FAKE SMART MARKETING™

In April 2026, I published a piece about Visby.ai and an AI visibility problem I called Fake Smart Marketing™ — software that creates the emotional experience of precision and control without providing the operational clarity to know what to do next. The concept resonated because it named something practitioners had been experiencing for years without a precise description.

The response clarified something important: Visby.ai was not the problem. It was a symptom. The actual problem is a category-wide pattern that emerges every time a new measurement domain opens up faster than the interpretive frameworks that make measurements actionable.

We saw it with social media analytics. We saw it with attribution modeling. We saw it with SEO authority scores. We are seeing it now with AI visibility. Every time a new type of search behavior becomes monetizable, software vendors race to measure it before anyone has established what the measurements should be used for.

The result is always the same: beautiful dashboards with numbers that create emotional certainty without operational clarity. The scores feel precise. The decisions remain unclear. The vendor gets paid. The user gets a dashboard.

This is why Fake Smart Marketing™ becomes a recurring lens rather than a one-time verdict. Every few weeks, a new AI SEO tool launches. Every few weeks, the same question applies: does this tool close the gap between signal and decision, or does it add another score to the pile?

To apply the Fake Smart Marketing™ test systematically, I developed five questions that separate genuine decision support tools from dashboard theater.

Question 1: Can you leave the dashboard and immediately know the three most important things to do this week? If the answer is that you need to think about it, the tool has surfaced data but not closed the gap to decisions.

Question 2: Are the scores and metrics connected to specific actions? A GEO score of 72 is not connected to an action. Adding FAQPage schema to these 14 pages is connected to an action.

Question 3: Can the tool explain exactly how its proprietary scores are calculated in enough detail to verify them independently? If the methodology is opaque, the score is a black box that creates emotional certainty without operational clarity.

Question 4: Does the tool make you smarter about AI visibility, or just more aware of your score? There is a difference between a tool that teaches you why certain content gets cited and a tool that just shows you a citation score.

Question 5: Would your AI visibility actually improve if you followed every recommendation the tool makes? Run the tool for 90 days, implement every recommendation, and measure actual AI citation frequency. If it has not improved meaningfully, the tool was measuring the wrong things.

Smarter Clicks AI (smarterclicks.ai) is a tool I have been watching since it appeared. The premise is interesting: connecting AI search visibility signals to both organic and paid strategy. The claim is that AI-generated answers influence what users click, and that optimizing for AI citation should inform how you allocate PPC spend, not just how you produce content.

This is a genuinely novel angle. Most AI visibility tools focus exclusively on organic content optimization. The paid-organic connection is underexplored. If AI answers are shaping click behavior before users make organic or paid clicks, then AI visibility strategy should influence ad targeting, bidding, and creative — not just SEO content.

Question 1 result: The tool surfaces AI visibility scores and competitive positioning. Whether it produces immediate action priorities depends on the specific implementation. The category of AI-influenced click behavior is real, but translating it to bid X on keyword Y today requires a more complete attribution model than most tools in this category currently provide.

Question 2 result: This is where the value proposition lives. If Smarter Clicks AI can genuinely connect your brand appears in 35% of AI answers for this query cluster to therefore increase bids on these branded terms — that is operational clarity. The question is whether the connection is mechanical and verifiable or inferential and approximate.

Questions 3-5: Score transparency is limited but standard for early-category tools. The paid-organic angle is genuinely educational. The ultimate test — does actual AI visibility improve after 90 days of following recommendations — requires independent verification against your own data.

Calling something Fake Smart Marketing is not a permanent verdict. It is a diagnosis of a current state. Early Ahrefs was confusing. Early Google Analytics produced data that most users could not act on. The category maturity problem is real and temporary.

The tools that move from Fake Smart Marketing to Genuine Decision Support follow a predictable evolution. First generation: surface data that did not exist before. Second generation: add benchmarks and context. Third generation: produce specific recommendations. Fourth generation: automate the implementation, closing the loop entirely.

Most AI visibility tools are first or second generation. The recommendation layer is thin or absent. The automation layer does not yet exist.

The practitioners who get the most out of early-category tools treat the data as inputs to their own framework, not as complete answers. If you have a clear AI citation strategy — entity infrastructure, FAQ schema, topical cluster architecture — then AI visibility tool data becomes genuinely useful feedback on whether that strategy is working.

While AI visibility tools mature from dashboard theater to genuine decision support, here is the implementation framework that produces measurable results without requiring proprietary scores.

Manual citation testing is the most reliable measurement method available right now. Once per week, query 10-20 target topics across ChatGPT, Claude, Perplexity, and Bing AI. Record which sources are cited, in what context, and whether your brand appears.

Structured data completeness audit produces the highest-impact improvements. Run a systematic inventory of your Schema.org coverage. Schema gaps are the most direct, fixable AI citation gaps — and they do not require proprietary score interpretation to identify.

Entity chain building is the investment that compounds across all future AI citation. Creating a Wikidata entry, connecting it via sameAs to your website entity, building cross-domain mention patterns — these are one-time infrastructure investments that increase AI citation probability forever.

Topical cluster depth is the content strategy that AI systems recognize. A site with 50 interconnected articles on a topic is more citable than a site with one comprehensive guide on that topic.