Solution / Marketing analytics
Measure marketing as a decision system.
Marketing analytics is the measurement layer behind budget, channel and campaign decisions: how credit is attributed, what incremental effect a channel really produced, and which numbers a team is allowed to act on. Quorlytic designs that measurement system, states the limits of each method, and connects the result to a named decision rather than a vanity dashboard.
By QuorlyticUpdated
Attribution, incrementality and channel reporting answer different questions and disagree by construction. Decide which question the business is actually asking, then build the measurement that can support it — and say plainly where it cannot.
Problem, approach and outcome.
| Problem | Budget allocation, campaign optimisation, channel evaluation and post-campaign reporting are four different questions. Last-touch attribution flatters closing channels, multi-touch spreads credit by rule, and incrementality testing measures a change against a holdout. All are defensible for some purpose; none is universally correct, so the decision being made decides which one is honest. |
|---|---|
| Approach | A controlled test — geo holdout, audience split or time-based holdout — estimates what would not have happened without the spend. It is the strongest evidence available and it is not always feasible: small budgets, brand campaigns and long consideration cycles resist clean tests. Where a test is impossible, say so and keep conclusions proportional to the evidence. |
| Typical outcome | Measurement question and method decision |
The approach
- 01
Agree the question before the model
Budget allocation, campaign optimisation, channel evaluation and post-campaign reporting are four different questions. Last-touch attribution flatters closing channels, multi-touch spreads credit by rule, and incrementality testing measures a change against a holdout. All are defensible for some purpose; none is universally correct, so the decision being made decides which one is honest.
- Decision and owner named before the method
- Method and its blind spots documented together
- One agreed number per decision, not one per tool
- 02
Incrementality is the standard, not the default
A controlled test — geo holdout, audience split or time-based holdout — estimates what would not have happened without the spend. It is the strongest evidence available and it is not always feasible: small budgets, brand campaigns and long consideration cycles resist clean tests. Where a test is impossible, say so and keep conclusions proportional to the evidence.
- Holdout design, power and duration agreed up front
- Feasibility stated honestly rather than assumed
- Results reported with intervals, not single point estimates
- 03
Identity and consent come before measurement
Attribution depends on being able to follow a journey, which depends on identity and consent. Customer 360 owns the profile and permission model; measurement consumes it. Where consent, device limits or walled gardens break the journey, the model has to show the gap rather than interpolate through it.
- Profile and consent context consumed, not re-implemented
- Coverage gaps visible in every report
- Modelled values labelled as modelled
- 04
Channel measurement that survives scrutiny
Channel reporting fails in predictable ways: spend recorded in one system and revenue in another, currencies and time zones mismatched, campaign naming that changes mid-quarter, and imported conversion windows that flatter one channel. Reconcile the inputs before interpreting the output, and keep the definition of a conversion in one governed place.
- 05
Design the decision the numbers feed
Measurement is only useful if a budget, a creative decision or a channel plan changes because of it. Give each report a decision, a cadence and an owner, and record what was decided. Where the measurement cannot resolve the question, the honest output is a recommendation to test — not a confident ranking built on a rule nobody can defend.
Attribution approaches and what they answer
| Dimension | Question it answers | Where it stops |
|---|---|---|
| Last-touch attribution | Which touchpoint closed the conversion. | Ignores everything that created the demand in the first place. |
| Multi-touch attribution | How credit spreads across a defined journey. | The distribution rule is an assumption, not a measurement. |
| Incrementality test | What would not have happened without the spend. | Needs a holdout, sufficient volume and enough time. |
| Marketing mix modelling | Aggregate channel contribution over long periods. | Too coarse to resolve campaign-level decisions. |
| Channel reporting with modelled conversions | Coverage where identity and consent are limited. | Modelled values must be labelled, bounded and explained. |
What the work produces
- Measurement question and method decision
- Attribution and incrementality design with stated limits
- Channel input reconciliation and definitions
- Decision cadence, owner and review pack
Delivery stages
- 01Name the question
- 02Choose the method
- 03Reconcile the inputs
- 04Decide and review
Ask what was actually held out of the measurement.
A marketing model can predict withheld sales well without proving that media caused those sales. The word holdout can describe a withheld outcome in model evaluation or a group whose exposure was deliberately changed in an experiment. In this hypothetical budget discussion, the measurement record identifies which comparison was performed before anyone turns a model-fit result into a spending recommendation.
Scope and assumptions: This example distinguishes evaluation designs; it does not report a campaign, estimate incremental revenue or recommend a budget. Google's Meridian implementation is a concrete example of modelling choices. A media-mix model, attribution rule and randomised intervention each require assumptions that predictive fit alone cannot validate.
| Dimension | Comparison being discussed | What the evidence supports and what remains open |
|---|---|---|
| Withheld KPI observations | Reserve specified geography/time outcome observations from model fitting and evaluate prediction against them. Record exactly which variables are withheld, rather than assuming that every observation for that geography disappears from the model. | Meridian documents a holdout that excludes KPI data while retaining media data because it can affect subsequent adstock. Good prediction on these outcomes is evidence about that predictive task; it is not itself a treatment-versus-control experiment. |
| Changed exposure | For an incrementality experiment, define the unit assigned to a different exposure policy, the comparison condition and the outcome window before observing the result. Specify leakage, spillover and implementation failures that could weaken the contrast. | Retain assignment and actual-delivery evidence separately. A geography reserved for predictive validation has not automatically received a lower or different media exposure, so it cannot be relabelled as an experimental control. |
| Repeated model choices | If the same withheld outcomes repeatedly guide variable selection, priors or model revisions, they are influencing development. Keep that role visible instead of presenting the final fit on them as a wholly untouched assessment. | Save each choice and the evidence used to make it. Reserve a suitable final evaluation or state the limitation when a genuinely independent comparison is unavailable. |
| Calibration evidence | If an experiment informs a prior or calibration constraint, retain its product, market, period, channel and uncertainty. Ask whether those conditions still describe the budget decision under discussion. | Keep calibration evidence distinct from later validation evidence, and disclose overlap. A narrow experiment does not establish that its effect transfers unchanged to another market, spend range or campaign mix. |
| Budget interpretation | Name the decision: explanatory reporting, model diagnosis or a proposed spending change. Retain uncertainty and unsupported ranges. Compare the recommendation with operational limits and the existing budget rule. | State which part of the recommendation relies on observational assumptions and which has intervention evidence. Where the current evidence cannot resolve a consequential choice, define a bounded next measurement rather than publishing a confident causal ranking. |
At narrow widths, scroll the worksheet to read every column. With the table focused, use the arrow keys to scroll.
Worked decision: a presentation calls withheld KPI weeks a geo experiment, but media ran normally in those geographies. Correct the interpretation to predictive validation and retain the result for that purpose. Any claim about incremental return needs its own causal identification and suitable evidence. This distinction improves the decision record without inventing an experiment or claiming that an attribution model has measured causality.
- Write the withheld unit, withheld variable and exposure policy in plain language. The label holdout is too ambiguous to serve as the entire method description.
- Keep measurement definitions with the budget output: outcome, currency, returns treatment, time window and permitted population. Different definitions can produce different recommendations even with an identical model specification.
If the next question changes an offer or price rather than media exposure, use pricing analytics to define that intervention separately.
Official references for this example:
Questions
Questions worth asking.
Which attribution model is best?
Whichever answers the decision honestly. Last-touch is simple and flatters closing channels; multi-touch spreads credit by an agreed rule; incrementality testing measures cause rather than correlation but needs a holdout. Most teams need one governed model for reporting and a test programme for decisions, with the differences explained rather than hidden.
Can we measure marketing without cookies?
Yes, with narrower claims. Consent-limited identity, aggregate channel data, geo holdouts and modelled conversion estimates all remain available. What changes is the confidence and the granularity: reporting has to expose coverage and modelled values instead of presenting a complete journey that no longer exists.
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