OpenAI is rolling out the ability to upload audience lists to ChatGPT Ads. The option is under the “Tools” section and is named “Audiences.” I assume this lets ChatGPT Ads target based on the audience lists you upload to the ad platform for better ad targeting.
More details. You can upload raw or hashed emails and phone numbers to use as audience filters for your campaigns on ChatGPT Ads.
What it looks like. We spotted screenshots of this from Craig Graham and Joss Froggatt on LinkedIn – here are those screenshots:
Why we care. OpenAI continues to add more customizations and targeting options to its new ChatGPT Ads platform.
This should make for a more robust ad platform for advertisers and marketers and improve the overall conversions and ROI of the ads.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/07/audiences-chatgpt-ads-6N5Akr.jpg?fit=833%2C577&ssl=1577833Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-07 11:27:152026-07-07 11:27:15ChatGPT Ads rolling out audience lists
Content creators and publishers use many channels beyond their own websites to reach their audiences.
As people gravitate toward firsthand perspectives and different content formats, we want to make
it easier for site owners and creators—even those without their own website—to get a consolidated view
of how all of their content is getting discovered on Search.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-07 00:00:002026-07-07 00:00:00See how content from social and video platforms performs on Google Search
OpenAI rolled out a new feature to let ChatGPT Ads generate ads for you – I suspect using AI. The feature is available under the “Add new ad” option, and says “generate ads for you.”
The advertiser or marketer can then opt to let ChatGPT create the ad, and then review, edit and approve the ad for delivery on the ChatGPT Ad platform.
What it looks like. Anthony Higman posted a screenshot of this feature on the social media platform named X. Here is that screenshot:
As you can see, it says, “We generated an ad variation based on your website and campaign settings. Review, edit as needed, and activate when you’re ready.” Then you can “Review and create.”
There is also a quick duplicate ad option that Higman spotted as well:
Why we care. It makes sense that OpenAI would leverage AI to help advertisers create ads. The hope is it would lead to more ads being created and submitted on ChatGPT Ads sooner. This way the AI company can earn more money on ChatGPT.
As a marketer, you should be very careful when using AI to generate ads. Make sure to review the ads that were generated for you carefully so they meet your branding and marketing criteria, including your ROI goals.
Be the brand AI recommends.
See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/07/chatgpt-create-ads-ai-for-you-DoIC1C0f-qXESiw.webp?fit=1401%2C1506&ssl=115061401Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-06 16:22:462026-07-06 16:22:46OpenAI can generate ChatGPT Ads for you
Twelve months ago, the industry was betting on which AI platform would win discovery. Perplexity looked like the search-native challenger. Copilot looked like the enterprise Trojan horse. Neither bet paid off.
Previsible (disclosure: I’m its CPO and co-founder) just published its third AI Traffic Study, analyzing 6.77 million LLM-driven sessions. The data shows consolidation. Monthly LLM sessions grew 9.9x, reaching 644,478 in May 2026. And 92.4% of that traffic comes from one platform.
The plateau was a pause
In mid-2025, AI traffic appeared to be peaking in some sectors. It wasn’t.
Sessions rose from 65,249 in November 2024 to 396,278 by August 2025, then dropped sharply in November 2025, before hitting new highs of 428,203 in February 2026 and 644,478 in May.
That November dip needs context.
Sessions fell 50% in one month, driven almost entirely by ChatGPT referrals dropping from 448,412 to 213,345. Other platforms held steady. This was likely a model-related change; we’ve seen modest product tweaks massively swing referral traffic, like last fall when many sites lost half their ChatGPT traffic because the model began favoring Wikipedia and Reddit. Sessions recovered to 442,609 by December.
The lesson: one vendor’s product decisions can halve your AI traffic overnight. Plan for the volatility.
Consolidation, not competition
When we last published in December 2025, ChatGPT held roughly 84% share, followed by Perplexity at 8.9%, Gemini at 4.5%, Copilot at 2.1%, and Claude at 0.6%. Six months later, the field has collapsed toward the leader.
Across the full dataset, ChatGPT commands 92.4% of trackable LLM referral traffic, growing 12.8x over 19 months with no sign of slowing. It’s the only LLM sending meaningful referral volume at scale. Optimizing for “AI visibility” without prioritizing ChatGPT means optimizing for an abstraction.
Important framing: this measures standalone LLM referral traffic. AI discovery inside Google’s own results, including AI Overviews, almost certainly drives more AI traffic than all standalone platforms combined, but it operates on a different measurement paradigm and is excluded here.
The challengers flipped
The surprise isn’t at the top. It’s who’s moving underneath.
Claude
Claude grew 64x, from 133 sessions in November 2024 to 8,528 in May 2026, and overtook Perplexity in March 2026 for the first time. It stayed ahead.
Claude was flat through 2025, then accelerated 4x in two months as its agentic tools and enterprise integrations gained adoption. The enterprise advantage the industry expected Copilot to win may be materializing for Claude instead.
If your audience includes technical buyers, developers, or professional services, Claude visibility is now material, and the window for early positioning is open.
Gemini
Gemini is the quiet number two: 3.2x growth with almost no volatility. Its Workspace and Android integration mean referral numbers likely undercount its real discovery footprint.
Perplexity & Copilot
Perplexity peaked at 17,507 monthly sessions in March 2025 and has fallen 61% since. Copilot collapsed 96% from its August 2025 peak, from 8,651 sessions to 339.
Neither is a growth bet for traffic acquisition anymore. Both are shifting toward keeping users inside their own experiences: browsers, agents, and modes where they don’t need to send you traffic at all.
Where LLMs send users, and why it should change your roadmap
The study’s most actionable finding isn’t market share. It’s landing pages.
ChatGPT sends 28.8% of its traffic to internal search results pages. Across industries, roughly 25% of AI-referred traffic lands on internal search.
The model trusts your domain but can’t pick the right page, so it sends users to your search box and lets them navigate. This pattern persists across verticals and time periods, suggesting it’s structural to retrieval-augmented generation rather than a temporary quirk.
Think about what that means. The model did the hard work of choosing your domain. Your internal search UX now determines whether that high-intent visit converts or bounces.
For most sites, internal search is a neglected navigation feature, not an acquisition surface. That has to change.
The vertical view tells several different stories:
SaaS traffic lands on search pages (34.6%).
Publisher traffic lands on news pages (54%), yet against 120+ million organic sessions, publisher penetration is 0.11%; publishers produce the content LLMs cite and capture almost none of the resulting traffic.
Ecommerce traffic lands on product pages, with purchase intent already formed.
Health traffic lands on About pages (42.1%), with users evaluating the source before the content.
Legal traffic spreads across blog, about, contact, and location pages: the full evaluation arc.
The platforms have personalities, too:
ChatGPT and Gemini are search-pattern models: domain trust, page-level uncertainty.
Perplexity and Claude are content-selection models that pick specific pages and over-index on long-form.
If your strategy depends on editorial content driving qualified traffic, Perplexity and Claude matter disproportionately to their share.
What to do now
Optimize for ChatGPT first. Expand elsewhere when volume justifies it.
Monitor Claude. It overtook Perplexity in March. Early positioning compounds.
Treat product pages as AI entry points. Product pages capture 43% of e-commerce LLM traffic. Structured, comparable product data is a discoverability requirement now.
Make pricing machine-readable. “Contact us for pricing” gives AI systems nothing to summarize, compare, or recommend.
Prioritize internal search. It’s an acquisition tool, not a navigation feature.
Track AI traffic by page type, not site-wide. Your site average hides where AI traffic concentrates. Your pricing page might run 3x your site-wide penetration.
The next question is the one nobody has answered: conversion rate by LLM platform. Which platforms send users who buy, and which send users who bounce?
We built this dataset to answer that. If the last 19 months are any indication, the answers will change faster than most teams are ready for.
About the data
166 GA4 properties, November 2024 through May 2026, spanning SaaS, ecommerce, finance, legal, health, insurance, education, publishing, and ticketing. All 166 properties are present throughout the full 19-month window, so the trajectories reflect behavioral change rather than sample expansion.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/07/ai-traffic-search-EtDxTJ.webp?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-06 16:15:002026-07-06 16:15:00ChatGPT commands 92% of AI referral traffic. Here’s what 6.77 million sessions reveal.
AI visibility and AI profitability are not the same thing. Most teams are growing one without building the other.
The four most common failure modes are optimizing for mentions over conversions, measuring AI visibility like rankings, chasing tactics without a revenue connection, and running AEO/GEO in a silo.
AI-referred visitors convert at 8.3 times the rate of traditional traffic, close 62 percent faster, and generate 7 times more revenue per visitor. Those numbers only hold if your conversion architecture is built to receive them.
The highest-performing campaigns share four traits: retrieval-ready content, strong authority signals, multi-channel distribution, and conversion systems designed for low-click environments.
You can start building toward profitability in 90 days without a full overhaul, but the phases have to run in order.
You might be showing up in ChatGPT answers. Getting cited in Google’s AI Overviews. Watching your brand mentions climb across the web.
And still not seeing it move the revenue needle.
That’s the problem a lot of marketing teams are grappling with right now. AI visibility is growing. Profitability isn’t keeping pace. After analyzing more than 100 AEO and GEO campaigns at NP Digital, I can tell you the issue isn’t the strategy itself. Most teams are simply optimizing for the wrong outcomes.
If you already know what AEO and GEO are and you’re ready to actually make money from them, this post is for you. I’m going to break down exactly where the profitability gap comes from, what the winning campaigns have in common, and how to build toward revenue, not just visibility.
Why Most AEO/GEO Efforts Don’t Make Money
Getting cited is not the same as getting paid. That distinction sounds obvious, but most AEO/GEO programs are structured around the former and hope the latter follows automatically. It doesn’t.
After auditing campaigns across industries, NP Digital identified four failure modes that consistently prevent AI visibility from converting into revenue.
Optimizing for mentions and citations. Mentions don’t pay the bills; conversions do. If your entire AEO/GEO program is oriented around getting named in AI responses, you’re measuring a proxy, not an outcome. A citation that doesn’t connect to a conversion path is brand awareness you can’t prove.
Measuring AI visibility like rankings. Citation volume tells you nothing about pipeline. Teams that treat AI mention counts the same way they used to track keyword rankings end up with
dashboards full of activity metrics and no way to show leadership what any of it is worth.
Chasing AI-specific tactics in isolation. Schema updates, prompt engineering, entity optimization do matter, but tactics without distribution don’t compound. Teams that bolt on AEO/GEO tactics without building content and authority infrastructure underneath them tend to see short-term citation spikes that fade quickly.
Running AEO/GEO separately from revenue goals. This is the biggest one. Visibility disconnected from business outcomes is overhead. The teams getting budget approved for AI search have tied it to pipeline, not impressions.
NP Digital data tells the story clearly. AI visibility index climbed to 133 across tracked brands, while the profitability outcomes index reached 174. The gap between those two numbers is the opportunity this post addresses.
The Profitability Gap: What Changes When Buyers Use AI
Buyers who find you through AI tools are not the same as buyers who find you through traditional search. They arrive differently, they behave differently, and they convert differently.
The traditional funnel started with discovery through search, a click-through to compare options, an early-stage arrival that needed nurturing, and multiple touchpoints before a decision. The AI-influenced funnel runs differently. Research happens inside AI tools. Buyers validate brands before they ever click. They arrive later, already informed, and convert faster when trust exists.
That shift is an advantage, but only if your conversion architecture is built to receive it.
NP Digital data across 40-plus B2B and B2C campaigns makes the opportunity concrete. AI-referred visitors convert at 5.97 percent. Traditional traffic converts at 0.72 percent. Time to conversion drops from eight days to three. Revenue per visitor rises from $2.56 to $18.04.
The volume is still small. AI traffic accounts for about 0.58 percent of total traffic but drives 5.09 percent of sales. Lifetime value is also stronger at $325, up from $271 for Google-referred traffic.
The math works. But capturing those numbers requires a funnel built for visitors who arrive intent-driven rather than still in the research phase.
What the Profitable Campaigns Have in Common
Across the campaigns NP Digital analyzed, the ones generating real pipeline from AI search shared four traits. These traits reinforce each other, which is why building them together matters.
Content Built for Retrieval
The content types that drive both AI citations and conversions are high-intent formats that answer specific questions buyers ask when they’re close to a decision. Not top-of-funnel awareness pieces.
Comparison pages and alternatives content convert AI-referred traffic at 6.8 percent, the highest of any page type NP Digital tracked. First-party research and original data earn citations because they can’t be replicated elsewhere; they become reference points AI engines return to repeatedly. Bottom-funnel educational content and FAQ frameworks round out the top performers.
Format is as important as topic. Lists and listicles account for 48 percent of AI citations in NP Digital’s research. Step-by-step guides come in at 17 percent. AI engines pull from content structured for easy parsing. Content not formatted for retrieval tends not to get retrieved.
Strong Authority Signals
NP Digital scored six trust signals across ChatGPT, Gemini, Copilot, Claude, and Perplexity on a one-to-five scale. Third-party citations scored between 4.5 and 4.8, the single most consistent signal across every platform. Expert authorship scored between 4.0 and 4.6.
AI engines reward signals that are difficult to manufacture: named, credentialed authors; external sources citing your content; consistent brand presence across multiple platforms. Publishing on your own site still matters, but earning coverage and mentions outside it is what drives citations.
Multi-Channel Distribution
NP Digital tracked 75 brands across AI platforms and found a direct correlation between monthly publishing channels and AI visibility score. AI engines validate authority through repetition and consistency. Presence across YouTube, LinkedIn, Reddit, and PR channels signals to AI tools that your brand is real and relevant, not just self-published.
Conversion Architecture for Low-Click Environments
AI-referred visitors arrive pre-qualified. They’ve already done the research, compared options, and formed an opinion. A landing page designed for someone at the top of the funnel is the wrong tool for a visitor who’s already at the bottom.
The brands capturing revenue from this traffic have built accordingly: fast pages, strong trust indicators placed prominently, simplified calls to action, bottom-funnel calculators and tools, and conversational paths that confirm a decision rather than explain a product category.
How to Measure AEO/GEO for Revenue, Not Just Visibility
The metrics most teams track are measuring the wrong thing. Rankings, raw traffic, click-through rate, AI mention counts, these are visibility metrics. They tell you whether people are seeing your brand. They don’t tell you whether it’s generating revenue.
The teams getting AEO/GEO budgets renewed are the ones connecting citations to pipeline. That requires a different measurement stack.
Stop tracking: raw rankings, organic traffic volume as a primary metric, click-through rate, AI mention counts, raw citation tracking, vanity impressions.
Start tracking: influenced conversions, brand search lift, assisted pipeline, returning visitor quality, and conversion rate by intent source.
NP Digital’s outcomes-first measurement framework organizes this into three tiers. At the foundation: visibility and influence signals, including brand search volume, share of voice, community engagement, and earned media. In the middle: demand signals, including multi-touch attribution, AI-driven lead scoring, behavioral intent, and consumption depth. At the top: business outcomes, including revenue, CAC:LTV ratio, retention, expansion, and advocacy.
Build reporting from the bottom up. Track from the top down. The goal is a dashboard leadership reads as a business document, not a marketing activity report.
NP Digital research shows how much KPI priorities have shifted. Leadership priority for rankings dropped from 88 to 63 between 2024 and 2026. Pipeline contribution rose from 23 to 70. Revenue growth held steady at 96 to 98. Your measurement framework needs to reflect where leadership attention already sits.
A practical starting point: for every vanity metric on your current dashboard, add one outcome metric alongside it. That shift is often enough to change the budget conversation.
The 90-Day Plan to Turn AEO/GEO Into Revenue
You don’t need to overhaul everything at once. You do need to run the phases in order. Each phase builds on the one before it, and skipping ahead consistently produces weaker results.
Days 1 to 30: Audit and Fix the Foundation
Start by auditing your current AI visibility across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews. Search your brand name and core topics. Note where you appear, where competitors appear instead, and where no one appears. Those gaps are your priority list.
From there, identify high-intent content gaps where competitors are getting cited and you aren’t. Improve structured formatting across your highest-traffic pages with clear headers, FAQ sections, and concise direct answers. Strengthen author and entity signals. Clean up trust indicators including reviews, third-party citations, and brand consistency across platforms. Apply schema and retrieval-friendly formatting throughout.
One consistent finding across NP Digital’s audits: brand authority, PR and mentions, and community visibility are almost always the lowest-scored areas. Start there before investing more in content production.
Days 31 to 60: Create and Distribute for Profitability
Create the content types that drive both citations and conversions: comparison pages, original research and proprietary data, buyer guides, and FAQ expansions. These formats earn citations and convert the traffic those citations send.
Distribute across LinkedIn, YouTube, PR placements, expert commentary opportunities, and community channels like Reddit. The goal is consistent presence across multiple ecosystems. AI engines validate authority through repetition across platforms, not just depth on your own site.
Days 61 to 90: Optimize Conversion and Measurement
With the foundation fixed and the content layer built, optimize for what happens when AI-referred visitors arrive.
Improve bottom-funnel UX for high-intent visitors. Add calculators, tools, and simplified calls to action. Optimize assisted conversion flows. On the measurement side, track influenced pipeline from AI-assisted traffic, compare conversion quality across platforms, and build an executive dashboard tied to revenue rather than visibility metrics.
The window to establish AI search presence is real and won’t stay open indefinitely. The brands building this infrastructure now are accumulating authority signals that compound over time and become increasingly difficult for competitors to overcome.
FAQs
How do you connect AEO/GEO to revenue?
The connection runs through your measurement framework and your conversion architecture. On the measurement side, track influenced conversions, assisted pipeline, and brand search lift rather than citation counts. On the conversion side, build landing pages and CTAs designed for visitors who arrive already informed. AI-referred visitors are pre-qualified and need a fast path to a decision, not an introduction to your product category.
What metrics should you track for AEO/GEO profitability?
Move away from rankings, raw traffic, and citation volume as primary KPIs. The metrics that connect to profit are influenced conversions, brand search lift, assisted pipeline, returning visitor quality, and conversion rate by intent source. Build toward a three-tier measurement stack: visibility and influence at the foundation, demand signals in the middle, and business outcomes at the top.
What content converts best from AI-referred traffic?
Comparison pages and alternatives content convert AI-referred traffic at 6.8 percent, the highest of any page type in NP Digital’s research. First-party research, bottom-funnel educational content, and FAQ frameworks also perform well. Format matters as much as topic. Lists and listicles account for 48 percent of AI citations because they’re structured for easy extraction.
Conclusion
The winners in AI search don’t just focus on earning the most citations but make sure they can turn citations into pipeline.
That requires connecting visibility to conversion architecture, measuring outcomes rather than activity, and building the content and authority signals that AI engines reward consistently over time. None of those things happen by accident.
The brands doing this work now are building compounding advantages. Authority signals accumulate. Citation patterns stabilize. Conversion infrastructure improves with data. Starting later means starting behind.
If you want support building an AEO/GEO strategy tied to revenue rather than just visibility, NP Digital’s team works through exactly this kind of profitability infrastructure with clients.
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Advanced architecture is no longer just technical structure. It determines whether your content can be found, understood, and surfaced by search engines and AI systems.
Our next SMX Now on July 15 features Shari Thurow, co-founder, information scientist, and search director at the Information Architecture Gateway. She’ll explain how advanced architecture works and where most AI, SEO, and site development workflows fall short.
The session introduces a five-phase framework Thurow has tested through decades of client work with organizations including Microsoft, Google Cloud, Abbott Laboratories, CVS Pharmacy, WebMD, Sony Music, the Library of Congress, Best Buy, and Merriam-Webster. You’ll learn how architecture decisions shape labeling systems, wayfinding networks, taxonomy, wireframes, and AI access to valuable content.
It also challenges long-standing misconceptions, including the three-click rule, the idea that taxonomy is only a hierarchy, and the belief that AI can generate effective wireframes without a deeper architectural model.
You’ll leave with a practical framework for building sites that communicate more clearly with users, search engines, and human-centered AI systems.
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Has your website seen better days, and are you looking to restore it to its former glory? Look no further. We will help you get your site up to today’s standards and make that splash online. We cover all the key areas you need to consider. Feel free to skip the steps that are already up to date, but be honest with yourself about what work still needs to be done. Your website only gets a millisecond to make a first impression, so let’s make the most of it. Now it’s time to peek under the hood, brush off the dust on those pages, and refresh your content.
Consider making big changes to your old website first, such as updating the domain or CMS.
Check and update your CMS, plugins, and data privacy setup to meet modern standards.
Refresh your content and keyword research to ensure relevance and improve SEO performance.
Evaluate technical aspects like mobile-friendliness, core web vitals, and accessibility for better user experience.
Use the Yoast SEO plugin to streamline the process of updating your old website and maintain its performance.
Check your site’s set-up
Want to make any big changes? Do that first
Before we delve into the depths of your old website, it’s worth considering any major changes you want to make. Maybe you’ve always wanted to change your domain name or your URL structure, but it was too much work when everything was up and running. Or perhaps you could benefit from switching to a different CMS or hosting provider. If you do want to make sitewide changes, you’ll be far better off planning this from the start.
Old or new domain?
You will have registered your domain when your website was still live, but that doesn’t necessarily mean it’s still available now. Check whether your old domain is still yours or available. If it’s not, you’ll need to choose another one. This can give you a chance to start over fresh with a whole new website.
When you’re looking to get a new domain, consider how you want to build your website. You can hire someone to custom-build a website for you. But if you want to save on costs, website builders like Wix and WordPress let you create a website without coding. Although both allow you to create websites yourself, we recommend WordPress if you want the option to grow and alter your website beyond what the available templates offer.
There are also AI builders to consider when creating a new website. For example, Bluehost’s AI Website Builder generates your website by giving a prompt and answering a few follow-up questions. A quick way to get back online, with hosting, a domain, and even human support included if needed.
If your website was popular in the past, you will probably have built up some domain authority. Meaning that people know your brand name and can have (positive) associations with it. So when you change your domain name, you’ll lose any domain authority you’ve gained previously.
Your old URLs will no longer work, and you’ll need to plan to migrate your content to your new domain. This can be a lot of work, so don’t underestimate it!
Are your CMS and plugins up to date?
The next thing you’ll want to check is whether your CMS is up to date. Depending on how old your site is, that could mean all kinds of things. As new possibilities arise in web development, a good CMS will adopt these and implement them for you (mostly) automatically. So it’s well worth updating your CMS as a first step! Make sure to back up your site and test the changes first, though.
The impact of out-of-date plugins really depends on which plugins you have installed. But whichever plugins you use, you should check these too. The same goes for themes; they can stop working if they’re too old or no longer supported. Online, the older your technology is, the more vulnerable it is to hacking, so make sure to update what you plan to use and remove unnecessary plugins.
Tip: If you’re using WordPress, go to the Site Health section, located under Tools > Site Health in the backend. This will give an overview of what needs to be done to get your website healthy again.
Check your robots.txt / indexing settings
Noindexing a page means you block search engines from indexing it and showing it in their search results. Some people prefer to noindex their site while they make big changes to it, to avoid leaving users with a bad impression. But you shouldn’t really play around with this unless you know what you’re doing. I would also not recommend doing this if you still get a decent amount of traffic to your site.
To noindex your site, you’ll need to update your robots.txt file. If you’re a Yoast SEO user, you can manage your indexing in yourconfigurationsettings without ever touching your robots.txt file. An easier way to update your site behind the scenes is by using the LightStart plugin.
Check your data privacy set up
If your site has been around for a few years, there’s a good chance your data privacy setup doesn’t meet modern standards. For instance, if your site uses cookies to track user behavior, it’s now a legal requirement to ask users for permission in most regions worldwide. Similarly, if you have user data stored on your site, you absolutely need to ensure it’s stored securely and used in a legally compliant way. If that data is old user data, the safest option is probably just to delete it all.
Check your content
Refresh your keyword research
When it comes to updating the content on your old website, refreshing your keyword research is a good place to start. The words people use in their search queries change over time, so the longer your content has been out of action, the more likely it is that you need to do this. When checking your keywords, you should see whether you’re still using the most suitable ones for your site and audience, and whether you can still compete for those rankings.
Also, you should take AI search into account when researching how to update your content. AI search is now an undeniable part of people’s online search behavior, whether it’s Google presenting users with an AI overview or people using AI models like ChatGPT and Gemini. So make sure to optimize for AI search as part of your SEO efforts.
Is the quality still good enough? Is there room to improve the content by applying the E-E-A-T principles?
Is your information still accurate and up to date? Are there new insights that you could add to the content?
Do the external and internal links still work? Do the meta description or SEO title need updating?
Take a look at each page, and be critical. What could be improved? Do you really need to keep each page? Will you need to rewrite the whole thing, or will some small adjustments be enough? It could take a while to get through all of your content. Make sure to start with your most important pages first.
Check your internal linking and site structure
Making sure your content is high-quality and well-optimized is only half the story. It needs to be findable too. By linking related pages, you make your content easier for your users to find. And on top of that, if you make sure your most important pages get the most internal links, it helps Google get a better idea of your site structure. As a result, those central pages (which we call cornerstone content) are likely to rank higher in search results!
Check the technical SEO
Mobile-friendly is essential
Most people are using their phones or other mobile devices to access the internet. As a result, Google switched to mobile-first indexing years ago. This means that if your site doesn’t work well on mobile, this can impact your overall visibility in search. Responsive site design and proper mobile usability testing are more of a requirement now than a nice extra. Make sure everything works, and that it looks good on all kinds of screen sizes. Don’t treat the desktop version of your website as the default.
Core Web Vitals and page experience
Back in the days of dial-up internet, you always had to wait patiently for pages to load. But that’s a thing of the past; pages that load quickly are a basic expectation nowadays. Loading quickly isn’t the only consideration, because your pages need to actually work well once they’ve loaded. Google has a ranking factor to measure things like this. So you need to make sure you’re meeting expectations for aspects such as:
Loading performance (how fast does stuff appear on the screen?)
Responsiveness (how fast does the page react to user input?)
Visual stability (does stuff move around on the screen while loading?)
The details behind these factors are quite technical, but it’s worth delving into these Core Web Vitals to make sure your technical SEO isn’t holding you back.
Check your media usage
Another thing that changes over time is the best practice for using images and videos on your site. Nowadays, most users expect high-quality images that load quickly, too. Make sure to optimize and properly tag them before mindlessly adding them to your page. When it comes to video, these can increase your visibility on platforms such as YouTube and social media. They also tell search agents that the content on your page is rich and valuable, so make sure to get going with a proper video strategy for your site and other platforms.
Accessibility
For most websites, accessibility is an afterthought. Which is a big shame, as this also means you’re missing out on a whole group of potential customers. Don’t just consider how you experience a website; also accommodate the needs of different types of visitors. Accessibility means making small adjustments and additions that let everyone enjoy your content. You don’t need to redesign everything; there are simple improvements you can make, such as adding alt text to your images.
Structured data
If you want to have the best-looking search results in Google, you’ll need to start adding structured data to your site. Structured data is a way of telling Google about the context or purpose of different types of content. You can label your news items as news, for example, and Google can identify that and add your content to its News section. Or you could label your products using structured data and have a chance of getting listed in Google’s Shopping results. Structured data helps AI search engines and chatbots better understand your site. There are loads of ways structured data can boost your content, so give it a try!
Start publishing and sharing
Check your robots.txt / indexing settings (again)
Give your indexing a final check and make sure the pages you want Google to index are crawlable. You can start by checking your robots.txt or your indexing settings. Google Search Console can be a great help at this point. Submit your sitemap. This will let Google know your site is ready for indexing again, and help it understand what’s changed. Search Console will flag any crawl errors, so you can easily check whether everything is set up correctly.
Start (re)publishing and sharing content
And now for the final step in updating your old site: start publishing content again! Publishing content regularly and sharing it on social media will help you to build awareness of your site. Plus, you might gain some new fans and followers! If your social media pages are outdated, give them a refresh to let people know your site is back and ready to welcome them!
Update your site’s SEO with Yoast
As you can see, there’s a lot to check when updating SEO and refreshing your old website. Once you’ve restored your site to its former glory, make sure you maintain it and your SEO. Otherwise, in a few years, you might be doing this all over again. Luckily, our Yoast SEO plugin can help you update and maintain your old site, too!
Refresh your website with Yoast SEO Premium
Get Yoast SEO Premium and get feedback on your content, access to our SEO training and helpful tools to clean up your site!
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-06 12:46:522026-07-06 12:46:52Got an old website? Update it and refresh your SEO
We’ve fixed that. Yoast SEO now reads your content straight from Elementor’s atomic editor, so every check works the way you’d expect: text length, keyphrase density, images, links, and more. Keyphrase highlighting works too. Click the eye icon on any check, and you’ll see your keyphrase marked in your text, right there in the editor.
If you’re still using Elementor’s classic editor, nothing changes for you. Everything keeps working as it did before.
What you need to do
Update your Yoast plugins to the latest version. That’s it. Your existing content gets picked up automatically the next time you open a page.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-06 08:52:072026-07-06 08:52:07Yoast SEO now works with Elementor’s new atomic editor
We’re excited to officially announce the next stop for
Search Central Live Deep Dive Europe 2026! Mark your calendars: based directly on
your feedback, we’re headed to
Barcelona, Spain, from September 30 to October 2, 2026.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-06 00:00:002026-07-06 00:00:00Search Central Deep Dive Europe 2026: Apparently we’re going to Barcelona
AI shopping agents have raised consumer expectations for personalization well beyond name tokens and basic segmentation.
Zero-party data (information customers share directly) and first-party behavioral data are the strongest inputs for personalized email programs.
Advanced segmentation, conditional logic in automations, and predictive churn modeling are the tactics separating high-performing email programs from average ones.
Personalization drives measurable gains in conversion rate, retention rate, and ROI across industries.
Every Email Service Provider (ESP) has different capabilities, but any increase in personalization tends to move performance metrics in the right direction.
A “Hi, [first name]” token in a subject line used to feel personal. Today it barely registers. Consumers have seen it so many times that it reads as the absence of personalization rather than the presence of it.
AI has changed what’s possible in email marketing, and in doing so, it’s changed what people expect. AI-powered shopping agents can now anticipate what a customer wants before they’ve searched for it. When that’s the comparison point, a generic batch-and-blast email doesn’t just underperform. It actively signals that your brand isn’t paying attention.
Here’s what email personalization actually looks like in 2026, and how to build a strategy that keeps up.
Why the Personalization Bar Moved
Consumers have always wanted to feel like more than a number on a list. That’s not new. What’s new is the benchmark they’re measuring you against.
AI-powered shopping assistants, personalized recommendation engines, and other AI marketing tools have made highly contextual experiences the norm. When a consumer’s phone already knows they’re running low on a product they buy regularly, or when a shopping agent surfaces the exact item they were about to search for, their tolerance for generic email content drops proportionally.
Research from Klaviyo consistently shows that personalization based on zero-party and first-party data drives higher conversion rates, better retention, and stronger ROI across industries. The brands that are seeing those results aren’t relying on a silver bullet tactic, but using better data and more deliberate segmentation to deliver messages that actually fit the person receiving them.
The brands that aren’t doing this make themselves easier to ignore or unsubscribe from.
The Data Foundation: Zero-Party vs. First-Party
Before you can personalize effectively, you need the right inputs. Two data types matter most here.
Zero-party data (ZPD) is information a customer gives you directly and intentionally. Product preference quizzes, style surveys, onboarding forms that ask about goals or challenges, and opt-in preference centers all generate ZPD. The customer knows they’re sharing it and chooses to do so. That intent makes it highly reliable.
First-party data is behavioral: purchase history, browsing activity, email engagement, content interactions. You collect it passively through your owned channels. It reflects what customers actually do, which often differs from what they say they’ll do.
The most effective email programs pull both data types into a unified customer profile and use that profile to drive segmentation, automation logic, and send timing. Running these as separate efforts is one of the most common gaps in email strategy. The brands getting the most out of personalization treat ZPD collection as a systematic part of the customer journey, starting at onboarding, not as an occasional survey blast.
What Advanced Email Personalization Actually Looks Like
Generic segmentation by geography or purchase category is a starting point, not a strategy. Here’s what moving beyond the basics looks like in practice.
Conditional Logic in Automations
Take the abandoned cart workflow as a representative example. Most brands send a single recovery email to everyone who abandons. A better approach uses conditional splits based on cart value.
A customer with $250 in their cart is probably not abandoning because they need a discount. They may need reassurance, a review, or a reminder. A customer with $35 in their cart might convert on a 10 percent offer. Treating those two scenarios with the same message ignores obvious signals you already have.
The same logic applies to your welcome series, post-purchase flow, and win-back campaigns. Conditional splits let you match the message to the moment instead of averaging across your list.
AI Segmentation for Churn Prevention
Waiting until a subscriber unsubscribes to try to win them back is too late. AI segmentation tools can identify high-risk churn subscribers based on engagement decay patterns, purchase cadence changes, and behavioral signals before they disengage.
Getting in front of those subscribers with a relevant message at the right moment is significantly more effective than a reactive win-back campaign three months after they’ve gone quiet. A targeted re-engagement email with a personalized offer based on their purchase history outperforms a generic “We miss you” message sent to a cold list segment.
Scheduled newsletters have their place, but the highest-performing email programs are increasingly event-driven. A customer who views a product page three times without purchasing is a better candidate for a targeted email right now than they are for your next weekly send.
Setting up behavioral triggers requires more upfront work, but it produces messages that arrive when the customer’s interest is actually active. That timing advantage is difficult to replicate with a fixed send schedule.
Personalizing Beyond the Subject Line
Subject line personalization is the most visible layer, but email body content, product recommendations, and calls to action can all be personalized based on the data you have. Dynamic content blocks let you serve different images, copy, or offers to different segments within a single email send.
For e-commerce brands, product recommendations based on purchase history and browsing data are one of the clearest performance drivers in email. According to research from Klaviyo, personalized product recommendations in email consistently outperform static content blocks across conversion and click-through metrics.
Building a More Personalized Email Program: Where to Start
You don’t need to overhaul your entire program at once. Incremental personalization improvements add up. Here’s a practical sequence:
Audit your current segmentation. If you’re sending the same email to your full list with no behavioral or preference-based splits, that’s the first thing to address.
Add a ZPD collection touchpoint to your welcome flow. A short preference survey, a product recommendation quiz, or a style selector at signup gives you first-party intent data you can act on immediately.
Build one conditional split into an existing automation. Your abandoned cart or welcome series is the right place to start. Pick one variable (cart value, product category, acquisition source) and split accordingly.
Review your suppression logic. Are you sending promotional emails to customers who just made a purchase? Sending re-engagement campaigns to active subscribers? Small gaps like these erode the experience in ways that accumulate over time.
Separate your measurement. Track personalized segments and general sends independently. Conversion rate, click-through rate, and unsubscribe rate will tell you whether the personalization is working. Without separate tracking, you’re flying blind.
Your ESP’s capabilities will set some limits here, but most platforms support at least basic segmentation and conditional logic. Start with what’s available and build from there.
FAQs
What is email personalization?
Email personalization is the practice of tailoring email content, timing, and offers to individual recipients based on data about their preferences, behaviors, and history with your brand. It goes well beyond name tokens to include segmentation, dynamic content, behavioral triggers, and predictive recommendations.
What is zero-party data in email marketing?
Zero-party data is information a customer shares with you directly and intentionally, such as quiz responses, stated product preferences, or answers to onboarding surveys. It differs from first-party data, which is collected through observed behavior like browsing and purchase history. Both are valuable inputs for personalization.
How does AI improve email personalization?
AI tools improve email personalization in a few ways: by identifying high-risk churn subscribers before they disengage, by powering product recommendation engines that surface relevant items based on purchase history and browsing behavior, and by enabling more sophisticated segmentation than manual rule-building allows.
What email segmentation strategies work best?
Behavioral segmentation outperforms demographic segmentation in most cases. Splitting by purchase history, engagement level, browsing behavior, and acquisition source produces more relevant messages than splitting by age or location alone. Combining behavioral data with ZPD preference data gives you the sharpest segments.
Do I Need a New ESP to Improve Personalization?
Not necessarily. Most ESPs support basic segmentation and conditional logic. The bigger gap is usually in data collection and workflow design, not platform capability. Start by improving your ZPD collection and segmentation logic before assuming your current platform is the constraint.
Conclusion
Email personalization in 2026 means understanding what your customers are looking for before they tell you, and sending the right message at the moment it’s relevant. That’s a different standard than what most email programs are currently operating at.
The good news is that the inputs are largely within your control. Zero-party data collection, conditional automation logic, and behavioral segmentation don’t require a massive platform overhaul. They require a more deliberate approach to how you collect, organize, and act on the data you already have. You can also work with the NP Digital team if you want hands-on support building a smarter email personalization strategy.
