Decision-grade modeling the rest of the operation reads against. Strategy reweights to the model, Optimization moves on the residuals, Ad Engineering tests against the cohort.
Analytics and operational infrastructure, built to surface decisions, not reports.
MMM, incrementality, cohort/LTV, attribution, funnel decomposition. Paired with operational dashboards across performance, tracking, business intelligence, and CRM.
InsideAlways-on reporting stack · models refreshed as data accumulates
Buyer trigger
Slot · The Reconciliation-Deficit OperatorSignal · Active
The Reconciliation-Deficit OperatorThree sources of truth disagree by double digits. Blended ROAS does not reconcile to the bank statement. Your in-house analyst can ship a GA4 screenshot, not a contribution-margin model with residuals, and the board wants commentary it can defend.
Signals firing the engagement
Platform attribution, GA4, and the warehouse all report different numbers and nobody can reconcile them
CFO asked for confidence ranges on the next forecast and the team is still pulling the spreadsheet
Last attribution decision was made off a screenshot, the residuals were never read
Decision-grade modeling the operation can read against, with the backoffice that lands it.
PROOF · 01
Attribution and MMM with documented assumptions and geo-holdout validation.
Decision-grade modeling layer
- Marketing-mix model integrating digital and offline spend, residuals reported on holdouts.
- Assumptions documented before the model is run, confidence ranges shipped with the output.
- Audit-able by your in-house analyst or any external reviewer, no proprietary black box.
PROOF · 02
Cohort and LTV reads segmented by acquisition channel and offer.
Cohort and unit-economics layer
- Payback by cohort, LTV:CAC, retention curves segmented by acquisition channel and offer type.
- Reweights the channel mix where the modeled cohort tells a different story than last-click.
- Reads back to the boardroom, decisions you can defend, forecasts the CFO can sign.
PROOF · 03
Operational reporting stack so the model output actually moves the operation.
Backoffice and reporting layer
- Performance, tracking-health, and CRM hygiene dashboards engineered as one operational stack.
- Anomalies surfaced before they compound, alert-to-action lag instrumented as a KPI.
- API-callable outputs available, signals integrate directly into platforms when the team wants them to.
How Dynamic Analytics & Backoffice actually runs.
Decision-grade clarity. Every dollar mapped to revenue with documented assumptions and stated confidence ranges.
The dashboard is not the problem, the absence of a model is.
Most analytics teams produce outputs nobody acts on. The dashboard shows what happened last week, nobody uses it to change what happens next week. The failure is rarely missing data, it is the absence of a model that connects data to a decision and the absence of operational work to make the decision move.
Decision-grade modeling, with assumptions on the page.
Attribution audit, MMM, and cohort and LTV models built with documented assumptions, residuals on holdouts, and confidence ranges shipped alongside every output. If your current attribution cannot show the residuals, it is a screenshot, not a model. The deliverable is a model your team can audit, your CFO can defend, and the operation can reweight to.
Modeling layer plus the backoffice that lands it.
Operational dashboards across performance, tracking-health, business intelligence, and CRM hygiene run alongside the models. The decision-grade outputs tell the operation what to do, the reporting stack keeps the operation running while it does it. One measurement layer, not two retainers.
Common objection
My agency does attribution already.
Most agency attribution is a screenshot of GA4. Ours is a model: documented assumptions, tested against holdouts, with a stated confidence range on the output. If your agency can show you the residuals on their attribution model, stay with them. If they cannot explain their methodology, call us.
What's included in Dynamic Analytics & Backoffice.
Each engagement scopes independently. Always-on reporting stack · models refreshed as data accumulates.
- Attribution Audit
What your numbers actually mean. With confidence ranges.
- MMM & Incrementality
Statistical mix model with documented assumptions and geo-holdouts
- Cohort & LTV Model
Payback, LTV:CAC, retention curves segmented by acquisition
- Semantic Reporting Stack
Performance, tracking-health, CRM dashboards in one stack
How Dynamic Analytics & Backoffice compounds forward.
The measurement layer of Lead OS and Commerce OS. The operation reads against it.
The model and the assumptions are documented, your team owns the artifact, the reporting stack stays live. Models compound as data accumulates, the operating record compounds with every read.
Questions buyers ask before scoping Dynamic Analytics & Backoffice.
What analytics platforms do you work with?
GA4, BigQuery, Looker Studio, native Meta/Google Ads reporting, plus CRM dashboards (HubSpot, Salesforce, custom). We build a unified view across platforms so you see blended ROAS and cohort LTV in one place.
What is marketing mix modeling (MMM) and when do I need it?
MMM is a statistical model that estimates the contribution of each marketing channel to revenue using historical data. You need it when your channel mix has grown beyond what last-click attribution can explain, typically when you're running 3+ channels and have 12+ months of data.
What is the "backoffice" part?
Operational dashboards and reporting work that sits alongside the analytical models: performance dashboards for the marketing team, tracking-health dashboards for the data team, business KPI dashboards for leadership, and CRM hygiene reports. The decision-grade models tell you what to do; the backoffice keeps the operation running while you do it.
How long does a model build take?
A one-off attribution or LTV model is scoped to data quality and complexity. Data audit and preparation come first; model build, validation, and documentation follow.