When I compare Goodie and Semrush for AI search visibility, I’m looking beyond traditional SEO dashboards. I want to understand how each platform supports answer engine optimization, from monitoring AI visibility to improving the signals that influence AI-generated answers.
A modern AEO performance dashboard brings AI search visibility, brand mentions, traffic attribution, and revenue signals into one measurement view.
For me, the key difference comes down to focus. Goodie is built around AEO monitoring, optimization, agentic commerce, and revenue attribution, while Semrush brings the depth of a broader SEO and competitive research platform.
A Semrush project dashboard brings SEO health into one view, from keyword rankings and site audit trends to optimization ideas and backlink toxicity signals.
In this comparison, I look at how both platforms help brands get discovered, cited, and recommended across AI search experiences, and how each one connects visibility to measurable business impact.
I’ve noticed a significant shift in the SEO industry toward senior, strategy-focused roles. As AI increasingly handles execution tasks, the demand for seasoned strategists has grown, along with an increase in salaries and responsibilities that span multiple channels.
The change in hiring trends is evident when looking at a recent Semrush analysis of 3,900 job listings. It appears companies are now prioritizing leadership skills, innovative experimentation, and cross-channel visibility over purely technical execution.
Why it matters to me. The landscape for SEO careers and skillsets is evolving. Entry-level positions are mostly focused on execution, while leadership roles require a firm grasp of strategy across various domains such as search, AI assistants, and paid channels, ensuring they drive significant revenue.
What’s changing now. Senior roles account for 59% of job listings, clearly dominating the landscape. In contrast, mid-level positions like specialists and managers are less prevalent, with only 15% and 10%, respectively.
Companies are redirecting their budgets towards strategic roles as AI tools begin to absorb more of the technical workload.
The shift in skills. The skills in demand now extend beyond traditional SEO to include coordination, experimentation, and decision-making capabilities:
Project management is mentioned in over 30% of the listings, highlighting its importance.
Communication is highlighted in 39.4% of non-senior roles, indicating its fundamental role in the industry.
Experimentation is noted in 23.9% of senior roles, compared to just 14% of other roles.
Technical SEO appears in approximately 6% of postings, showing its niche but crucial role.
Tools and channels. The modern SEO toolkit now includes analytics, paid media, and comprehensive data tools.
Google Analytics is cited in up to 47.7% of job listings, underlining its importance.
Google Ads features in 29% of the listings, showcasing its growing relevance.
Demand for SQL skills is rising, especially at the senior level.
AI tools, such as ChatGPT, are increasingly mentioned, reflecting their future role in SEO.
AI expectations. AI literacy is shifting from being a nice-to-have to an essential skill:
31% of senior roles now reference AI capabilities.
Nearly 10% of listings highlight familiarity with LLMs.
Concepts such as AI search and AEO are increasingly common in job descriptions.
Pay and positioning. SEO is being increasingly recognized as a vital business function:
The median salary for senior roles has reached $130,000, markedly higher than the $71,630 for other roles, with some positions offering even more.
Preferred degrees are leaning towards business and marketing, reflecting the strategic emphasis.
Remote work prevalence. Remote options are available in over 40% of job listings, indicating a shift towards flexible work environments across all levels.
About the data. This analysis by Semrush covers 3,900 SEO job listings in the U.S., gathered from Indeed as of November 25. The roles were deduplicated and segmented by seniority before a semantic keyword extraction analysis was applied.
I’m excited about the opportunity to influence the future of search marketing events. You can help shape SMX Advanced 2026 by sharing your insights and preferences. The event is happening from June 3-5 at the Westin Boston Seaport, and we want to know what you’re eager to learn and who you’re interested to hear from.
Reflecting on June’s event, it was thrilling to reunite in person for the first time since 2019 at SMX Advanced. It was more than just a conference; it felt like a global reunion for search marketers to connect, share ideas, and dive into cutting-edge insights.
The world of search is ever-evolving, with swift changes in AI SEO, algorithm updates, and the delicate balance of AI with a human touch. Advanced, actionable education is more crucial than ever, and that’s where you come in.
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I’ve been deeply involved in the compelling discussions around AI, especially the intriguing intersection of ‘AI hype meets AI reality.’ Tools like Semrush One and its Enterprise AIO tool have taken center stage, offering invaluable insights into what’s happening inside LLMs. The big questions I often ponder are: How many citations are we capturing and just how many mentions are our brands accumulating?
When this data first emerged, it felt revolutionary. However, it quickly prompted other questions, like ‘What’s the ROI here?’ and ‘How can I integrate this data into my team’s marketing strategy?’ Ensuring that this valuable and fascinating data translates into actionable insights is a challenge I enjoy tackling.
It’s no secret that the data these tools provide is incredibly valuable. But, what steps do I take next? Let’s uncover this journey together.
The Fundamental Challenges of Tracking LLMs
Tracking LLMs can be more challenging than traditional metrics like Google rankings. Google rankings may show where I stand, but ranking doesn’t always correlate with traffic or revenue. Even if I rank highly, an AI Overview could dominate the search, reducing my traffic for a given keyword. I need to ask myself, is this the right traffic for my business goals?
The big difference between traditional SEO rankings and LLM visibility is the straightforward correlation between strong rankings and increased revenue, which is more complex with LLMs. I can easily track user behavior after they land on my site from organic search, but it’s not so clear-cut with LLMs.
SEO effectively drives traffic to my site, allowing me to evaluate the success of my conversion rate optimization (CRO) strategies. However, LLMs operate differently, leaving me with the task of creatively connecting the dots.
The Problem with Methodology
As I dive deeper into using LLM-related data, I realize this approach requires me to step out of my comfort zone as a performance marketer. My usual reliance on direct attribution and data points is shifted toward constructing a narrative that ties LLM visibility to larger brand storytelling.
This method isn’t novel, however. Brand marketers have dealt with indirect metrics since the days of billboard advertising. Still, the shift requires me to create insights from what might seem like fragmented LLM data.
Metrics and Approach to LLM Impact Measurement
Uncovering the true value brought by LLM visibility metrics is a layered and comprehensive process. To do this accurately, I need to understand the wider ecosystem of my organization’s promotional efforts. This understanding allows me to determine the root cause of site traffic or branded searches effectively.
For instance, if a TV ad campaign runs concurrently with optimizing for LLM mentions, analyzing their impact becomes essential. Only with complete awareness of such activities can I identify true causality or correlation.
From here, I find that LLM visibility data is usually just the starting point. It’s unlike traditional SEO insights, which might be more apparent and direct. My task is to delve deeper, probing these data points to uncover richer insights.
The Branded Search of It All
I’ve noticed that brand search provides exceptional insights into LLM performance, offering a rich vein of marketing intelligence. The comparison between two competing chicken wing chains, Buffalo Wild Wings and Wingstop, brightened this understanding for me. While their LLM citations differ, their brand awareness through social media presence offers a clearer picture of market positioning.
Simply examining the branded search traffic showed me how both brands performed similarly on Google, despite their different social media followings. Here lies the heart of utilizing search data creatively to find LLM visibility data strategies.
Rather than merely counting traffic, I am now compelled to consider the number of branded keywords involved, providing a sometimes surprising view on brand awareness and diversity. This approach provides a richer understanding of LLM visibility’s impact.
Direct Traffic: My Trusted LLM Data Companion
I’ve come to see direct traffic as an essential part of my LLM data narrative. Far from being a black hole, direct traffic can often indicate brand awareness and affinity, especially when correlated with LLM visibility metrics. Understanding these correlations allows me to paint a clearer picture of AI’s practical impact on consumer behavior.
For instance, if I compare LG and TCL, LG’s superior direct traffic and increasing momentum in LLM visibility suggest a tangible AI-driven influence, a possibility I must explore through multi-metric analysis.
Considering various metrics together and identifying shared trends offer insight into how LLM visibility might be affecting my brand’s overall recognition and engagement.
Not Just One Metric: Stitching Together LLM Data Stories
Ultimately, it’s about developing a comprehensive data story from LLM visibility insights. This story goes beyond direct KPIs, utilizing various data sources, such as bounce rates and organic traffic, to add depth and relevance to the narrative. Every piece of performance-focused data stands as testimony to the expertise we can bring to LLM visibility.
Total LLM visibility data, when creatively amalgamated with performance data, can transform insights into actionable strategies that align with pragmatic business objectives, showcasing our value in the AI-driven landscape.
