In the produced universe, Evals have become a standard practice: they allow you to test the precise capacities of an AI model on targeted tasks. But in the marketing field, this logic is still marginal. However, in the era where assets (texts, visuals, videos, prompt) are generated, modified, tested and broadcast at high speed, the Marketers need objective benchmarks. Evals represent a robust method to measure the quality, impact or consistency of marketing productions-beyond simple a posteriori performance.
What is an Eval in a marketing context?
An Eval is a standardized test subject to a model to measure its ability to meet a specific objective. Applied to marketing, it amounts to asking a simple question: Is this asset good?
But “good” is no longer defined by an isolated CTR score, but by a series of criteria analyzable upstream:
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- Message clarity
- Alignment with brand tone
- Relevance for a target audience
- Originality or differentiation perceived
- Level of emotion or persuasion
These dimensions, formerly left to the subjectivity of the teams, can today be partially objectified by the models themselves, provided they provide them with well-designed Evals.
“The models are intelligent enough to learn everything they are taught-but it is still necessary to provide them with the right benchmarks.”
– Kevin Weil, CPO, Openai
Three types of evals to integrate into an IA-Native marketing process
1. Tone of tone (tone consistency)
Allows you to test if content respects brand grammar.
Prompt: “You are a brand manager expert. Evaluates this text according to the following values and tone: (brand brief). Gives a note out of 10 and explains why.”
2. Evaluing intention (persuasion / Clarity Test)
Measure the immediate understanding of the message and its ability to take action.
Prompt: “You are a B2B prospect targeted by this offer. Do you understand what this company offers? Do you feel convinced? What emotion do you feel?”
3. Eval comparative (A/B/C Testing assisted)
Instead of launching long and expensive advertising tests, the model compares several variants of the same message on specific criteria.
Prompt: “Compare these 3 versions of a LinkedIn message for the CMO target. Classify them according to their clarity, originality and emotional impact. Justifies each classification.”
Why these evals change the situation
Speed of iteration
Instead of publishing and waiting for results, we can Filter, classify and improve Dozens of upstream variants.
Qualitative standardization
Evals allow qualitative criteria to be made Stable and sharedavoiding recurring subjective debates in the editorial committee.
Structured feedback for Fine-Tuning
The well-written Evals serve as the basis for refining an AI model specific to the brand (fine-tuning), which guarantees continuous improvement of assets.
Acceleration of the ideation phase
A well -calibrated model via Evals can in return offer suggestions for creative, targeted, and aligned improvement.
Limits and good practices
- An Eval Marketing does not replace the real testhe completes it in the preparatory phase.
- He must be contextualized : A good message for students is not necessarily so for CFOs.
- It is necessary to avoid the prompt waves (“Is it good?”) In favor of precise, scorable, and interpretable criteria.
Towards “Eval-Driven” marketing
Like the user test in the UX or the A/B testing in Growth, the Marketing Evals will become a full discipline. They allow not only to be benchmarker of existing assets, but also to lead to an AI model to understand the specific requirements of a brand.
As generative agents will be integrated at all levels of marketing production, Knowing how to write Evals will become a central competenceat the crossroads of branding, semantic analysis and prompt engineering.
