How to Track the Effectiveness of AI and LLM Optimization Strategies? Top KPIs to Consider

Remember when ChatGPT was first released? It was November 2022, and social media timelines were flooded with screenshot after screenshot of AI-generated responses. We were all fascinated. Fast-forward just three years and it’s all become just another everyday online activity (this means data, lots and lots of valuable data, we’ll come back to this later).

Today, AI-powered search and conversational agents are a primary influence in consumers’ purchase decision-making processes. This trend spans B2B, B2C and beyond: whether someone is searching for home solar panels or a corporate cybersecurity solution, they first ask AI. 

What are AI Search success KPIs? 

It’s a new era, and powerful SEO practices are no longer sufficient. You now need measurable indicators that reveal how well your strategies are performing in AI Search, how engaged your audience truly is at every stage, and where the opportunities for AI Search growth lie.

So, let’s explore several KPIs that can help you evaluate your success in attracting, engaging, and converting traffic driven through large language models (LLMs). 

Traffic Quality & Funnel Depth

Perhaps the most foundational KPI in the context of LLM traffic is understanding who your visitors are, how they arrive, and what they do afterward. To do this effectively, we segment traffic from AI search engines (like ChatGPT) into three progressively valuable tiers. So:

What’s the best way to layer engagement in AI Search?

Tier 1: Users arriving from ChatGPT, simply landing on your site after an initial prompt.

Tier 2: Users from ChatGPT that actively engage by navigating beyond the landing page, e.g. reading content, exploring different sections, or clicking links.

Tier 3: Users who move beyond engagement to take meaningful actions such as booking demos, signing up for services, or completing conversions.

This layered “who, what, how” segmentation allows you to evaluate not just your visibility i.e., how many users find you, but how deeply they are exploring your offerings and whether they are converting into leads or customers. It’s a powerful way to measure funnel depth in an AI-centric environment.

A real-world example: layering AI Search engagement 

You are operating in the health tech sphere. You have a cutting-edge app for people suffering from diabetes. The app helps users track their blood sugar levels, manage their diet, access a live health advisor, and more. Upon analyzing AI Search metrics, you find out that the vast majority of users coming to your website are in Tier 1. Before we go deeper into this example, let’s exemplify the kinds of prompts that are low intent (Tier 1) versus Tier 2 and Tier 3 (high intent). 

AI Prompt Examples:

Low-Intent Questions (General information, curiosity)

What are the best diabetes management apps available?

How do I choose the right diabetes app for my needs?

Are diabetes apps easy to use? 

Medium-Intent Questions (Functional inquiries, initial considerations)

Can a diabetes app help me control my blood sugar levels?

Can I use a diabetes app to track my diet and carbohydrate intake?

Are there apps specifically designed for type 1 or type 2 diabetes?

High-Intent Questions (Action and decision-focused)

What features should I look for in a good diabetes app?

Can a diabetes app automatically log my glucose readings and insulin doses?

Can a diabetes app predict hypoglycemic or hyperglycemic episodes?

Can I share my diabetes data with my healthcare provider through an app?

If most of your visitors are in Tier 1, what are some actions you can take to increase their intent? Perhaps your visitors have only recently been diagnosed and don’t possess much information about the disease. They are not aware of the potentially life-saving features of your app. Perhaps they are overwhelmed by the idea of using a health app; they don’t know how easy it can be. How can you educate them? How can you let them know your app is incredibly easy to use? 

The specific questions your visitors ask AI are questions they would ask you over coffee if they could. If your brand takes the opportunity to answer their questions and address their concerns, then you’ll have their attention and you’re more likely to convert them into customers. 

LLM Visibility & Brand Presence

Brand exposure in AI and LLMs isn't just about being mentioned. It’s about being the right answer to the right questions, and being seen by the right people in the right context. To gauge this, we measure brand presence across three levels:

Is Your Brand Seen by the Right AI Search Audience?

• Level 1: Your brand appears within responses generated by LLMs. This indicates a direct mention, though perhaps not in a prominent position.

• Level 2: Your brand shows up in relevant prompts: those that align with your ideal customer profile (ICP). This demonstrates that your brand is part of conversations your target audience is having.

• Level 3: Your brand is featured prominently, such as the top answer or the first mention in responses to ICP-relevant prompts. This is where visibility turns into influence.

Tracking these levels helps you ensure that your brand isn't just visible, but visible to the right audience, in the right situations, and with the right prominence.

A real-world example: gauging your audience’s level

You are in the industrial construction business. Your technology measures the readiness level of concrete as it’s being mixed. It’s a pioneering real-time solution but you are not getting enough traffic from AI Search. 

Upon running an analysis, you find out that your brand is in Level 2. This means, your brand shows up as part of the answer to the relevant prompts asked by your ICP. Now you know your brand is part of the conversation but for some reason it’s not “the” answer AI chooses. 

What information is your audience missing? What information is AI missing? Perhaps it does not know how important it is to have a real-time approach as opposed to intermittent measuring. Can you make sure LLMs capture why your solution is better? A few seconds is a long time in concrete mixing. Can you make sure AI understands this and that’s why your solution is the best? With some content tweaks or additions, you can. 

Sentiment Analysis: Understanding Brand Perception

Beyond levels of visibility, equally important is understanding “how” your brand is being perceived within AI-generated content and conversations. This is about gauging the emotional tone, trust level, and overall sentiment that shapes public perception.

How to best analyze AI Search sentiment:

Sentiment analysis provides insights into whether references to your brand are positive, neutral, or negative.

• Neutral: Mentions are factual, informational, or ambivalent.

• Positive: Mentions reflect approval, trust, or satisfaction.

• Negative: Mentions contain criticism, dissatisfaction, or brand disadvantages.

By tracking sentiment trends, you can identify potential issues early, gauge the effectiveness of your messaging, and refine your brand image in the AI space.

A real-world example:

Your brand offers accessibility solutions for websites. When analyzing prompts from relevant users about a particular feature, you observe that ChatGPT consistently responds with a negative sentiment towards your brand.

Image 1: Negative sentiment surrounding your brand’s “Website Accessibility Automation” feature. 

When a user asks: "Compare automated platforms that ensure my website meets accessibility standards?", AI might reply:  

• Competitor 1 — positive explanation

• Competitor 2 — positive explanation

• Your brand — negative explanation

In this scenario, your brand's mention is associated with a negative sentiment across all platforms. Your task is to understand what is causing this perception and why. Based on this insight, you can then undertake strategies for adding reliable, authoritative content to change the narrative. For example, your KPI could track sentiment improvement, aiming to increase positive mentions by a certain percentage (e.g., X%) over time.

ROI on Visibility Efforts: Measuring Impact and Value

Finally, all of these metrics need to translate into tangible business results. The ROI on your AI visibility efforts is a critical KPI: it measures the effectiveness of your investments in increasing LLM visibility.

To calculate the ROI on AI Visibility, we correlate:

• The volume and quality of traffic coming from AI interactions.

• The revenue attributable directly or indirectly to this traffic.

• The resources invested (content creation, SEO, AI optimization).

Powered by data, this comprehensive view ensures you’re not just gaining exposure but driving meaningful growth, sales, and customer engagement through your AI search strategies.

KPIs for Effective AI Search: Next Steps 

Measuring success in the era of AI and LLMs requires a nuanced, layered approach. It’s about more than just visibility; it’s about understanding user intent, engagement depth, brand perception, and ultimately, the business value generated. By leveraging these key KPIs, your brand can continuously refine its AI search optimization strategies, attract higher-quality traffic, and unlock new growth opportunities in this exciting new world.