Every platform claims the same revenue
Last-click and platform-native reports overstate channel performance because each platform evaluates its own version of the journey.
Measure what platform attribution cannot prove.
MetricMaven helps growth teams answer the questions platform dashboards cannot: which channels are incremental, how spend should move, where LTV is changing, and what budget scenarios are worth testing.
Custom scope - best for mature spend and strategic budget decisions
MetricMaven
Measurement lab
Mode
Model
MMM Channel contribution Modeled
Tests Incremental lift Measured
LTV Revenue quality Forecast
Budget Scenario planning Mapped
MMM
channel-level measurement
Lift
incrementality tests
LTV
forecasting and retention
MMM
channel-level measurement
Lift
incrementality tests
LTV
forecasting and retention
The problem
At some point, attribution is not enough.
Platform attribution helps with operational reporting, but it cannot prove incrementality, forecast revenue quality, or explain how channels interact across time.
Budget moves need stronger evidence
Leadership needs to know whether spend changes create incremental growth or only shift credit between channels.
LTV and payback are unclear
Acquisition quality changes by channel, offer, cohort, and sales motion, but most reports stop at lead or deal creation.
Deliverables
What marketing science can include
These projects are scoped around a business decision, not a modeling exercise for its own sake.
Output 1
Measurement design
Define the question, decision owner, data requirements, assumptions, and confidence threshold before analysis starts.
Output 2
MMM and channel contribution
Model channel-level contribution across spend, seasonality, lag effects, external factors, and revenue outcomes.
Output 3
Incrementality and holdout testing
Design and analyze tests that estimate lift, budget waste, cannibalization, and marginal return.
Output 4
Forecasting and scenario planning
Translate measurement results into budget scenarios, LTV expectations, payback views, and executive recommendations.
Process
How the work runs
Every step has a concrete output, so the engagement keeps moving toward a working business artifact.
01
Frame the decision
We identify the spend, growth, or forecasting decision the analysis needs to support.
02
Audit the data
We review source quality, history, granularity, spend coverage, conversion definitions, and data gaps.
03
Model or test
We run the right method for the question: MMM, incrementality testing, cohort analysis, forecasting, or scenario planning.
04
Translate to action
The output is a recommendation your team can use in budget planning, channel strategy, or experimentation.
Proof
Where this work creates leverage
Marketing science is most useful when the decision is expensive enough that better evidence changes the outcome.
Budget allocation
Estimate which channels are underfunded, overcredited, or showing diminishing returns.
Experiment planning
Design holdouts and lift tests that can survive executive scrutiny.
Revenue quality
Evaluate acquisition quality through LTV, payback, retention, and cohort behavior.
Questions
Before you book.
When is a team ready for marketing science? +
Usually when spend is large enough that budget changes materially affect growth, and when the team has enough historical data to support modeling or test design.
Is this a replacement for attribution? +
No. Marketing science complements attribution. Attribution supports operational reporting, while MMM, incrementality, and forecasting support higher-level budget and strategy decisions.
What data do we need? +
The required data depends on the question, but common inputs include spend, conversions, revenue, channel history, CRM outcomes, seasonality, promotions, and business events.
Ready to fix the gap?
Measure what platform attribution cannot prove.
We will scope the right next step and show what needs to happen before the numbers can be trusted.