Done-for-you offer · Fractional CMO with AI Swarm · predictive-anomaly-forecasting skill
Completions forecasts marketing anomalies 14-30 days ahead of revenue impact
Operators running 50-1,500 multi-location and DTC ecommerce brands work above a strong forecasting + causal-attribution + ML-platform + per-source primitives layer (XGBoost + LightGBM + CatBoost + Facebook Prophet + AWS DeepAR + N-BEATS + Temporal Fusion Transformer + Transformer time-series + LSTM + statsmodels ARIMA/SARIMA/Holt-Winters/state-space + PyMC Bayesian structural time-series + Pyro/NumPyro + Stan for forecasting; CausalML + DoubleML + EconML + PyMC + Stan for causal attribution; Vertex AI + AWS SageMaker + Azure ML + Databricks ML for ML platforms; Google Search Console + Google Ads + Meta Business Manager + GBP API + Yelp + Placer.ai + Safegraph + Klaviyo + Twilio + Recharge for per-stream source; Temporal + AWS Step Functions + Apache Airflow + Dagster + Prefect for cross-agent orchestration). The orchestration that sits above those primitives — per-stream per-location per-cohort multi-model forecast ensemble at 14-30 day forward windows across 9 marketing streams, per-location per-cohort per-stream lift attribution via causal-uplift CATE meta-learner ensemble, cross-stream correlation forecasting with lead-lag structure, forward-window threshold-crossing detection, pre-emptive marketing-action coordination across the swarm, per-stream compliance overlay, calibration and backtesting — is operator-side architecture. Most observability stacks detect anomalies after they have reached revenue; the forecasting layer surfaces drift in the lead indicators (organic-rank, GBP- impression, email engagement) before the lagging indicators (foot-traffic, calls, unsubscribes, revenue) record it. Completions architects the predictive-anomaly-forecasting skill on the anomaly-detection agent end-to-end and coordinates it with the location-benchmarking, master-record-canonicalization, subscription-lifecycle, local-context-ingestion, and compliance-overlay-manager siblings. Operator owns every artifact: model code, calibration baselines, causal-uplift code, cross-stream correlation graph, forward-window threshold registry, brand spec, compliance overlay, audit trail. Operator can 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 a multi-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 + Stan) with stacking + meta-learner + Bayesian model averaging combiner.
Per-location per-cohort per-stream lift attribution via the 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, segment-stratified, matched-control, DiD, synthetic-control, pre-post, A/B test, bandit-control-arm) that operator data-science maintains.
Cross-stream correlation forecasting with lead-lag structure calibrated against operator data. Typical funnel positioning sees organic-rank leading paid-spend leading GBP-impression leading foot-traffic leading revenue, with lag windows that operator data-science calibrates against the operator catalog and traffic profile. When the organic-rank forecast falls in the forward window, the system surfaces a recommendation set (ad-budget reallocation, creative rotation, email featured- product swap, GBP post cadence adjustment, lifecycle stage trigger, per-location bid-strategy adjustment) for the operator team to review.
Forward-window threshold-crossing detection across 14-day, 21-day, and 30-day windows with the priority bands operator counsel and ops jointly set. 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 Articles 13/14/15) expressed as a policy-as-code gate (OPA Rego, AWS Cedar, Casbin, Cerbos, Oso) when forecasts surface in public filings or franchisee-shared dashboards.
Where the orchestration above forecasting, causal-attribution, and ML-platform primitives compounds at portfolio scale
The vendor primitives are strong. Forecasting libraries implement gradient-boosted trees, deep time-series, and Bayesian state-space models. Causal-attribution libraries implement the CATE meta-learner ensemble. ML platforms handle model lifecycle, scheduling, and deployment. Per-source vendors expose APIs. The orchestration above those primitives is what compounds at multi-location portfolio scale. Per-stream per-location per-cohort multi-model forecast ensemble sits above per-source event emission and per-location canonical-ID resolution. Per-location per-cohort per-stream lift attribution sits above the operator-maintained holdout structure. Cross-stream correlation forecasting sits above the causal- graph layer with operator-calibrated lead-lag windows. Forward-window threshold-crossing detection sits above event- driven architecture. Pre-emptive marketing-action coordination sits above the swarm orchestration layer. Per-stream compliance overlay sits above a policy-as-code gate that counsel reviews. Calibration and backtesting sit above the data-science layer.
Completions coordinates this orchestration layer under a Tier 3 Fractional CMO with AI Swarm engagement (1-2 days/wk embedded), tying the anomaly-detection agent to the location-benchmarking, master-record-canonicalization, subscription-lifecycle, local-context-ingestion, and compliance-overlay-manager siblings.
How the engagement progresses
Tier 1 AI Readiness Assessment (2-3 weeks, diagnostic). Completions audits the operator current predictive forecasting operation across seven axes — per-stream per-location per-cohort multi-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 (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 the 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 ( 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 coordinates predictive marketing anomaly forecasting across 9 marketing streams per-location per-cohort with 14-30 day forward-window detection above a strong forecasting + causal-attribution + ML-platform + per-source primitives layer (XGBoost + LightGBM + CatBoost + Facebook Prophet + AWS DeepAR + N-BEATS + Temporal Fusion Transformer + Transformer time-series + LSTM + statsmodels ARIMA/SARIMA/Holt-Winters/state-space/dynamic-linear-model + PyMC Bayesian structural time-series + scikit-learn Gaussian-process regression + Pyro/NumPyro + Stan for forecasting; CausalML + DoubleML + EconML + PyMC + Stan for causal attribution; Vertex AI + AWS SageMaker + Azure ML + Databricks ML for ML platforms; Google Search Console + Google Ads + Meta Business Manager + GBP API + Yelp + Placer.ai + Safegraph + Klaviyo + Twilio + Recharge for per-stream source data; Temporal + AWS Step Functions + Apache Airflow + Dagster + Prefect for cross-agent orchestration). Each stream gets a multi-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 + Stan) 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, segment-stratified, matched-control, DiD, synthetic-control, pre-post, A/B test, bandit-control-arm) that operator data-science maintains. Cross-stream correlation forecasting with lead-lag structure calibrated against operator data (typical funnel positioning sees organic-rank leading paid-spend leading GBP-impression leading foot-traffic leading revenue, with lag windows operator data-science calibrates). Forward-window threshold-crossing detection across 14-day, 21-day, and 30-day windows with the priority bands operator counsel and ops jointly set, triggering pre-emptive marketing-action coordination recommendations (ad-budget reallocation + creative rotation + email featured-product swap + GBP post cadence adjustment + lifecycle stage trigger + per-location bid-strategy adjustment) for the operator team to review. 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 Articles 13/14/15) expressed as a policy-as-code gate (OPA Rego, AWS Cedar, Casbin, Cerbos, Oso) when forecasts surface in public filings or franchisee-shared dashboards. Operator owns every artifact: model code, calibration baselines, causal-uplift code, cross-stream correlation graph, forward-window threshold registry, brand spec, compliance overlay, audit trail. Completions owns the swarm orchestration on the anomaly-detection agent and its coordination with the location-benchmarking, master-record-canonicalization, subscription-lifecycle, local-context-ingestion, and compliance-overlay-manager siblings.
Where does the orchestration layer above forecasting + causal-attribution + ML-platform primitives compound at multi-location portfolio scale?
Forecasting tooling (XGBoost + LightGBM + CatBoost + Facebook Prophet + AWS DeepAR + N-BEATS + Temporal Fusion Transformer + Transformer time-series + LSTM + statsmodels ARIMA/SARIMA/Holt-Winters/state-space/dynamic-linear-model + PyMC Bayesian structural time-series + scikit-learn Gaussian-process regression + Pyro/NumPyro + Stan), causal-attribution tooling (CausalML + DoubleML + EconML + PyMC + Stan), ML platforms (Vertex AI + AWS SageMaker + Azure ML + Databricks ML), per-stream source vendors (Google Search Console + Google Ads + Meta Business Manager + GBP API + Yelp + Placer.ai + Safegraph + Klaviyo + Twilio + Recharge), and cross-agent orchestration platforms (Temporal + AWS Step Functions + Apache Airflow + Dagster + Prefect) ship strong primitives. The orchestration layer above them is operator-side architecture, and at multi-location portfolio scale it spans seven workstreams: (1) per-stream per-location per-cohort multi-model forecast ensemble across the 9 marketing streams (organic + paid + GBP + reviews + foot-traffic + email + SMS + lifecycle + subscription) sitting above per-source event emission and per-location canonical-ID resolution; (2) per-location per-cohort per-stream lift attribution via the causal-uplift CATE meta-learner ensemble (T-learner + S-learner + X-learner + DR-learner + CausalML + DoubleML + EconML + Bayesian-treatment-effect + counterfactual-prediction + causal-forest) sitting above the operator-maintained holdout structure (portfolio-wide, segment-stratified, matched-control, DiD, synthetic-control, pre-post, A/B test, bandit-control-arm); (3) cross-stream correlation forecasting with lead-lag structure (typical funnel positioning sees organic-rank leading paid-spend leading GBP-impression leading foot-traffic leading revenue, with lag windows operator data-science calibrates) sitting above the causal-graph layer; (4) forward-window threshold-crossing detection across 14-day, 21-day, and 30-day windows with the priority bands operator counsel and ops jointly set sitting above event-driven architecture (Temporal + AWS Step Functions + Apache Airflow + Dagster + Prefect); (5) pre-emptive marketing-action coordination across ad-budget + creative rotation + email + GBP + lifecycle + bid-strategy flows sitting above the swarm orchestration layer; (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 Articles 13/14/15) when forecasts surface in public filings or franchisee-shared dashboards expressed as a policy-as-code gate (OPA Rego, AWS Cedar, Casbin, Cerbos, Oso) that counsel reviews; (7) calibration and backtesting across walk-forward + rolling-window + out-of-time + out-of-cohort holdouts with MAPE + MASE + CRPS + pinball-loss + quantile-loss + coverage-gap metrics sitting above the data-science layer. Completions coordinates this orchestration layer under a 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 (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 (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 (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 does Completions commit to reporting on a Tier 3 engagement?
Tier 3 engagements report against a pre-engagement baseline that the Tier 1 assessment establishes for the operator stack. The reporting cycle covers six workstreams: (1) per-stream per-location per-cohort forecast performance observed across 14-day, 21-day, and 30-day forward windows via MAPE + MASE + CRPS + pinball loss + quantile loss + coverage gap against walk-forward, rolling-window, out-of-time, and out-of-cohort backtests that operator data-science maintains; (2) per-stream calibration coverage observed via isotonic + Platt + temperature + Venn-Abers + conformal calibration diagnostics, with per-method coverage-gap breakdowns reported; (3) cross-stream correlation forecast surfacing observed via the causal-uplift CATE meta-learner ensemble against operator-maintained holdouts (portfolio-wide, segment-stratified, matched-control, DiD, synthetic-control, pre-post, A/B test, bandit-control-arm), with per-architecture diagnostics reported; (4) pre-emptive marketing-action coordination surface observed across the cross-agent fan-out flows (ad-budget, creative rotation, email, GBP, lifecycle, bid-strategy), with per-action handoff latency reported; (5) per-stream compliance gate pass rate observed 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 Articles 13/14/15 scope when forecasts surface in public filings or franchisee-shared dashboards; (6) audit-trail completeness observed across the per-statute retention windows operator counsel sets on WORM storage in the operator cloud account. Monthly reports surface to the C-suite + franchisee council + finance with per-cohort breakdowns. Caveats: per-source vendor API rate limits + per-source ingestion completeness + ML platform availability + LLM-vendor availability + model-update cadence + per-statute retention windows shifting with operator counsel policy sit outside Completions control and are reported alongside observed performance; attorney-client privilege on counsel-reviewed lead-lag relationships and pre-emptive action rules is preserved through every layer of the reporting cycle. Completions does not commit to fixed numeric SLAs on forecast MAPE, calibration coverage gap, cross-stream precision, coordination latency, or compliance pass rate when those KPIs depend on vendor performance, data infrastructure availability, or counsel policy decisions.
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). If your operation is ready to absorb the forecasting, the assessment hands off to the AI Swarm Setup Sprint (Tier 2, 4-8 weeks). 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, 1-2 days/wk embedded). Operator owns every artifact at every tier. Operator can in-house at any time.