While creating content for AEO is becoming more manageable, the bigger challenge lies in answering one crucial question: How do you measure the performance of your AEO efforts? Unlike traditional SEO, where rankings and traffic data are straightforward and consistent, AEO operates in a probabilistic space—the same question might yield different answers across different AI platforms, or even from the same model. These answers are often non-clickable, making performance tracking less direct and far more nuanced.

In this blog, we will break down exactly how to track and evaluate your AEO initiatives—what to measure, why it matters, and how to do it well.

AEO vs SEO Goals

With AEO, the goal isn’t necessarily to drive instant traffic or direct conversions. Instead, it’s about brand discovery and presence within AI-generated responses.

Why is this important? Because users often don’t go beyond the initial AI-generated answer. There’s no need to click a link, scroll through pages, or open multiple tabs. The AI delivers the entire response in one go. And yet, this interaction can significantly influence user behavior.

Even without a click, an AI-generated brand mention:

• Builds brand awareness
• Establishes credibility through AI’s “endorsed” answer
• Influences future searches, conversations, and buying decisions

Example: If your brand is listed in ChatGPT as a top tool in your industry, that mention may not lead to immediate traffic, but it increases the chances of users later searching for your brand, visiting your site, or converting through another channel.

Why don’t traditional SEO metrics apply to AEO? In SEO, rankings, impressions, and click-through rates provide a clear map of performance. These are based on deterministic systems—if your site ranks #1 today, chances are it still ranks #1 tomorrow, unless something significantly changes.

AEO doesn’t work like that. LLMs rely on probabilistic models, meaning:

• Responses vary based on the model’s training and external web sources
• Results aren’t fixed or consistent
• Answers may not include clickable links at all

That’s why AEO performance must be evaluated differently. You’re not tracking just traffic or rankings—you’re measuring presence, prominence, sentiment, and how those influence user behavior over time.

What AEO Performance Metrics Should You Track?

Here are 3 types of metrics that can help you evaluate AEO performance meaningfully:

1. Visibility Metrics (Primary metric)

Visibility is the foundation of AEO. These metrics help you assess how often and how prominently your brand is being included in AI-generated answers.

• Brand visibility score in ChatGPT, Gemini, Perplexity, etc: This reflects how frequently your brand is mentioned in AI responses relevant to your category. For example, if you ask Gemini, “What’s the best task management tool for teams?” and your brand is listed in the top three, that’s a win, even if it doesn’t lead to a click.
• Position or priority in LLM answers: Are you mentioned first, last, or somewhere in the middle? Higher placement often reflects stronger brand authority. Being listed in a response should be considered a win.
• Sentiment in brand mentions: LLMs learn from online content, including reviews (e.g., G2, Capterra), discussions (e.g., Reddit), and articles. Positive sentiment in these sources makes favorable mentions more likely in AI answers.

2. Engagement Metrics

Much of the brand discovery and evaluation happens within the AI interface itself, before a user ever visits your website. As a result, while your overall website traffic may decrease, traffic quality is likely to improve. Users who do click through have already done a fair amount of research through AI and are coming in more informed, curious, and ready to engage. This often translates to higher-quality sessions on your site.

To measure this effectively, focus on the following engagement metrics:

• Bounce rate: Are visitors sticking around or leaving right away?
• Average time on site: Are they exploring your content in depth?
• Pages per session: Are they engaging across multiple pages?

Engagement metrics tend to improve when your brand’s visibility and positive sentiment for the brand increase. For example, if Nike sees a rise in brand visibility for “best running shoes” across AI searches, they should also expect improved engagement on their running shoe product pages—across all traffic channels, including organic, direct, and paid.

3. Conversion Metrics

Although AEO primarily plays a role at the top of the funnel, it’s important to track its impact on downstream conversions. You may have noticed that many AI platforms—like ChatGPT—are now starting to appear in your website analytics as referral sources, often tagged with UTM parameters such as utm_source=chatgpt.com. As traffic from these LLM-powered platforms grows, you may begin to see a lift in bottom-of-funnel metrics, including:

• Actions initiated (form submissions, demo requests, or product views)
• Actions completed (lead forms submitted, purchases made, or demos scheduled)
• Live chat engagements
• Product support inquiries
• Direct revenue generated

The Challenge of Measuring AEO: Rag Is a Strong Influencer of Performance

Retrieval-Augmented Generation (RAG) is a technique used by most leading LLMs to enhance the quality and relevance of their responses. RAG-enabled AI models pull in real-time information from the web to supplement their answers.

Because these models reference external sources dynamically, any changes in the availability, credibility, or ranking of your brand across these sources can directly influence the outcome of an AI-generated response, similar to how Google’s algorithm updates shift the weight given to certain websites over others.

RAG allows LLMs to:

• Actively retrieve live data from the internet at the time of the query
• Combine it with their existing training knowledge
• Generate fresh, contextually rich responses using high-authority sources

Example: Ask Perplexity, “What are the top CRM tools for nonprofits?” and it might reference current blog posts, software comparison sites, and industry articles to compile its answer. If your brand is mentioned frequently in those sources, it increases your chances of being included.

Manual vs. Automated AEO Performance Tracking

When it comes to tracking your brand’s presence in AI search, there are two primary approaches: manual audits and automated tools. Unlike the Google search ecosystem, where we have tools like Keyword Planner, SEMRush, or Ahrefs to help us with search performance over time, similar reporting metrics are still a black box for the AI search ecosystem.

1. Manual Tracking: Hands-On Learning

Manual tracking typically involves running periodic queries across AI platforms like ChatGPT or Gemini to observe how and where your brand appears. This includes:

• Asking the same set of queries each week to check for consistency
• Noting any changes in phrasing, ranking, or brand mentions
• Reviewing the sentiment and context in which your brand is mentioned

Example: Maintain a simple spreadsheet tracking whether your brand shows up for queries like “best CRM for small businesses” or “top productivity tools for remote teams” every week across key AI search platforms.

While manual audits can be time-consuming, they offer significant value in today’s rapidly evolving AI landscape. These hands-on checks provide deeper insight into how different models interpret and represent your brand—nuances that automated tools may still miss. Though not easily scalable, manual analysis remains a powerful complement to automation and should be part of your routine, even if you already use other tracking tools.

2. Automated Tracking: Scalable & Consistent Monitoring

As the space matures, tools like Goodie AI have emerged to help brands monitor their AI search performance at scale. These platforms offer features such as:

• Brand visibility scores across ChatGPT, Gemini, Perplexity, and others
• Sentiment and positioning analysis
• Competitor benchmarking
• Topic-level reporting tied to your business objectives
• Optimization suggestions to improve inclusion and prominence

Because AEO is about visibility rather than traffic, these tools focus on discoverability metrics—measuring how often, and how favorably, you appear in AI-generated answers.

Goodie in Action: AEO Power Tool for Modern Brands

Goodie AI is a leading platform built specifically for AEO tracking and optimization. It offers a full suite of features to support both measurement and improvement:

• Real-time tracking of brand mentions across AI search engines
• Sentiment analysis across LLMs like ChatGPT, Gemini, Claude, Perplexity, and Deepseek
• Dual optimization strategies for both AI and traditional SEO
• AI-driven content recommendations to close visibility gaps
• Traffic and conversion attribution from AI interactions

💡 Why it matters: In a probabilistic AI environment where exposure = influence, tools like Goodie AI helps you track what matters—and take action to stay ahead.

As AI-driven search continues to evolve, the brands that adapt early—and measure smartly—will lead the way. Traditional SEO metrics no longer tell the whole story. AEO demands new tools, new KPIs, and a new mindset.

Your brand doesn’t need to “rank”—it needs to be recognized, recommended, and remembered. Tracking visibility, sentiment, and brand presence across LLMs is no longer a “nice to have”—it’s a competitive necessity.

The post How to Measure Performance of Answer Engine Optimization (AEO) appeared first on NoGood™: Growth Marketing Agency.

To address this gap, our team of experienced marketers conducted the first impartial study assessing the leading LLMs in the market. We recognized the need for a study that was not only conducted for marketers by marketers but also one that focused on the practical applications and challenges faced by marketing professionals in their daily work.

Background

The global AI market has already been valued at $241.8 billion in 2023 as compared to $135 billion in 2022, and it is expected to surge to a staggering $740 billion by 2030, reflecting a CAGR of 17.3%, according to Statista. This surge is being spearheaded by the United States, boasting the world’s strongest AI research capabilities, followed closely by Europe. Enterprises are seeing a steep adoption rate. According to OpenAI, 90% of Fortune 500 brands are actively using their tools and API internally.

Generative AI Adoption in Marketing

The marketing and advertising industry has emerged as a frontrunner in AI adoption, with a remarkable 37% of professionals already utilizing this technology in their daily tasks.

This rapid embrace is not surprising, considering the unique blend of creativity and data analysis inherent to marketing. AI seamlessly integrates with this blend, offering powerful tools for both creative exploration and data-driven decision-making.

In a 2023 study conducted with marketers in the United States, 73% of respondents reported using generative artificial intelligence tools, such as chatbots, as a part of their company’s work. This widespread adoption indicates a growing comfort level and understanding of the potential benefits these tools offer.

Generative AI, particularly Large Language Models (LLMs), has revolutionized the way people work across industries, providing innovative solutions to complex tasks. It has found prominent usage in various job functions, notably in industries such as IT and Technology, Finance, Healthcare, and Marketing.

Marketing Evaluation to Leading LLMs Report

The study involved a panel of 20 seasoned marketers, each with a minimum of 6 years of experience in the field. These experts were tasked with reviewing the output of anonymized leading LLMs across a range of the most common marketing use cases, including copywriting, marketing strategy development, content creation, creative ideation, and campaign planning. To ensure an unbiased evaluation, we carefully crafted prompts that were highly practical, realistic, and consistent across each model. This approach guaranteed that no single model was favored or engineered to outperform others, a common issue in LLM evaluations published by the creators of the models themselves.

The LLMs covered in the study:

• LLama 2 by Meta
• Gemini 1.5 by Google
• Perplexity by Perplexity
• ChatGPT (publicly available GPT 3.5) by OpenAI
• Claude 2.1 by Anthropic
• Claude 3 – Sonnet by Anthropic

By conducting this study, we aim to provide marketers with valuable insights into the strengths and weaknesses of each LLM, empowering them to make informed decisions when incorporating these tools into their marketing strategies and workflows. Our findings shed light on the variability in performance across different aspects, such as creativity, thoroughness, and coherence, highlighting the importance of selecting the right LLM for specific marketing tasks. The data presented in this report demonstrates the significant potential of AI applicability use case in marketing as the industry continues to evolve.

The graphs below offer a broad overview of how various Large Language Models (LLMs) performed across different marketing tasks. For a more in-depth analysis and breakdown of these findings, download the free report by clicking the download button below.

As the use of Generative Artificial Intelligence (Gen AI) continues to grow, a critical question emerges: Which Gen AI tool is best suited for what purpose, and how do the leading solutions compare? This report addresses this question by evaluating the performance of six Large Language Models (LLMs) across various marketing tasks and against diverse evaluation criteria.

The post Marketing Generative AI Report: A Comprehensive Evaluation of Leading LLMs appeared first on NoGood™: Growth Marketing Agency.

Enters contextual marketing, a strategy that is known for its ability to offer relevant content to the right audience at the perfect moment. In this comprehensive blog post, we will dive into the benefits of contextual marketing, common challenges faced by marketers with contextual marketing and how to overcome those and useful tips to execute an effective and successful contextual marketing strategy.

What is Contextual Marketing?

Harvard Business Review published a magazine in 2000, and this is how they explained contextual marketing. The article reads, “Instead of trying to create destinations that people will come to, they need to use the power and reach of the Internet to deliver tailored messages and information to customers at the point of need.“

They need to become what we call contextual marketers.” This definition still holds. In other words, “it’s delivering the right piece of communication using the right medium to the right user at the right time in their purchase journey.” And if you get enough things done right, you have a perfectly executed contextual marketing campaign. Here’s a classic example of contextual ads.

For example, if a user is reading an article about the best credit cards for travel rewards, contextual ads may promote a travel credit card ad. Or, webmail ads could target users actively engaging with email platforms. The relevance works to grab attention when consumer interest peaks to turn their intent into conversions.

Here’s a classic example of contextual advertising:

Benefits of Contextual Marketing

1. Enhanced relevance: Relevant ads and content that are not intrusive and aligned with the interests and needs of the users help in getting higher engagement or better click-through rates than the traditional targeting methods.
2. Building long-term relationships: Contextual marketing fosters genuine connections between a brand and a customer as the communication is based on the customer’s current needs and interests, which eventually leads to lasting loyalty.
3. Cost-effectiveness: Targeting based on context can be more cost-effective than other traditional methods.
4. Better ROI: By focusing on meaningful engagement, contextual ads help in delivering measurable return on investment.

Common Challenges with Contextual Marketing

1. Limited Data 

For years, 3rd party cookies played a crucial role in digital marketing, especially behavioral advertising, like tracking user behavior across websites and enabling highly targeted advertising, but also raised privacy concerns. Deprecation of 3rd party cookies is fundamentally changing the game for marketers.

Mozilla Firefox was the first to stop 3rd party cookies in 2019, followed by Apple’s Safari browser. Now, the industry giant Google Chrome has joined the movement, phasing out third-party cookies by the end of 2024.

To overcome this challenge, ensure you focus on enriching your first-party data through website content, user engagement, and surveys. Leverage user actions and search queries/ search terms on your website to create segments. Also, utilize the customer preferences and purchase history from the CRM system.

2. Scaling Effectively

Utilize automation tools and pre-built contextual segments offered by marketing platforms. Prioritize high-impact channels and audiences for initial campaigns. Utilizing marketing automation tools alongside CRM systems has transitioned from being merely optional to becoming a mandatory practice.

3. Overcoming Ad Fatigue 

To combat ad fatigue, marketers can use dynamic content optimization to rotate advertisements and personalize messages based on user interaction history. Not all platforms offer this functionality; in such cases, you can opt to have a “winner vs contender model”.

For example, you run an A/B test with Creative A vs B for 2 months, then at the end of the testing cycle, you can evaluate the performance based on metrics that are relevant for the experiment. You can retain the winner and add a new contender creative for the next testing cycle. The duration of the test can be defined based on various factors like platform, product, conversion cycle, etc. But the model should work for all types of tests.

4. Measuring Success

Go beyond just clicks and impressions. Focus on metrics like level of engagement, time spent, conversions, and downstream sales to measure the true effectiveness of your contextual campaigns. It has been the most demanded feature by digital marketers and yet one of the most difficult when it comes to execution.

Thankfully, there have been advancements in the way last mile measurement can be tracked. Well, it might not be possible, especially for brands and products where the life cycle of the product is longer. In such cases, it’s best to start with an alternative metric that aligns with the marketing objective and can be measured in the short term while you keep inching closer toward sharper metrics.

Tips for Executing a Successful Contextual Marketing Strategy

1. Structure Campaigns by Buying Stages 

Go beyond generic top-of-funnel targeting with a customer journey viewpoint. Use the AIDA model to reach interested users at multiple touchpoints

2. Embrace Contextual Advertising Platforms

Partner with platforms that offer contextual ad networks, enabling you to display your ads on websites and apps aligned with your target audience’s interests and current context. For contextual ads, Google Demand Gen, Taboola, Outbrain, and Quora could be used. This is not the complete list of ad platforms available out there. You should pick the relevant ones that add value to your brand and business objectives.

3. Expand Beyond Paid Channels 

Contextual marketing strategies can be done effectively without relying solely on paid campaigns. Here are some examples

CRO and SEO: Interlink relevant content on your website. You should guide the user by sharing relevant internal links or contextual popups that are non-intrusive and offer personalized recommendations based on their interest or previous selections on the website. This activity should ideally expose customers to relevant product and service pages based on their purchase funnel.

Content Marketing: Craft content in the form of blog posts, FAQs, product pages, tutorial videos, and testimonials that address current events, industry trends, or common pain points within your target audience. This establishes your expertise and resonates with the customers. With the ever-increasing consumption of video content and social media, it is advisable to create visually appealing infographics or videos that share valuable information in an engaging format. 

Email marketing: Email marketing, when combined with contextual data, can be a powerful tool and can deliver personalized messages that resonate deeply and drive action. It has become a common practice to target behavior segments like welcome emails, cart abandoners, and browsing specific products or product categories. However, in addition to these, marketers can also indulge in email conversions by sharing interactive or personalized content.

Examples of Interactive content include:

• Quizzes: recommendations based on their answers, 
• Dynamic Polls to gauge user preference and tailor future campaigns.

Examples of personalized content:

• Content related to trending topics
• Promotion of events
• Dynamic product recommendations
• Webinars/Product demo invitations.

Influencer partnerships: Collaborate with influencers or brand advocates who align with your brand values and target audience. Partnering with influencers can help you reach new audiences and build credibility and trust with their followers. This could include sponsored posts, product reviews, tutorials, or behind-the-scenes glimpses that resonate with the influencer’s audience and align with your brand messaging.

4. Experiment and Measure

Like any marketing strategy, continuous testing and analysis are crucial. Experiment with different contextual targeting options, platforms, and creative formats, and measure the impact on key metrics like engagement, conversion rates, and ROI. No one nails it perfectly in their first attempt, but constant monitoring and adjustments will help you find the sweet spot for effective and well-received contextual marketing.

5. Strike the Right Balance 

Contextual marketing can be a powerful tool, but when overdone, it can backfire. To achieve scale, it’s a common practice to go overboard by getting in the area of irrelevant ads, intrusive channels, or untimely messages.

Remember the annoyance of the remarketing ads that followed you everywhere or excessive push notifications that eventually made you uninstall an app? Intrusive contextual marketing can create negative experiences. Be mindful of getting the right balance for a winning strategy.

Final Thoughts

With the deprecation of third-party cookies, the need for contextual marketing is at its peak! Contextual marketing offers numerous benefits, such as enhanced relevance, fostering long-term relationships, cost-effectiveness, and better ROI. However, it also presents challenges such as limited data, scaling effectively, overcoming ad fatigue, and measuring success.

To execute a successful contextual marketing strategy, marketers should structure campaigns based on buying stages, embrace contextual advertising platforms, expand beyond paid channels through methods like CRO, SEO, content marketing, email marketing, and influencer partnerships, experiment and measure results, and strike the right balance to avoid intrusive experiences.

The post Unlocking Contextual Marketing: Benefits, Challenges, and Strategies for Success appeared first on NoGood™: Growth Marketing Agency.