When you’re fine-tuning a generative AI model—whether it’s writing subject lines, creating product copy, or crafting promotional emails—the most important question is simple:
Which model version will actually drive results?
Traditional evaluation metrics like BLEU or ROUGE focus on how closely a model’s output matches a reference text. That might work in machine translation, but for marketing content, it misses the point entirely.
You don’t just want the output to be semantically correct.
You want it to be compelling
You want it to perform
In this post, we’ll introduce a few common approaches to evaluating generative AI. We will then dive deep into the one that matters most for marketers: using AI to estimate Business-Aligned Proxy Metrics.
Summary
Traditional metrics like BLEU fall short for evaluating generative AI output in marketing—they focus on text similarity rather than business impact.
Instead, marketers should adopt business-aligned proxy metrics that reflect real performance outcomes.
These metrics leverage AI to analyze historical campaign data—such as subject lines, body content, open rates, and CTRs—to predict how new AI-generated content is likely to perform.
Alternative Evaluation Methods for Gen AI Models: Why They Fall Short
Before we focus on the main act, here’s a quick overview of some of the other popular methods for evaluating language model outputs.
| Evaluation Method | Description | Pros | Limitations |
|---|---|---|---|
| LLM-as-a-Judge | Uses a large language model (e.g., GPT-4, Claude) to rate content based on clarity, tone, and persuasiveness. | Fast, scalable, consistent; useful for preliminary quality checks. | Based on modeled human preference, not real-world performance data; may not reflect actual user behavior. |
| Human Evaluation | Real human reviewers rate outputs on attributes like creativity, fluency, and emotional impact. | High-quality insights; good for subjective or brand-sensitive content. | Time-consuming, costly, not scalable; subjective results may not align with actual marketing outcomes. |
The Case for Business-Aligned Proxy Metrics
Business-aligned proxy metrics are business-outcome-oriented metrics designed to estimate how well a piece of AI-generated content will perform against real business goals. These might include:
- How likely an email is to be opened
- Whether a product description leads to clicks
- Whether a subject line fits the brand voice
- Whether the copy is compliant with legal and marketing policies
These metrics are called “proxy” because they don’t directly measure outcomes like open rates or conversions, but they’re trained or designed to predict them using historical data, behavioral models, or other performance signals.
The result: an evaluation method that tells you not just whether content is good, but whether it’s likely to work.
How Business-Aligned Proxy Metrics Work in Practice
Let’s walk through how this approach plays out in a real-world generative use case: marketing email generation. The metrics chosen for evaluation include
- Email Open Rate Prediction: What is the estimated open rate if we were to use model generated subject line?
- Click-Through Rate Estimation: What CTR can we expect if we use the model generated email body?
- Brand Voice Consistency: CTR/Open rate may be great but what is the degree of brand-voice alignment?
- Compliance Scoring: What is the compliance score of the generated content?
Of these, 1, and 2 are outcome-based metrics, while 3, and 4 are designed to ensure brand consistency and compliance. Let’s dig in!
Need hands-on technical implementation details?
Check out a deep-dive, developer-focused recipe for implementing predictor models for assessing LLM performance.
1- Email Open Rate Prediction
You’re using an LLM to generate email subject lines. Instead of simply choosing the ones that sound good, you build a model that predicts how likely each subject line is to be opened.
What You’ll Need
- Historical email campaign data — This usually comes from your email service provider (ESP) like Mailchimp, HubSpot, Salesforce Marketing Cloud, or Klaviyo.
Each record should include:
- Subject line
- Actual open rate
- Campaign type (promo, welcome, reminder)
- Audience segment (e.g., new, returning)
- Send time and date
How You Train the Model
You treat this as a regression or classification problem:
- Input: The subject line (plus optional metadata)
- Output: The open rate (as a number or category)
To make this work, you convert the subject lines into features the model can understand, such as:
- Text embeddings (semantic representation of the text)
- Word or character count
- Use of personalization tokens
- Sentiment or tone indicators
- Presence of urgency words or numbers
You can also include contextual features like day of the week, time of day, and segment type if available.
Use a simple predictive model (logistic regression, XGBoost, or any AutoML tool). These models learn patterns from the data, like:
- Subject lines that start with “Don’t miss” tend to perform well on Fridays.
- Emojis reduce performance in B2B campaigns but increase it in consumer ones.
- Personalized subjects perform better for new subscribers.
How You Use It
Once trained, you can run each subject line generated by your LLM through the model. It outputs a predicted open rate for each line—giving you a data-backed way to rank them and identify which outputs are most likely to succeed.
Subject Line | Predicted Open Rate |
“20% Off Bottles That Love the Planet” | 32.8% |
“Sip Sustainably. Save Now.” | 27.1% |
“Your Water Bottle’s New Look Is Here” | 24.9% |
This replaces guesswork with real performance signals—even before the email is sent.
2- Click-Through Rate Estimation
For the body of the email, you might use a model that predicts the likelihood of a user clicking through to your landing page or product.
This model can be trained on historical campaign data with:
- Email body copy
- CTR outcomes
- Features like CTA wording, message length, tone, or structure
Even if the text is fluent and on-brand, this tells you whether it’s likely to inspire action.
3- Brand Voice Consistency
Unlike open rate or click-through predictions, brand voice evaluation is not an outcome-based metric. You’re not trying to predict user behavior. Instead, you’re assessing whether the generated content matches your company’s tone, style, and vocabulary.
This typically involves comparing outputs to a library of brand-approved content using embedding similarity or tone classification models.
Why it matters:
While it doesn’t predict opens or clicks, brand consistency is critical to maintaining trust, recognition, and alignment across campaigns. It’s best used as a quality filter, helping you ensure that only content that sounds like your brand moves forward for further evaluation or testing.
4-Compliance and Safety Checks
Compliance scoring also doesn’t predict user behavior. Instead, it identifies outputs that should not be used, even if they might perform well technically.
For example, you can build rule-based or model-based checks to catch:
- Overpromising claims (“Guaranteed results in 3 days”)
- Inappropriate or exclusionary language
- Missing legal disclaimers
These checks are constraint-based, not outcome-based, but they’re critical for reducing legal risk, protecting brand integrity, and staying within ethical boundaries.
Two Types of Evaluation Metrics
To clarify how these all fit together, it helps to group evaluation metrics into two categories:
Metric Type | Examples | Role in Evaluation |
Outcome-Oriented | Open rate prediction, CTR, conversion rate | Optimize toward these |
Constraint-Based | Brand consistency, compliance checks | Filter or validate outputs |
Outcome-oriented metrics tell you how well something might perform.
Constraint-based metrics tell you whether it’s safe or acceptable to use.
Both are essential. You optimize for the former, and enforce the latter as guardrails.
Using These Metrics to Evaluate LLMs
Once you’ve built these proxy models and constraint checks, you can use them to evaluate outputs from each LLM variant. For example:
Model | Predicted Open Rate | CTR Estimate | Brand Voice Score | Compliance Pass |
A | 24.7% | 3.2% | 0.91 | Yes |
B | 27.3% | 3.5% | 0.88 | No |
C | 26.5% | 3.8% | 0.93 | Yes |
From here, selecting the best model depends on your priorities. Models that fail hard constraints (like compliance) are typically disqualified, even if their performance scores are high. Among the remaining candidates, you can apply weighted scoring across your outcome-oriented metrics, such as assigning more importance to CTR than open rate or tone. This lets you calculate a composite score for each model and rank them accordingly. If two models score closely, you might run a live A/B test or human review as a final validation step before choosing a winner.
Summary: Evaluate Like a Marketer, Not Just a Modeler
Generative AI is powerful, but the true test isn’t whether the text looks good—it’s whether it performs.
That’s where business-aligned proxy metrics shine. They let you evaluate model outputs based on what actually matters to your campaigns: open rates, clicks, conversions—and equally important—brand tone and policy compliance.
Here’s the key idea:
- Optimize toward outcomes.
- Enforce essential constraints.
- Measure your AI like you measure your marketing—with real signals and boundaries.
Whether it’s a subject line or a product description, ask:
“Will this move the needle?”
And also: “Is this safe and on-brand?”
That’s how you move from good AI outputs to great business results.
Got Questions?
If you’re exploring how generative AI can drive real performance—and not just generate text—we’d love to help.
Learn more about our AI-in-marketing solutions, or book a free discovery call with our team to see how we can support your goals.
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