If you’re a marketer, you’ve probably invested heavily in content marketing over the years, but the Organic Search payoff isn’t delivering, and you’re questioning your SEO strategy. 

For example, for those in the finance industry, your retirement planning guides might rank pretty well, your pages explaining investment strategies get decent traffic, and your calculator tools actually help people. But maybe lately, something feels off. 

Your rankings are holding steady, yet Organic Search traffic keeps declining. Is this the impact of AI Search? 

Maybe you’re barely showing up in AI Overviews or AI Mode results for topics that are core to your business goals, and when potential clients ask ChatGPT or Perplexity about retirement planning, your firm doesn’t get mentioned at all. 

The problem might be that you have an outdated perspective on SEO. You’re familiar with optimizing for single keywords, but that’s not how it works in AI Search. AI Search Platforms are leveraging query fan-out. 

When someone searches for “What’s the best way to save for retirement,” Google’s Gemini is simultaneously running dozens of related searches for AI Overviews, such as 401(k) contribution limits, Roth IRA comparisons, retirement calculators, savings benchmarks by age, and common mistakes to avoid. If your content doesn’t include passages that satisfy your search across all of these relevant sub-queries, you’re losing visibility, brand awareness, and performance metrics.

So what’s a better, more modern strategy for AI Search? Let’s approach this problem using a fictional mid-sized financial services firm as an example. I’ll walk through the complete process: map out fan-out queries, analyze what actually ranks and gets cited by AI (including which formats and sources dominate), and build an omnimedia content plan. You can’t only worry about your owned properties, like your website. You need to cover every channel that contributes to AI Search results: YouTube, Reddit, industry trades, and every other channel where your prospects are searching. 

Let’s get started. 

What Is Query Fan-Out and Why Does It Matter for SEO Now?

Today, your content might rank on page one for your target keyword and still be invisible in AI Search results, and a lot of that has to do with query fan-out.

Query fan-out is the process by which AI systems and modern search engines expand a single user query into related sub-queries that run simultaneously in the background. Rather than treating your search as an isolated request, these systems interpret it as a starting point for exploration.

When you ask Google a question in AI Mode, what appears to be one search is actually dozens or even hundreds of synthetic queries working in parallel. As Mike King explained in his detailed technical analysis, these include:

• Related queries that are semantically adjacent to your original search.
• Implicit queries that capture what you likely meant but didn’t explicitly state.
• Comparative queries that help you make decisions between options.
• Personalized queries tailored to your search history, location, and behavioral patterns.

Mike’s research into Google’s patent applications revealed that it uses large language models like Gemini to generate these synthetic queries through structured prompting that emphasizes intent diversity, lexical variation, and entity-based reformulations. (If this feels like too much jargon, revisit Chapter 6 of our AI Search Manual, where we discuss some of these fundamentals that influence information retrieval.)

Modern AI Search systems use query fan-out to understand and satisfy your full search intent.

This approach enables AI systems to:

• Synthesize comprehensive answers by pulling all relevant information.
• Build reasoning chains that connect different aspects of a topic logically.
• Anticipate follow-up questions before users ask them.
• Diversify information sources to avoid relying on a single perspective.

The query fan-out process helps ensure that AI-generated responses are robust, multi-dimensional, and genuinely helpful. 

Helping a Financial Brand Win Retirement Searches

What’s the practical application?

Let’s say you’re working with a mid-sized financial services firm specializing in retirement planning and investment management.

Their primary business goal is to increase qualified leads from people actively researching retirement planning options. To achieve this, they need to earn more visibility in search than direct and indirect competitors.

This company’s core audience persona is a mid-career professional in their 40s, earning over $90K annually, who has been contributing to a 401(k) for years. They have a couple of kids who are in high school and parents who have retired, so now they’re thinking about their own financial future.

The core audience also:

• Have accumulated some retirement savings but lack confidence in their strategy.
• Feel overwhelmed by financial jargon and competing advice.
• Want personalized guidance but aren’t ready to commit to a financial advisor.
• Increasingly turn to AI Search tools for quick answers to complex financial questions.

This person doesn’t search for “financial advisor near me” or “best IRA provider” (at least not yet). They start with broader, more exploratory questions.

So, we’re going to ask a broad question to start our search. 

The Query

For this analysis, we’ll focus on a query that represents the prospective customer’s starting point in the retirement planning journey:

“What’s the best way to save for retirement?”

This question is exploratory rather than transactional, requires synthesized information from multiple sources, and triggers dozens of related questions that the searcher hasn’t yet articulated.

A little digging around in Ahrefs showed that this question is a difficult keyword for which to rank. The more succinct “best way to save for retirement” is more popular than the question form, which isn’t surprising. People tend to ask full questions to LLMs rather than Google these days. 

We can also see where this phrase appears most often, including sites for the Department of Labor, Trinity College, Fidelity, Merrill Edge, Bankrate, and Reddit (remember these sites because they’ll appear often later in our research). 

But for the financial company, ranking well for this single query in traditional search isn’t enough. They need to appear across all subqueries about 401(k) contribution limits, retirement calculators, average retirement savings by age, and much more.

Now that we’ve researched the initial query, let’s get to the query fan-out. 

Generating a Query Fan-Out with Qforia

Qforia is a tool developed by Mike King at iPullRank to simulate a query fan-out. Begin by visiting the Qforia site, then enter a paid Gemini API key (the free ones won’t work) and enter your query. After a few moments of thinking, your results will appear:

Qforia leverages the same Gemini model used by AI Overviews and AI Mode, so the output is still probabilistic, but the goal is accuracy, not precision. Plus, Mike’s tool bakes in a lot of useful information to the results:

• The “query” column shows you other related queries. 
• The “type” column shows you what kind of question the query is likely asking. Is it comparative like a 401K vs. Roth IRA or personalized like “retirement savings strategies for someone in their 30s”? 
• The “user intent” column tells you what the searcher is looking for with a particular query. Do they need to understand the basic options? Are they seeking advice tailored to a specific life stage? These classifications help you group your queries based on who is asking and why. 

Implicit and personalized queries seem to be common in this fan-out, showing that asking about the best way to save for retirement can be very specific to a certain person. 

Another useful bit of data is the “format reason” column. This is a relatively new feature of Qforia that lays out what type of content would be best to answer the query, be it a guide, article, table, interactive tool, etc. 

In this example, Qforia suggests a table for a side-by-side comparison of 401Ks and Roth IRAs. And for “retirement savings strategies for someone in their 30s”, it recommends a checklist with actionable steps to take. The “steps to open an IRA account” phrase is another implicit query that would benefit from a numbered or bulleted step-by-step list to follow. 

The query “what is an employer 401K match” is considered to be a frequently asked question, so Qforia recommends answering it within an FAQ page about retirement benefits. An interactive tool seems to be the best way to help someone answer “how much money should I save for retirement?”

Now that we’ve performed our fan-out research, let’s take a look at the search engine results pages (SERPs) and see what we can learn.

Analyzing the SERPs for Fan-Out Queries

For this experiment, I will choose five of the fan-out queries Qforia suggested (in addition to our original query) and see what the SERPs look like for each:

1. Beginner’s guide to saving for retirement
2. What are the different types of retirement accounts?
3. How much money should I save for retirement? 
4. Retirement savings strategies for someone in their 30s
5. Common retirement savings mistakes to avoid

Let’s start with the SERP for the initial query: What’s the best way to save for retirement?

Once you get past the litany of sponsored posts and the AI Overview, the rest of the results give a wide range of helpful resources. There are top 10 lists of tips from Merrill Edge and the U.S. Department of Labor, Reddit and Quora forum answers, YouTube videos, and articles from universities and credit unions. 

Bankrate even offers a retirement calculator to estimate how much you should save, even though I didn’t even ask that yet (we’ll see if it shows up again later for that query). 

Business	Media Type	Content Type	Website Type
U.S. Dept. of Labor	PDF article	Listicle	Government site
Reddit	Forum posts	Various	Forum
Trinity College	Article	Beginner’s guide	College site
Texas Hospital Association	Article	Listicle	Brand site
Bankrate	Calculator	Informational	Brand site
Space Coast Credit Union	Article	Informational	Brand site
California Credit Union	Article	Listicle	Brand site

When it comes to the AI Overview and AI Mode results, they both offer a bulleted list of strategies for saving. The AI Overview is more detailed, though, with a table showing the different types of retirement accounts and an action plan to follow:

Now let’s see the results for “Beginner’s guide to saving for retirement”:

We see many similar results to the original query, such as the “6 Essential Steps” video, the “Retirement 101” article from Trinity College, the “top 10 ways to prepare for retirement” article from the U.S. Department of Labor, and the Reddit thread on how to start saving for retirement. The AI Overview was also another list of tips and steps to take to save. 

Business	Media Type	Content Type	Website Type
NerdWallet	Calculator	Interactive	Brand site
SuperGuy	Video	List	Brand account
Trinity College	Article	Informational	College site
Amazon (Author Dan Clay)	Book	Informational	Brand site
U.S. Dept. of Labor	PDF article	Informational	Government site
Bankrate	Article	Informational	Brand site
Reddit	Forum posts	Multiple	Forum

The process can be applied to every fan-out sub-query, but here we want to focus on the organic rankings.

Next up is “What are the different types of retirement accounts?”: 

This query is very specific, and so the results were highly specific lists of definitions. The AI Overview and AI Mode both had the same bullet lists, and the rest of the SERP featured articles from sources like Charles Schwab, Northwestern Mutual, and American Express that were all laid out in list form. 

There’s also a “people also ask” section at the top of this query with even more related and highly specific questions.

 

Business	Media Type	Content Type	Website Type
IRS	Table of contents	Links to informative webpages on the site	Government site
Fidelity	Article	Listicle	Brand site
U.S. Dept. of Labor	Article	Informational	Government site
Thrivent	Article	Informational	Brand site
Reddit	Forum posts	Multiple	Forum
Equifax	Article	Informational	Brand site
Charles Schwab	Article	Listicle	Brand site
Northwestern Mutual	Article	Listicle	Brand site
American Express	Article	Informational	Brand site

 

As for “How much money should I save for retirement?”:

The results consisted of:

• Articles that all gave the same recommendation: save between 10 and 15% of your annual income, implying that it really is a best practice that everyone follows (or simply everyone is copying each other’s advice). 
• A couple of retirement calculator tools from NerdWallet and Merrill Edge but there weren’t nearly as many as I expected to see (maybe they are difficult to build or not very accurate), so perhaps our financial organization can capitalize on that with a better one. 

Based on the lack of diversity in results, it would appear that Google feels this question has already been answered and nothing else is needed.

Business	Media Type	Content Type	Website Type
NerdWallet	Calculator	Interactive	Brand site
Citizens Bank	Article	Informational	Brand site
Texas Hospital Association	Article	Listicle	Brand site
The People’s Bank	Article	Informational	Brand site
Merrill Edge	Calculator	Interactive	Brand site
Brookings	Article	Informational	Brand site

The AI Overview had some recommended guidelines, a table with age-based benchmarks for saving, and a list of methods to estimate how much, including a link to the NerdWallet calculator. The same result was found in AI Mode. 

The query “Retirement savings strategies for someone in their 30s” gave a bunch of highly specific results mainly consisting of articles and videos making recommendations. The results even had a “find related products and services” section, implying that this query is so bottom of the funnel that people searching this very specific query probably want to select an actual account/financial institution. 

However, one thing to note is that some of the results misunderstood the question. The article from Farther Financial talks about early retirement, as in how to retire when you’re in your 30s instead of starting to plan in your 30s. 

Overall, nothing linked on this SERP was a repeat of the other queries:

Business	Media Type	Content Type	Website Type
Securian Financial	Article	Informational	Brand site
Reddit	Forum posts	Multiple	Forum
Fidelity	Article	Listicle	Brand site
Bankrate	Article	Listicle	Brand site
Thrivent	Article	Informational	Brand site
Hancock Whitney	Article	Informational	Brand site
MassMutual	Article	Informational	Brand site
Farther Financial	Article	Informational	Brand site
America’s Wealth Management Show – YouTube	Video	Informational podcast	Brand account

Aside from a few videos up top, the results for “Common retirement savings mistakes to avoid” were hilariously all list articles ranging from 10 important tips to 5 mistakes to avoid. AI Mode and the AI Overview simply listed out the mistakes grabbed from many of the articles listed on the SERP:

Business	Media Type	Content Type	Website Type
T. Rowe Price – YouTube	Video	Informational	Brand account
CBS Philadelphia – YouTube	Video	News story	News station account
MeaningfulMoney – YouTube	Video	Informational	Brand account
Louisiana Office of Financial Institutions	PDF Article	Informational	Government site
Morgan Stanley	Article	Listicle	Brand site
Charles Schwab	Article	Listicle	Brand site

 

Next, we’re going to look at all these results together and start analyzing what it all means. 

Matching Content Formats to Intent

It’s clear that format choice matters just as much as topic choice. As we’ve seen from the screenshots, the SERPs seem to favor list articles, videos, and interactive calculator tools. 

One thing I noticed (and was pretty excited about) was how many reputable sources were cited. Actual financial institutions with experience in the subject matter like Fidelity, Charles Schwab, Wells Fargo, and others topped the SERPs and were cited in AI Overviews and AI Mode. I read the AI Overviews and actually believed they were telling the truth, which isn’t something that happens often. 

This is great for the people doing the searching, but a challenge for our financial organization to overcome in rankings. They will have to work hard to produce the kind of content that can compete with these huge firms. But thanks to our query fan-out and SERP research, we know what type of content ranks well to give them an idea of where to focus their marketing efforts. 

However, it’s also important to consider the fact that a new type of content that hasn’t been seen on the SERPs like an FAQ page or guide might capture more eyeballs than the same listicle over and over. It’s all about experimentation and seeing what works.

A successful content strategy will need to be creative, helpful, and omnimedia to focus on every search channel in order to compete on these SERPs.

Expanding Beyond Web Pages into Omnimedia

What is an omnimedia and omnichannel strategy? It focuses on everything in every channel and in every media format: videos on YouTube, text in Reddit forums, social media, review site tables, TikTok videos, literally anywhere a person can search for something.

In our finance company example, there weren’t any instances of social media or TikTok posts showing up in the SERPs, but we did see quite a few YouTube videos and Reddit and Quora forum posts. 

I looked at the same queries in a few LLMs (ChatGPT, Gemini, and Perplexity) to analyze their results. Rather than inundate you with mountains of screenshots, I will just sum up the findings for a few of the queries. I started with the original query of “What’s the best way to save for retirement”:

• ChatGPT’s answer was a simple list explaining the types of retirement accounts, a recommended target percentage of income to save, and some tips for smart saving. I didn’t see any links or citations in this summary. 
• Gemini focused on tables that could be exported to Google Sheets that showed age benchmarks and how to choose the right account type. At the bottom, there was a sources button that opened a window on the right with a list of sources including AARP, T. Rowe Price, Fidelity, and Empower. 
• Perplexity offered links to cited sources throughout its summary after each section, which included Guardian, Schwab, Vanguard, and even Reddit.

For the “Beginner’s guide to saving for retirement” prompt:

• ChatGPT had an overly simplistic list of steps with no cited sources.
• Perplexity had a 5-step list with cited sources throughout (mainly the same ones cited the last time like Schwab and Fidelity). 
• Gemini had a 3-step list, a table of benchmarks, and a checklist, but the most interesting part for me was it specifically recommended several companies with which to open a Roth IRA. This is important for brands to note. With enough content on this topic, financial companies could be added to this list: 

For the next query of “What are the different types of retirement accounts”:

• ChatGPT gave a list with brief explanations and no citations again.
• Perplexity detailed what each plan was and how it worked, citing a source at each line.
• Gemini did the same citing many different sources:

For the sake of brevity, let’s go straight to “Common retirement savings mistakes to avoid” since this query is a different style – a serious prompt with negative implications on a person’s life, aka. Your Money or Your Life (YMYL) prompts:

• ChatGPT offered a list of mistakes with a “why this hurts” and “do this instead” line for each.
• Gemini gave a much more detailed list of mistakes with sources such as Discover, LPL Financial, T. Rowe Price, and Kiplinger. These sources differed from those that Gemini cited for the other prompts.
• Perplexity cited 10 sources in their brief list of mistakes to avoid, this time with a few reputable news sources like the New York Times and The Harvard Gazette. 

Since this prompt opened up the citations to new sources we hadn’t seen with the other prompts, it shows that organizations like Fidelity and Schwab don’t have a monopoly on citations for this topic. That means our financial business could possibly join the citations. 

It also means that this type of prompt that’s focused on YMYL issues, such as making mistakes with your retirement savings, can impact the sources cited. Therefore, depending on their status in the industry, this may not be the right prompt for our financial company to focus on. 

Let’s look at the results as a whole to see how our finance firm can fit in. 

Reverse-Engineering Citations to Inform Content Creation

AI citations, derived from analyzing SERP features and LLM responses, help content planning by revealing the types of sources, formats, and channels necessary to gain visibility, especially in competitive areas like financial services.

AI Overviews and LLMs draw from a diverse ecosystem of content, which can be categorized by the credibility and nature of the source and the channels they utilize:

Source Type	Examples of Channels and Sources	Insight for Content Planning
Institutional/Financial Firms	Web pages, articles, and tools from established firms like Fidelity, Charles Schwab, T. Rowe Price, Vanguard, and LPL Financial.	These sources dominate citations for core financial topics, signaling the need for high-authority, expert-level content to compete.
Official/Educational	Articles and guides from government agencies and educational institutions, such as the U.S. Department of Labor, Trinity College, and AARP.	Citations favor unbiased, fact-checked authority, especially for complex or sensitive topics like retirement planning.
Reputable Media/News	Articles from established publishers and news sources like NerdWallet, Bankrate, Kiplinger, The New York Times, and The Harvard Gazette.	Targeting queries focused on specific angles, like “Common retirement savings mistakes to avoid” (a negative prompt), can broaden citation opportunities beyond traditional financial firms into reputable media sources.
Community/ User-Generated	Posts and threads from forums like Reddit and Quora.	An omnimedia strategy should recognize and target platforms where prospective customers are having exploratory, non-transactional conversations, as these are cited in both SERPs and LLMs.

The channels cited are primarily traditional web pages/articles, but an effective content strategy must be omnimedia, focusing on all channels where people search.

The type of content that receives citations is often determined by the specific query intent and is heavily favored by formats that are easily synthesized by AI. Cited assets tend to be highly structured and actionable:

• List Articles (Listicles): Many SERP results and AI Overviews for queries like “mistakes to avoid” or “types of retirement accounts” heavily feature content formatted as lists.
• Tables: AI Overviews frequently use tables to detail complex information, such as age-based benchmarks for saving or comparisons of different retirement accounts.
• Interactive Tools and Calculators: For questions about specific quantities, such as “How much money should I save for retirement”, interactive tools and calculators from sources like Bankrate and NerdWallet are featured prominently in results and AI Overviews. Qforia also specifically recommends interactive tools for certain queries.
• Actionable Formats: Both Qforia analysis and LLM outputs favor step-by-step lists and checklists for actionable queries like retirement strategies for someone in their 30s.
• Videos: Videos are visible on SERPs across multiple exploratory queries, indicating the necessity of including video production in an omnimedia strategy.

Analyzing these citation patterns transforms competitor analysis into actionable content strategy, allowing a financial firm to focus its marketing efforts:

1. Prioritize Format Over Topic Saturation: Since many SERPs repeat listicles, opportunity may lie in creating different asset types, such as FAQ pages, comprehensive guides, or tables, that might capture more attention than yet another listicle. 
2. Target Implicit Transactional Intent: Although the customer starts with exploratory questions like “What’s the best way to save for retirement”, AI models like Gemini are willing to suggest specific companies for opening accounts. This creates an opportunity for a financial business to position itself to be specifically recommended by AI.
3. Use Specific Query Styles to Bypass Citation Monopolies: While major firms like Fidelity and Schwab often dominate general retirement citations, queries focusing on specialized or negative topics, like “mistakes to avoid” (which touches on YMYL issues), open up citations to different types of high-authority sources, including news publications. This suggests either focusing resources on specific, highly authoritative content addressing nuanced concerns, or just letting this query go.
4. Adopt an Omnimedia/Multi-Channel Presence: The appearance of Reddit, Quora, and YouTube content in both SERPs and LLM citations shows that high-quality content planning must extend beyond official website articles. To fully compete, content must be tailored for, and distributed across, these community and video channels.

Now that we have an idea of how these results can inform content strategy, we’ll put together a generalized omnimedia strategy for our hypothetical finance business. 

Building an Omnimedia Strategy

Here’s what we do at iPullRank:

• Keyword Matrix: Establishes the groundwork for topical presence in AI Search by identifying synthetic keyword gaps, potential opportunities, and valuable terms at the passage level. It reveals which areas are strengthening or weakening your AI presence, enabling you to allocate resources toward terms that create competitive differentiation.
• Omnimedia Content Audit: Comprehensive assessment of content spanning all formats and channels, covering owned, earned, and shared media properties. It uncovers what’s limiting your authority while pinpointing where focused resources will generate results.
• Omnimedia Content Plan: Strategic blueprint for enhancing Content Resonance and expanding coverage across discovery channels, complete with content frameworks and governance structures. It provides a defined pathway to greater prominence in AI-generated responses.
• AI Search Measurement Plan: Monitor input indicators, channel data, and performance benchmarks across AI Search environments. It identifies how AI Search influences growth trajectories, allowing you to connect spending with outcomes and refine your approach based on evidence.

Given that our fan-out research gave us the keywords we need and we already analyzed their presence in searches, let’s put together a plan for our financial company. This plan leverages the query fan-out analysis for retirement planning to create resonant, structured content across multiple channels.

Query Fan-out Prompt	Recommended Asset Type & Structure	Omnimedia Objective
“What’s the best way to save for retirement” (Core)	Pillar Guide / Comprehensive List: Create a definitive, long-form guide. Structure the article so it begins with the core answer or takeaway, followed by supporting detail and nuance.	Semantic Resonance: Ensure the title, description, and internal linking structure establish this as the canonical source for all related fan-out queries.
“Beginner’s guide to saving for retirement”	Actionable Checklist/Step-by-Step Guide: Develop a simplified, numbered list of steps for beginners. Content should be clear and simple.	Passage-Level Extraction: Optimize for easy extraction of the 3 or 5 essential steps by LLMs (like ChatGPT’s simplistic lists).
“What are the different types of retirement accounts?”	Comparison Table and FAQ Page: This requires highly specific definitions. Create a dedicated FAQ page featuring a comparison chart or table detailing account types and their features.	Structured Data: Ensure the data is provided in a clean, easily digestible format (table) that AI Overviews and LLMs already favor when detailing complex information.
“How much money should I save for retirement?”	Interactive Tool/Calculator: The primary format requested for this query is an interactive tool. This must be accompanied by structured data (tables) showing age-based benchmarks for saving.	High-Value Utility: Provide functional utility (the tool) and support the quantifiable answer with authoritative data (the table), increasing the chance of citation over static articles.
“Retirement savings strategies for someone in their 30s”	Personalized Checklist/Segmented Guide: Content should be highly specific and actionable for a segmented audience. Use the product grouping concept to show a progression_from or complementary_with path for financial products relevant to this age group.	Targeted Personalization: Customize the content to fit the behavior and specific needs of this persona, making the recommendations feel personalized, which is crucial for AI agents.
“Common retirement savings mistakes to avoid”	YMYL List Article with Solutions: Create a list detailing the mistakes, each paired with a solution. Must be accurate, with citations and data sourced from trusted research.	Trust and Authority: This content relates to YMYL issues. It must be highly detailed and authoritative to compete with reputable news sources and large financial institutions (otherwise don’t bother).

 

Because LLMs often prioritize user-generated content, the content plan must actively generate and integrate these signals across all channels:

Channel	Content Focus based on Retirement Queries	Strategic Goal
YouTube	How-To Videos & Guides: Create short, instructional videos for the “Beginner’s guide” and “Strategies for someone in their 30s” queries. Ensure video descriptions include relationship attributes to related website guides and link to the interactive tool.	Capture Exploratory Search: Videos are crucial for user education in the finance industry. Position the firm as an authority where consumers compare accounts and learn how they work.
Community Forums (Reddit/Quora)	Monitoring and Engagement: Focus resources on monitoring discussions related to “Common retirement savings mistakes to avoid” and “Beginner’s guide”, as these forums appear frequently in SERP and LLM citations.	Influence Citation Flow: While the firm cannot directly control these posts, understanding the conversation context allows for developing content that directly answers community-driven questions with clarity and context.
LinkedIn	Thought Leadership: Utilize LinkedIn Pulse for “Retirement savings strategies for someone in their 30s”. Publish educational, semi-formal posts that bridge technology and business to capture a broader professional audience.	Professional Authority: Establish individual and corporate credibility on a channel that ranks well for thought leadership.

By implementing this omnimedia plan, the financial company treats every content touchpoint from the interactive tool on its site to a checklist shared on LinkedIn as a signal that reinforces a clear, emotionally grounded, and contextually rich narrative about retirement planning to maximize visibility in SERPs and LLMs.

But what if your financial business is enterprise-sized with a massive library of webpages? How can you scale your content strategy? And how can you easily prune old, irrelevant content that is no longer serving your site?

Scaling Query Fan-Out and Omnimedia Planning

For a single topic or article, traditional content pruning involved manual work that was often time-consuming and subjective. This process required manually pulling traffic data, checking rankings, looking at backlinks, and skimming posts to make a “best guess” about relevance. 

The content pruning methodology we’ve developed at iPullRank uses a Relevance Engineering framework that blends AI-driven semantic relevance analysis with SEO and content metadata to make data-driven decisions at scale. This will help ensure all the content on your site is relevant and identify what content should be removed. 

The steps for executing this workflow involve defining your strategic benchmarks, quantifying meaning using vector embeddings, integrating performance data, and applying a final decision framework.

Here are the steps we follow to perform the content pruning:

Step 1: Define Strategic Focus

The entire analysis is grounded in business reality by defining what relevance means for the organization.

• Work with the client to solidify their core solution areas or strategic pillars.
• For each area, develop representative keyword portfolios reflecting user intent and product capabilities.
• Draft a concise business relevance statement (a short paragraph capturing the ideal focus).

Step 2: Generate Topic & Business Relevance Centroids

This step translates the strategic pillars into quantitative benchmarks.

• Using the chosen embedding model, generate an embedding for each individual keyword within a topic’s portfolio (developed in Step 1).
• Calculate the average of all keyword embeddings within that cluster. This averaged vector becomes the topic centroid, serving as a robust mathematical representation of the topic’s semantic space.
• Generate a single embedding for the overall business relevance statement.

Step 3: Generate Article Embeddings

The meaning of every post in the library is represented numerically.

• Run the extracted article content through basic Python cleaning routines (removing excess whitespace or stray HTML tags).
• Combine the Title text and the cleaned main body content for each article.
• Generate a single embedding for each article using the same model used in Step 2 for consistency.
• Store these individual article embeddings, typically in a NumPy array paired with their corresponding URLs.

Step 4: Calculate Similarity Scores

This step objectively measures how semantically aligned each article is with the strategic targets.

• Using Scikit-learn in Python, calculate the cosine similarity between each article’s embedding (from Step 3) and each of the topic centroid embeddings (plus the business relevance embedding from Step 2).
• The output is a matrix added to the spreadsheet, showing scores between -1 and 1, where scores closer to 1 indicate a stronger semantic alignment.

Step 5: Layer SEO Performance & Metadata

Real-world performance and freshness data are integrated with the relevance scores.

• Pull standard SEO metrics, primarily trailing 3 to 6 months of organic clicks from Google Search Console, focusing on a recent window to avoid rewarding historical performance.
• Pull Publish Date and Last Modified Date from the CMS or crawl data.
• Join this performance and freshness data to the master spreadsheet containing the URLs and similarity scores.

Step 6: Apply the Decision Framework (Making Informed Choices)

The combined data points are translated into actionable categories to guide pruning and optimization efforts.

• Establish data thresholds to categorize each article into three main actions:
  • KILL: Candidates typically have low similarity scores across all core topics, low recent GSC clicks, and are old (more than 5 years with no significant updates).
  • KEEP: Articles that qualify if they have high similarity to at least one core topic or have strong recent SEO performance.
  • REVIEW/REVISE: Articles that are relevant but underperforming, outdated, or highly similar to other posts (potential consolidation targets).
• Conduct manual review of edge cases, confirm decisions, and identify specific actions.

Reframing SEO with Query Fan-Out

For firms competing against giants on the SERPs and in LLMs, focusing on an omnimedia query fan-out approach to content strategy is essential. The citation analysis we walked through shows that specialized queries, especially those focused on specific concerns like “mistakes to avoid” or personalized scenarios like “strategies for someone in their 30s”, open up opportunities for smaller firms to break through with unique and truly valuable content. 

You can’t out-budget the big players, but you can out-execute them on format diversity, omnimedia presence, and targeting the fan-out queries they’re ignoring. And when you scale this with the Relevance Engineering framework for content pruning, you’re optimizing your entire content library to align with how AI Search works.

We know that this can be overwhelming for a limited staff to achieve, but we can handle it for you. If you want to do this for your own business, talk to iPullRank about our AI Search Strategy Program with a full Omnimedia Content Audit and Content Plan, tied directly to a Keyword Portfolio Matrix, designed to improve visibility across traditional search, AI search, and third-party platforms. Let us help you be the first thing customers see (and trust) wherever they search.

The post Query Fan-Out in Practice: Turning One Search into an Omnimedia Content Plan appeared first on iPullRank.

Your product feed used to be a technical checklist for Google Shopping that your dev team handled while your content team told the real story elsewhere. They were different systems with different goals.

That time is over.

OpenAI’s product feed specification recognizes that your catalog is your content now. Every title, attribute, and image becomes training material for AI models that will decide whether to recommend your product or your competitor’s.

But the product feed itself is the lever you control. Your merchant feed is the canonical source that powers the broader shopping graph, the web of product information that AI systems (and traditional Google Search) use to understand what you sell.

Everything else (your reviews, videos, blog posts, product detail pages, etc.) contributes to whether AI recommends your product. The feed is where you define the product’s semantic identity.

The models care about all of it: what’s in the feed, what’s in your reviews, what’s in your YouTube descriptions, and whether all of those things tell the same coherent story. 

Discovery now happens through conversation with AI, and conversations require context, emotion, relationships, and narrative consistency across every place your products appear.

This is omnimedia strategy for AI search: turning fragmented product information into a unified content engine that works whether a customer finds you through Google Shopping, asks ChatGPT for recommendations, or watches a YouTube review.

Here’s how ecommerce brands can make it work.

Your Product Feed Is Now Content

The Google Shopping feed traditionally operated with content strategy being pretty siloed: technical teams optimized product feeds for crawling, indexing, and ad targeting based on keywords, while creative storytelling and user-generated content (UGC) like reviews and Q&A often remained separate. Google’s system was designed for a world of keywords and clicks.

But we are now seeing the emergence of a new standard, shown by OpenAI’s Product Feed specification, that turns your product feed into your main content engine. Instead of describing your products solely for a search engine, you are now describing them to an AI system that can reason about them.

Every field within your feed, including titles, descriptions, and attributes, is no longer just structured data but conversational training data.

In essence, your catalog becomes content. If your descriptions lack voice, clarity, or context, you are basically training the model to recommend someone else’s product.

You need to align all of your content channels, from customer reviews to YouTube videos, into this single narrative. Every piece of content, including your product image and Q&A, is now considered a retrieval object that AI models can access to understand, compare, and recommend your products. 

But we need to distinguish between two different things here:

• The product feed (your merchant feed) is the structured data file you submit to Google Merchant Center, Amazon, or other platforms. This is your direct input into the shopping graph and the foundational layer of product understanding from which commerce platforms build. 
• Content that includes your product is everything else: customer reviews, YouTube videos, Reddit discussions, blog posts, Q&A threads, social media mentions, and product detail pages. You influence it through customer experience and content marketing, but you don’t control it directly.

Both matter, but they matter differently.

That means your product feed needs voice, not just keywords. It needs use cases, not just features. It needs emotional context that helps AI understand not just what something is, but why it matters to someone in a specific situation.

As Gianluca Fiorelli noted in his framework for increasing visibility in AI search: “Each unit of content — whether a full article or a single section — should begin with the core answer or takeaway, followed by supporting detail, nuance, and optional depth.” 

This structure makes content highly scannable for humans and enables passage-level extraction by AI. Your feed establishes the core answer, and your content provides the supporting detail.

What Google and ChatGPT Are Reading

The transition from traditional ecommerce visibility to AI-driven discovery is really just a shift from structured feeds to multimodal signals.

The Google Shopping feed used to be mostly concerned with machine-readable text attributes like titles, prices, and keywords that were optimized for crawling and indexing. Now, ecommerce seems to be moving toward a richer, more comprehensive understanding of the product.

The Rise of Multimodal Data

AI systems are built for a world of questions and reasoning. To facilitate this reasoning, they require more than just descriptive text.

Google itself has been moving past simple structured data, evidenced by its patents focused on product understanding from visual embeddings. This signals that Google is also working to extract meaning directly from images and visual assets to better understand and recommend products.

The OpenAI Product Feed specification, however, supports a unified approach to all media types. The specification expands the media attributes beyond traditional images to include video_link and model_3d_link. These attributes, which are new or only partially supported via separate methods in the Google system, enable AI models to access and analyze 3D models and videos directly so every product image, Q&A, and review functions as a retrieval object.

This focus on omnimedia goes hand-in-hand with a consolidation of data. Where Google traditionally splits product information, pricing, inventory, and reviews across multiple separate feeds (e.g., Core Product Feed, Inventory Feed, Product Reviews Feed), OpenAI merges it all.

This unified schema ensures that the AI has access to a complete, consistent picture of the product at all times, making it easier for the AI to reason about the product’s value and use case. For example, user-generated content (UGC) like full review text and Q&A pairs (raw_review_data, q_and_a) are now consolidated within the product record, offering the model more surface area for complex reasoning.

Using this multimodal and unified data approach can help your feed become conversational training data that allows the AI to tell your brand’s story with voice, clarity, and context.

How We Build an Omnimedia Content Plan

Optimizing your presence throughout the internet might sound obvious, but many businesses are not staying on top of this. 

Your content should be aligned across all channels with consistent tone, language, and voice. 

At iPullRank, we offer Omnimedia Content Audits and Plans for our clients that focus on gaps and potential wins across YouTube, Reddit, LinkedIn, and other places. Let’s look at a specific case study from a FinTech company that’s a client of ours: 

Keyword Matrix

We start with creating our Keyword Matrix, which is a more comprehensive Keyword Portfolio incorporating deeper segmentation and data points to assess keyword opportunity, viability, relevance, and ranking potential. We use iPullRank’s own Qforia tool to perform query fanout research. 

Using client-provided information and tools like Semrush and Ahrefs, we look at parts of speech, entities, word count (classifying keywords as head, chunky, or long-tail), search volume, trends, Google ranking, and well as the rank zone, which shows keyword ranking distribution.

Omnimedia Content Audit

We then use the Keyword Matrix to perform an Omnimedia Content Audit. We take synthetic queries from the Keyword Matrix and see the top ranking landing pages for those queries. Then we’ll identify where our client is visible, where there are visibility gaps and, of those top landing pages, what kind of content is ranking. 

For this particular FinTech company, we tracked over 1,200 synthetic queries to see which domains ranked the highest and which type of content is the most successful (knowing that Google often links to itself within its SERP features). We look at priority topic clusters for a deeper dive into what it takes to win visibility for specific types of queries.

We also looked at the client’s social media presence and tagged them by topic cluster to see where there were gaps in coverage or opportunities to expand into new conversations. We found video to be a crucial part of user education for FinTech, focusing on use cases, helping users understand what these tools can do, and where there may be limitations or risks. 

LinkedIn Pulse posts from individuals or companies also rank well in this space. These thought leadership and corporate insight pieces are educational and semi-formal, including a broader professional audience into the conversation around benefits, comparisons, and future-thinking that bridges technology and business.

Omnimedia Content Plan

From our Audit findings, we were able to develop a strategy for our client’s content production. We looked at the types of articles that Yahoo ranks well for, as well as what other companies in the FinTech space like IBM are writing about, to suggest topics that our client may consider covering.

In general, we often recommend these types of content to our clients:

• Data journalism with proprietary data
• Custom graphics
• Interactive tools within the site
• Traditional blog article

None of these should be broad, TOFU content anymore. That branch is pretty much covered by AI summaries. New content you create should be highly specialized, unique to your brand and can be used as a source or cited in other material, giving LLMs something unique to cite and view you as an authority.

Why Feed Resonance Matters

When a customer watches your YouTube unboxing video, reads a review on your site, and later sees your product in Google Shopping, they’re encountering fragments of the same story. Feed resonance is what happens when those fragments create a coherent narrative that both human shoppers and AI systems can understand and trust.

Without resonance, you’re speaking in disconnected dialects. Your YouTube video calls it a “portable espresso maker for travelers.” Your feed says, “compact coffee machine.” Your reviews mention “camping coffee gear.” To a human, these might seem related, but to an AI parsing semantic relationships, you’ve just created three separate concepts competing for attention instead of one unified signal.

The Emotional Architecture of Your Feed

Traditional product feeds are transactional: SKU, price, availability. But AI models are trained on human language, which means they respond to emotional and value-based descriptors that explain why something matters, not just what it is.

Before: Transactional

• title: “StainlessSteel Insulated Tumbler 20oz” 
• description: “20-ounce stainless steel tumbler with vacuum insulation”

After: Resonant

• title: “StainlessSteel Insulated Tumbler 20oz” 
• description: “20-ounce stainless steel tumbler with vacuum insulation that keeps morning coffee hot through your entire commute because your first sip at the office should taste as good as the one at home” 
• value_proposition: “temperature retention for busy professionals” 
• emotional_benefit: “consistent comfort in unpredictable schedules” 
• use_context: “daily commute, desk work, travel”

Clustering with Relationship_Type Fields

Ecommerce catalogs are naturally relational, but most feeds treat products as isolated entities. Adding relationship_type fields creates explicit semantic connections that help AI models understand your product ecosystem the way customers actually experience it.

Strategic Relationship Types:

• complementary_with: Products used together
  • Yoga mat → yoga blocks, straps
  • “Customers who practice at home often need both stability and support”
• alternative_to: Similar products for different needs
  • Lightweight running shoe → stability running shoe
  • “Same performance philosophy, different biomechanical support”
• progression_from: Journey-based relationships
  • Beginner guitar → intermediate guitar
  • “Natural next step as skills develop”
• occasion_paired_with: Context-based groupings
  • Cocktail shaker → bar spoons, jiggers, bitters
  • “Complete home bartending setup for entertaining”
• problem_solution_chain: Sequential pain point resolution
  • Sensitive skin cleanser → hydrating serum → barrier repair cream
  • “Layered approach to reactive skin concerns”

Implementation Example:

Encouraging Fresh, Contextual Customer Reviews

User-generated content is the highest-trust signal for AI models because it represents actual usage patterns and authentic language. But not all reviews are equally valuable for feed resonance.

“Great product! 5 stars.” = Zero semantic value

“Works as described.” = Confirms nothing meaningful

“Fast shipping!” = Wrong signal entirely

Some tips for soliciting better reviews:

1. Post-Purchase Prompts with Specific Context

• Instead of:

“How would you rate this product?”

• Try:
  • “How did this yoga mat perform during your first hot yoga class?”
  • “Which features made the biggest difference during your morning commute?”
  • “What surprised you most about using this for camping trips?”

2. Temporal Review Requests

• Capture evolving experiences:
  • 7 days: First impressions and setup experience
  • 30 days: Daily use patterns and unexpected benefits
  • 90 days: Durability and long-term value assessment

Each window generates different semantic data. Early reviews capture “unboxing,” “easy setup,” “initial quality.” Later reviews contribute “holds up well,” “worth the investment,” “still performs like new.”

3. Scenario-Based Review Frameworks

• Guide customers to describe context not just satisfaction:
  • “Tell us about a specific moment when this product made a difference:”
  • Solved an unexpected problem
  • Made a routine task easier
  • Worked perfectly in challenging conditions
  • Exceeded expectations over time
• This generates reviews like:
  • “Used this tumbler during a power outage and it kept ice frozen for 14 hours when our fridge went out” instead of “Keeps drinks cold.”

4. Cross-Channel Review Integration

• Surface reviews in:
  • YouTube video descriptions (“Real users say…”)
  • Product feed structured data markup
  • Social media carousel posts
  • Email nurture sequences

When the same authentic story appears in your feed, in video metadata, and in social content, AI models recognize pattern consistency.

Measuring Resonance

You’ll know your feed resonance strategy is working when:

• Cross-channel query matching increases: The same product appears for semantically similar queries across Google, Amazon, and LLM-based shopping.
• Related product discovery improves: AI recommends your complementary items without explicit ads.
• Review themes align with feed descriptors: Customer language mirrors your value propositions.
• Long-tail semantic traffic grows: You rank for “why” and “how” queries, not just product names.

Feed resonance transforms your product catalog from a static inventory list into a dynamic semantic network that follows your customer across every touchpoint. 

In an AI shopping world, ecommerce shops need their products to tell the most coherent, emotionally resonant, and semantically rich stories across every surface where discovery happens.

Visibility in the Future of Ecommerce

The change from keyword optimization to omnimedia strategy may feel large, but the opportunity is proportional to the challenge. Ecommerce shops that treat their product feed as isolated technical infrastructure while investing heavily in “content marketing” aren’t understanding that the feed is the content. The reviews are the story. The YouTube videos, customer Q&A, product images, and attribute data are different expressions of the same semantic truth about what you sell and why it matters.

Getting this right requires recognizing that your 200-word product description will be read by more AI models in the next month than human shoppers will read in a year. It requires understanding that a well-structured relationship_type field does more for discovery than another generic Instagram post. 

It’s about having the most resonant content where every signal across every channel reinforces the same clear, emotionally grounded, contextually rich narrative.

What story is your product feed actually telling?

The post Omnimedia Strategy for Ecommerce: Turning Your Product Feed into a Content Engine appeared first on iPullRank.

Today, they’ll expose the problems with modern rank tracking and provide you with a way forward. 

Watch the replay here:

Here’s what we covered in this webinar: 

• What is rank tracking? [0:38]
• The history of rank tracking [1:08]
• Why rank tracking matters [2:51]
• The fundamental flaw with rank tracking software [4:19]
• How Google and SEOs measure rankings  [4:35]
• How rank tracking has changed [6:29]
• How should you be rank tracking? [12:47]
• The gaps in rank tracking [21:39]
• Why you should be tracking SERPs [28:05]
• The relationship between rank tracking, SERP tracking, and topic clustering [32:11]
• Rank parsing re-run to analyze historical data [39:12]
• The wall between paid and organic [40:01]
• Using Exact Science to address these rank tracking problems [41:58]

What is rank tracking? [0:38]

If you’re an experienced marketer, you probably know this one.

Rank tracking is the process of tracking a web page’s search engine position for a select set of keyword queries. That’s the thing about rank tracking — definitions are simple; however, the practice of rank tracking has become incredibly complex. 

It’s an issue that is, for the most part, neglected by rank tracking tools. 

How did we get here? 

To answer that question, we’ll need to look at the origins of rank tracking. 

The history of rank tracking [1:08]

Let’s go way back to 1995. 

AOL bought Web Crawler, an old-school search engine. Web crawler would later be sold to Excite. Later, AOL would launch its own branded search engine, called AOL Search. AOL would eventually create a partnership with Google. 

Why do these dinosaur search engines matter? 

Many of these search engines operated on the original clickthrough rate model. Larry Kim (formerly of Wordstream) wrote: 

“Clicks on the search results page are basically a zero-sum game. If there’s an increase in CTR for one part of the SERP, some other part is losing that click. There must be a decrease in CTR elsewhere. And that includes the ads.” Kim’s research showed us that the higher the ranking, the higher the clickthrough rate. The more traffic you receive, the higher you’ll rank for competitive search terms.”

Why rank tracking matters [2:51]

Traffic comes from rankings. 

But there’s more to it than that. You’ll need to identify the various decreases in traffic, monitor the competitive landscape, spot various types of opportunities (and threats), and look for low-performing content or gaps in the space. 

Here’s the caveat. 

Great rankings don’t automatically mean you’re driving a lot of traffic. In fact, you can rank well for lots of queries but fail to drive any meaningful traffic. Rank tracking is a valuable tool, but as Mike King points out, “it’s not the end-all-be-all of organic search performance.” 

Gathering rankings for keywords that do nothing to move the needle for your organization means your search campaigns are unsustainable. If there’s no improvement to revenue, no financial lift, you won’t be able to sustain your rank for very long.

It’s better than viewing rank tracking as the start of the journey. 

The fundamental flaw with rank tracking software [4:19]

Rank tracking software needs to deceive Google. 

Over the years, Google has introduced localization, added personalization, rich snippets, and various other helpful features. What’s common across each of these features is Google’s increasing dependence on meaning and context. Marketers know this, and they work within these parameters. 

When rank tracking is done, this goes out the window. 

The process begins with a clean computer — a computer that’s logged onto the internet for the first time, a browser that has no cookies, no history, and little to no location data. 

That’s not an actual user. 

This is a problem for obvious reasons. The SERPS and rankings this sanitized computer sees and displays to you, the marketer, isn’t the same thing actual users see. As a result, ranking data is immediately suspect. 

Searchers using the query “football” will see very different things depending on their location (Chicago vs. Manchester).  

How Google and SEOs measure rankings  [4:35]

Google measures rankings (or they call it position) differently from SEOs. Looking at Google’s documentation, we see that they measure position in a kind of S shape, a sideways S shape; The first five positions in the organic are measured, then they start measuring the knowledge graph and other features like that are above the fold. SEOs tend to go straight down the page.

What does this mean? 
If you’re comparing your position in GSC versus a rank value from an SEO rank tracking tool, you’re seeing two completely different values. Your rank tracking tools should be able to account for that.

How rank tracking has changed [6:29]

Mobile and desktop SERPs are completely different. 

• Google captures more location data on the mobile side
• There’s continuous scrolling on mobile along with site links. Some brands have multiple links. 
• Depending on the query, you also have personalization (a search done in New York versus Pittsburgh returns different results) 
• Google’s also added indented results, filters, and pills
• Then there are rich snippets 
• Time of search plays an important role as well (e.g., trending news stories vs. product listings based on open/close times of a specific store)

Then there’s also the number of features in a particular SERP. 

Some SERPs include lots of features. Videos and music links, a knowledge panel, more videos, people also ask images, top stories, and links — all before the first result.

It’s a lot to keep track of. 

Maybe this is why many rank tracking tools haven’t been tracking these details? 

How should you be rank tracking? [12:47]

When you do rank tracking the way it’s always been done, you’re not looking at your SERPS. You don’t really understand what you’re up against. If you rank number one for coffee grounds here, you think you’re doing well, but realistically you’re probably not driving many clicks on this keyword. This may be a keyword that you’ve spent a lot of time on that wasn’t actually valuable. 

You should be able to bring more comprehensive data into your rank tracking. It’s a necessity in order to be more strategic about which keywords you should be targeting based on where your true opportunities are. Looking at search volume, looking at CTR models, just looking at pure rankings — it’s not enough to give you the whole story.

Some of the data you need: 

• Legacy Rankings – All platforms should continue to compute rankings the way they previously have to allow for reporting continuity. Introducing something different from how rankings are currently considered is difficult because making the switch will potentially tank all of your reporting. Legacy ranking models should still be computed and added to reports. The new rankings model could also be applied for those tools that maintain SERP archives to give marketers a good sense of what these metrics illustrate over time.
• Web Rankings – This is the metric that we’ve been using. However, we’d count the presence of a featured snippet as part of this metric. This would be the equivalent of STAT’s base rankings
• Absolute Rankings – A position complete with all features on the SERP, including ads.
• Feature Rankings – Organic Rankings that include all Organic SERP features. Presumably, this could be used to better compare GSC data with a ranking. [This includes featured snippets, video, ads, thumbnails, knowledge panels, site links, top stories, etc.]
• Offset – The pixel position of the ranking from the top of the page. This would be measured by the distance that the a tag has from the body tag.
• Page – The page of the SERP that the ranking was seen on.

The gaps in rank tracking [21:39]

This gets into kind of some of these cracks that we talked about and things that need to be seen across the board in rank tracking. This includes details like understanding the pixel height of different SERPS, your rankings, and how it compares with the other features on the page.

A Google placement of #3 organically may mean you’re actually the 25th link down because of a knowledge panel, a local pack, people also ask, and all these other featured snippets.

What does the industry need then? 

At its core, we need better insights for change.  

Murch paints the picture for us. “Basically, there are things on a SERP that are actually changing, but you may only be ranking or checking your rankings for one URL. So you’ll say, Hey, it went from position ten to a two. Awesome. Great. What else changed on that page? Right? Someone who was at position #83 is now at #10? There’s a new competitor who is actively going after this. Do I know about that? Are there content changes to pages that are currently ranking? We may rank within the top three positions, but someone else is actively updating their page. We should know about it or see what changes they’re making.”

Google search console gives us a lot of data, it’s true, but being able to integrate and showcase that with ranking data helps paint a clearer picture. It offers other insights showing SEOs what has increased or decreased across various SERPs. 

Why you should be tracking SERPs [28:05] 

The reason? There are different types of rank tracking. 

You may have new links that show up and missing links that change. You’ll have different sites whose rankings improve and others that deteriorate; seeing that fluctuation on a per rank basis means you’ll be able to see what other things are happening with these SERPs. 

If you’re doing this daily or hourly, every 30 or 15 minutes, you have historical data you can use to map these SERP changes over time. This is invaluable as it gives you hard data on the stability or volatility of specific keyword sets, topic clusters, and SERPs. 

It’s data you can use to make good decisions. 

The relationship between rank tracking, SERP tracking, and topic clustering [32:11]

Topic clustering is another essential component that’s missing from rank tracking tools. 

Imagine that you’re tracking X number of URLs in the top 10. 

Now, there are lots of different tools that do top three, top four, top seven, but imagine that you were able to say, “okay, here are the SERPs that are very similar between these keywords. So if we go after this cluster of keywords, we can get a better sense of how Google ranks pages and topics in this industry, niche, or category. The amazing part about all of this is the fact that this gives you insight into how Google approaches similar keywords or meanings. 

What do I mean? 

We can analyze different synonyms and keyword permutations; it’s you being able to provide all of that data. There’s so much data that Google basically just gives us as part of the SERP. We should be leveraging that as SEOs. 

What about Google’s API?

Google Search Console offers a lot of data; their UI and tool are great, but their API provides more data. Breaking this data down on a per-day basis and overlaying that with Google Search Console data and your rankings data gives you deeper insights you can use to make (much) better decisions. 

Rank parsing re-run to analyze historical data [39:12]

Then there’s historical data analysis. 

Murch discusses a topic that’s near and dear to his heart — a rank parsing re-run. “I found a few issues when we pulled different SERPs from different providers and, although they can fix the issue, it doesn’t fix my historical data. So the idea of being able to re-run data or if there’s something new and it’s confirmed is an important one.

With a re-run, I go backward and say, “Hey, I can re-parse this data and state confidently, these are the changes.”

This means you can monitor historical data and go back in time with the new knowledge that you may have learned by analyzing your SERPs (both paid and organic data). 

The wall between paid and organic [40:01]

Are your paid and organic search teams sharing their data? 

Most aren’t.

Murch explains, ” I don’t know why, but paid and organic are always on two different teams; there’s a wall in between, and there’s a lot of PPC data that should be shared on the SEO side. If you’re targeting keywords or even just trying to rank for them, it’s a good idea to test their conversion value by throwing up a landing page and running paid ads. That should be step one.” When you have this data, you can say, “Hey, these keywords convert. Let’s now spend some time and money ranking for them.” 

Is there a rank tracking tool that can do all of this? 

Using ExactScience to address these rank tracking problems [41:58]

“We’re going to be launching ExactScience, which is our rank tracking software that addresses a lot of these problems because we feel like there’s a gap in the market. There are a lot of things that I just want to highlight and showcase in some of these screenshots that we’ve done.



You should be able to show pixel position, different types of ranking (whether it be branding competitive or the site), some of the features, and the different kinds of tagging and categorization you could do. There’s much more to come about this, but we wanted to have a big announcement. And for all those asking the next question, yes, sign up to become a beta tester. If you go to iPullRank.com/beta-testers, you can sign up and join our waitlist. We’ll be slowly reaching out to you and testing some of our solutions against some of your problems. If you have specific ranking challenges or ideas, please reach out and let us know. 

About Mike King, Founder and Managing Director at iPullRank

Mike is the Founder and Managing Director of iPullRank. He has an extensive background in software, web development, and creative. He’s an industry leader, and his work is frequently featured on sites like Moz, SearchEngineWatch, Unbounce, and Distilled. He’s a sought-after speaker speaking internationally at SMX, SearchLove, SES, MozCon, and LinkLove. 

About John Murch, Director of Software Engineering at iPullRank

John has 15+ years of experience in SEO and software development. He’s a developer with a background in online marketing, and he loves building products and services. 

The post Webinar Recap – Why Your Rank Tracking Is Not Enough appeared first on iPullRank.

In December of 2019, Coronavirus first appeared in Wuhan, Hubei, China and many of us immediately knew the virus posed a great risk to the world. While no one was sure just how dangerous the disease was at first, its quick spread and lethality made everyone take note. Today, saying that the widespread pandemic has affected everyone in some way would be a gross understatement. In fact, there are few things in this world that have not seen an impact from this virus.

Adjustments and Demand Shifts During a Pandemic

Living in NYC as it now appears to be the new epicenter of the virus, I have the unfortunate opportunity to see its effect on nearly every part of my life. Everyday activities like working, looking for a new apartment, filing a car insurance claim have all become virtual experiences (ask me about how GEICO appraised my accident damage some time). If I were to attempt to document everything that’s not the same amidst this global crisis, I may never finish writing and only have more to write with each passing day. And in the time I was writing this, I ended up catching COVID-19 myself (more on that in a future post probably). Instead, I wanted to take a look at something many Americans cared about before the pandemic became our new reality – the 2020 elections.

There is no hierarchy of current events in my mind that makes me prioritize the election over the path toward a  recession, spikes in unemployment, or the hospitals at their capacity with COVID-19 patients (at the time of this writing). However, I wanted to show how search is shifting through this example to demonstrate that everyone has a limited attention span that’s being occupied by the virus. The hypothesis was that with a seemingly never ending concern for your safety, less engaging topics like politics may fall by the wayside.

In what will follow, I will be looking at:

• Data on Google Trends to see how this virus has the capacity to affect our election process and review how American people are searching for election-related queries. 
• Reviewing search trends around the election in the major epicenters of Washington and New York 
• Diving deep into specific political issues and how the pandemic is either driving or diminishing search interest

The tool uses a 0-100 scale based on searches being conducted on Google. If the same date ranges during the election years have numbers that are close, the search demand can be deemed unaffected. If, however, the numbers are different enough, we can assume that the search demand is affected by something. And while this isn’t a controlled experiment (different candidates, changes in technology, different forms of media, etc.), we will be able to see if anyone is still concerned about the presidential race during the pandemic.

COVID-19 Enters the US Scene and Everyone Takes Notice

To start this off, we first need to understand America’s interest in Coronavirus in the realm of Google Search. By seeing when people’s priorities begin to shift in the direction of this pressing health threat, we can deduce at which point their interest in politics may shrink. 

On January 21, some search interest began to appear around the term “coronavirus” in the United States. This is no coincidence and was a direct result of the first identified US case in Snohomish County, Washington identified just 2 days prior.

The COVID-19 nomenclature gained popularity on February 11 after WHO gave the virus its new official name. The two names have been used interchangeably for the most part since.

It’s important to establish the timeline for the interest in this deadly disease as we will dive into how it has affected the overall interest in the US election. I will look at search trends before and after the pandemic begins to pick up search interest and also check how those trends compare to previous searches in the 2016 election.

Do We Care Who’s Running?

The early informational searches related to the election may be around finding the candidates running. Though the 2020 Democratic field was a who’s who of politics, I wanted to compare the run-up to 2016 to see how search interest compared.

I quickly realized that this year proved to have a greater search interest around the term “who’s running for president?” than 4 years ago (even when looking at November 2016 numbers). Granted, early March saw 4 candidates drop out of the race following bad outcomes in primary votes (Buttigieg, Klobuchar, Warren, and Bloomberg all dropped out within 5 days of each other), it looked like Americans were still paying attention amid coronavirus concerns and NYC on the brink of a shut-down.

Of course, it’s not impossible that the denial of the severity of the virus would leave many areas not significantly impacted from the search perspective. While we were stocking up on non-perishables and toilet paper in NYC, there were reports of many “brave” souls still partying it up in Florida as late as March 25. The most affected areas initially were Washington and New York. Naturally, I wanted to dig deeper to see if these epicenters dropped all interest in the election when the virus began spreading in their backyard.

Does COVID-19 Affect Certain States More?

Taking a closer look at the states affected most early on, we can see that the pattern remains somewhat similar even in states like Washington and New York.

Washington’s search interest in “presidential candidates” was higher in early March 2020 than around the same time in 2016. As the state that had the first confirmed COVID-19 cases, this suggests that the pandemic was not enough to distract Washington residents from following the election.

But did that same trend hold true for New York?

It did not. And as someone who is in New York throughout this time, it makes sense as unease was noticeable in people early on. While the difference year over year is not huge, we know that overall interest nationwide was up. Trying to get even more granular, I decided to look at the local data for areas that had first confirmed cases:

This graph more closely aligns with my expectations. But even here the difference was not one I’d call extreme (roughly a 21% decline year over year). 

Are There Political Issues That Are More Important Now?

Although the interest in the upcoming election may take a backseat on the local level when the virus is prevalent in an area, the individual policies associated with candidates running could see a different change. Issues that become extremely pertinent during a crisis could actually see a spike in search interest. With lots of uncertainty around income, health benefits, and unemployment, people are searching for information on topics raised again and again during the election cycle. But how does this compare to 2016?

While medical insurance has been a topic of interest for a long time now, the pandemic highlights people’s need for coverage that is not tied to the employer. 

Though “universal healthcare” did not see any major shifts in search demand in the months leading up to the 2016 election, the term spiked abruptly throughout final Democratic debates and the emergence of COVID-19 in America. This is not necessarily a correlation between the virus and people’s concern for health coverage but it may continue its path as many lose their coverage with their jobs.

Another political issue that has been raised and effectively led to the passing of the recent stimulus bill is the idea of a basic income. As many struggle to make ends meet, a safety net was deemed necessary and most of us got a check from Uncle Sam to help out. The term “basic income” is trending upward and will likely keep the conversation going. It is very possible that this isn’t the last time you see a stimulus check during the pandemic.

Keeping in line with financial struggles that everyone is going through, I wanted to include search demand for “unemployment insurance.” Though not a political talking point itself, the chart below demonstrates the severity of the situation brought about by mass unemployment. And while I’m hopeful things can get back to normal soon, the dependence on support from non-employment revenue is clear. As businesses were forced to close, many lost their job and began collecting unemployment. This type of reaction can be another argument that can reinforce the need for health benefits that are not tied to the employer and a basic income that acts as a safety net.

As the pandemic lingers, many Americans are also anxious to see a government response to the crisis. And as the race for the next US president tightens, that means citizens want to hear both from the sitting president and his opponent. With uncertainty brewing, Donald Trump’s actions and statements on COVID-19 are being searched more and more. But what is interesting to see is that Joe Biden’s view on the crisis is also being searched and potentially weighed against the incumbent’s actions.

Conclusion

I initially hypothesized that the COVID-19 pandemic would take over all search interest and people would forget the election was still happening. I was pleasantly surprised that except for specific areas (and I’m sure it’s not just limited to NYC), Americans are still very much engaged and searching for information as the election approaches. Specific issues raised by candidates are even seeing an uptrend as the pandemic highlights the hardships many of us are going through. While there is not going to be much concrete direction for anything until a new normality is established, we can see how COVID-19 is affecting US Google searches and how it may shape policies for the years to come.

How are your industry’s search trends being impacted by the pandemic? What uptrends are you seeing? Please comment below.

The post The Changes in Search Demand During COVID-19 and Why the 2020 Election Will Be as Big as Ever appeared first on iPullRank.

Most SEO strategies are going after unbranded keywords because that is where the majority of incremental opportunity lies. It’s how customers who are not looking for your brand directly are searching. Branded queries, on the other hand, are driven by existing customers or searchers who have been made aware of your brand through other channels (TV, social media, etc.)  and are explicitly looking for your brand. 

The Value of Branded Keywords

Most SEO strategies focus on unbranded keywords due to their high volume and traffic potential. But branded keywords are viable and should not be ignored. They represent existing and potential customers and just because a visitor is searching on your brand’s name or a specific product does not mean that they have purchased before. In fact, it signals a higher likelihood to convert. 

According to the Advanced Web Rankings CTR Study, the CTR for the no. 1 position for branded queries is 40% vs. 27% for the no. 1 position for unbranded queries.

For example, consumers searching for “running shoes” could be comparison shopping or doing a broad research search. Whereas searchers looking for the branded “adidas running shoes” are specifically looking for the brand Adidas thus signalling greater intent.

If your brand is strong and benefits from high visibility, branded keywords will likely have high search volume and be valuable to you.

Branded keywords will also be more important with the growing popularity of voice search. Machines like Google Home, Siri, and Alexa typically return one result or answer. Having your brand optimized to meet those branded voice queries will be more important than ever.

An SEO strategy that is not focusing on branded search could be forfeiting traffic to review sites, shopping engines, Paid Search, and squatters. Here are 10 common ways brands squander their branded search visibility and how you can avoid these pitfalls in an SEO branded search strategy.

10 Ways Brands Lose in Branded Search

1. Believing Branded Keyword Search Is Minimal 

Compared to unbranded search, branded keyword queries generate less potential search volume and ultimately less traffic. However, in aggregate branded search volume can represent a significant portion of traffic and when reviewed in conjunction with direct traffic, branded traffic may represent a larger percentage of your overall organic traffic than unbranded terms.

How to Capitalize: Pay attention to your branded positions and clicks in GSC. Compare organic traffic against direct and your top organic entry pages. You can connect your GSC to Google Analytics to merge visibility with page-level data.

For this brand, mentions of their brand name represented 25K clicks to their site over a 3-month period. That’s not minimal and should be monitored to ensure its steady-state and growth.

By filtering down to a specific URL, you can monitor its Organic visibility and corroborate this data in analytics. This is great intel for finding branded keywords and pages that are under-performing to help improve their visibility.

2. Not Correlating Direct Traffic’s Role

Direct traffic is when a user types your URL directly into a browser. While this is not the same as a user going to Google and searching on your brand, the two are correlated. A user who has searched on your brand or been made aware of your brand through an unbranded query may return via direct traffic.

Brand awareness and visibility drive both direct traffic and branded queries. SEOs should monitor fluctuations in both channels as well as track returning visitors to know their point of entry and their most used returning channel. 

3. Ignoring Contact or Support Queries

Branded searches come in many forms and intents. A lot of branded queries are from users who have interacted (or purchased) your products or services and are looking for customer service. This means queries such as “[your brand]” +“support” or “contact [your brand]” are highly intentional. Ignoring these keywords can spell disaster for customer loyalty.

This also goes for informational branded queries. Customers may search on your brand with how-to and instructional queries and they should be able to find the right information. 

4. Neglecting to Segment Your Branded Searches

Like #3, branded searches come in all shapes and sizes. There are informational branded searches, transactional ones, research-driven, purchase-oriented. You name it. Brands do themselves a vast disservice by lumping all branded queries together. By segmenting branded searches, you can see what content you have and don’t have onsite to match these queries as well as which products and services are more popular with consumers.

At iPullRank, we leverage semantic tools to generate highly targeted keyword lists for our clients. Based on the opportunity size, we segment lists based on category, need state, rank zone and unbranded vs. branded. 

5. Character Shaving Brand Mentions

In optimization, the brand name is often the first thing to get cut. While it does take up prime character length, brands must ensure that their brand is being parsed in other ways. 

Those ways may include:

• Domain name: Your domain should be your brand name. Search engines will be able to correlate branded searches with the URL, pending the page also has enough relevant copy for the query.
• On-page copy: The title tag is not the end all be all and any and all copy on your site should be branded. What that means is it should drive users into your funnel and move toward a conversion. With relevant, authoritative copy, search engines will be able to make a correlation with your brand.
• Backlinks: Links build brand equity. That includes inbound links and internal links between pages. Having authoritative sites in your industry link to your pages can be more powerful than any brand name appended to a title tag. 
• Schema Markup: By adding your brand name to your website’s schema it will appear in the URL breadcrumb in the SERPs, giving users and search engines a stronger correlation between your brand and the query. 

6. Letting Squatters Take Your Real Estate

Comparison sites, aggregators, affiliates and other companies may squat on your branded terms to co-opt your traffic in both Organic and Paid Search. If your SEO or Paid strategy is ignoring these valuable queries, these squatters will capture the traffic meant for your site. In the case of aggregators, they may capture that traffic, make a conversion and take a percentage of your sale. 

7. Offline Efforts are not Synced to Online Efforts

One of the biggest issues with branded search is that other channels are often drivers. There has to be brand awareness in order for consumers to know the brand. While this may cause an issue with attribution, for SEOs it’s a win-win. If offline channels such as TV are running spots, SEO can correlate any spike or residual branded interest back to those efforts. The play between offline and online can be made strong and brand messaging can be consistent to capture consumers who see a TV ad, do a Google search and find the same deal they were advertised.

When offline and online are not synced, it can be a huge fail. Take Super Bowl commercials for instance. It’s the biggest advertising day of the year and if a brand is running a unique (and expensive) spot, their search strategy should be on-point. Customers will search with phrases from the TV spot and if you’ve done your work, your brand should show up. 

Req did a great post on brands that capitalized on the Super Bowl ads in search and the brands that, unfortunately, did not.

8. Ignoring Comparison or Reviews Queries

All competition on branded search terms is not cannibalizing. Comparison shopping engines and review sites can bolster awareness of your products and services. And it also clues you into the ways searchers are doing research.

Review queries such as “[your product]” + “reviews” are great tools to see what is important to consumers. It also provides ample inspiration for your content strategy. If your branded comparison or review keywords are garnering significant volume, there should be a page on your site to address consumers in this research phase.

9. Wrong Pages Ranking for Branded Queries

Another way brands forfeit their branded rankings is by not assessing if the right pages are ranking for their branded queries. It’s easy to let the homepage accumulate most of the brand visibility, but for highly intentional queries like the Contact Us page or Support content may be more relevant.

By tracking your branded keywords, you can see which pages are ranking and if there are better entry pages for searchers. Properly aligning the right pages to a query can aid in increased engagement and higher conversions. 

10. Not Nurturing or Growing Return Visits

The end game of search is to grow and nurture your traffic and customer base. By tracking and optimizing for your branded search terms, companies can get a better picture of return visitors. Consumers searching on branded terms are already familiar with your brand, but for whatever reason they are not going directly to your site.

By examining their queries and the pages they visit, you can optimize your conversion funnel. Maybe these repeat branded searches need to convert into leads, so they can receive lead nurturing emails? Maybe they need to be prompted to create an account? Branded keyword searches are fertile ground for moving searchers into the funnel. Look at the pages that rank and optimize your lead generation efforts.

Branded search is a crucial portion of your keyword optimization efforts. To ignore or reduce efforts on branded keywords is a strategic error that can lead to holes in your content strategy, marketing, and ultimately your sales funnel. Consumers searching on your brand are valuable and most importantly, they already know about your products or services.  You don’t have to alert them. You do, however, have to provide them with relevant content and nurture them toward action.

How much of your keyword mix and targeting is geared to branded search? Which of these efforts can your company improve on?

The post 10 Ways Brands Forfeit Their Branded Search appeared first on iPullRank.