As marketing leaders, we are under constant pressure to adapt our strategies to the ever-evolving digital landscape. The rise of AI assistants has been a particularly disruptive force, and many in our industry have rushed to "optimize for AI" under the assumption that these platforms are simply a new channel for commercial queries. However, a recent analysis by Dan Petrovic, Director of the AI SEO agency Dejan, challenges this assumption and suggests that we may be fundamentally misreading how consumers are using these powerful new tools. Petrovic's research, which analyzed 4.4 billion characters, 613 million words, and 3.9 million conversation turns, reveals that a staggering 65% of AI chats have no commercial intent whatsoever. This finding has profound implications for our content strategies, resource allocation, and our understanding of the customer journey in the age of AI. How Users Actually Engage with AI Assistants The data shows that AI users behave very differently from traditional searchers. While a typical search engine query is a discrete event, an AI chat is often a multi-step task. The median chat is just two turns—a quick question and a quick answer—but this masks a long tail of more complex interactions. Over 80% of chats are under 1,000 words, but a small percentage (4.2%) exceed 2,500 words, representing high-value tasks such as editing, coding, tutoring, and data analysis. The typical user contributes only 16-17% of the conversation, with the AI assistant generating approximately 1.5 times more content than the user inputs. This pattern reveals that users are not simply querying for information; they are engaging in collaborative problem-solving sessions where the AI serves as an active partner rather than a passive search engine. When we examine what users are actually doing in these conversations, the non-commercial nature becomes even more apparent. Petrovic classified 24,259 sessions across 42 intent categories and found that the vast majority of interactions fall into categories such as brainstorming (7.7%), planning (6.5%), conversation and emotional support (6.2%), analysis (5.7%), learning (4.7%), transformation tasks like summaries and translations (4.6%), and creation activities including writing and coding (3.9%). A full 25% of interactions fell into an "other" category that included highly specialized requests, roleplay scenarios, and various experimental uses of the technology.

The SPARK Framework™ breaks down AI search optimization into five interconnected pillars. Understanding how these work together is critical for implementation success. Semantic Optimization ensures your content speaks the language AI models understand. Platform Authority builds your credibility across multiple touchpoints. Answer Readiness structures your content for direct synthesis. Relevance Signals help AI systems understand your topical expertise. Knowledge Integration connects your content to the broader knowledge graph. Each pillar reinforces the others. When you optimize semantically but lack platform authority, AI systems may understand your content but not trust it enough to cite it. The framework is designed to address all dimensions simultaneously. Question for the community: Which of these five pillars do you think presents the biggest challenge for most businesses, and why?

As SEO professionals, we're witnessing a fundamental shift. While we've mastered tracking clicks and rankings, AI Overviews and direct answers from platforms like ChatGPT are changing the game. Our clients' content is being used, but the value isn't always captured in our traditional analytics. This isn't just about adapting; it's about evolving our strategy. The SPARK Framework™ was developed for this exact challenge. It provides a structured methodology to move from a keyword-centric approach to an entity-based one, focusing on becoming the citable authority that AI models trust. For example, the Answer Readiness pillar isn't just about FAQs; it's about structuring data so effectively that AI systems can directly synthesize it into answers, positioning your brand as the source. Question for the community: How are you adapting your client reporting to demonstrate value when success is a citation in an AI answer, not just a click-through?

As marketing leaders, we are constantly seeking to optimize our advertising spend and drive better results. The Google Ads search terms report is a powerful, yet often underutilized, tool in this endeavor. While many practitioners are familiar with its basic function, a deeper understanding of its nuances can unlock significant improvements in campaign performance, leading to better targeting, reduced waste, and a clearer picture of customer intent. This guide provides a strategic overview of the search terms report, designed to help you and your teams move from a reactive to a proactive approach to paid search management. Foundational Concepts: Keywords, Search Terms, and Campaign Coverage Before diving into advanced strategies, it is essential to have a clear understanding of the fundamental concepts. A keyword is the term you add to a campaign, along with a specific match type, to instruct Google on the types of searches you want to target. In contrast, a search term is the actual phrase a user types into Google that triggers your ad. This distinction is critical because it highlights the difference between your intended targeting and the reality of how your ads are being served. Your ad can be triggered by either manually entered keywords or through keywordless targeting systems such as Shopping Ads, Dynamic Search Ads (DSA), AI Max, or Performance Max. Understanding this distinction is the first step in effectively analyzing the search terms report and identifying opportunities for optimization. It is also important to recognize that the search terms report is not limited to traditional search campaigns. It is available for all three campaign types that utilize search queries: Search Campaigns (both keyword-based and keywordless, such as AI Max), Shopping Campaigns, and Performance Max. All three of these campaign types also allow for the use of negative keywords, making the search terms report an invaluable tool for refining your targeting across the board. No matter which campaign type you are running, the search terms report remains your best window into user intent and how your ads are appearing for real searches.

For over a decade, the definition of SEO success was narrowly confined to metrics like keyword rankings, organic traffic volume, and domain authority. While these metrics were tangible and easy to present in a boardroom, they were fundamentally insufficient. We have reached a critical turning point where these older definitions no longer align with true business value or the reality of changing search behavior. The Chief Digital Marketing Officer must now lead the conversation to reposition SEO as a strategic contributor, moving the focus from technical vanity to measurable commercial outcomes. The Narrow Success Window and Its Limitations The classic metric stack—keyword positioning leading to impressions, clicks, and eventually conversions—no longer tells the full story. Rankings are ultimately vanity metrics; if they improve without leading to qualified traffic or revenue, the SEO team may appear successful, but the business does not benefit. The limitations of this narrow view are now starkly apparent. We must begin with the end in mind, asking what the business goal truly is, what value each new lead brings, and how the website supports those aims. The conversation must shift from keyword counts to the broader question of how much value organic search adds to the business's bottom line. The Forces Driving the Need for Change Several converging forces are rendering the older success yardsticks unreliable. Firstly, search behavior has fundamentally changed. Users now expect fast, direct answers, and search engines deliver these through "zero-click" results. If users receive what they need without visiting a site, traditional click-based metrics lose their relevance. Secondly, the attribution chain is growing more complex. Organic traffic often plays an indirect role, supporting brand engagement or influencing the decision-making journey early on. The connection between a search visit and a tangible business outcome can be difficult to track with confidence. Finally, the data itself is becoming noisier due to bot traffic, privacy constraints, and changes in user interaction. Metrics like bounce rate and click-through rate are now vulnerable to misinterpretation, forcing SEO teams to deliver clear business value, not just improved rankings.