Interview with David Lange, Digital Marketing Strategist, The Query Post

This interview is with David Lange, Digital Marketing Strategist, The Query Post.

For Connectively readers, how do you describe your work today as a Digital Marketing Strategist and founder of The Query Post, and the specific visibility problems you help brands solve in an AI-driven landscape?

With The Query Post, I scan the web every day for emerging stories, platform updates, and early signals in SEO, AI search, PPC, and digital marketing. The goal is to spot the topics brands should understand before they become obvious or overcovered.

What key experiences—from early PBN-era SEO to scaling a crypto news site and optimizing retail catalogs—most shaped your path toward focusing on AI search and modern visibility strategy?

A lot of my thinking comes from seeing SEO change over time.

Early on, I worked in a period when tactics like PBNs and aggressive link building could still move rankings quickly. That experience was useful, but it also showed me how fragile visibility can be when too much depends on shortcuts.

Later, working with large retail catalogs changed how I viewed SEO. At that level, it is not about one clever article or one campaign; it is about structure. Categories, product pages, internal links, crawlability, search intent, and conversion all have to work together. Small decisions can affect thousands of pages.

That shift eventually pushed me toward a broader view of visibility. Search is no longer just about ranking a page on Google. Brands now need to be found, understood, and trusted across search engines, AI answers, review sites, marketplaces, and third-party sources.

So my work today is still rooted in SEO, but the focus is wider: helping brands become visible and credible wherever people now look for answers.

Building on that shift from “checklist SEO” to visibility strategy, what is the first 30-day change you’d advise a marketing team to make to stay competitive right now?

The first 30-day change I would make is to stop treating SEO as a publishing checklist and start running a visibility audit.

Most teams already have enough content in motion. The bigger problem is that they do not really know where the brand is visible, where competitors are being recommended instead, and which sources are shaping buyer decisions.

I would take 10 to 20 high-intent customer questions and search them across Google, AI overviews, ChatGPT, Perplexity, Reddit, review sites, and industry lists. Then I would document three things: who gets mentioned, which sources are being cited, and whether our brand is missing, misrepresented, or weakly supported.

That exercise usually reveals the fastest opportunities. Maybe the company has strong product pages but no third-party validation. Maybe competitors are showing up in listicles and review sites. Maybe AI tools understand the category but not the brand. Maybe the content is good, but it is not structured clearly enough to be cited or trusted.

So the first 30 days should not be about producing more content for the sake of it. It should be about finding the visibility gaps that already exist and fixing the easiest ones first.

That might mean:

• Improving one key service page
• Updating brand information across third-party profiles
• Earning a few relevant mentions
• Strengthening reviews
• Rewriting content so it answers the questions buyers and AI systems are actually using

The practical goal is simple: by the end of 30 days, the team should know where the brand is invisible and have started fixing the signals that matter most.

From your testing across ChatGPT, Perplexity, and Gemini, which on-page elements most reliably increase the chances of being cited in AI answers?

The biggest on-page element is a clear, direct answer near the top of the page. If a page takes 800 words to explain what it is about, AI systems often have better options. A short definition, summary, or practical answer in the first few paragraphs helps a lot.

The second is structure. Pages with clean H2s, specific subtopics, comparison tables, FAQs, step-by-step sections and clear examples tend to perform better because the model can understand what each part of the page is trying to answer:

• Clean H2s
• Specific subtopics
• Comparison tables
• FAQs
• Step-by-step sections
• Clear examples

I also see strong results from including concrete details: numbers, dates, examples, screenshots, author experience, product names, use cases and limitations. Generic content is easy to ignore. Specific content gives AI systems something worth citing.

For local or commercial topics, trust signals matter too. A page should clearly show who is behind the information, what the company does, where it operates and why it has credibility. That can include:

• Author bios
• Customer examples
• Reviews
• Case studies
• Consistent entity information across the site

The practical rule I use is simple: write the page so a human, Google and an AI answer engine can all quickly understand the same thing. What is the topic? What is the answer? What proof supports it? And why should this source be trusted over ten similar pages?

When you restructured content to be more answer-ready, what repeatable page pattern has delivered the best balance of AI retrievability and human engagement?

The pattern that has worked best for me is what I call a “direct answer plus proof” structure.

I don’t start with a long introduction anymore. I start the page with a short, clear answer to the main question — two or three paragraphs that explain the core point in plain language. That makes the page easier for AI systems to extract and easier for a human reader to understand quickly.

After that, I build the page in layers:

1. The direct answer
2. The context
3. Examples
4. The proof or data behind the claim
5. Practical steps or a short checklist

That structure works because it serves both sides. AI systems can quickly identify the answer and the supporting sections. Human readers still get enough depth to trust the page and keep reading.

The biggest mistake I see is hiding the answer too far down the page. A lot of content still starts with generic background, definitions, and filler before saying anything useful. That may have worked for old SEO templates, but it is weak for AI retrieval and not great for readers either.

The best pages now feel more like a useful expert briefing: clear answer first, then enough detail to prove why the answer is worth trusting.

Switching to AI shopping, what is one concrete workflow a mid-sized retailer can adopt to clean product data with AI and quickly improve search relevance and product discovery?

A practical workflow is to start with one messy product attribute rather than the whole catalog.

For a mid-sized retailer, I would take the top 500 to 1,000 products by traffic or revenue and run an AI-assisted product data cleanup around three fields:

• Product titles
• Attributes
• Customer-facing descriptions

The workflow looks like this:

1. Export the product feed with titles, descriptions, categories, attributes, search terms and performance data.
2. Use AI to flag missing or inconsistent attributes, such as size, material, color, fit, compatibility, use case or product type. The goal is not to rewrite everything; it is to find the gaps that stop products from matching real customer searches.
3. For example, a retailer may have one product listed as “men’s waterproof jacket,” another as “rain coat,” and another as “outdoor shell,” even though customers search across all three patterns. AI can help normalize those terms, suggest missing attributes and group products into cleaner categories.
4. Have a human merchandiser review the suggestions before pushing changes live. That review step is important because AI can make confident mistakes, especially with sizing, materials or technical product claims.
5. The fastest win is usually improving internal search relevance. Once titles and attributes are cleaner, products become easier to match to queries, filters, recommendations and AI shopping experiences.

In 30 days, the goal should be simple: clean the highest-value products first, standardize the most important attributes and measure whether search exits, zero-result searches and product discovery improve.

Zooming out to trust, what signals are you prioritizing today to influence both traditional rankings and the emerging AI ranking heuristics you’re seeing in the wild?

The trust signals I prioritize are the ones that make a brand easier to verify outside its own website.

For traditional SEO, you still need the basics: strong pages, technical health, clear internal structure, real expertise, and relevant links. But for AI-driven visibility, I’m paying much more attention to corroboration. Can the brand be confirmed by third-party sources? Are people reviewing it? Is it mentioned in trusted industry lists, comparison pages, podcasts, news articles, or community discussions?

That matters because AI systems do not only look at what a company says about itself; they often lean on the wider web to decide which sources and brands are safe to mention.

So the signals I care about most today are:

• A clear brand entity
• Consistent company information across the web
• Strong review profiles
• Relevant third-party mentions
• Expert authorship and real experience
• Original examples, data, or case studies
• Pages that answer specific questions clearly
• Technical structure that makes the content easy to understand

The mistake is thinking trust is one thing. It is not. It is a pattern. If your website says one thing, your reviews say another, and the rest of the web barely mentions you, that weakens both human trust and AI visibility.

The brands that will win are not just the ones publishing more content. They are the ones that look credible from multiple angles.

To track progress beyond clicks, how are you measuring AI-driven visibility—citations, assistant referrals, and brand mentions—and which metric has become your north star?

Honestly, you cannot track it perfectly yet.

What I do is build a fixed set of high-intent prompts and run them regularly across ChatGPT, Perplexity, Gemini, and Google AI Overviews. Then I track:

• whether the brand is mentioned
• whether it is cited
• which competitors appear instead
• whether the answer describes the brand correctly

I do not look at one answer in isolation because AI results move around. I look for patterns over time.

Analytics referrals are useful when they appear, but they are incomplete. So the closest thing to a north star for me is share of answer: how often the brand appears in the answers that matter commercially compared with competitors.

Finally, looking 12 months ahead, what underappreciated SEO trend at the intersection of AI shopping, citations, or ranking should marketers prepare for now and how should they prepare?

One underappreciated trend is that product and brand data quality will become a ranking factor in more places than traditional search.

AI-driven shopping will not only depend on who has the best ad budget or the strongest category pages; it will also depend on whether systems can clearly understand what a product is, who it is for, how it compares, and whether the information is consistent across the website, feeds, reviews, marketplaces, and third-party mentions.

Many retailers still treat product data as backend admin work. I think that will become a real visibility problem.

If your titles, attributes, descriptions, categories, and reviews are messy, AI systems will struggle to recommend you confidently. The same applies to brands: if your company is described differently across your site, review platforms, articles, and directories, you make it harder for AI systems to understand and trust you.

The way to prepare is not complicated:

• Clean the product feed.
• Standardize attributes.
• Improve category and comparison pages.
• Make reviews easier to interpret.
• Keep brand information consistent across the web.
• Make sure the pages you want cited actually answer the questions customers ask before buying.

The next 12 months will reward brands that are easy to understand, easy to verify, and easy to recommend.

Thanks for sharing your knowledge and expertise. Is there anything else you'd like to add?

I would only add that the brands most likely to win over the next few years are the ones that stop treating visibility as a channel-by-channel problem.

SEO, AI search, social search, reviews, citations, and paid media are becoming more connected. A weak brand presence in one place can affect how people and AI systems understand you somewhere else.

That is one reason I started The Query Post. I wanted a place to track these shifts in search, AI visibility, and digital marketing before they become obvious. The landscape is moving quickly, but the practical goal stays the same: help brands become easier to find, easier to trust, and easier to choose.