Brand marketing is often the silent driver behind successful digital campaigns.

People are far more likely to read, watch, click, and ultimately buy from a brand they already know and trust. That’s why doing the harder, slower work of building a strong brand pays dividends when it comes to performance marketing efforts like SEO and PPC. We know this intuitively.

But proving the impact of brand marketing is much harder. Unlike SEO rankings or PPC conversions, brand-building results are not always immediately visible, which is why these efforts often get under-credited – or worse, neglected altogether – in favor of easier-to-measure tactics. This is a mistake.

Why Brand Marketing Matters More Than Ever

The irony is that large-scale studies repeatedly show brand-related factors at the forefront of digital visibility.

Semrush’s 2025 ranking factor study found that authority, traffic, and backlink signals – closely tied to brand strength – are still among the most important correlating factors for high search rankings.

Similarly, as AI Overviews and large language model (LLM)-powered search expand, brand strength is proving to be the key to visibility. In its 2025 study, Ahrefs found that branded mentions, branded anchors, and branded search volume are the top three factors correlated with AI Overview presence.

All of these point to one conclusion: Brand marketing is increasingly the engine that drives both human trust and algorithmic preference.

The challenge, however, is demonstrating its impact in a way that stakeholders can understand and value. That’s why it’s critical to learn how to measure your brand marketing efforts using both qualitative and quantitative metrics, tied back to clear key performance indicators (KPIs).

The Situation For Digital Marketing Leaders

Consider the role of an in-house SEO director. Your KPIs might look like this:

• Grow organic traffic by 25% year-over-year.
• Increase lead generation downloads by 40%.
• Drive 20% more sales from organic.

But with Google’s AI Overviews cutting click-through rates by more than 34% and users increasingly turning to LLMs for top-of-funnel research, traditional SEO tactics alone won’t get you there.

Instead, your future success depends on brand strength. Stronger brand signals lead to better visibility in AI-driven search results, higher trust with customers, and greater resilience in an evolving digital landscape. That means, even as an SEO professional, your path forward relies on executing and measuring brand marketing strategy effectively – and proving its business impact.

The good news is that as an SEO professional, you’ve likely already got quite a bit of the data you need. It may just require you to repackage some of your efforts. It may also require you to collaborate more with your fellow digital marketers, particularly those in PR, social media, and PPC, to show brand visibility growth more holistically.

Tying Metrics To The Sales Funnel

When it comes to your brand marketing, there are really four categories of efforts:

• Awareness.
• Consideration.
• Conversion.
• Loyalty & Advocacy.

Ultimately, you are looking to increase your brand strength in every area of the funnel.

You want more people to hear of your brand, which then drives them to search for it to learn more about it.

More brand familiarity and trust should then ultimately lead to more conversions.

And the more customers and followers of the brand you have, the more you would expect to see an increase in loyalty and advocacy.

All of your brand marketing tracking should tie back to one of those four categories. Therefore, the next sections of this article are broken down by stage of the funnel.

Brand Awareness Metrics

Brand awareness metrics help you measure whether your brand is becoming more recognizable in the right contexts. At the top level, awareness is measured by reach and visibility signals: metrics like impressions, social mentions, and share of voice across channels.

On the digital side, you can monitor branded search impressions and clicks in Google Search Console, track direct traffic growth in Google Analytics 4, and use SEO tools like Semrush or Ahrefs to compare your brand’s share of voice against competitors.

These metrics reveal whether people are actively seeking you out and whether brand exposure is translating into traffic.

Equally important are perception-based metrics, which capture how audiences actually recall and recognize your brand.

Brand lift studies and recall surveys ask consumers whether your brand comes to mind within your category – both aided (i.e., Have you heard of [brand]?) and unaided (i.e., What brands come to mind for [category]?). These are especially powerful after large brand campaigns, such as a national TV spot or a major podcast sponsorship, to see if awareness efforts are resonating with the right audience.

Key Awareness Metrics

It is important that you’re measuring both the quantitative signals of awareness (search, traffic, mentions) and the qualitative signals (surveys, brand lift). Together, these provide a complete picture of how visible and memorable your brand really is.

Consideration Metrics

While awareness tells you whether people recognize your brand, consideration metrics show whether they are actively evaluating your brand as a viable option. This stage of the funnel is all about engagement and intent. We’re looking at signals that potential customers are digging deeper, comparing you against competitors, and gathering the information they need to make a decision.

On your website, key metrics include pages per session, time spent on product or service pages, and return visits to your site, which often indicate research and deeper evaluation. Growth in traffic to product-related pages and increases in branded product queries (i.e., “Brand X running shoes”) are also strong signals that awareness is moving into intent.

Beyond on-site behavior, content downloads such as case studies, whitepapers, or product comparison guides show that audiences are engaging with assets that help them evaluate their choices.

Similarly, a rise in third-party product reviews or mentions on industry forums and social media reflects growing consideration and social proof that others are weighing your brand seriously in the buying process.

Key Consideration Metrics

By tracking both behavioral signals on your owned channels (site engagement, return visits, content downloads) and external validation (third-party mentions), you build a clear picture of whether your brand is moving beyond recognition and into active consideration.

Conversion Metrics

Conversion metrics show how effectively brand strength translates into tangible business outcomes. At this stage, the focus shifts from evaluation to action.

We’re looking at whether people are requesting demos, signing up for free trials, or making purchases. Strong branding makes these conversions more likely by building the trust and credibility necessary to reduce friction at the decision point.

On your website, look for form fills, demo requests, trial sign-ups, and completed transactions as clear indicators of conversion. Tracking conversion rates from branded search campaigns in Google Ads or measuring pipeline influenced by brand-related traffic in your customer relationship management (CRM) also provides valuable insight.

Additionally, monitoring add-to-cart and checkout completions in GA4 can highlight how often brand equity is driving purchase intent to completion.

Key Conversion Metrics

Loyalty And Advocacy Metrics

Loyalty and advocacy metrics reveal whether brand strength translates into long-term customer relationships. At this stage, the goal is not just to retain customers but to turn them into advocates who actively promote your brand.

Strong loyalty reduces churn, increases lifetime value, and builds a customer base that supports sustainable growth.

Key metrics here include customer retention rates, repeat purchase behavior, and customer lifetime value (CLV), which quantify how effectively you’re keeping customers over time.

Net Promoter Score (NPS) and customer satisfaction surveys capture how likely customers are to recommend your brand. Monitoring referrals, user-generated content, and social sharing also provides qualitative proof of advocacy.

Review platforms and communities can be another strong signal. Growth in positive product reviews or customers organically defending your brand in forums shows that loyalty has translated into advocacy.

Key Loyalty & Advocacy Metrics

Turning Metrics Into A Compelling Data Story For Stakeholders

The real value of measuring brand marketing comes not just from tracking the right metrics, but from connecting them into a story that stakeholders can understand.

By aligning awareness, consideration, conversion, and loyalty metrics to the sales funnel, you create a framework that shows how brand-building efforts impact the entire customer journey.

A brand dashboard is one of the most effective tools for communicating this story. Tools like Looker Studio or Power BI will allow you to consolidate signals from multiple sources to present a holistic view of brand health.

Rather than overwhelming leadership with granular reports from different platforms, you’re providing them with a clear line of sight from brand activity to revenue impact. It can look something like: Google Search Console for branded queries, GA4 for site engagement, CRM data for conversions, and social listening tools for sentiment and share of voice.

When sharing results, keep in mind that executives often care less about the technical details and more about the outcomes. Frame your reporting around KPIs tied to growth:

• Did brand awareness lift lead to more traffic and higher-quality leads?
• Did stronger consideration metrics translate into more demo requests or trial sign-ups?
• Did higher loyalty scores reduce churn or drive referrals?

By mapping brand marketing metrics to outcomes stakeholders already value – pipeline growth, revenue impact, and customer retention – you position branding not as a “soft” investment, but as a measurable driver of business performance.

More Resources:

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Featured Image: Master1305/Shutterstock

https://www.searchenginejournal.com/how-to-measure-brand-marketing-efforts-and-prove-their-roi/554496/

Google is hiring a new anti-scraping czar, whose job will be to analyze search traffic to identify the patterns of search scrapers, assess the impact, and work with engineering teams to develop new anti-scraping models for improving anti-scraping defenses.

Search Results Scraping

SEOs rely on SERP tracking companies to provide search results data for understanding search ranking trends, enabling competitive intelligence, and other keyword-related research and analysis.

Many of these companies conduct massive amounts of automated crawling of Google’s search results to take a snapshot of ranking positions and data related to search features triggered by keyword phrases. This scraping is suspected of causing significant changes to what’s reported in Google Search Console.

In the early days of SEO, there used to be a free keyword data source via Yahoo’s Overture, their PPC service. Many SEOs used to search on Yahoo so often that their searches would unintentionally inflate the keyword volume. Smart SEOs would know better to not optimize for those keyword phrases.

I have suspected that some SEOs may also have intentionally scraped Yahoo’s search results using fake keyword phrases in order to generate keyword volumes for those queries, in order to mislead competitors into optimizing for phantom search queries.

&num=100 Results Parameter

There is a growing suspicion backed by Google Search Console data that search result scraping may have inflated the official keyword impression data and that it may be the reason why Search Console Data appears to show that AI Search results aren’t sending traffic while Google’s internal data shows the opposite.

This suspicion is based on falling keyword impressions that correlate with Google’s recent action to block generating 100 search results with one search query, a technique used by various keyword tracking tools.

Google Anti-Scraping Engineering Analyst

Jamie Indigo posted that Google is looking to hire an Engineering Analyst focused on combatting search scraping.

The responsibilities for the job are:

• “Investigate and analyze patterns of abuse on Google Search, utilizing data-motivated insights to develop countermeasures and enhance platform security.
  Analyze datasets to identify trends, patterns, and anomalies that may indicate abuse within Google Search.
• Develop and track metrics to measure scraper impact and the effectiveness of anti-scraping defenses. Collaborate with engineering teams to design, test, and launch new anti-scraper rules, models, and system enhancements.
• Investigate proof-of-concept attacks and research reports that identify blind spots and guide the engineering team’s development priorities. Evaluate the effectiveness of existing and proposed detection mechanisms, understanding the impact on scrapers and real users.
• Contribute to the development of signals and features for machine learning models to detect abusive behavior. Develop and maintain threat intelligence on scraper actors, motivations, tactics and the scraper ecosystem.”

What Does It Mean?

There hasn’t been an official statement from Google but it’s fairly apparent that Google may be putting a stop to search results scrapers. This should result in more accurate Search Console data, so that’s a plus.

Featured Image by Shutterstock/DIMAS WINDU

https://www.searchenginejournal.com/google-is-hiring-an-anti-scraping-engineering-analyst/556262/

AIOs, LLMs & the New Rules of SEO

AI Overviews are changing everything.

Your impressions might be up, but the traffic isn’t following. Competitors are showing up in AI search while your brand remains invisible.

How do you measure success when ChatGPT or Gemini doesn’t show traditional rankings? How do you define “winning” in a world where every query can produce a different answer?

Learn the SEO & GEO strategies enterprise brands are using to secure visibility in AI Overviews and large language models.

AI Mode is growing fast. Millions of users are turning to AI engines for answers, and brand visibility is now the single most important metric. 

In this webinar, Tom Capper, Sr. Search Scientist at STAT Search Analytics, will guide you through how enterprise SEOs can adapt, measure, and thrive in this new environment.

You’ll Learn:

• How verticals and user intents are shifting under AI Overviews and where SERP visibility and traffic opportunities still exist.
• Practical ways to leverage traditional SEO while optimizing for generative engines.
• How to bridge the gap between SEO and GEO with actionable strategies for enterprise brands.
• How to measure success in AI search when impressions and rankings no longer tell the full story.

Register now to gain the latest, data-driven insights on maintaining visibility across AI Overviews, ChatGPT, Gemini, and more.

🛑 Can’t attend live? Sign up anyway, and we’ll send you the recording.

https://www.searchenginejournal.com/win-brand-visibility-in-ai-search/555017/

YouTube has announced a suite of monetization updates designed to help creators diversify their revenue streams.

Key updates include dynamic sponsorship for long-form videos, brand linking in Shorts, AI-powered product tagging for Shopping, and side-by-side live ads.

The updates come as YouTube revealed it paid out over $100 billion to creators, artists, and media companies globally over the past four years.

What’s New

Dynamic Sponsorship

YouTube is introducing a new way for creators to manage sponsorships in their videos.

Creators will soon be able to dynamically add brand segments to their content, rather than having to permanently embed them.

YouTube’s announcement reads:

“This new format enables you to remove the sponsorship when the deal is complete, resell the slot to another brand or eventually sell the same slot to multiple brands in different markets — transforming your videos into living assets to grow your business. Creators can choose the perfect moment to insert the branded segment, and will see detailed performance insights directly in YouTube Studio, which can also be shared with the brand.”

Testing starts with a small group early next year.

Shorts Links

YouTube is adding the ability to link directly to a sponsor’s website from Shorts.

YouTube states:

“For Shorts creators, they’ll soon be able to add a link to a brand’s site specifically for brand deals. This will make it easier for viewers to discover and buy products, while giving creators a powerful way to drive results for brand partners.”

Shopping

YouTube Shopping is getting a series of updates to improve the shopping experience for both creators and viewers.

The platform is adding automatic timestamps that show when products are available in videos, making it simpler for viewers to find and buy featured items.

YouTube is also automating product selection in Merchant Center, which reduces the manual work creators have to do to tag and link products to their content.

YouTube’s announcement reads:

“We know tagging products can be time-consuming, so to make the experience better for creators, we’re leaning on an AI-powered system to identify the optimal moment a product is mentioned and automatically display the product tag at that time, capturing viewer interest when it’s highest. We’ll also begin testing the ability to automatically identify and tag all eligible products mentioned in your video later this year.”

These updates are planned for later this year.

Live Streaming

Live streaming, which draws more than 30 percent of YouTube’s daily logged-in viewers, according to company data, is getting new features to help creators earn more money.

YouTube is rolling out live ads that show up next to streams, rather than interrupting them.

YouTube’s announcement reads:

“The new side-by-side ads are a less intrusive format for viewers, while helping creators get paid without pulling their audience away.

YouTube is also introducing a feature that lets live streams transition directly to member communities and channel memberships.

The company adds:

… We’re rolling out a new feature that allows channel membership creators to easily transition from public to members-only livestreams, without disruption. This makes it easy to create premium, members-only content, while strengthening your community and attracting new paid members.

Why This Matters

These updates are a move toward giving creators more control over how they make money from their content, while also giving brands more ways to partner with them.

By opening up new revenue streams beyond traditional pre-roll and mid-roll ads, YouTube is equipping creators with tools that could make the platform more attractive for full-time publishing.

https://www.searchenginejournal.com/youtube-monetization-updates-across-long-form-shorts-live/556245/

YouTube announces A/B title testing availability, along with AI-powered conversational analytics for YouTube Studio.

• Title tests are coming to YouTube Studio
• ‘Ask Studio’ lets you query analytics in plain language to surface actionable insights.
• YouTube also teased Veo 3 Fast for Shorts.

https://www.searchenginejournal.com/youtube-adds-title-a-b-testing-and-ask-studio-analytics/556232/

Boost your skills with Growth Memo’s weekly expert insights. Subscribe for free!

Here’s what I’m covering this week: How to build user personas for SEO from data you already have on hand.

You can’t treat personas as a “brand exercise” anymore.

In the AI-search era, prompts don’t just tell you what users want; they reveal who’s asking and under what constraints.

If your pages don’t match the person behind the query and connect with them quickly – their role, risks, and concerns they have, and the proof they require to resolve the intent – you’re likely not going to win the click or the conversion.

It’s time to not only pay attention and listen to your customers, but also optimize for their behavioral patterns.

Search used to be simple: queries = intent. You matched a keyword to a page and called it a day.

Personas were a nice-to-have, often useful for ads and creative or UX decisions, but mostly considered irrelevant by most to organic visibility or growth.

Not anymore.

Longer prompts and personalized results don’t just express what someone wants; they also expose who they are and the constraints they’re operating under.

AIOs and AI chats act as a preview layer and borrow trust from known brands. However, blue links still close when your content speaks to the person behind the prompt.

If that sounds like hard work, it is. And it’s why most teams stall implementing search personas across their strategy.

• Personas can feel expensive, generic, academic, or agency-driven.
• The old persona PDFs your brand invested in 3-5 years ago are dated – or missing entirely.
• The resources, time, and knowledge it takes to build user personas are still significant blockers to getting the work done.

In this memo, I’ll show you how to build lean, practical, LLM-ready user personas for SEO – using the data you already have, shaped by real behavioral insights – so your pages are chosen when it counts.

While there are a few ways you could do this, and several really excellent articles out there on SEO personas this past year, this is the approach I take with my clients.

Most legacy persona decks were built for branding, not for search operators.

They don’t tell your writers, SEOs, or PMs what to do next, so they get ignored by your team after they’re created.

Mistake #1: Demographics ≠ Decisions

Classic user personas for SEO and marketing overfocused on demographics, which can give some surface-level insights into stereotypical behavior for certain groups.

But demographics don’t necessarily help your brand stand out against your competitors. And demographics don’t offer you the full picture.

Mistake #2: A Static PDF Or Shared Doc Ages Fast

If your personas were created once and never reanalyzed or updated again, it’s likely they got lost in G: Drive or Dropbox purgatory.

If there’s no owner working to ensure they’re implemented across production, there’s no feedback loop to understand if they’re working or if something needs to change.

Mistake #3: Pretty Delivered Decks, No Actionable Insights

Those well-designed persona deliverables look great, but when they aren’t tied to briefs, citations, trust signals, your content calendar, etc., they end up siloed from production. If a persona can’t shape a prompt or a page, it won’t shape any of your outcomes.

In addition to the fact classic personas weren’t built to implement across your search strategy, AI has shifted us from optimizing for intent to optimizing for identity and trust. In last week’s memo I shared the following:

The most significant, stand-out finding from that study: People use AI Overviews to get oriented and save time. Then, for any search that involves a transaction or high-stakes decision-making, searchers validate outside Google, usually with trusted brands or authority domains.

Old world of search optimization: Queries signaled intent. You ranked a page that matched the keyword and intent behind it, and your brand would catch the click. Personas were optional.

New world of search optimization: Prompts expose people, and AI changes how we search. Marketers aren’t just optimizing for search intent or demographics; we’re also optimizing for behavior.

Long AI prompts don’t just say what the user intends – they often reveal who is asking and what constraints or background of knowledge they bring.

For example, if a user prompts ChatGPT something like “I’m a healthcare compliance officer at a mid-sized hospital. Can you draft a checklist for evaluating new SaaS vendors, making sure it covers HIPAA regulations and costs under $50K a year,” then ChatGPT would have background information about the user’s general compliance needs, budget ceilings, risk tolerance, and preferred content formats.

AI systems then personalize summaries and citations around that context.

If your content doesn’t meet the persona’s trust requirements or output preference, it won’t be surfaced.

What that means in practice:

• Prompts → identity signals. “As a solo marketer on a $2,000 budget…” or “for EU users under GDPR…” = role, constraints, and risk baked into the query.
• Trust beats length. Classic search results are clicked on, but only when pages show the trust scaffolding a given persona needs for a specific query.
• Format matters. Some personas want TL;DR and tables; others need demos, community validation (YouTube/Reddit), or primary sources.

So, here’s what to do about it.

You don’t need a five or six-figure agency study (although those are nice to have).

You need:

• A collection of your already-existing data.
• A repeatable process, not a static file.
• A way to tie personas directly into briefs and prompts.

Turning your own existing data into usable user personas for SEO will equip you to tie personas directly to content briefs and SEO workflows.

Before you start collecting this data, set up an organized way to store it: Google Sheets, Notion, Airtable – whatever your team prefers. Store your custom persona prompt cards there, too, and you can copy and paste from there into ChatGPT & Co. as needed.

The work below isn’t for the faint of heart, but it will change how you prompt LLMs in your AI-powered workflows and your SEO-focused webpages for the better.

1. Collect and cluster data.
2. Draft persona prompt cards.
3. Calibrate in ChatGPT & Co.
4. Validate with real-world signals.

You’re going to mine several data sources that you already have, both qualitative and quantitative.

Keep in mind, being sloppy during this step means you will not have a good base for an “LLM ready” persona prompt card, which I’ll discuss in Step 2.

Attributes to capture for an “LLM-ready persona”:

• Jobs-to-be-done (top 3).
• Role and seniority.
• Buying triggers + blockers (think budget, IT/legal constraints, risk).
• 10-20 example questions at TOFU, MOFU, BOFU stages.
• Trust cues (creators, domains, formats).
• Output preferences (depth, format, tone).

Where AIO validation style data comes in:

Last week, we discussed four distinct AIO intent validations verified within the AIO usability study: Efficiency-first/Trust-driven/Comparative/Skeptical rejection.

If you want to incorporate this in your persona research – and I’d advise that you should – you’re going to look for:

• Hesitation triggers across interactions with your brand: What makes them pause or refine their question (whether on a sales call or a heat map recording).
• Click-out anchors: Which authority brands they use to validate (PayPal, NIH, Mayo Clinic, Stripe, KBB, etc.); use Sparktoro to find this information.
• Evidence threshold: What proof ends hesitation for your user or different personas? (Citations, official terminology, dated reviews, side-by-side tables, videos).
• Device/age nuance: Younger and mobile users → faster AIO acceptance; older cohorts → blue links and authority domains win clicks.

Below, I’ll walk you through where to find this information.

Qualitative Inputs

1. Your GSC queries hold a wealth of info. Split by TOFU/MOFU/BOFU, branded vs non-branded, and country. Then, use a regex to map question-style queries and see who’s really searching at each stage.

Below is the regex I like to use, which I discussed in Is AI cutting into your SEO conversions?. It also works for this task:

(?i)^(who|what|why|how|when|where|which|can|does|is|are|should|guide|tutorial|course|learn|examples?|definition|meaning|checklist|framework|template|tips?|ideas?|best|top|list(?:s)?|comparison|vs|difference|benefits|advantages|alternatives)\b.*

2. On-Site Search Logs. These are the records of what visitors type into your website’s own search bar (not Google).

Extract exact phrasing of problems and “missing content” signals (like zero results, refined searches, or high exits/no clicks).

Plus, the wording visitors use reveals jobs-to-be-done, constraints, and vocabulary you should mirror on the page. Flag repeat questions as latent questions to resolve.

3. Support Tickets, CRM Notes, Win/Loss Analysis. Convert objections, blockers, and “how do I…” threads into searchable intents and hesitation themes.

Mine the following data from your records:

• Support: Ticket titles, first message, last agent note, resolution summary.
• CRM: Opportunity notes, metrics, decision criteria, lost-reason text.
• Win/Loss: Objection snapshots, competitor cited, decision drivers, de-risking asks.
• Context (if available): buyer role, segment (SMB/MM/ENT), region, product line, funnel stage.

Once gathered, compile and analyze to distill patterns.

Qualitative Inputs

1. Your sales calls and customer success notes are a wealth of information.

Use AI to analyze transcripts and/or notes to highlight jobs-to-be-done, triggers, blockers, and decision criteria in your customer’s own words.

2. Reddit and social media discussions.

This is where your buyers actually compare options and validate claims; capture the authority anchors (brands/domains) they trust.

3. Community/Slack spaces, email newsletter replies, article comments, short post-purchase or signup surveys.

Mine recurring “stuck points” and vocabulary you should mirror. Bucket recurring themes together and correlate across other data.

Pro tip: Use your topic map as the semantic backbone for all qualitative synthesis – discussed in depth in how to operationalize topic-first SEO. You’d start by locking the parent topics, then layer your personas as lenses: For each parent topic, fan out subtopics by persona, funnel stage, and the “people × problems” you pull from sales calls, CS notes, Reddit/LinkedIn, and community threads. Flag zero-volume/fringe questions on your map as priorities; they deepen authority and often resolve the hesitation themes your notes reveal.

After clustering pain points and recurring queries, you can take it one step further to tag each cluster with an AIO pattern by looking for:

• Short dwell + 0–1 scroll + no refinements → Efficiency-first validations.
• Longer dwell + multiple scrolls + hesitation language + authority click-outs → Trust-driven validations.
• Four to five scrolls + multiple tabs (YouTube/Reddit/vendor) → Comparative validations.
• Minimal AIO engagement + direct authority clicks (gov/medical/finance) → Skeptical rejection.

Not every team can run a full-blown usability study of the search results for targeted queries and topics, but you can infer many of these behavioral patterns through heatmaps of your own pages that have strong organic visibility.

2. Draft Persona Prompt Cards

Next up, you’ll take this data to inform creating a persona card.

A persona card is a one-page, ready-to-go snapshot of a target user segment that your marketing/SEO team can act on.

Unlike empty or demographic-heavy personas, a persona card ties jobs-to-be-done, constraints, questions, and trust cues directly to how you brief pages, structure proofs, and prompt LLMs.

A persona card ensures your pages and prompts match identity + trust requirements.

What you’re going to do in this step is convert each data-based persona cluster into a one-pager designed to be embedded directly into LLM prompts.

Include input patterns you expect from that persona – and the output format they’d likely want.

Optimizing Prompt Selection for Target Audience Engagement

Reusable Template: Persona Prompt Card

Drop this at the top of a ChatGPT conversation or save as a snippet.

This is an example template below based on the Growth Memo audience specifically, so you’ll need to not only modify it for your needs, but also tweak it per persona.

You are Kevin Indig advising a [ROLE, SENIORITY] at a [COMPANY TYPE, SIZE, LOCATION]. Objective: [Top 1–2 goals tied to KPIs and timeline] Context: [Market, constraints, budget guardrails, compliance/IT notes] Persona question style: [Example inputs they’d type; tone & jargon tolerance] Answer format: - Start with a 3-bullet TL;DR. - Then give a numbered playbook with 5-7 steps. - Include 2 proof points (benchmarks/case studies) and 1 calculator/template. - Flag risks and trade-offs explicitly. - Keep to [brevity/depth]; [bullets/narrative]; include [table/chart] if useful. What to avoid: [Banned claims, fluff, vendor speak] Citations: Prefer [domains/creators] and original research when possible.

Example Attribute Sets Using The Growth Memo Audience

Use this card as a starting point, then fill it with your data.

Below is an example of the prompt card with attributes filled for one of the ideal customer profiles (ICP) for the Growth Memo audience.

You are Kevin Indig advising an SEO Lead (Senior) at a Mid-Market B2B SaaS (US/EU). Objective: Protect and grow organic pipeline in the AI-search era; drive qualified trials/demos in Q4; build durable topic authority. Context: Competitive category; CMS constraints + limited Eng bandwidth; GDPR/CCPA; security/legal review for pages; budget ≤ $8,000/mo for content + tools; stakeholders: VP Marketing, Content Lead, PMM, RevOps. Persona question style: “How do I measure topic performance vs keywords?”, “How do I structure entity-based internal linking?”, “What KPIs prove AIO exposure matters?”, “Regex for TOFU/MOFU/BOFU?”, “How to brief comparison pages that AIO cites?” Tone: precise, low-fluff, technical. AIO validation profile: - Dominant pattern(s): Trust-driven (primary), Comparative (frameworks/tools); Skeptical for YMYL claims. - Hesitation triggers: Black-box vendor claims; non-replicable methods; missing citations; unclear risk/effort. - Click-out anchors: Google Search Central & docs, schema.org, reputable research (Semrush/Ahrefs/SISTRIX/seoClarity), Pew/Ofcom, credible case studies, engineering/product docs. - SERP feature bias: Skims AIO/snippets to frame, validates via organic authority + primary sources; uses YouTube for demos; largely ignores Ads. - Evidence threshold: Methodology notes, datasets/replication steps, benchmarks, decision tables, risk trade-offs. Answer format: - Start with a three-bullet TL;DR. - Then give a numbered playbook with 5-7 steps. - Include 2 proof points (benchmarks/case studies) and 1 calculator/template. - Flag risks and trade-offs explicitly. - Keep to brevity + bullets; include a table/chart if useful. Proof kit to include on-page: Methodology & data provenance; decision table (framework/tool choice); “best for / not for”; internal-linking map or schema snippet; last-reviewed date; citations to Google docs/primary research; short demo or worksheet (e.g., Topic Coverage Score or KPI tree). What to avoid: Vendor-speak; outdated screenshots; cherry-picked wins; unverifiable stats; hand-wavy “AI magic.” Citations: Prefer Google Search Central/docs, schema.org, original studies/datasets; reputable tool research (Semrush, Ahrefs, SISTRIX, seoClarity); peer case studies with numbers. Success signals to watch: Topic-level lift (impressions/CTR/coverage), assisted conversions from topic clusters, AIO/snippet presence for key topics, authority referrals, demo starts from comparison hubs, reduced content decay, improved crawl/indexation on priority clusters.

Your goal here is to prove the Persona Prompt Cards actually produce useful answers – and to learn what evidence each persona needs.

Create one Custom Instruction profile per persona, or store each Persona Prompt Card as a prompt snippet you can prepend.

Run 10-15 real queries per persona. Score answers on clarity, scannability, credibility, and differentiation to your standard.

How to run the prompt card calibration:

• Set up: Save one Prompt Card per persona.
• Eval set: 10-15 real queries/persona across TOFU/MOFU/BOFU stages, including two or three YMYL or compliance-based queries, three to four comparisons, and three or four quick how-tos.
• Ask for structure: Require TL;DR → numbered playbook → table → risks → citations (per the card).
• Modify it: Add constraints and location variants; ask the same query two ways to test consistency.

Once you run sample queries to check for clarity and credibility, modify or upgrade your Persona Card as needed: Add missing trust anchors or evidence the model needed.

Save winning outputs as ways to guide your briefs that you can paste into drafts.

Log recurring misses (hallucinated stats, undated claims) as acceptance checks for production.

Then, do this for other LLMs that your audience uses. For instance, if your audience leans heavily toward using Perplexity.ai, calibrate your prompt there also. Make sure to also run the prompt card outputs in Google’s AI Mode, too.

Watch branded search trends, assisted conversions, and non-Google referrals to see if influence shows up where expected when you publish persona-tuned assets.

And make sure to measure lift by topic, not just per page: Segment performance by topic cluster (GSC regex or GA4 topic dimension). Operationalizing your topic-first seo strategy discusses how to do this.

Keep the following in mind when reviewing real-world signals:

• Review at 30/60/90 days post-ship, and by topic cluster.
• If Trust-driven pages show high scroll/low conversions → add/upgrade citations and expert reviews and quotes.
• If Comparative pages get CTR but low product/sales demos signups → add short demo video, “best for / not for” sections, and clearer CTAs.
• If Efficiency-first pages miss lifts in AIO/snippets → tighten TL;DR, simplify tables, add schema.
• If Skeptical-rejection-geared pages yield authority traffic but no lift → consider pursuing authority partnerships.
• Most importantly: redo the exercise every 60-90 days and match your new against old personas to iterate toward the ideal.

Building user personas for SEO is worth it, and it can be doable and fast by using in-house data and LLM support.

I challenge you to start with one lean persona this week to test this approach. Refine and expand your approach based on the results you see.

But if you plan to take this persona-building project on, avoid these common missteps:

• Creating tidy PDFs with zero long-term benefits: Personas that don’t specify core search intents, pain points, and AIO intent patterns won’t move behavior.
• Winning every SERP feature: This is a waste of time. Optimize your content for the right surface for the dominant behavioral patterns of your target users.
• Ignoring hesitation: Hesitation is your biggest signal. If you don’t resolve it on-page, the click dies elsewhere.
• Demographics over jobs-to-be-done: Focusing on characteristics of identity without incorporating behavioral patterns is the old way.

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Featured Image: Paulo Bobita/Search Engine Journal

https://www.searchenginejournal.com/personas-are-critical-for-ai-search/556162/

This week’s Ask an SEO question comes from an anonymous user:

“Should we still publish high volumes of content, or is it better to invest in fewer, higher-authority evergreen pieces?”

Great question! The answer is always higher-authority content, but not always evergreen if your goal is growth and sustainability. If the goal is quick traffic and a churn-and-burn model, high volume makes sense. More content does not mean more SEO. Sustainable SEO traffic via content is providing a proper user experience, which includes making sure the other topics on the site are helpful to a user.

Why High Volumes Of Content Don’t Work Long Term

The idea of creating high volumes of content to get traffic is a strategy where you focus a page on specific keywords and phrases and optimize the page for these phrases. When Google launched BERT and MUM, this strategy (which was already outdated) got its final nail in the coffin. These updates to Google’s systems looked at the associations between the words, hierarchy of the page, and the website to figure out the experience of the page vs. the specific words on the page.

By looking at what the words mean in relation to the headers, the sentences above and below, and the code of the page, like schema, SEO moved away from keywords to what the user will learn from the experience on the page. At the same time, proactive SEOs focused more heavily on vectors and entities; neither of these are new topics.

Back in the mid-2000s, article spinners helped to generate hundreds of keyword-focused pages quickly and easily. With them, you create a spintax (similar to prompts for large language models or LLMs like ChatGPT and Perplexity) with macros for words to be replaced, and the software would create “original” pieces of content. These could then be launched en masse, similar to “programmatic SEO,” which is not new and never a smart idea.

Google and other search engines would surface these and rank the sites until they got caught. Panda did a great job finding article spinner pages and starting to devalue and penalize sites using this technique of mass content creation.

Shortly after, website owners began using PHP with merchant data feeds to create shopping pages for specific products and product groups. This is similar to how media companies produce shopping listicles and product comparisons en masse. The content is unique and original (for that site), but is also being produced en masse, which usually means little to no value. This includes human-written content that is then used for comparisons, even when a user selects to compare the two. In this situation, you’ll want to use canonical links and meta robots properly, but that’s for a different post.

Panda and the core algorithms already had a way to detect “thin pages” from content spinning, so although these product pages worked, especially when combined with spun content or machine-created content describing the products, these sites began getting penalized and devalued.

We’re now seeing AI content being created that is technically unique and “original” via ChatGPT, Perplexity, etc, and it is working for fast traffic gains. But these same sites are getting caught and losing that traffic when they do. It is the same exact pattern as article spinning and PHP + data feed shopping lists and pages.

I could see an argument being made for “fan-out” queries and why having pages focused on specific keywords makes sense. Fan-out queries are AI results that automate “People Also Ask,” “things to know,” and other continuation-rich results in a single output, vs. having separate search features.

If an SEO has experience with actual SEO best practices and knows about UX, they’ll know that the fan-out query is using the context and solutions provided on the pages, not multiple pages focused on similar keywords.

This would be the equivalent of building a unique page for each People Also Ask query or adding them as FAQs on the page. This is not a good UX, and Google knows you’re spamming/overoptimizing. It may work, but when you get caught, you’re in a worse position than when you started.

Each page should have a unique solution, not a unique keyword. When the content is focused on the solution, that solution becomes the keyword phrases, and the same page can show up for multiple different phrases, including different variations in the fan-out result.

If the goal is to get traffic and make money quickly, then abandon or sell the domain, more content is a good strategy. But you won’t have a reliable or long-term income and will always be chasing the next thing.

Evergreen And Non-Evergreen High-Quality Content

Focusing on quality content that provides value to an end user is better for long-term success than high volumes of content. The person will learn from the article, and the content tends to be trustworthy. This type of content is what gets backlinks naturally from high-authority and topically relevant websites.

More importantly, each page on the website will have a clear intent. With sites that focus on volume vs. quality, a lot of the posts and pages will look similar as they’re focused on similar keywords, and users won’t know which article provides the actual solution. This is a bad UX. Or the topics jump around, where one page is about the best perfumes and another is about harnesses for dogs. The trust in the quality of the content is diminished because the site can’t be an expert in everything. And it is clear the content is made up by machines, i.e., fake.

Not all of the content needs to be evergreen, either. Companies and consumer trends happen, and people want timely information mixed in with evergreen topics. If it is product releases, an archive and list of all releases can be helpful.

Fashion sites can easily do the trends from that season. The content is outdated when the next season starts, but the coverage of the trends is something people will look back on and source or use as a reference. This includes fashion students sourcing content for classes, designers looking for inspiration from the past, and mass media covering when things trended and need a reference point.

When evergreen content begins to slide, you can always refresh it. Look back and see what has changed or advanced since the last update, and see how you can improve on it.

• Look for customer service questions that are not answered.
• Add updated software features or new colors.
• See if there are examples that could be made better or clearer.
• If new regulations are passed locally, state level, or federally, add these in so the content is accurate.
• Delete content that is outdated, or label it as no longer relevant with the reasons why.
• Look for sections that may have seemed relevant to the topic, but actually weren’t, and remove them so the content becomes stronger.

There is no shortage of ways to refresh evergreen content and improve on it. These are the pillar pages that can bring consistent traffic over the long run and keep business strong, while the non-evergreen pages do their part, creating ebbs and flows of traffic. With some projects, we don’t produce new content for a month or two at a time because the pillar pages need to be refreshed, and the clients still do well with traffic.

Creating mass amounts of content is a good strategy for people who want to make money fast and do not plan on keeping the domain for a long time. It is good for churn-and-burn sites, domains you rent (if the owner is ok with it), and testing projects. When your goal is to build a sustainable business, high-authority content that provides value is the way to go.

You don’t need to worry about the amount of content with this strategy; you focus on the user experience. When you do this, most channels can grow, including email/SMS, social media, PR, branding, and SEO.

More Resources:

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Featured Image: Paulo Bobita/Search Engine Journal

https://www.searchenginejournal.com/ask-an-seo-high-volumes-or-high-authority-evergreen-content/553873/

Google adds an Achievements report in Search Console so you can track click-based milestones, see in-progress goals, and jump to Insights and tips.

• Achievements now live inside Search Console.
• Report is divided into In progress and Achieved sections.
• Goals are based on clicks from Google Search.

https://www.searchenginejournal.com/google-search-console-adds-achievements-report-to-track-milestones/556146/

New research from OpenAI and Harvard finds that “Seeking Information” messages now account for 24% of ChatGPT conversations, up from 14% a year earlier.

This is an NBER working paper (not peer-reviewed), based on consumer ChatGPT plans only, and the study used privacy-preserving methods where no human read user messages.

The working paper analyzes a representative sample of about 1.1 million conversations from May 2024 through June 2025.

By July, ChatGPT reached more than 700 million weekly active users, sending roughly 2.5 billion messages per day, or about 18 billion per week.

What People Use ChatGPT For

The three dominant topics are Practical Guidance, Seeking Information, and Writing, which together account for about 77% of usage.

Practical Guidance remains around 29%. Writing declined from 36% to 24% over the past year. Seeking Information grew from 14% to 24%.

The authors write that Seeking Information “appears to be a very close substitute for web search.”

Asking vs. Doing

The paper classifies intent as Asking, Doing, or Expressing.

About 49% of messages are Asking, 40% are Doing, and 11% are Expressing.

Asking messages “are consistently rated as having higher quality” than the other categories, based on an automated classifier and user feedback.

Work vs. Personal Use

Non-work usage rose from 53% in June 2024 to 73% in June 2025.

At work, Writing is the top use case, representing about 40% of work-related messages. Education is a major use: 10% of all messages involve tutoring or teaching.

Coding And Companionship

Only 4.2% of messages are about computer programming, and 1.9% concern relationships or personal reflection.

Who’s Using It

The study documents rapid global adoption.

Early gender gaps have narrowed, with the share of users having typically feminine names rising from 37% in January 2024 to 52% in July 2025.

Growth in the lowest-income countries has been more than four times that of the highest-income countries.

Why This Matters

If a quarter of conversations are information-seeking, some queries that would have gone to search may go toward conversational tools.

Consider responding to this shift with content that answers questions, while adding expertise that a chatbot can’t replicate. Writing and editing account for a large share of work-related use, which aligns with how teams are already folding AI into content workflows.

Looking Ahead

ChatGPT is becoming a major destination for finding information online.

In addition to the shift toward finding info, it’s worth highlighting that 70% of ChatGPT use is personal, not professional. This means consumer habits are changing broadly.

As this technology grows, it’ll be vital to track how your audience uses AI tools and adjust your content strategy to meet them where they are.

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Featured Image: Photo Agency/Shutterstock

https://www.searchenginejournal.com/chatgpt-study-1-in-4-conversations-now-seek-information/556104/

Google appears to have disabled or is testing the removal of the &num=100 URL parameter that shows 100 results per page.

Reports of the change began around September 10, and quickly spread through the SEO community as rank-tracking tools showed disruptions.

Google hasn’t yet issued a public statement.

What’s Happening

The &num=100 parameter has long been used to retrieve 100 results in one request.

Over the weekend, practitioners noticed that forcing 100 results often no longer works, and in earlier tests it worked only intermittently, which suggested a rollout or experiment.

@tehseoowner reported on X:

Google has seemingly removed the ability to do &num=100, can anyone confirm?

If you use the parameter, only 2 pages show. This “2 pages” filter has been tested for a year, but usually if you clicked to page 2 they have the rest the top 100. Now it shows nothing after page 2.

— SEOwner (@tehseowner) September 11, 2025

Keyword Insights wrote:

‼️ Google has killed the n=100 SERP parameter. Instead of 1 request for 100 SERP results, it now takes 10 requests (10x the cost). This impacts Keyword Insights’ rankings module. We’re reviewing options and will update the platform soon.

— Keyword Insights (@keywordinsights) September 14, 2025

Ripple Effects On Rank-Tracking Tools

Clark and others documented tools showing missing rankings or error states as the change landed.

Some platforms’ search engine results page (SERP) screenshots and daily sensors briefly stalled or displayed data gaps.

Multiple SEO professionals saw sharp declines in desktop impressions in Google Search Console starting September 10, with average position increasing accordingly.

Clark’s analysis connects the timing of those drops to the &num=100 change. He proposes that earlier desktop impression spikes were partly inflated by bots from SEO and AI analytics tools loading pages with 100 results, which would register many more impressions than a normal 10-result page.

This is a community theory at this stage, not a confirmed Google explanation.

Re-Examining “The Great Decoupling”

Over the past year, many teams reported rising impressions without matching clicks and associated that pattern with AI Overviews.

Clark argues the &num=100 change, and the resulting tool disruptions, offer an alternate explanation for at least part of that decoupling, especially on desktop where most rank tracking happens.

This remains an interpretation until Google comments or provides new reporting filters.

What People Are Saying

Clark wrote about the shift after observing significant drops in desktop impressions across multiple accounts starting on September 10.

He wrote:

“… I’m seeing a noticeable decline in desktop impressions, resulting in a sharp increase in average position.

“This is across many accounts that I have access to and seems to have started around September 10th when the change first begun.”

Keyword Insights said:

“Google has killed the n=100 SERP parameter. Instead of 1 request for 100 SERP results, it now takes 10 requests (10x the cost). This impacts Keyword Insights’ rankings module. We’re reviewing options and will update the platform soon.”

Ryan Jones suggests:

“All of the AI tools scraping Google are going to result in the shutdown of most SEO tools. People are scraping so much, so aggressively for AI that Google is fighting back, and breaking all the SEO rank checkers and SERP scrapers in the process.”

Considerations For SEO teams

Take a closer look at recent Search Console trends.

If you noticed a spike in desktop impressions in late 2024 or early 2025 without clicks, some of those impressions may have been driven by bots. Use the week-over-week changes since September 10 as a new baseline and note any substantial changes in your reporting.

Check with your rank-tracking provider. Some tools are still working with pagination or alternative methods, while others have had gaps and are now fixing them.

Looking Ahead

Google has been reached out to for comment, but hasn’t confirmed whether this is a temporary test or a permanent shift.

Tool vendors are already adapting, and the community is reevaluating how much of the ‘great decoupling’ story stemmed from methodology rather than user behavior.

We’ll update if Google provides any guidance or if reporting changes show up in Search Console.

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Featured Image: Roman Samborskyi/Shutterstock

https://www.searchenginejournal.com/google-modifies-search-results-parameter-affecting-seo-tools/556080/