Measurement data
Agree the business results to measure and connect the spend, campaign and outcome data needed for analysis.
- Measurement plan
- Campaign and event definitions
- Spend and sales data connections
- Checks for missing or inconsistent data
Enterprise marketing AI / Use case 05
Attribution & Measurement
Measure how marketing contributes to sales and other business results. Combine campaign data with attribution, experiments and marketing mix modelling to help teams compare channels and plan budgets.
Investment review
Observed return vs estimated incremental return
Illustrative interface · Sample data, not client results
What we do
Marketing performance and budget decisions
We connect spend, campaign exposure and business outcomes, then choose a measurement method suited to the data and decision. Attribution shows which interactions receive credit. Experiments estimate what changed because of a campaign. Marketing mix models estimate channel contribution over time. Budget reviews include the assumptions and uncertainty behind each result.
Who this is for
Marketing, media and finance teams deciding how to divide budgets across brands, channels and markets.
What you receive
Attribution reports, experiment results and budget scenarios with their assumptions and limitations.
How it works
- 01
Connect campaign spend, sales and other business outcomes
- 02
Compare attribution with experiments or marketing mix models
- 03
Estimate channel contribution and document uncertainty
- 04
Review budget options with marketing and finance
Implementation scope
What we build.
We agree the required components after reviewing your current software, data and team. A project can include the following.
Attribution, experiments and modelling
Select the methods that can answer the question with the available data. Compare results when more than one method is used.
- Attribution reports
- Campaign and geographic test designs
- Marketing mix models where suitable
- Comparison of model estimates and test results
Budget scenarios
Estimate how outcomes could change under different budget allocations. Include the spending limits and assumptions used.
- Channel response estimates
- Forecasts and uncertainty ranges
- Budget scenario comparisons
- Constraints by market, brand or channel
Results and budget reviews
Prepare analysis for marketing and finance to review together. Record approved changes and compare subsequent results with the forecast.
- Budget review dashboards
- Recommendations with supporting calculations
- A schedule of proposed experiments
- A record of budget decisions and results
Potential data sources
SpendExposureConversionCommerceExperimentsIncrementality studiesFinance outcomesExternal factors
We connect only sources your organisation has permission to use. Source rights, retention and the permitted purpose are recorded before integration.
Enterprise applications
Sample Use Cases
These sample use cases describe possible deployments, including inputs, review steps and decisions. They are not accounts of client results.
CONSUMER
Budget allocation across brands and channels
Brand, performance, retail and market budgets are reviewed with incompatible evidence and time horizons.
Workflow
- 01
Unify spend and outcome data
- 02
Estimate channel contribution
- 03
Calibrate with experiments
- 04
Apply spending limits by brand and market
- 05
Present allocation scenarios
Decision supported
How should investment move across brands, markets and channels?
Measures to track
Incremental returnForecast errorBudget changes supported by evidenceConfidence coverage
FINANCIAL SERVICES
Acquisition quality measurement
Low-cost acquisition can conceal poor activation, risk or long-term value.
Workflow
- 01
Connect acquisition cohorts
- 02
Compare activation, retention and customer value
- 03
Estimate customer value over the agreed period
- 04
Compare incremental return
- 05
Agree the minimum return required for further spend
Decision supported
Which acquisition sources create acceptable risk-adjusted value?
Measures to track
Incremental quality customersPaybackValue confidenceAllocation change
DISTRIBUTED MARKETS
Marketing experiment tracking
Previous test results are not searchable across markets, so teams repeat similar tests without reviewing earlier findings.
Workflow
- 01
Register hypothesis
- 02
Check prior evidence
- 03
Design valid test
- 04
Analyse and grade
- 05
Publish reusable learning
Decision supported
What do we know, what remains uncertain and what should be tested next?
Measures to track
Valid experiment rateLearning reuseDecision coverageTime to evidence
Sample interface
Attribution and campaign measurement dashboard
A measurement review distinguishes observed returns from incremental estimates and highlights missing tests. Budget changes remain subject to review of assumptions and uncertainty.
Investment reviewIllustrative interface · Sample data
Quarterly planning · Illustrative channel comparison
Tested channels3 / 4
One lacks experiment evidence
Experiment window8 weeks
Sample test design
Budget approvalPending
Finance review required
Illustrative values, not model outputs. Incremental estimates depend on design and uncertainty; do not use these values for investment decisions.
Evidence available
Search
ReviewHoldout test; interval to be reviewed
Video
ReviewGeo test; regional variation noted
Retail media
GapNo valid holdout available
Next review
Review confidence intervals and test limitations before proposing a budget change.
Evaluation
How we measure results.
Record the current process first. Review the same measures after rollout, including manual exceptions, model costs and time spent checking outputs. The comparisons below describe evaluation areas; they are not before-and-after results.
Measurement coverage
- Baseline
- Spend supported by an appropriate model or valid experiment.
- Evaluate
- Coverage by market and channel, with unsupported areas identified.
- Owner
- Measurement lead
Forecast accuracy
- Baseline
- Out-of-sample error for the current forecast and time horizon.
- Evaluate
- Error on held-out periods, including bias and interval coverage.
- Owner
- Analytics lead
Incremental outcomes
- Baseline
- Outcome definition, test design and existing causal evidence.
- Evaluate
- Incremental estimate, uncertainty and test limitations.
- Owner
- Finance and marketing
Evidence reuse
- Baseline
- Previous tests accessible with their design and limitations.
- Evaluate
- Planning decisions that reference relevant prior results.
- Owner
- Measurement lead
Controls for this use case
Data access and approvals.
Assign source access and review responsibilities before launch. Define which failures pause the workflow, which can be retried and who receives the exception.
Read the governance details- Model cards and assumptions
- Confidence ranges
- Experiment registry
- Independent validation
- Finance reconciliation
- Decision and override log
Questions before scoping
Common questions.
No. Perfect attribution does not exist. We build a portfolio of evidence, show disagreement and uncertainty, and match the method to the decision being made.
No. The choice depends on spend, the amount of historical data, variation across markets and whether a controlled experiment is practical.
A dashboard reports recorded results. Attribution, experiments and statistical models help explain marketing contribution. We show the method, assumptions and uncertainty so the team can judge what the result supports.