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Measurement

Part of Why connected TV advertising measurement needs a plan before launch

How to assess connected TV advertising benchmark evidence

A practical method for assessing connected TV advertising benchmarks: define the decision, score evidence consistently and report its limitations clearly.

What to take away

  • A benchmark is only useful when you can name who measured it, when, and on which supply path.
  • Start from the metric you report to finance, not the one the platform finds easiest to expose.
  • Score every source with a rubric before it enters the plan.
  • Rebuild quarterly, because streaming inventory moves faster than annual planning cycles.

A planner in Manchester is asked to justify a connected TV advertising budget for two quarters. Finance wants one chart. Four platform decks arrive, each with a different completion rate and a different definition of a view. That gap is where benchmark research starts.

Decide what the benchmark must answer

Write the question before you collect numbers. A benchmark that answers "is connected TV working for us?" is too broad to test. One that answers "what did a 30-second spot on broadcaster video-on-demand cost per completed view last quarter?" can be checked.

Too Broad vs Testable Question

Too broad

Question
Is CTV working?
Specificity
No unit defined
Supply path
Unstated
Period
Vague
Decision tied
None

Testable

Question
Cost per completed view
Specificity
30-second spot
Supply path
Broadcaster VOD
Period
Last quarter
Decision tied
Budget or placement

Keep the question tied to a decision. If no budget, creative or placement choice changes when the number moves, the metric is decoration. Our guide to connected TV advertising measurement and reporting treats reporting as a design choice rather than a monthly chore.

Where benchmark numbers come from

Public sources rarely publish connected TV rates at the granularity planners want. Regulators publish rules, not price lists. Useful UK context includes BARB’s television audience measurement, Thinkbox’s TV research and Ofcom’s media-market reporting; these can frame audiences and viewing, but they are not interchangeable CTV price datasets. Platforms publish their own definitions, which are not comparable across sellers.

Comparison of public versus internal sources for connected TV benchmark numbers (Connected TV advertising benchmark research without the vanity metrics)
Section 1 contrasts public sources with internal campaign, agency and panel data you can actually verify. Image: Connected Video

Most usable benchmarks are therefore internal: your past campaigns, an agency's aggregated buys, or a syndicated panel you pay for. Label the origin of every figure.

For audience planning rather than price benchmarking, consult Thinkbox’s BARB glossary: it explains that TGRs from Kantar Media’s TGI Survey are fused with BARB TV data to support planning against broader target audiences. Treat this as audience context, not a CTV-specific cost or outcome benchmark.

For example, a team paying £400 a month for a small always-on test can calculate cost per completed view from its own invoices rather than quoting a market average it cannot verify.

Build a scoring rubric

Score each candidate source out of 20 before it reaches the plan. Anything below 12 is context only, never the basis for a budget line.

CriterionWhat good looks likeScore
Named sourcePublisher, analyst or platform is identified0-4
DatePublication date is within the last four quarters0-4
Method statedSample, panel or census method is described0-4
Definition clarityView, impression and completion are defined0-4
ComparabilitySupply path and device mix are stated0-4

Run the same rubric over your own historical data. Internal numbers often score well on method and date but poorly on comparability, because buying routes changed between campaigns.

Check the rules and the consent trail

Advertising compliance is placement-specific. The Online Safety Act explainer provides wider online-safety context, not a substitute for advertising codes. Check the ASA’s CAP Code or the BCAP Code as applicable, and record which rules applied when the data was collected.

Consent and tracking are the second half of that picture. The ICO's guidance on online tracking and ad tech compliance explains what counts as valid consent for advertising technology. Assess UK GDPR and PECR alongside the ICO guidance when a benchmark relies on personal data or tracking. Where a benchmark relies on cross-device matching, note how consent was captured.

Link benchmarks to attribution

A benchmark tells you what a typical result looks like. Attribution tells you whether your campaign caused it. Planners who skip the second step end up defending last-click numbers in a channel built for reach.

Our breakdown of connected TV advertising attribution methods compares the main approaches and their trade-offs. Read it before promising finance a clean cost per acquisition.

Present the benchmark without overclaiming

One table, one source note, one limitation. State the period, the supply path and the sample. If the data covers England only, say so. If it covers the whole UK, say that instead.

Media planning is an organisational process as much as an analytical one. Use a source note such as: source and owner; reporting period; supply path; sample; metric definition; and known limitation. This gives reviewers the context needed to assess the figure.

Common questions

How many sources should a benchmark use?

Three to five is usually enough. One gives no sense of range, and more than five slows the review without improving the decision.

Should benchmarks use cost per completed view or cost per impression?

Include both where the seller supplies them. Completion rates vary by format and placement, so one impression-based figure hides too much.

How often should benchmarks be refreshed?

Quarterly for active channels, annually for planning assumptions. Note the refresh date on every chart so nobody quotes stale figures.

Can we use platform-published benchmarks?

Yes, but score them first. Platform data often scores well on definition clarity and poorly on comparability, because it covers only that platform's inventory.

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