An Introduction to AI Evals for Marketers

If you're running AI-powered marketing campaigns, you're probably wondering: "How do I know if this stuff actually works?" You're not alone. Most marketers are flying blind when it comes to measuring AI performance, making tweaks based on gut feeling rather than data.

That's where AI evaluations (or "evals" as the cool kids call them) come in. Think of them as your quality control system for AI outputs – a systematic way to measure, improve, and maintain consistency in your AI-driven marketing efforts.

What Are AI Evals and Why Should You Care?

AI evals are structured assessments that measure how well your AI tools perform specific marketing tasks. Whether you're using AI for content creation, customer segmentation, or campaign optimisation, evals help you understand what's working and what isn't.

Here's the thing: AI isn't magic. It makes mistakes, produces inconsistent outputs, and sometimes completely misses the mark. Without proper evaluation, you might be publishing subpar content, targeting the wrong audiences, or making strategic decisions based on flawed AI insights.

The benefits are straightforward:

• Measure actual performance – Know exactly how well your AI tools handle specific tasks

• Spot improvement opportunities – Identify weak points before they damage your campaigns

• Maintain quality standards – Ensure consistent output across all AI-generated materials

• Build confidence – Make data-driven decisions about AI tool adoption and usage

The Four Types of AI Evals Every Marketer Should Know

Not all evals are created equal. Here are the four main types you'll encounter, along with their pros and cons:

1. Code-Based Evals

These assess the technical performance of AI algorithms – think accuracy rates, processing speed, and error frequencies. For marketers, this might involve measuring how accurately your AI tool segments customers or predicts campaign performance.

Pros:

• Objective and quantifiable

• Can be automated

• Great for benchmarking

Cons:

• Requires technical expertise

• May not capture creative quality

• Limited insight into user experience

2. Human Evals (Human-in-the-Loop)

Real people review AI outputs for quality, relevance, and brand alignment. This is particularly valuable for content creation, where nuance and creativity matter.

Pros:

• Captures subjective quality measures

• Understands context and nuance

• Can assess brand alignment

Cons:

• Time-consuming and expensive

• Subject to human bias

• Difficult to scale

3. LLM-Judges

Large language models evaluate AI-generated content automatically. You might use GPT-4 to assess the quality of blog posts generated by another AI tool, for example.

Pros:

• Scalable and fast

• Can handle complex criteria

• Cost-effective for large volumes

Cons:

• May inherit biases from training data

• Limited understanding of brand-specific requirements

• Can be inconsistent across evaluations

4. User Evals

Direct feedback from your target audience about AI-generated content or experiences. This might involve A/B testing AI-generated email subject lines or surveying customers about chatbot interactions.

Pros:

• Reflects real user preferences

• Directly measures business impact

• Provides actionable insights

Cons:

• Requires significant sample sizes

• Can be slow to implement

• May not capture long-term effects

How to Choose the Right Eval for Your Marketing Needs

The eval type you choose depends on what you're measuring and your available resources. Here's a practical framework:

Building AI Evals Into Your Marketing Workflow

Here's where most marketers get it wrong: they treat evals as a one-off exercise rather than an ongoing process. The real power comes from integrating evaluations into your regular workflow.

Start Small and Scale Up

Don't try to evaluate everything at once. Pick one AI tool or process that's critical to your marketing success and start there. For example, if you're using AI for social media content creation, begin by evaluating post quality and engagement rates.

Create Evaluation Criteria

Define what "good" looks like for your specific use case. This might include:

• Brand voice alignment (1-10 scale)

• Factual accuracy (pass/fail)

• Engagement potential (predicted vs actual)

• Grammar and readability scores

Automate Where Possible

Manual evaluation doesn't scale. Use tools and scripts to automate routine assessments, reserving human review for high-stakes content or complex creative work.

Act on the Results

This sounds obvious, but many teams collect evaluation data and then ignore it. Create a clear process for addressing poor-performing AI outputs – whether that means adjusting prompts, switching tools, or adding human oversight.

Real-World Example: Evaluating AI-Generated Blog Content

Let's say you're using AI to generate blog posts. Here's how you might implement a comprehensive evaluation system:

Step 1: LLM-Judge evaluates each post for readability, structure, and SEO optimisation
Step 2: Human reviewer assesses brand voice alignment and factual accuracy for 10% of posts
Step 3: User evals track engagement metrics (time on page, social shares, comments)
Step 4: Code-based eval measures SEO performance (rankings, organic traffic)

This multi-layered approach gives you comprehensive insight into content quality while remaining manageable and cost-effective.

Common Pitfalls to Avoid

Based on what I've seen working with marketing teams, here are the mistakes you'll want to sidestep:

• Over-evaluating everything – Focus on high-impact areas first

• Ignoring context – A blog post and a social media caption need different evaluation criteria

• Relying on single metrics – Combine multiple eval types for comprehensive assessment

• Setting and forgetting – Review and update your evaluation criteria regularly

• Perfectionism paralysis – Start with basic evals and improve over time

The Future of AI Evals in Marketing

AI evaluation tools are becoming more sophisticated and accessible. We're seeing the emergence of platforms that can automatically assess content quality, predict campaign performance, and even suggest improvements in real-time.

The marketers who embrace systematic AI evaluation now will have a significant advantage as these tools become more prevalent. They'll have cleaner data, better processes, and more confidence in their AI-driven decisions.

Getting Started Today

Don't overthink this. Pick one AI tool you're currently using and ask yourself: "How do I know if this is working well?" Then design a simple evaluation process to answer that question.

Start with basic metrics, involve your team in defining quality standards, and gradually build more sophisticated evaluation systems as you learn what matters most for your specific marketing goals.

The goal isn't perfection – it's continuous improvement. AI evals give you the feedback loop you need to make that happen systematically rather than relying on guesswork.

By implementing AI evaluations, you're not just improving your current marketing performance – you're building the foundation for faster learning and better decision-making as AI tools continue to evolve. And in a competitive market, that systematic approach to improvement might just be your secret weapon.

Growth Method is the only AI-native project management tool built specifically for marketing and growth teams. Book a call to speak with Stuart, our founder, at https://cal.com/stuartb/30min.