model ensemble operation + per-stream calibration baseline maintenance + causal-uplift CATE meta-learner ensemble operation + cross-stream correlation graph maintenance + forward-window threshold-crossing detection + pre-emptive marketing-action coordination + per-stream compliance overlay management + cross-agent coordination + per-stream vendor credentials hand-off + ML platform credentials 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 forecasting, 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 predictive forecasting 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.