B2B SaaS companies often have complex products that require education before purchase. AI search is changing how prospects discover and evaluate solutions, making it critical to position your product correctly. The SPARK Framework™ helps SaaS companies become the answer to specific problem queries. When someone asks an AI, "What's the best tool for X?", your goal is to be mentioned in that response. This requires clear product positioning, detailed feature documentation, case studies that demonstrate outcomes, and comparison content that AI can synthesize. The key is making it easy for AI to understand not just what your product does, but what problems it solves and for whom. Question for the community: How are you structuring your SaaS content to improve discoverability in AI-powered search?

As an SEO, there are few things that stoke panic like seeing a considerable decline in organic traffic. Getting to the answers isn’t always straightforward or simple, because SEO is neither of those things. The success of an SEO investigation hinges on the ability to dig into the data, identify where exactly the performance decline is happening, and connect the dots to why it’s happening. It’s a little bit like an actual investigation: Before you can catch the culprit or understand the motive, you have to gather evidence. In an SEO investigation, that’s a matter of segmenting data. Using Data To Confirm There’s An SEO Issue Just because organic traffic is down doesn’t inherently mean that it’s an SEO problem. So, before we dissect data to narrow down problem areas, the first thing we need to do is determine whether there’s actually an SEO issue at play. After all, it could be something else altogether. In which case, we’re wasting unnecessary resources chasing a problem that doesn’t exist. Is This A Tracking Issue? In many cases, what looks like a big traffic drop is just an issue with tracking on the site. To determine whether tracking is functioning correctly, there are a couple of things we need to look for in the data. The first is consistent drops across channels. Zoom out of organic search and see what’s happening in other sources and channels. If you’re seeing meaningful drops across email, paid, etc., that are consistent with organic search, then it’s more than likely that tracking isn’t working correctly. The other thing we’re looking for here is inconsistencies between internal data and Google Search Console. Is This A Brand Issue? Organic search traffic from Google falls into two primary camps: brand traffic and non-brand traffic. Non-brand traffic is directly affected by SEO work. Whereas, brand traffic is mostly impacted by the work that happens in other channels. When marketing efforts in other channels are scaled back, the brand reaches fewer users. Since fewer people see the brand, fewer people search for it. Either way, it’s not an SEO problem. But in order to confirm that, we need to filter the data down. Go to Performance in Google Search Console and exclude any queries that include your brand. Then compare the data against a previous period. Do the same for queries that don’t include the brand name. If non-brand traffic has stayed consistent, while brand traffic has dropped, then this is a brand issue.

For years, SEO has been a game of keywords. We targeted them, tracked their rankings, and built our content around them. But the ground has shifted. Modern search engines, powered by sophisticated AI, no longer just match strings of text; they understand the real-world concepts—the people, places, and ideas—behind the words. This is the world of entity-based SEO, and for marketing leaders, it represents a fundamental change in how we build authority and win visibility. Understanding and mastering this shift is no longer optional. It’s the foundation of a resilient, future-proof SEO strategy that ensures your brand is not just seen, but understood by both users and the AI systems that guide them. Beyond Keywords: What Is an Entity? In the simplest terms, an entity is a single, well-defined thing or concept. It can be a person (Elon Musk), a place (the Eiffel Tower), an organization (Apple Inc.), or a concept (climate change). Unlike a keyword, which is just a string of text, an entity has attributes and relationships that give it context. Google’s Knowledge Graph, a massive database of billions of entities, knows that the Burj Khalifa is a building, that it’s the world’s tallest, and that it’s located in Dubai. It’s this web of understanding that allows search engines to answer complex questions, not just point to pages with matching words. This distinction is critical. A keyword is the language a user types; an entity is the meaning they intend. By focusing on entities, you align your strategy with how search engines actually think, moving from a purely linguistic game to a conceptual one. Why Entities Are the Bedrock of AI-Powered Search The rise of generative AI and Large Language Models (LLMs) has accelerated the importance of entities exponentially. AI systems like Google’s AI Overviews and ChatGPT don’t just crawl your content for keywords; they ingest it to learn about the world. They build their understanding of your brand, products, and expertise based on the entities you are associated with.

For years, SEO has been a discipline of meticulous, often tedious, manual labor—hours spent exporting keywords, wrangling spreadsheets, and fixing markup. But a new class of tools is emerging that promises to automate the grunt work and elevate the strategic value of SEO. These are not standalone AI novelties, but rather a collection of custom GPTs and Model Context Protocols (MCPs) that plug directly into ChatGPT, giving it access to real-time, proprietary data from platforms like Google Analytics and Ahrefs. This integration of conversational AI with live data is transforming SEO workflows, moving the practice from manual analysis to high-level strategic decision-making. For marketing leaders, this represents a critical opportunity to empower their teams, accelerate insights, and focus on what truly matters: making better business decisions. From Data Pulling to Conversational Insights The most significant shift is the ability to "talk" to your data. Instead of navigating clunky interfaces and exporting CSV files, SEOs can now ask complex questions in plain English and receive immediate, data-backed answers. Tools like the Google Analytics + ChatGPT MCP allow analysts to query their GA4 data directly, asking questions like, "Which landing pages get the most traffic but have the highest bounce rates?" or "What are the most common navigation paths before conversion?" This turns hours of report-building into a minutes-long conversation, freeing up valuable time for strategic analysis. Similarly, the Ahrefs + ChatGPT MCP connects the AI to live SEO data, enabling sophisticated competitor analysis and keyword research on the fly. An analyst can upload competitor keyword files and ask the AI to "make sense of everything," receiving back fully formed topic clusters, traffic potential analysis, and even data visualizations. This is a world away from the manual keyword clustering that once consumed entire workdays. Optimizing for AI: A New Frontier Beyond workflow automation, a new category of tools is emerging to address a fundamentally new challenge: optimizing for AI itself. As more users turn to AI assistants for research, ensuring your brand is accurately and favorably represented in AI-generated responses is becoming a critical marketing function. This has given rise to tools like Steve Toth’s LLM Info Page Generator, a custom GPT that creates structured web pages designed to be a clean, authoritative "source of truth" for AI models.

For decades, marketing leaders have relied on a predictable set of metrics to measure their digital presence: keyword rankings, share of voice, and organic traffic. However, the ground is shifting. With large language models (LLMs) like ChatGPT and Gemini now acting as the primary research layer for a growing majority of B2B buyers, a new, more abstract metric is emerging as the true indicator of brand relevance: LLM perception drift. This metric measures the month-over-month change in how AI models reference and position brands within a given category. It is the digital equivalent of brand perception, but instead of happening in the minds of consumers, it is happening inside the neural networks of AI. As new data shows, this perception is volatile, measurable, and increasingly critical to business success. For executives, the question is no longer just "How do we rank?" but "How does AI remember us?" The Forces Shaping AI's Memory Recent analysis of the project management software space reveals just how quickly an AI's understanding of a market can change. Brands that were once category leaders can see their association weaken in a matter of weeks, while others rise to prominence. This drift is driven by two primary forces: 1. Category Entanglement: LLMs do not think in rigid silos. They are increasingly blending related concepts, pulling project management tools into broader discussions around "workflow orchestration," "digital transformation," and "enterprise productivity." This is why established software brands are now appearing alongside consulting giants like Deloitte and KPMG in AI-generated responses. The boundaries of your market are becoming blurrier, and your competitive set is expanding in unpredictable ways. 2. The Ecosystem Advantage: The data shows a clear pattern: brands with a strong, interconnected digital ecosystem are building a more stable presence in the AI's memory. Companies like Atlassian, Microsoft, and Google, which offer multiple integrated products supported by extensive documentation and a high density of contextual information, are seeing their brand signals strengthen. The models favor brands that exist across multiple contexts, reinforcing the long-held principles of entity-based SEO in a new, accelerated form.