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Marketing Analytics & Revenue Attribution

We build marketing analytics on definitions your leadership agrees on: LTV, CAC, payback, attribution, and cohorts computed from reconciled CRM, billing, and ad-platform sources rather than whichever number a tool happens to report. Historical results and forecast estimates are kept separate and labeled, attribution differences between networks are documented instead of merged, and questions the data cannot answer come back as explicit unknowns.

  • One metric dictionary for LTV, CAC, payback and attribution
  • Cohort and channel views reconciled against source exports
  • Forecasts labeled as estimates, never mixed with actuals
  • Explicit unknowns where the data cannot support an answer
Revenue mix, hover a channelSample

Revenue by channel

9.2%

Revenue, last 30 days: $117,630, up 9.2% against the previous period

  • Email & SMS$0
  • Google Ads$0
  • Meta Ads$0
  • Organic$0
  • Affiliates$0
Largest: Email & SMS

Sample illustration with illustrative numbers, not a verified client result.

Who this is for

Growth and finance teams that quote different LTV:CAC numbers in different meetings, leaders who need to know which channels actually produce revenue, and subscription businesses that need cohort economics leadership can defend. The fit is strongest when you can name the metrics in dispute and grant access to the CRM, billing, and ad sources behind them.

What we need from you

The model starts with an agreed metric dictionary — customer and cohort definitions, acquisition cost scope, revenue sources, refund treatment, and the currency, timezone, and cutoff for every figure.

  • CRM, billing or subscription data, ad platform exports, and cost sources.
  • Agreed definitions for the metrics leadership will rely on.
  • The cohort rules: how customers and acquisition cohorts are defined.
  • A named owner for the assumptions record and its version.

How the analytics are built

Sources are reconciled into one model where historical and collected results stay separate from forecast LTV, every estimate is labeled as an estimate, and missing inputs stay unknown rather than filled with an assumption. Attribution views show channel and campaign contribution with documented confidence, real-time revenue monitoring checks the sales and payment sources you scope against agreed baselines, and every alert traces back to the figures, window, and threshold that triggered it.

Controls, limits, and acceptance

The model reports economics; it does not promise lower CAC, higher revenue, or a universal target ratio, and it will not present a custom expression as a universal accounting formula. Alerting speed is bounded by source freshness, delayed feeds are flagged rather than treated as zero revenue, and thresholds reduce noise without eliminating false positives. Acceptance means an agreed sample cohort can be recalculated independently from your source exports, with a reviewer able to explain the denominators and distinguish late-arriving data, refunds, and forecasts from actuals.

How a pilot runs

We agree the metric dictionary and source access on a call, reconcile the scoped sources into the model, and validate it against a sample cohort your team recalculates independently. Forecast assumptions are documented with a named owner, monitoring thresholds and baselines are set where alerting is in scope, and the model is handed over with its definitions, provenance, and version record.

Workflow examples

Example scopes and acceptance checks for custom builds. These are illustrative workflows, not verified client case studies.

Intelligence01

Marketing Unit Economics — LTV:CAC & Profit Dashboard

A reconciled LTV:CAC and unit economics view built on your own definitions — with forecast estimates clearly labeled.

Nightly LTV:CAC by channelSample

LTV:CAC, paid channels

3.8+0.6

Nov

  • Google Ads4.1
  • Meta Ads3.3

Forward LTV projected on your own formula, nightly

A marketing unit economics model is built around the definitions your leadership agrees on — customer and cohort rules, acquisition cost scope, revenue, refunds, and cost sources — with assumptions, currency, and cutoffs documented. Historical results and forecast LTV are kept separate, and estimates are labeled as estimates.

Explore workflowDiscuss
Intelligence02

Real-Time Revenue Monitoring & Anomaly Detection

Intraday sales and payment monitoring with traceable alerts — and explicit flags when a feed is late or missing.

Revenue anomaly alertsSample
Anomaly

Checkout revenue down 41% since 13:00

Card payments fail on Safari after the 12:55 deploy. Orders from other browsers are normal.

A revenue monitoring system checks the sales and payment sources you scope against agreed baselines and thresholds, and routes alerts to a named operator with a runbook. Statistical anomaly detection filters noise, feed delays and missing sources are flagged as their own state, and alert timing depends on source freshness — reported honestly rather than promised unconditionally.

Explore workflowDiscuss

Related guides

Frequently asked questions

Because the definitions differ: customer rules, acquisition cost scope, revenue sources, refunds, and cutoffs are all choices. The model starts from a metric dictionary your leadership agrees on, so everyone quotes the same defined number or an explicit ambiguity flag.
We build attribution views on reconciled sources with documented confidence levels, and we keep platform-reported attribution separate from settled revenue. Where the data cannot support a confident answer, the model says so instead of blending numbers into a plausible guess.
No. Historical and collected results are kept strictly separate from forecast LTV, every estimate is labeled as an estimate, and the assumptions record has a named owner and version.
Yes — real-time revenue monitoring checks the sales and payment sources you scope against agreed baselines and routes traceable alerts to a named operator. Detection speed is bounded by source freshness and is reported honestly.
The affected figures are flagged as delayed or unknown — never silently treated as zero — and feed health is visible per source so a reviewer can tell a data problem from a real revenue change.

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