How should AI-supported marketing work be evaluated?

Construct validity, factual support and the limits of automated judgement

This retrospective review considers source versions available by 1 December 2025. The cutoff defines the evidence included; preparation and source checking took place on 21 October 2026.

Keywords: evaluation; construct validity; language models; marketing; factuality

1. The problem of evaluating useful work

A marketing deliverable can be fluent, factually wrong and commercially unhelpful at the same time. It can also be accurate yet unsuitable for its intended audience. Evaluation becomes difficult when these properties are compressed into a single judgement that an output is good. A research question about AI-supported work therefore needs a specified object: the model, its answer, the surrounding retrieval system, the human review process or the finished business task. The appropriate measurement depends on that choice.

This review examines which distinctions established evaluation research supplies for assessing language-model-supported marketing work. It concentrates on the validity of evaluation, rather than attempting to rank current products. A benchmark can show that a system answers a defined class of questions. A local comparison can show that a particular configuration improves a defined assignment. Neither observation establishes that all tasks in a marketing function improve. The central issue is the relationship between the measurement and the claim attached to it.

Evaluation also has a temporal dimension. A result belongs to the model version, prompt, available documents, tools and scoring procedure used when it was obtained. Changing any of these can alter the intervention. A reproducible record consequently needs enough information to distinguish a new system from a rerun of an old one. That requirement applies equally to a small internal trial and to a public benchmark; the scale of the exercise does not remove the need to define what was tested.

2. Review method and scope

Targeted searches on 21 October 2026 located primary benchmark papers, evaluation frameworks and official NIST publications. Selection focused on complementary measurement problems: multidimensional performance, behavioural robustness, factuality, retrieval, automated judges and documentation. Twelve references were retained. Author and publisher abstracts and official summaries support the empirical statements below. This is a purposive narrative review, without independent screening, a preregistered protocol or full-text risk-of-bias appraisal.

The synthesis separates a method's proposed purpose from evidence that it works under every deployment condition. An automated evaluator may be useful on the tasks used to validate it while remaining unvalidated on marketing claims, visual identity or commercial strategy. Historical benchmark results are discussed as evidence about measurement design. They are not current estimates of the performance of models available in October 2026. No pooled score or league table is constructed because the referenced assessments target different constructs.

Marketing examples identify how an assessment question would be specified. They are analytical examples, not observations from a new experiment. The review makes no claim that its own selection of criteria has been empirically validated as a complete marketing evaluation instrument. This boundary is necessary because assembling plausible criteria does not itself establish reliability, predictive validity or sensitivity to improvements in professional performance.

3. Benchmark coverage and construct validity

HELM evaluates language models across scenarios and multiple dimensions, including accuracy, calibration, robustness, fairness, bias, toxicity and efficiency. Its author summary explains that evaluation coverage was previously uneven and that standardised comparisons exposed gaps [1]. For marketing, the methodological consequence is that a score on one dimension leaves the others unresolved. Lower generation cost cannot establish factual accuracy, and accuracy on a selected question set cannot establish suitable representation across audiences.

MMLU was introduced as a broad test covering 57 academic and professional subjects [2]. BIG-bench expands the range of tasks used to probe language-model capabilities [3]. Their breadth is useful for characterising models, but a broad collection is still a collection of specified tasks. The relationship between those tasks and a particular marketing assignment requires examination. Producing a defensible consumer insight, for example, includes selecting relevant evidence and preserving the uncertainty of a source; a multiple-choice test may not exercise either requirement in its deployed form.

Construct validity asks whether the measurement captures the property that the evaluation claims to assess. This is separate from whether the score was calculated correctly. A carefully calculated preference score measures preference under the elicitation procedure. Describing it as strategic quality requires an additional justification. The same applies when grammatical polish becomes a proxy for reasoning, or when a source link becomes a proxy for factual support. Clear naming of the outcome is therefore part of measurement, not merely a reporting preference.

4. Behavioural tests reveal what averages conceal

CheckList introduced a task-agnostic behavioural testing approach organised around linguistic capabilities and test types. Its demonstrations identified failures in models that had already undergone substantial testing; a practitioner study found more bugs with the method [4]. The contribution is to make particular expected behaviours testable. An overall score can remain high while a narrow but consequential capability fails repeatedly.

For an evidence-summary task, a behavioural specification might ask whether the system preserves a qualification when a source sentence is paraphrased. Another might ask whether the answer changes appropriately when a product specification changes. These are different tests: stability under an irrelevant change and responsiveness to a relevant change. A system that never changes its answer would pass a crude stability test while failing to use new evidence. The expected relationship between input and output must therefore be stated before a pass has meaning.

A useful evaluation set also distinguishes ordinary task variation from deliberate stress cases. Common cases estimate routine performance only to the extent that their distribution represents routine work. Rare, adversarial or difficult cases investigate vulnerabilities. Combining the two into one percentage without explaining the mixture obscures the interpretation. A high failure rate on deliberately difficult cases does not estimate the production incident rate; a low failure rate on easy examples does not establish resilience to the difficult cases.

Task boundaries must include the instructions that make a response unacceptable. Omitting a required qualification may be a factual omission, a compliance failure and a failure to follow the brief. Recording those dimensions separately preserves diagnostic value. Otherwise the evaluator knows that an answer failed but cannot distinguish whether the remedy concerns source retrieval, instruction following or the acceptance rule itself.

The CheckList manuscript distinguishes minimum-functionality tests, invariance tests and directional-expectation tests. A minimum-functionality test isolates a simple behaviour; an invariance test changes an input in a way that should preserve the prediction; a directional test specifies how the response should move. The authors organise these against capabilities such as negation, temporal reasoning and named entities [4]. This matrix offers a more diagnostic description than a single accuracy figure because the failure remains attached to a stated behavioural expectation.

5. Factuality, support and completeness

TruthfulQA tests whether models reproduce common falsehoods, using questions designed around misconceptions [5]. Its historical results show why plausible human-like answers and truthful answers can diverge. The benchmark does not supply an error rate for a contemporary marketing assistant. It supplies a test-design lesson: an answer should be checked against evidence, including where a familiar response may be wrong.

FActScore decomposes a long answer into atomic factual claims and estimates the share supported by a reliable knowledge source. Its reported application to generated biographies illustrates why binary whole-answer labels lose information [6]. A single paragraph may contain a correct product name, an incorrect date and an unsupported causal claim. Treating the whole paragraph as correct because most sentences are plausible conceals the unsupported elements.

Factual precision and completeness answer different questions. A response containing one well-supported fact may achieve high precision while omitting the evidence needed to answer the assignment. Conversely, a comprehensive response may introduce unsupported details. An evaluation of accepted work needs to preserve both dimensions if both matter to the brief. This observation follows from the definitions of the measures; it does not require assuming that a particular scoring framework already measures completeness.

Source support also has levels. A cited document may exist, discuss the subject and still fail to support the exact claim. A statement about association can be accurately quoted yet incorrectly presented as causal. Checking the claim-source relation therefore requires attention to population, intervention and outcome, as well as the presence of a link. The validation target is the proposition asserted in the answer, including its strength and scope.

6. Retrieval evaluation needs separate components

RAGAs distinguishes retrieval quality, faithful use of retrieved material and generation quality. Its framework proposes metrics that do not depend on ground-truth human annotations for every example [7]. The distinction is important because a system can retrieve the relevant paragraph and still misstate it. It can also write a faithful summary of an irrelevant paragraph. A final-answer score alone may not identify which component caused the failure.

An evaluation corpus for brand or product knowledge has its own constraints. The relevant document may be absent, superseded or accessible under a different name. These are failures in the evidence environment as well as possible failures in retrieval. A model cannot recover a missing internal fact by being instructed to be accurate. The evaluation record should indicate whether the answer was available in the authorised corpus and whether the retrieved version was the intended one.

Reference-free metrics can shorten feedback cycles, but the term describes a method's input requirements rather than universal independence from judgement. A metric still embodies assumptions about what counts as relevant context or a faithful answer. Where evaluation relies on another model, its errors can enter the assessment. Validation on representative examples with known answers remains a separate question from the convenience of running the metric.

7. Automated judges and the meaning of agreement

Zheng and colleagues examine model-based judging through MT-Bench and Chatbot Arena. Their paper reports substantial agreement with human preferences while documenting position, verbosity and self-enhancement biases, alongside reasoning limitations [8]. These two findings belong together. Agreement in the tested setting establishes useful evidence about that setting; the documented biases identify conditions under which the judgement can change for reasons unrelated to the intended quality criterion.

A marketing comparison may involve two answers that differ in both substance and length. If the judge prefers the longer answer, the result is ambiguous unless the evaluation can distinguish additional useful content from a length preference. The same problem arises when one response repeats the rubric's language. Apparent compliance may be easier to recognise than the underlying quality the rubric intends to measure.

Human evaluation also needs a defined task. Experts can disagree because they apply different standards, have different information or interpret the brief differently. Agreement between judges is informative about consistency, but consistency alone does not establish that the shared judgement predicts business performance. A panel can consistently prefer an advertisement that produces no incremental effect. The endpoint remains preference until a separate study connects it to another outcome.

Independence of review concerns information and incentives as well as identity. A reviewer who sees which output used AI may apply a different standard from a reviewer who does not. Randomising presentation order, concealing unnecessary condition labels and recording the rubric before scoring can make the comparison more interpretable. These are design considerations arising from the documented judge biases; the review does not assign them an unverified percentage improvement.

8. Documentation makes comparisons interpretable

Model cards describe intended uses, evaluation conditions and performance across relevant groups [9]. Datasheets document a dataset's motivation, composition, collection and recommended uses [10]. Both frameworks address the context needed to interpret a number. A model score without the test population or a dataset without its construction history leaves important validity questions unanswered.

For a marketing evaluation, the corresponding record includes the task definition, corpus version, prompt, model identifier, tool permissions, scoring criteria and date. These details identify the treatment being assessed. They also expose which changes make an earlier comparison obsolete. Updating a product fact in the corpus changes the task environment; changing the evaluator changes the measurement process. Both may be justified, but they should not disappear into an unchanged label.

The unit of observation must also be explicit. Ten outputs from one brief are not ten independent briefs. Multiple ratings of the same output are not additional outputs. Repeated generations can reveal variation within a configuration, while repeated assignments can reveal variation across tasks. Confusing these levels exaggerates the apparent amount of independent evidence. The necessary sample size depends on the variation and precision required, rather than on a universal number of examples.

9. Risk, acceptance and deployment context

NIST's AI Risk Management Framework is voluntary guidance for incorporating trustworthiness into AI design and use [11]. Its Generative AI Profile extends that work to risks specific to or amplified by generative systems [12]. These are institutional frameworks, not experimental demonstrations that a particular review process eliminates errors. Their relevance is the need to connect measurement to the system's intended use and potential consequences.

Acceptance thresholds depend partly on what an error can do. An incorrect internal brainstorming suggestion and an incorrect published product claim have different consequences. Averaging them into one error score imposes a value judgement about their relative importance. A defensible assessment identifies consequential failures separately and states how they affect acceptance. Reporting only a weighted total can conceal a failure that should determine the decision.

Deployment introduces conditions absent from a static test: new documents, changing users, larger workloads and different task combinations. An evaluation establishes evidence within the tested conditions. Ongoing monitoring examines whether those conditions and outcomes continue to hold. The two activities have different roles. Monitoring can discover a new failure, but it cannot retrospectively make an inadequately designed pre-deployment comparison causal or representative.

A result also needs a denominator that matches its use. Counting every generated sentence weights long answers more heavily than short answers. Counting every assignment gives equal weight to assignments that may differ in difficulty or consequence. Neither convention is automatically correct. The report should explain which quantity a rate describes and why that quantity bears on the intended decision. An error rate per claim, an acceptance rate per assignment and a failure rate per user interaction cannot be compared as though they were alternative estimates of the same probability. Where repeated observations come from the same source material, uncertainty should reflect that shared origin. These measurement choices can change the interpretation even when the underlying outputs are identical. They therefore belong in the evaluation specification before a score is used to compare configurations.

10. Synthesis and limitations

The selected literature supports a layered assessment of language-model-supported work. Benchmark breadth, behavioural tests, factual support, retrieval quality and preference judgements each reveal different properties. Their results become useful when the evaluated object and the intended claim match. A composite number can help a decision only when its components, weighting and exclusions remain visible.

The review is limited by purposive selection and summary-level access. It does not reproduce benchmark results, inspect statistical code or establish how well every method works on contemporary marketing tasks. Several references predate current systems. The practical transfer is therefore methodological: they identify questions that an evaluation should answer, while the answers for a particular system require evidence from that system and setting.

The central conclusion is that useful evaluation requires more than identifying a preferred response. It requires specifying the property being assessed, collecting observations that bear on it and keeping the uncertainty of that inference visible. A marketing team can then distinguish a capable model from a reliable configured system, and a reliable system from a commercially effective use of its output. Those distinctions prevent evidence from one level being asked to establish another.

Source access and appraisal register

The 12 references below were located through targeted title and topic searches on 21 October 2026. Author or publisher abstracts, official research summaries and publication records were consulted. The discussion uses the claims available in those materials; full manuscripts, supplementary analyses and underlying data were not independently appraised. Institutional guidance is treated as guidance, rather than a controlled estimate of effectiveness. Where a working paper is cited, its provisional status remains part of the interpretation. The evidence map is selective and does not estimate the prevalence of results in the complete literature.

The CheckList full manuscript was additionally inspected for its test taxonomy and behavioural examples (Sections 2–3). Other source descriptions remain at abstract, official-summary or record level. Reading selected full-text sections does not constitute independent replication or a formal risk-of-bias appraisal.

References