Five attribution mistakes to check before changing a plan

Collection starts before permission is checked

Collection before permission review is the costliest mistake. A technically available identifier is not automatically permitted for every purpose, and later analysis cannot repair collection that should not have happened. Check the actual configuration before gathering data, rather than waiting until results have arrived.

The ICO's storage and access technologies guidance covers cookies, tracking pixels, device fingerprinting and similar methods under PECR and, where relevant, the UK GDPR. Treating review as a post-report task is too late: sound analysis does not make unsuitable data collection appropriate.

Map each technology, purpose, data field, recipient, retention period, and any exception or consent analysis before collection; then obtain qualified review of the actual setup. This order keeps permission assessment ahead of data gathering, rather than turning it into a response to results already in hand.

A borrowed threshold is treated as a universal rule

Borrowing an acceptance threshold from another decision is a mistake: cost, reversibility and potential harm can differ, making a pass-or-fail result misleading. The cut-off is incomplete unless its origin and the decision it serves are visible to the people using it.

HM Treasury's Green Book 2026 applies to its stated public-sector work and warns against judging value for money from an arbitrary benefit-cost-ratio threshold alone. It does not prescribe a private marketing rule, so use its warning to mark a boundary, not as a ready-made cut-off for a buyer's allocation.

Leave acceptance thresholds blank until the buyer defines the consequences of a false positive, false negative or delayed decision. Record who approved the eventual threshold, the evidence available and the condition that prompts review; schedule dated review points in a ninety-day plan.

Platform totals are combined despite mismatched definitions

Adding platform totals is a mistake when dashboards use the same label for different events, sources, windows or dates. Their sum may then describe no single system's reproducible result; tidy formatting cannot make unlike measures comparable, and supplier definitions can diverge further.

Google's Ads attribution-report record describes differences within its product in event timing, network coverage, conversion source and lookback window. Other suppliers may define each field differently, so matching labels alone cannot show that platforms counted the same event over the same period. This remains true even when display labels match exactly.

Keep platform credit in separate columns and reconcile underlying source records against one buyer-owned outcome definition before making a comparison. Do not force events that cannot be matched into a shared total. A channel strategy built around aligned definitions makes this correction practical.

Model-assigned credit is reported as caused value

Treating platform credit as caused value is a mistake: attribution assigns recorded credit under a model, but does not automatically estimate what would have happened without the marketing intervention. Reading the total as incremental impact converts an allocation into a causal claim the report label cannot establish.

HM Treasury's Quality in Policy Impact Evaluation says a good counterfactual faces the same influences as the policy group, except for effects of the policy. That source concerns policy evaluation, not commercial attribution, so use it to distinguish method boundaries rather than as a commercial rule.

Give the report distinct fields for reported attributed outcome and estimated incremental outcome, leaving the latter blank unless the design supports it. This separates model-assigned credit from an estimate of incremental outcome, consistent with the distinction in measurement mistakes.

A reported figure cannot be reproduced

Reporting a number without its query, extraction date and transformation is a mistake because later reviewers may have nothing to inspect beyond the result. A polished total cannot substitute for a record showing how the figure was produced or let a reviewer retrace the work.

The Government Analysis Function's quality and uncertainty guidance asks producers of official statistics to describe source quality, bias and distortive effects. Attribution reporting is not official statistics, but the same disclosure discipline is useful when explaining how reported results were produced and what limits them.

Version the outcome definition, source tables, query, extraction date, transformations, code, identity rule, model and exclusions. Preserve corrections instead of overwriting earlier runs, so readers can connect each figure to the version that generated it. Show that version beside the result in a reporting dashboard, not separately from it.

Pause any proposed allocation change while a relevant row remains unresolved. If the next step is to clarify what evidence a decision needs, use the evidence plan and record buyer-set stop rules in the planning template. Recheck the decision only after the unresolved repair is addressed.