Robots.txt is a plain text file in your root directory that tells search engine and AI crawlers which pages on your site to crawl and which to skip.
By guiding bots away from technical clutter and low-value pages, you make sure they spend their time on the important, high-value content that drives results.
The four AI crawlers most worth knowing (GPTBot, ClaudeBot, Google-Extended, and CCBot) respect robots.txt directives and can be blocked individually with their user-agent strings.
Common robots.txt mistakes include using disallow: / on a live site, blocking CSS or JavaScript files (which hurts rendering), and confusing disallow with noindex, since a disallowed page can still be indexed if linked externally.
Think of your robots.txt file as your site’s GPS.
It tells web crawlers for search engines like Google or Bing (and now AI) where to look and what to index. That’s significant in today’s search world. Yet, it’s often an overlooked part of technical SEO.
Many treat robots.txt with a set-it-and-forget-it mentality, not realizing the toll that can take on search visibility.
With AI now claiming top positions on the search engine results pages (SERPs), the right robots.txt configuration is more important than ever.
To help you stay ahead, I’ve put together this refresher on how to create a robots.txt file that promotes modern-day visibility and delivers real business results.
What Is a Robots.txt File?
The robots.txt file, also known as the robots exclusion protocol or standard, is a text file that tells web robots (often search engine crawlers and AI scrapers) which pages on your site to crawl.
It also tells web robots which pages not to crawl.
Let’s say a search engine is about to visit a site. Before it visits the target page, it will check the robots.txt for instructions.
There are different types of robots.txt files, so let’s look at a few different examples of what they look like.
The asterisk after “user-agent” indicates that the robots.txt file applies to all web robots visiting the site.
The slash after “Disallow” tells the robot not to visit any pages on the site. However, it’s important to note that disallowing a page won’t prevent it from being indexed if external links are pointing to that page.
Why Robots.txt Matters for SEO
You might wonder why anyone would want to stop web robots from visiting their site.
After all, one of the major goals of traditional and AI SEO is to get search engine or AI bots to crawl your site easily, thereby increasing your visibility.
That’s where the secret to this SEO hack comes in.
You probably have a lot of pages on your site, right? Even if you don’t think you do, check. You might be surprised.
If a search engine crawls your site, it’ll crawl every single page.
And if you have a lot of pages, it’ll take the search engine bot a while to crawl them. That can negatively affect your ranking.
That’s because Googlebot (Google’s search engine bot) has a “crawl budget.” This breaks down into two parts.
The first is the crawl capacity limit, which is the maximum number of connections Google can use to crawl a site at any given time. Google goes into more detail here:
The second part is crawl demand, which is essentially Google’s appetite for your content. It comes down to how popular your pages are and how often you update them. Here’s a deeper explanation from Google:
Basically, crawl budget is “the number of URLs Googlebot can and wants to crawl.”
You want to help Googlebot spend its crawl budget for your site as efficiently as possible. That means you want it crawling your most valuable pages.
To make sure you’re leading bots to the right places, Google advises minimizing these common drains on your crawling resources:
Faceted navigation: URL parameters for sorting and filtering can create an “infinite space” that traps bots in a maze of redundant pages.
Duplicate content: When the same information exists across multiple URLs, consolidate them so crawlers can focus on your unique content.
Hurdles and dead ends: Soft 404 errors and long redirect chains waste crawl demand, forcing bots to work harder for no reward.
Server performance: If your site responds slowly, Google may not be able to read as much content from your site.
OK, let’s come back to robots.txt.
A well-structured robots.txt page tells search engine bots (and especially Googlebot) to avoid certain pages.
Think about the implications. By curating your robots.txt file, you’re highlighting your best work. You’re effectively steering the bots away from technical clutter and toward your most valuable content.
In other words, your robots.txt helps make sure that every second a bot spends on your domain is a worthwhile one. It’s the difference between a bot wandering aimlessly through your digital storage and one that heads straight for the pages that drive results.
Intrigued by the power of robots.txt? Let’s talk about how to create a robots.txt file and use it properly.
How to Create a Robots.txt File
Using robots.txt effectively starts with getting the basics right. Follow these steps to create a robots.txt file that gets your “website GPS” off to the right start.
Step 1: Open a Plain Text Editor
You can create a new robots.txt file by using a plain text editor, like Notepad on PC and TextEdit on Mac. Whatever you use, make sure it’s a plain text editor.
If you already have a robots.txt file, make sure you delete the text (but not the file) to give yourself a fresh start.
Step 2: Locate and Format Your File Properly
To start, you must name your file “robots.txt.” That may seem obvious, but it’s so important that it’s worth stating. If you get your naming wrong, nothing else that you do will matter.
Also note that each site can have only one robots.txt file. That file must also be placed at the root domain of the site it applies to.
Think of it as the technical fine print. Here are the three biggest things to keep in mind from Google’s guidance:
Location is everything: Your file must live at the root of your host (e.g., yoursite.com/robots.txt). If you tuck it away in a subfolder, crawlers simply won’t look for it.
Stay in your lane: A robots.txt file only has authority over its specific protocol (HTTP vs. HTTPS), subdomain, and port. If you have a mobile site (m.yoursite.com), it needs its own dedicated file.
Stick to UTF-8: The file must be a plain text file with UTF-8 encoding. If you use non-standard characters, Google might find your rules invalid and ignore them entirely.
Step 3: Write Your Robots.txt Rules
I’m going to show you how to set up a simple robot.txt file, putting the rules we mentioned above into action.
Every robots.txt file starts with the user-agent directive. This defines which crawlbot is subject to the rule. This example from Google’s robots.txt documentation sets Googlebot as the user.
The example also defines two rules: allow and disallow. They enable the robots.txt file to guide Googlebot toward any page under the root domain www.example.com, except for those with the /nogooglebot/ URL path. All other crawlbots are free to crawl any page within the site.
I know it looks super simple, but these two lines are already doing a lot.
This rule also links to an XML sitemap, but that’s not strictly necessary. It serves as a universal map for all crawlers, including AI. It’s especially important for larger sites, as it gives bots a direct path to your most valuable pages without them having to hunt for links.
Voila, you now have a basic robots.txt file with simple (but effective) rules in place.
As you get more familiar with using robots.txt, there are more rules you can use to your advantage. Google lists them all, along with what they do, here.
Step 4: Save and Upload to Your Root Directory
To do its job, your robots.txt file needs to be uploaded to your site’s root directory. How you do this depends on your hosting platform and your site architecture.
A common exception to this is WordPress, which can generate its own virtual robots.txt file when you launch a site. To change it, you may need a plugin or manual upload to override it.
When in doubt, though, contact your hosting platform or search through their support documentation for upload methods. You can usually do this by navigating to their help articles or knowledge base and searching “upload files [hosting company name].”
How to Block AI Crawlers with Robots.txt
Blocking AI crawlers gives you more control over how your content is used.
Some site owners do it to limit AI training use. Others do it to reduce crawler load, protect gated-style content that accidentally became public, or keep competitors from repackaging their work through AI tools.
The trade-off is visibility. If you block everything, you may protect more of your content, but you can also reduce your chances of showing up in AI-generated results.
The major AI crawlers worth knowing are GPTBot (OpenAI), ClaudeBot (Anthropic), Google-Extended (Google), and CCBot (Common Crawl). All four support robots.txt controls, and each publishes a specific user-agent string you can target.
CCBot is one that many people overlook, even though its public dataset powers dozens of open-source models, making it too impactful to leave out.
To block each crawler individually, list each user-agent with its own disallow rule:
User-agent: GPTBot
Disallow: /
User-agent: ClaudeBot
Disallow: /
User-agent: Google-Extended
Disallow: /
User-agent: CCBot
Disallow: /
The major AI crawlers worth knowing span both training and search functions. OpenAI runs GPTBot for training and OAI-SearchBot for search. Anthropic runs ClaudeBot for training and Claude-SearchBot for search. Google uses Google-Extended for training. CCBot, run by Common Crawl, powers dozens of open-source models, so it’s worth including even though many people overlook it.
That distinction matters in practice. Blocking GPTBot does not block OAI-SearchBot, and blocking ClaudeBot does not block Claude-SearchBot. If you want to stop both training and search crawling, you need separate rules for each bot.
All of these crawlers support robots.txt controls, and each publishes a specific user-agent string you can target. To block them individually, list each user-agent with its own disallow rule:
If you’d rather block every non-search bot at once, flip the logic. Disallow everything by default, then explicitly allow the search engines you want to keep.
User-agent: * Disallow: /
User-agent: Googlebot |Allow: /
User-agent: Bingbot Allow: /
Note that Google-Extended is a separate token from Googlebot. Blocking it opts you out of Google’s AI training data and has zero effect on how you rank in regular Google Search.
Keep in mind that while blocking AI crawlers stops your content from feeding model training, it also reduces your chances of getting cited in AI answers. It’s important to proceed with caution if you want to implement these rules.
If AI visibility is part of your strategy, use an llms.txt file for SEO to guide AI systems toward your best content rather than locking them out entirely, as you would with your robots.txt file.
How to Test Your Robots.txt File
After your robots.txt file goes live, confirm Google can read it correctly. Google retired the old standalone robots.txt Tester in late 2023 and replaced it with the robots.txt report inside Google Search Console.
To find it, open Search Console, pick your property, and click Settings in the left sidebar. The report shows which robots.txt files Google has fetched for your site, when each was last crawled, and any syntax errors or warnings it hit during parsing. If you’ve just pushed an update, you can request a recrawl right from that screen.
To test how a specific URL behaves under your current rules, switch to Search Console’s URL Inspection tool. It tells you whether Googlebot can access the page or whether a directive is blocking it.
This move is useful for catching a misplaced disallow rule before it tanks an important page. Make this part of your regular technical SEO site audit.
Another pro tip: Type the root domain followed by /robots.txt in your browser to view that site’s robots.txt file. It’s a quick way to see how competitors structure their rules, which directories they protect, and which AI crawlers they’re blocking.
Pair it with a full SEO audit for a complete picture of where you can improve and overtake your competition.
Common Robots.txt Mistakes to Avoid
Robots.txt mistakes are easy to make and hard to spot until traffic drops. Even small errors can have site-wide consequences.
Here are the most common missteps to watch for:
Using disallow: / on a live site. This single line blocks every URL on your site from every crawler, including your homepage. It usually slips into production when a staging file gets pushed live without being updated, so be sure to review your robots.txt after every migration.
Blocking CSS and JavaScript. Googlebot renders your pages the same way a browser does, so it needs access to your CSS, JavaScript, and image files to evaluate them properly. Blocking these resources can force Google to crawl your site “blind,” resulting in demoted rankings.
Confusing disallow with noindex. A disallow rule stops crawling but doesn’t prevent indexing. A blocked URL can still appear in Google Search if it’s linked from another site. To keep a page out of search results, use a noindex meta tag or password-protect the page instead.
Leaving the file empty or missing. A missing robots.txt won’t break your site. Google will assume everything is crawlable, but you lose the ability to point crawlers to your sitemap, manage crawl budget, or opt out of AI crawlers. Build it into your standing SEO checklist so it’s not an afterthought.
FAQs
How does robots.txt work?
Crawlers check yoursite.com/robots.txt before crawling your pages. The file uses user-agent and disallow directives to tell them which paths to skip. Compliance is voluntary, but major crawlers respect it.
Do I need a robots.txt file?
Not necessarily. Google can crawl your site without one, but the file lets you control crawl budget and block AI training crawlers, which is worth doing even for small sites.
What should a robots.txt file look like?
A minimal file that allows all crawlers and points to your sitemap looks like this:
User-agent: *
Disallow:
Sitemap: https://yoursite.com/sitemap.xml
Add disallow rules for any directories you don’t want crawled, like /wp-admin/ or /checkout/. Use a separate user-agent block per crawler you want to give different rules to.
How do I edit robots.txt in WordPress?
The easiest path is an SEO plugin like Yoast, which includes a robots.txt editor in its settings. Otherwise, edit the file via FTP or your hosting file manager and upload it to your site’s root directory.
How do I fix “Indexed, though blocked by robots.txt?”
This warning means Google indexed a URL it couldn’t crawl. Either remove the disallow rule so Google can read your page’s noindex tag, or password-protect (or remove) the page entirely.
Conclusion
Robots.txt is a small file with a big impact on how your site shows up across the web. A few well-placed directives can keep low-value pages out of search results and decide whether AI systems get to train on your content.
Already have a robots.txt file? Audit it against the mistakes covered above.
Starting from scratch? Build it using the steps in this guide and test it in Search Console before calling it done.
The conversation around robots.txt has shifted. What started as a tool for managing Googlebot and the SERPs now extends to handling AI’s rise in search and emerging standards like llms.txt.
Whatever comes next, robots.txt remains a foundational part of staying in control of your content.
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AI lead generation works best as a system, not a collection of separate tools. The three core layers are data, activation, and optimization.
Traditional lead gen breaks at scale because teams fragment strategy across locations, operate in silos, and rely on manual budget decisions.
Local search carries the highest purchase intent in digital marketing. Most multi-location brands are losing those searches due to inconsistent listings and weak profiles.
AI improves lead quality, not just volume. Lead-to-close rate by location is the metric that actually matters.
You don’t need a full overhaul to start. A focused 30-day rollout can produce measurable pipeline impact.
Multi-location brands are generating more leads than ever. And yet, many are still struggling to turn that activity into consistent revenue across every market they serve.
Here’s the real problem: traditional lead gen was never built for scale. It was built for one team, one market, one campaign at a time. The moment you’re managing dozens or hundreds of locations, that model cracks. Fragmentation sets in. Quality drops. And the manual work required to hold it all together eats your team alive.
AI lead generation changes the equation entirely, but only if you use it the right way. This isn’t about automating what you’re already doing. It’s about building a system that gets smarter across every location, every market, every campaign, at the same time.
This article lays out how to actually do that.
Why Traditional Lead Gen Breaks at Scale
Multi-location lead gen has three structural failure points. Once you can see them clearly, the solution becomes obvious.
Fragmentation. Different teams run different playbooks in different markets. There’s no shared learning system, no central source of truth, and no way to know why your top location outperforms your worst one. According to NP Digital survey data, only 16 percent of multi-location businesses report “very consistent” lead quality across their locations. The majority fall somewhere between “significant variation” and “highly inconsistent.”
Inconsistent quality. High lead volume in one region doesn’t translate to high revenue. The locations that look like top performers by lead count often rank near the bottom by close rate. Without visibility into lead quality at the location level, you’re optimizing for the wrong thing.
Manual optimization that can’t keep pace. Most teams still allocate budget manually, review performance monthly, and build campaigns market by market. That cadence worked when the scale was manageable. At 50 or 100 locations, it’s a liability. Budget decisions made quarterly can’t respond to demand signals that shift weekly.
Buyers make it harder, too. By the time someone contacts your business, they’ve already researched you using search, reviews, and word of mouth. 98 percent of consumers verify an AI-recommended brand before buying, and about 65 percent of Google searches now end without a click to any website. Your presence has to be consistent, accurate, and compelling long before a lead form ever gets filled out.
The old model is broken. The fix isn’t more campaigns. It’s a better system.
The AI-Powered Lead Gen Framework
The brands scaling successfully with AI for lead generation aren’t just using more tools. They’re using tools that connect.
Most companies have pieces of the puzzle. The problem is those pieces don’t talk to each other. Paid media AI can’t access your lead scoring data, so you optimize for clicks that don’t convert. Local listing data lives in a separate system, so top-performing locations can’t surface insights to underperformers. Performance data stays siloed in individual markets and never informs the broader strategy.
The AI-powered lead gen framework has three layers:
Data Layer: Location data, CRM signals, and customer behavior. This is the foundation. If your data is fragmented or inconsistent, everything built on top of it will be, too.
Activation Layer: Ads, SEO, social, and local listings. These are your channels. The goal is to run them from a centralized playbook while adapting execution to each market’s demand signals.
Optimization Layer: AI testing, budget allocation, and personalization. This is where the system learns. It improves not just individual campaigns, but the entire operation simultaneously.
The key distinction is centralized strategy with localized execution. Brand messaging, campaign frameworks, and budget guardrails are set at the top. Creative, offers, and targeting adapt to each market’s specific signals. AI models are trained on the full dataset, not just one region, so outputs are informed by what’s actually working across your entire footprint.
This is how you stop duplicating the same campaign across 50 markets and start building something that compounds. Scale doesn’t come from more campaigns. It comes from smarter systems,
AI and Local Search: Capturing High-Intent Demand at Scale
Your next customer isn’t searching for your brand name. They’re searching “near me.” And that intent matters enormously.
“Near me” searches carry some of the highest purchase intent in all of digital marketing. The problem is that most multi-location brands lose those searches before they ever have a chance to convert. The culprits are predictable: inconsistent Google Business Profiles, weak local SEO signals, and no coherent review strategy.
NP Digital’s research found that 59 percent of multi-location businesses are not tracking their Map Pack visibility at all. You can’t optimize what you don’t measure, and you can’t win local search if you’re not paying attention to it.
AI addresses each of these gaps directly.
Automated listing optimization keeps your business information accurate and consistent across every platform and every location simultaneously. Name, address, and phone number (NAP) inconsistency is one of the most common reasons brands lose local rankings. AI can audit and sync that data at a scale no manual process can match.
AI-generated localized content means each location gets landing pages, service descriptions, and posts that reflect its specific market, without requiring a dedicated content team for every region. Add schema markup so search engines and AI tools can surface your location data in map features and AI-generated answers.
Review sentiment analysis lets you monitor feedback across every location and flag negative trends early, before they compound into a visibility or reputation problem.
The metrics that matter at the location level: local visibility share, calls and direction requests, and location-level conversion rates. Track these per location, not just in aggregate, and the gaps in your strategy become obvious fast.
Scaling Paid Media Across Locations Without Wasting Budget
Manually managing paid ads across 100+ locations is where growth breaks.
Budget gets spread evenly across markets regardless of demand. Creative runs until someone manually pulls it. Performance gets reviewed monthly, by which point underperforming campaigns have already wasted weeks of spend. No one is learning what actually works in each market, because the data stays local.
AI fixes all three. Here’s how it works in practice:
Performance Max runs across Search, Display, YouTube, Maps, and Discovery from a single campaign structure. Rather than building separate campaigns for each location, you set the inputs and let AI distribute across channels based on where demand is showing up.
Dynamic creative optimization means AI is testing headline, image, and call-to-action combinations by market automatically. Creative adapts to what resonates locally, rather than running a single approved version everywhere.
Demand-based budget reallocation is the biggest unlock. NP Digital’s research shows that only seven percent of multi-location businesses use AI or automation to guide budget allocation. The majority allocate manually or based on historical performance. That means most brands are treating their best markets the same as their worst ones.
AI shifts spend toward the locations showing real-time opportunity signals. Same total budget, redistributed by what’s actually working right now. The result: the same dollar goes further because it’s going where it’s most likely to convert.
For more on building a paid strategy that generates more leads without inflating spend, this post breaks down the fundamentals.
Personalization Across Markets: Why One Message Doesn’t Fit All
Customers in Phoenix don’t behave like customers in New York. Generic messaging across locations produces low engagement and lower conversion rates.
NP Digital’s Personalization Maturity by Location data tells the story: 62 percent of multi-location brands are still “mostly standardized” in how they reach customers across markets. Only three percent are fully customized per location. The gap between standardized and partially customized is where most of the conversion lift is hiding.
AI enables three things that manual personalization can’t deliver at scale:
Location-based messaging adjusts the content, offers, and tone of your campaigns based on where a user is and what that market’s demand signals look like. A promotion that converts in one region might be irrelevant in another. AI can surface those distinctions without a marketer manually monitoring every market.
Behavioral personalization goes further. Rather than one-size-fits-all follow-up sequences, AI can trigger personalized responses based on how a specific lead has interacted with your content. The follow-up feels timely and relevant because it is.
Localized ad creative adapts headlines, images, and calls-to-action by market automatically. What works in a competitive urban market is often different from what converts in a suburban or rural one.
Each location also needs its own landing page with unique copy, local reviews, and the specific services offered there. Region-specific pages aren’t just an SEO play. They’re what closes the gap between click and conversion.
Relevance drives conversion. AI delivers relevance at scale.
Lead Quality Over Lead Volume: What AI Actually Optimizes For
More leads does not mean more revenue, especially across locations where quality varies wildly by region.
The metric most multi-location teams are missing is lead-to-close rate by location. It tells you which markets actually convert customers, not just which ones fill the top of the funnel. Without it, you’re optimizing for activity, not revenue.
NP Digital’s data shows that only 22 percent of companies can accurately track lead-to-close by location. Another 32 percent say they can’t do it at all. That means two-thirds of multi-location brands are flying blind on the metric that matters most for growth.
Three metrics separate volume from value:
Lead-to-close rate by location. Which markets are actually converting? This is the signal that tells you where to invest more and where to pull back.
Cost per qualified lead. Not cost per lead. Cost per lead that had a real chance of closing. The difference often reveals which channels are generating noise and which are generating pipeline.
Pipeline contribution. Which locations, channels, and campaigns are directly tied to revenue? This is the number that justifies more investment, and the one most teams can’t answer accurately.
AI addresses each of these through lead scoring models that evaluate more variables per lead than any human team can process manually, smart routing that gets the right lead to the right team within minutes based on location, service type, and availability, and predictive conversion optimization that improves over time as the system learns which signals actually predict a close.
For teams looking to build better systems for nurturing leads once they enter the funnel, that post covers the mechanics in detail.
The 30-Day AI Lead Gen Rollout Plan
You don’t need a full transformation to start seeing results. A focused, four-week rollout can produce measurable pipeline impact, and it gives your team a framework to build on.
Week 1: Audit location data and identify top performers. Pull all location data into a single view: listings, lead volume, close rates, and ad performance. Flag any locations with inconsistent or outdated NAP data. Rank locations by revenue contribution, and identify your top 10 percent and bottom 10 percent. The gap between them is your opportunity map.
Specifically: go into your Google Business Profile dashboard and note which locations are incomplete, missing photos, or haven’t had a review responded to in more than 30 days. That list becomes your Week 2 priority.
Week 2: Launch AI-driven campaigns and optimize listings. Launch Performance Max campaigns targeting your highest-opportunity locations first. At the same time, fully optimize Google Business Profiles across all locations, including photos, services, FAQs, and hours. Set up dynamic creative testing so ad variations can start adapting by market automatically. Fix the listing inconsistencies flagged in Week 1.
Week 3: Implement personalization and start lead scoring. Deploy location-based messaging on your top landing pages. Set up AI lead scoring to prioritize high-intent leads over raw form fills. Build region-specific landing pages for your highest-traffic markets. Automate lead routing so every inbound lead reaches the right team within minutes, not hours.
Week 4: Measure pipeline impact and reallocate budget. Pull lead-to-close rates by location and compare against your Week 1 baseline. Identify which campaigns and channels are driving qualified leads. Shift budget toward the markets and formats showing real pipeline contribution. Cut what isn’t working.
Small AI implementations compound quickly. The goal of this rollout isn’t to solve everything at once. It’s to build a feedback loop that makes your system smarter every week.
For teams that want to layer in automation across the nurturing side of the funnel, lead nurture automation is worth reading before you get into Week 3.
FAQs
How to use AI for lead generation?
Start with the data layer: consolidate your location data, CRM signals, and customer behavior into a unified view. From there, activate AI across your paid campaigns, local listings, and content. Use the optimization layer, AI testing, budget reallocation, and personalization, to improve performance across all channels simultaneously rather than one at a time.
How does AI lead generation work?
AI lead generation uses machine learning to identify high-intent prospects, score and route leads based on conversion likelihood, personalize outreach by market, and reallocate budget toward the channels and locations showing the best performance in real time. The key is building a system where these tools share data, rather than operating in separate silos.
How can AI agents boost lead generation and sales?
AI agents can handle the repetitive, data-intensive work that slows human teams down: monitoring listing consistency, running creative tests across hundreds of markets, scoring inbound leads, and routing them to the right sales rep within minutes. That speed and precision at scale is what produces conversion lift.
Conclusion
The brands that win won’t just generate more leads. They’ll generate better ones, faster, and across every market they serve.
Multi-location complexity is only going to grow. New locations, new markets, more channels, more data. The gap between brands that build AI systems now and those that wait will widen quickly. The difference between a system that scales and one that fragments under pressure isn’t budget; it’s infrastructure.
Start with the audit. Build the connective tissue between your data, activation, and optimization layers. And measure at the location level, because that’s where the real signal lives.
If you want support building out that system, NP Digital’s consulting team works with multi-location brands on exactly this. If you want deeper insights on this topic, check out the full webinar as well.
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Google is redefining Search as a decision-making experience. AI Overviews and AI Mode let users get curated summaries, compare options, and follow up within the search itself, without clicking through to a website.
Gemini is now positioned as an intelligence layer across all of Google’s products. The long-term direction points toward AI handling more research, task completion, and shopping on a user’s behalf.
Google Ads is moving toward a goal-in, AI-executes model. Tools like Ask Advisor, Asset Studio, and expanded Demand Gen features mean advertisers define business outcomes while the platform handles more operational work.
Keyword-first marketing is becoming less sufficient as Google’s systems shift toward inferring intent from behavioral signals, conversational patterns, and context rather than matching exact terms.
Measurement quality is becoming a competitive advantage. As automation absorbs more execution, the teams that benefit most will have clean first-party data, clear business goals, and strong incrementality measurement.
Brand authority may be one of the most important marketing investments over the next several years. AI systems surface brands that are consistently recognized as credible and trustworthy, making authority function as distribution.
Each year, Google hosts two major events that influence how people use the internet and how brands reach them.
The first is Google I/O, where the company introduces major consumer, developer, and platform innovations. The second is Google Marketing Live, where it outlines how advertisers can engage with those changes across Search, YouTube, commerce, and measurement.
Historically, the two events felt seperate. I/O focused on product vision and technical progress, while Google Marketing Live emphasized ad formats, campaign tools, and media performance.
In 2026, however, the connection between them was much clearer.
Taken together, both events point to the same strategic direction: Google is reshaping discovery, productivity, shopping, and advertising around Gemini-powered AI experiences and more agent-driven workflows.
AI is no longer being presented simply as a feature, an assistant, or a limited experiment, but the layer through which people access information, evaluate products, complete tasks, and interact with businesses.
Across Search, Gemini, shopping, Workspace, YouTube, and advertising, Google emphasized experiences in which AI helps curate information, summarize options, recommend actions, and in some cases, help complete the next step for the user.
If that direction continues, marketing teams will need to adapt quickly to a landscape defined less by manual navigation and more by AI-mediated discovery and decision making.
Google I/O 2026: Search Is Evolving Beyond Traditional Search
The biggest takeaway from Google I/O was that Google is fundamentally redefining Search.
For more than two decades, Search has worked in a relatively simple way: users typed in queries, Google returned links, and websites competed for clicks.
That model is changing.
Google made clear that AI experiences are becoming a central part of Search. Building on AI Overviews, the company highlighted a more conversational search experience and described AI Mode as a major step in that direction.
Rather than only directing users to sources, Google increasingly aims to answer questions directly, organize information, and support followup exploration within the experience itself.
That may sound subtle, but it changes the entire structure of the web economy: search is shifting from a discovery tool toward a more decision-oriented experience.
Users might still search for topics such as “best CRM software” or “where to travel in July,” but they are now encouraged to ask broader questions, continue the conversation, compare options, and rely on AI-generated summaries before deciding whether to visit individual sites.
In many ways, Google is becoming the homepage of the internet all over again, except this time the experience is conversational instead of navigational.
For marketers and publishers, this is a meaningful structural change:
Traffic patterns are going to change.
Organic click-through rates are going to change.
Content strategies are going to change.
Traditional rankings will still matter, but visibility within AI-generated responses may become increasingly important if users receive useful summaries before visiting a website. Potentially, these responses may become more important than traditional rankings themselves.
Gemini Is Becoming a Core Intelligence Layer Across Google
The other major story from I/O was Gemini.
Google no longer presents Gemini merely as a chatbot competitor. At I/O, the company positioned it as a core intelligence layer across many of its products and services.
That includes Search, Android, Workspace, YouTube, shopping experiences, developer tools, and even wearable devices.
More importantly, Google continues to invest in agent-based systems that do more than answer questions. The direction presented at I/O emphasized tools that can research, organize, recommend, and help complete tasks on a user’s behalf.
This is where things get interesting.
Google demonstrated experiences that can gather information, support shopping decisions, assist with workflows, and work across applications. The broader implication is that users may spend less time moving manually from one destination to another and more time working through an AI-mediated layer.
That creates a dramatically different internet experience.
Today, consumers browse. Tomorrow, AI may browse for them.
That changes how businesses compete online.
If AI systems become a primary gateway between consumers and brands, discoverability may depend less on traditional SEO alone and more on whether a business is consistently represented as relevant, credible, and useful within those systems.
The implications are massive.
Your future competition may not just be another brand ranking above you in Google Search.
In that environment, the competitive question is not only who ranks first, but also which brands are surfaced, summarized, or recommended by AI in the first place.
Google’s Hardware Direction Offers a View of What May Come Next
One of the more notable areas at I/O was Google’s continued investment in intelligent eyewear and Android XR experiences.
At first glance, smart glasses can feel gimmicky because the category has failed before. But this time is different because the technology finally has the AI layer needed to make wearables genuinely useful.
Google’s direction points toward ambient computing, where AI is available in the background and can respond to context in real time.
In practical terms, that could include systems capable of:
seeing what you see
hearing what you hear
understanding your surroundings
translating conversations live
offering recommendations instantly
guiding purchases contextually
The smartphone may still dominate today, but Google is already preparing for what comes after it.
For example, if wearable AI becomes mainstream over the next decade, consumer behavior could fundamentally change again:
Search may become more spoken.
Recommendations may become more proactive.
Shopping may become more conversational and contextual rather than centered on explicit queries.
Businesses that still think primarily in terms of websites and landing pages may eventually find themselves optimizing for entirely new interfaces.
See the full panel below:
Google Marketing Live 2026: Advertising Is Becoming More AI-Driven
While I/O focused on the consumer experience, Google Marketing Live revealed the business model powering all of it.
And the message was impossible to miss: Google Ads is moving further toward an AI-centered model.
Over the past several years, Google has automated more of the advertising workflow. At Google Marketing Live 2026, that direction became even clearer, with Gemini-based tools spanning campaign creation, creative development, measurement, reporting, and commerce. More importantly, Google moved beyond general AI messaging and attached that strategy to specific products such as Ask Advisor, Asset Studio, new AI Search ad experiences, and agentic commerce infrastructure.
The broader message was that marketers will increasingly provide goals, assets, data, and business constraints, while Google’s systems handle more of the operational execution. In practical terms, that means more campaign planning through conversational interfaces, faster creative iteration through Asset Studio, and more cross-platform guidance through Ask Advisor across Google Ads, Analytics, Merchant Center, and Google Marketing Platform.
This isn’t just incremental automation anymore. Google is attempting to abstract away the operational complexity of advertising itself.
Rather than managing every campaign detail manually, advertisers are being encouraged to define the business outcome they want, such as more leads, more purchases, more subscriptions, or more revenue, and let the platform optimize toward it.
Then the AI determines how to achieve it.
That’s a profound shift because it changes what marketing teams actually spend time doing.
As execution becomes more standardized through automation, strategic inputs such as positioning, creative quality, data quality, and measurement discipline become even more important.
Keyword-First Marketing Is Becoming Less Sufficient on Its Own
One of the clearest themes from Google Marketing Live was that traditional keyword dependency is becoming less sufficient on its own.
For years, digital marketing revolved around precision: exact-match keywords, manual bids, segmented audiences, and granular controls.
Google is increasingly shifting from rigid keyword matching toward broader intent understanding supported by AI, conversational search behavior, and richer contextual signals. Keywords still matter, but they matter inside a much larger system designed to interpret what a user wants rather than simply matching the exact words they typed.
The system no longer needs exact keywords to understand what users want. It can infer intent contextually through behavior, language patterns, browsing habits, purchase signals, and conversational interactions.
That gives Google enormous power, but it also creates tension for marketers.
On one hand, automation can improve efficiency and performance. On the other hand, advertisers may lose some transparency and control as more decisions move into systems that are harder to inspect directly.
The tradeoff is straightforward: Google is asking marketers to place greater trust in automated systems that promise stronger performance.
And whether advertisers are comfortable with it or not, that future is already arriving.
Measurement Is Becoming a Strategic Advantage, Not Just a Reporting Function
One of the most important implications of Google Marketing Live 2026 is that better automation increases the value of better measurement. As more execution moves into Gemini-powered systems, marketers need stronger inputs to guide those systems effectively.
That puts more pressure on signal quality, first-party data, conversion design, and experimentation discipline. Google’s emphasis on Ask Advisor and a more centralized measurement workflow suggests the company wants advertisers spending less time pulling reports and more time interpreting patterns, testing ideas, and improving decision quality.
In other words, the teams that benefit most from automation may not be the teams with the most manual platform expertise. They may be the teams with the clearest business goals, the cleanest data, and the strongest ability to measure incrementality, customer quality, and true business outcomes.
YouTube Is Becoming Even More Important Across the Funnel
Another area that deserves more emphasis is YouTube. Google Marketing Live did not position YouTube only as an awareness channel but a platform that can support both brand building and performance outcomes, especially as creator partnerships, Demand Gen, and AI-assisted media planning become more tightly connected.
That matters because it reinforces the broader idea that Google is not just reinventing Search. It’s redesigning how advertisers create demand and capture demand across its entire ecosystem. If Search becomes more conversational and AI-mediated, YouTube becomes even more valuable as a place to generate familiarity, trust, and preference before the user ever asks the question that leads to a purchase.
The creator and Demand Gen updates also suggest that Google sees YouTube as a stronger bridge between discovery and conversion, not just a top-of-funnel video platform. For marketers, that means the future media mix may depend less on separating brand and performance into distinct channels and more on orchestrating them across connected AI-driven surfaces.
Commerce Is Becoming More Conversational
Another major theme across both events was conversational commerce.
Google is developing shopping experiences in which AI does more than display products. It helps narrow options, provide context, and support purchase decisions within the conversation. Announcements around agentic commerce, Universal Commerce Protocol, and Universal Cart suggest Google is working toward a more connected path from product discovery to transaction.
Consumers will increasingly ask AI questions like: “What’s the best laptop for video editing under $2,000?” “Which protein powder is healthiest?” “What’s the best CRM for a small agency?”
Instead of receiving only a list of links, users may receive curated recommendations with explanations, comparisons, reviews, and direct paths to purchase embedded in the experience. If Google succeeds in building more seamless agentic shopping flows, the gap between product research and transaction could shrink even further.
This has the potential to shorten the traditional customer journey considerably.
The future funnel may no longer look like this:
Search → Website → Research → Cart → Purchase
Instead, it may increasingly look like this:
Ask AI → Receive recommendation → Buy
That means trust signals become more important than ever.
That means signals of trust become even more important. Brands that perform well in this environment are likely to be the ones with strong authority, clear expertise, credible reviews, and a consistent body of useful content.
Which leads to the single most important takeaway from this entire week.
To learn more, see my segment at the event below, starting at the 1 hour 31 minute mark:
Looking Ahead: Brand May Matter More Than Ever
Most companies still think about marketing in channels.
SEO
Paid ads
Social media
Email
Content marketing
But AI is collapsing those channels together.
When consumers increasingly rely on AI systems to recommend products, summarize information, and guide decisions, the real question becomes: Does the AI trust your brand?
That’s where things are headed.
For years, performance marketing dominated because attribution was easy. Businesses could rely heavily on targeting, retargeting, and optimization tactics to drive growth.
In an internet shaped more heavily by AI, brand becomes an increasingly important signal for discoverability. Think about it:
Strong brands are easier for AI systems to recognize.
Strong brands are cited more often.
Strong brands generate more searches.
Strong brands earn more mentions, reviews, and links.
Strong brands create trust at scale.
And trust is exactly what AI systems are trying to model.
This is why businesses that underinvest in brand today are going to struggle over the next five years.
AI may reduce the value of short-term tactical advantages, large volumes of weak content, and purely technical optimization. But it amplifies trust and clear authority.
The companies that win moving forward won’t necessarily be the ones producing the most content or spending the most on ads.
They’ll be the companies that become undeniable authorities in their category.
Because in a world where AI curates the internet for users, authority becomes distribution.
That’s the real story behind everything Google announced this week. It’s not about AI tools but reworking the broader discovery ecosystem around AI-assisted answers, recommendations, and commerce experiences.
If businesses want to remain visible in that environment, investing in a recognizable, authoritative, and trustworthy brand may become one of the most important marketing priorities over the next several years.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-21 13:45:172026-05-21 13:45:17Key Updates from Google I/O and Marketing Live 2026
Google Analytics 4 (GA4) replaced Universal Analytics in July 2023 and introduced a completely redesigned reporting interface.
Standard reports are pre-built and cover everyday metrics like traffic and engagement. Explorations is a separate section for custom analysis, such as funnels and path analyses.
Not every report deserves equal attention. The ones worth checking regularly are those tied to a specific question you’re trying to answer.
Checking a focused set of reports on a consistent schedule is more valuable than occasionally auditing everything at once.
If you’ve ever opened Google Analytics 4 and felt overwhelmed, you’re not alone.
GA4 replaced Universal Analytics in July 2023 and introduced a completely redesigned interface. With hundreds of data points across dozens of Google Analytics reports, it’s hard to know which ones are worth your time.
The good news? You don’t need to look at everything.
I’ve narrowed it down to the 12 best Google Analytics reports. These are the ones worth including in your metrics. I’ll also show you exactly where to find them in GA4 and how to put the data to good use.
What to Look for in a Google Analytics Report
GA4 organizes its reporting into two main categories: standard reports and explorations.
Standard reports are pre-built templates that live under the Reports section in the left-hand navigation menu. They simplify your performance analysis because they’re ready to use from the get-go and cover most of the user data you’d want to see, such as traffic and engagement.
Explorations live under Explore and are a separate section for more custom analysis. They go beyond standard reports, covering metrics like funnels and path analyses. They’re more powerful but require more setup. Think of standard reports as your regular dashboard and explorations as your analysis workspace.
The best reports are tied to a specific question you’re trying to answer. Where are users coming from? Which pages drive engagement? Where do people drop off before converting?
If a report doesn’t connect to a decision you can make, it’s not worth prioritizing right now.
The Best Google Analytics Reports for Marketers
Here are the 12 reports worth having on your regular radar, along with where to find them in GA4 and how to act on what they show.
1. User Acquisition Report
The user acquisition report shows how new users find your website for the first time. It’s broken down by channel: organic search, paid, social, direct, and referral. It’s your clearest read on which marketing efforts are growing your audience.
User acquisition tracks how users were first acquired, while the traffic acquisition report (which we’ll cover next) shows where sessions come from, including those from returning users.
If paid traffic looks strong in traffic acquisition but weak here, you’re likely good at re-engaging existing users but struggling to reach new ones. And that’s a different problem requiring a different fix.
Where it lives: Reports > Acquisition > User Acquisition.
2. Traffic Acquisition Report
GA4’s traffic acquisition shows where each visit comes from, not just how someone first found you, making it a better tool for week-over-week trend monitoring.
As a Google Analytics SEO report, it’s useful for quick diagnostics. For instance, you might use it to compare a specific date to historical performance or conduct a channel-by-channel scan.
A dip in organic traffic while other channels hold steady might point to a ranking change or technical SEO issue, not a site-wide problem. That distinction’s a big deal for deciding how to respond.
Where it lives: Reports > Acquisition > Traffic Acquisition.
3. Pages and Screens Report
Pages and screens reports break down page views, average engagement time, and other engagement metrics by individual page or screen (individual screens on a mobile app).
These are foundational content marketing analytics data points. They make a solid starting point for understanding which posts are pulling their weight and which aren’t. You can sort by views to find high-traffic pages, and then cross-reference the engagement rate.
For example, a page driving strong traffic but showing low engagement might signal a mismatch between what users expected and what they found. That’s a page worth auditing before creating more content on the same topic.
Where it lives: Reports > Engagement > Pages and Screens.
4. Landing Page Report
Unlike the pages and screens report, which measures all page activity, the landing page report focuses on the first page a user lands on during a visit. Landing pages reveal which content is pulling traffic from sources like social or paid campaigns.
A landing page with high sessions and a low engagement rate could be telling you the entry experience doesn’t match what brought users there. That can be where conversion problems start, and it’s the right place to diagnose them before testing other changes.
Where it lives:Reports > Engagement > Landing Page.
5. Engagement Overview Report
The engagement overview report gives you a quick pulse check on how actively people interact with your site. Use it to monitor engagement trends across your website and spot sudden changes before digging into individual pages or channels.
GA4 emphasizes engagement rate over the old UA bounce rate model. It measures the percentage of sessions that last longer than 10 seconds, involve a key event, or have at least two page or screen views.
According to Databox benchmark data, the median engagement rate across all industries sits at 56.23 percent.
That’s a helpful reference point, if not a universal target. A meaningful drop in one traffic channel can signal a content mismatch or a technical issue that’s cutting sessions short (like a slow-loading page).
Where it lives: Reports > Engagement > Overview.
6. Events Report
GA4 tracks user interactions as events, including page views, clicks, form submissions, and other actions you configure.
The events report shows what’s firing on your site and how often each action occurs. You’ll also be able to see the events you’ve marked as key events, aka conversions.
Use this report to check your conversion tracking before judging content performance. If a form submission or sign-up isn’t set up as a key event, for example, your content may look like it’s underperforming even when users are taking valuable actions.
Before you rewrite a page or change your strategy, make sure GA4 is tracking the outcome you care about.
Where it lives: Reports > Engagement > Events.
7. Demographic Details Report
Google’s demographic details report is great for seeing whether the people you’re reaching are genuinely your target audience. It breaks down your audience by details like age or interests. This pairs well with acquisition data if you’re monitoring Google Analytics for social media performance.
If campaigns targeting 35- to 54-year-old professionals are generating traffic that skews heavily under 25, that demographic mismatch shows up here before it turns up in the conversion numbers. That gives you a chance to correct targeting before spending more.
Where it lives: Reports > User Attributes > Demographic Details.
8. Tech Overview Report
Mobile accounts for more than half of global web traffic, which means a mobile performance problem can quickly become a revenue problem. The tech overview report is where you look to find those problems.
Sort by device category and compare conversion rates between mobile and desktop. A significant gap might indicate slow load times or a layout that doesn’t translate well to smaller screens.
Browser breakdown is worth checking, too, since compatibility issues often affect more users than you might expect.
Where it lives: Reports > User > Tech > Tech Overview.
Key event attribution is one of the more revealing Google Analytics SEO report views in the platform, showing how organic search contributes across multi-touch journeys.
Last-click attribution models give all the credit to the final channel a user touched before converting. The key event attribution paths report (formerly the conversions report) provides a fuller view, showing the touchpoints a user interacted with along the path to a conversion.
If social or display advertising consistently appears early in conversion paths, those channels deserve budget even when they don’t earn last-click credit.
Where it lives:Advertising > Key Events > Key Event Attribution Paths
10. Search Console Report
Once you link Google Search Console to GA4, you can view organic search data inside Analytics. Metrics like queries and clicks are all tied to the landing pages they lead to.
The Console-GA4 combination puts this among the most actionable Google Analytics SEO reports.
You can see which queries drive traffic to specific pages and where impression numbers don’t match click-through rates. The report can also uncover which pages rank but don’t convert.
Each data point provides key context, enabling you to fix multiple tracking issues all in one place.
Where it lives: Reports > Acquisition > Search Console (requires linking Google Search Console to GA4).
11. Realtime Pages Report
This report shows which pages people are viewing right now and how many users are on each page. It’s less useful for strategic analysis than the others on this list, but it’s genuinely valuable as a QA tool.
Say you’ve just pushed a campaign live. You can confirm tracking is firing before you make future spending decisions.
Realtime can also help you confirm whether new posts or key event changes are working before standard reports catch up.
Where it lives: Reports > Real-Time.
12. Retention Overview Report
Retention is where sustainable growth happens. The retention overview report shows whether users return to your site after their first visit and how engaged they are after they’re acquired. It’s broken down by cohort over time.
Getting people to come back builds compounding authority and revenue. A declining retention curve can reveal gaps in content quality or user experience issues.
These trends are worth investigating before pushing harder on acquisition, because more traffic will only amplify these issues.
Where it lives: Reports > Retention.
When to Use a Google Analytics Report Template
GA4 lets you customize reports and save them in your library. That way, you can reuse reports without rebuilding them each time.
If you or your team need to share performance data with clients or leadership, Data Studio (formerly Looker Studio) is usually the better option.
Data Studio is Google’s free data visualization tool and connects directly to GA4. You can also use pre-built Google Analytics report templates from providers like Supermetrics and Porter Metrics. These ready-made dashboards cover key data, including traffic overviews and ecommerce performance.
Templates let you stand up a shareable, auto-refreshing dashboard without building from scratch, a real time-saver for anyone reporting to stakeholders who don’t log into GA4 directly.
FAQs
How do I create reports in Google Analytics?
GA4 includes pre-built reports in the left navigation under Reports. To build a custom report, go to Reports > Library and select “Create new report.” For deeper analysis, like funnel exploration, use the Explore section. This operates separately from standard reports and offers more flexible visualization options.
How do I automate Google Analytics reports?
GA4 doesn’t offer native scheduled report delivery, but Data Studio (formerly Looker Studio) handles this cleanly. Connect your GA4 property, build or copy a template, then use the scheduled email feature to send reports at your preferred cadence automatically. Tools like Porter Metrics and Supermetrics extend this further for agencies managing multiple properties or clients.
Conclusion
GA4 populates a ton of data points. It’s on marketers to sift through the noise and boil things down to the reports that move the business needle.
A good place to start is picking two or three Google Analytics reports from this list that fit your current business goals.
If growing organic traffic is your focus, you might begin with the Search Console and traffic acquisition reports. If conversion rate is the priority, events and attribution paths can show you where the gaps are.
Whatever reports resonate with your business case, build a review cadence and stick to it. The more consistent you are, the easier it is to spot patterns and make better calls.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-20 19:00:002026-05-20 19:00:0012 Best Google Analytics Reports Used by Expert Marketers
An AI visibility report tracks how often your brand is cited across AI-generated responses. Think of it as a companion to your SEO reporting, not a replacement for it.
Your tracked prompt set is the foundation of every number Writesonic shows you. If you don’t understand what those prompts cover, you’ll misread your data.
Portfolios organize your tracked URLs by content type. Get this set up early and keep it updated as new content goes live.
Citation data is inherently noisy. A single-period dip rarely means anything. A sustained two-to-three-month trend does.
The Action Center is where the quick wins live. Use it to find pages with citation visibility gaps and start closing them.
Here’s something that should keep marketers up at night: your buyers are researching purchases in ChatGPT and Perplexity, and most brands have no idea whether they’re showing up in those answers.
That gap is exactly what an AI visibility report is built to close. It tells you how often your brand gets cited in AI-generated responses, which pages are driving those citations, and where competitors are outperforming you in the moments that matter most.
Writesonic has one of the more practical toolsets for building this kind of reporting. But I want to make one thing clear: I’m not trying to do a review of the platform. This is a working guide for content teams that need to get this reporting off the ground and want to understand what the data actually means before they put it in front of a client or a leadership team.
Why AI Visibility Reporting Matters for Marketing Teams
Buyers don’t just Google things anymore. A growing portion of them open ChatGPT, type a question, and act on whatever comes back. Salesforce research found that 41 percent of consumers used AI tools as part of their research process in 2024. That number has only grown since.
If your brand isn’t being cited in those responses, you’re losing potential customers.
AI visibility reporting helps you understand not just if you appear, but which topics you’re being cited for, how that’s changing over time, and who’s beating you in the answers your buyers are reading.
Where this fits in your stack matters, too. AI visibility reporting isn’t a replacement for organic search analytics or conversion data, but an added signal. This tells you whether AI systems find your content credible enough to surface. Teams that treat it as a complement to their larger organic strategy get more out of it than those trying to use it standalone.
The two questions it should help you answer: Are we showing up where buyers are actually looking? And if not, what do we fix first?
Understanding Your Prompt Set Before You Report on Anything
Every number in Writesonic traces back to your tracked prompt set. These are the specific questions the platform monitors across ChatGPT, Perplexity, Gemini, and other AI tools to see whether your content gets cited in the response.
Get this wrong, and everything downstream looks worse than it is.
The platform assigns default topic labels to clusters of prompts. Those labels are usually broad. A marketing blog running this kind of reporting might see their prompt topics labeled “content marketing” and “digital marketing.” Both are accurate but they are closely related terms that cover a huge swathe of subtopics. Due to the lack of specificity, you may encounter issues building and reporting on AI visibility if you only rely on the pre-populated topic list.
Here’s what works better: export the full prompt list, drop it into an AI tool, and ask it to summarize the underlying themes, intent types, and audience categories. That same marketing agency’s list of 100 prompts might actually break into much more specific themes, like Organic & search visibility, Paid media & SEM, and Email & conversion.
The screenshot above is a portion of Claude’s output when I asked it to perform this exercise. As you can see, there’s a lot more information here to guide our content reporting (and creation). Not only do we have a clearer idea of the GEO content pillars we’re tracking against, but also the audience and intent for each category.
This type of output influences how you read everything else. If you find that your prompt set skews heavily toward one audience, your citation numbers for content aimed at a different audience will look artificially low. You can’t treat this as losing ground. You’re just being measured against prompts that page was never written for.
The practical rule: only report on content that genuinely aligns with your tracked prompt themes. Flagging low citation share on a page that serves a completely different audience creates confusion in client reports. Know your prompt set first, then interpret your data.
To pull the list, navigate to the Prompts section and use the export option. Fifteen minutes of AI-assisted theme analysis is worth doing before you touch anything else.
Setting Up Portfolios to Track Your Content Over Time
Portfolios are folders. They allow you to organize the URLs you’re tracking by content type so you can report on categories rather than hunting down individual pages every time you pull a report.
Create them early and keep them simple. At minimum, you want separate portfolios for blog posts, core website pages, and comprehensive guides. If your client has distinct product lines or service areas, break those out too.
The part that really matters is the workflow. As soon as a new piece of content goes live, add the URL to its portfolio. Teams that skip this step spend far too much time during reporting cycles searching for pages that should have been tracked from day one. Make it part of the implementation process: publish, review, then add to portfolio.
One thing worth knowing: portfolios aren’t limited to your own content. You can add competitor URLs and track their citation performance in the same view. That’s useful when you need to show a client exactly where a competitor is outpacing them on a specific topic, without having to cross-reference separate reports mid-meeting.
How to Report on a Single Piece of Content
The path is: Overview > Citations > Content Performance. Set your date range and filter by URL slug.
You’ll mainly want to look at Citation Count or Citing Answers, which are how many times that page was cited across all tracked prompts in the selected period.
If you look at Citation Share, the number may appear small. That’s because this view measures a single page’s citation contribution across your entire prompt set, not just the prompts that are relevant to what the page covers. A tightly focused blog post will naturally have limited citation surface area relative to the full prompt universe you’re tracking.
Second, pay attention to the prompts the page is and more importantly, is not being cited for. You can see the full prompt set by clicking on the number in the ‘Answers citing your content’ tab. In this case, I clicked on the 100.
You’ll then be taken to the All Prompts & Answers view, where you can see which prompts and platforms are surfacing your content and which ones are not.
If a page is ranking well for some prompts but missing others that closely match its content, those gaps are actionable. Adding a structured FAQ section or a more direct answer to a specific question can sometimes close them — and that’s something Writesonic can help you generate.
Third, be careful with month-over-month comparisons. A single dip is not a signal. LLM citation patterns shift constantly as models update and competitive content changes. Before treating a decrease as a problem, remove the comparison period and look at a three-to-four-month trend line instead. A trough followed by recovery is a very different story than a genuine sustained decline.
When you do see a real downward trend, don’t touch the content first. Cross-reference with your SEO data and generative engine optimization metrics. Often, the issue is external, like a model update, and editing the content won’t fix it.
Reporting Content Categories with Portfolios
Another useful feature inside Writesonic is the ability to report on content performance at the portfolio level, not just the page level.
To access it, navigate to Overview > Page Tracker > Portfolios. If you’ve organized portfolios by content type, topic cluster, service area, or funnel stage, this view gives you a meaningful way to evaluate how a group of pages is collectively performing in AI-generated answers.
This matters because page-level reporting only tells you so much. When you’re managing a content program at scale, you need to be able to say, “our informational content about hotel amenities is being cited regularly” or “our location-based pages are getting picked up but not driving brand mentions.” Portfolios let you have that conversation at the category level, which is how most content strategies are built and how most stakeholders think about performance.
Two metrics worth understanding here are citation share and visibility contribution.
Citation share tells you what percentage of all AI answers cite at least one page from that portfolio. Think of it as reach for that content category. A 1.6% citation share, like the example above, means those pages appeared in roughly 660 out of 40,000 tracked answers. Reported at the portfolio level, this becomes a concrete benchmark you can share: how often AI tools are drawing from this type of content, and how that’s trending over time.
Visibility contribution is a layer deeper. It measures the percentage of your brand’s total AI visibility that comes from pages in that portfolio being cited alongside a brand mention. It tells you which content categories are driving brand recognition in AI answers, not just traffic or citations. A portfolio with strong visibility contribution means your content and your brand name are appearing together in AI responses, which is the outcome you’re optimizing for.
Together, these two metrics help you go beyond vanity reporting and start answering the questions clients and stakeholders actually care about: Is this content working? Are people seeing our brand name? Which content categories should we double down on, and which need attention?
If a portfolio has solid citation share but low visibility contribution, AI tools are referencing those pages frequently but not associating them with your brand. That’s a signal to look at how clearly your brand is represented within the content itself. If a portfolio is underperforming on both, that’s a prioritization conversation. And if a portfolio is driving strong numbers on both, that’s proof-of-concept worth scaling.
Understanding Volatility: What’s Signal and What’s Noise?
LLM citation data is noisy by nature. This isn’t a Writesonic-specific problem. It’s how these models work. AI citation drift, where sources shift in and out of responses as models retrain, re-rank sources, or adjust sampling, has been documented across platforms. Research from SISTRIX shows citation sources can change significantly week over week, even when the underlying content is untouched.
One data point tells you almost nothing. The question is always whether you’re looking at a trend or a snapshot.
For example, look at the graph above. This shows the number of citations a page has over a two-month span. As you can see, there are several peaks and valleys, even within the span of a few days. However, if you were to draw a trend line, the result would be relatively flat and even increase a bit towards the end of the second month.
That’s why it’s important to remember that a one-period decrease is not a call to action. A consistent downward pattern over two to three months is worth digging into. Before you touch any content, pull SEO performance and AI Overview impression data for the same window. If organic traffic is stable and AI Overview appearances are flat, the Writesonic dip is most likely a model or sampling artifact.
This is worth saying explicitly to leadership and clients. AI visibility reporting is newer and messier than traditional SEO reporting. Setting that expectation upfront builds credibility. Trying to explain unexpected volatility after the fact does the opposite.
What Writesonic Can’t Tell You
Transparency on limitations makes reporting more credible, not less.
As mentioned earlier, Writesonic tracks a defined prompt set, not every AI query relevant to your category. Your citation numbers reflect performance within that sample. That distinction matters when someone asks why results look lower than expected. The tracked set may simply not cover the full range of queries where your content performs well.
Other things to be aware of include:
Prompt volume isn’t search volume. AI platforms don’t publish query data the way Google does. Estimating how many times people search specific prompts in platforms like ChatGPT requires multiple data sources, a scoring methodology, and sampled user data. That means LLM prompt volume should always be taken with a grain of salt, no matter what AI visibility platform you’re using.
Citation change versus buyer behavior. A drop in citations might reflect a model update or a competitor adding a stronger page. It doesn’t necessarily mean fewer buyers are encountering your brand. Separating those two things requires additional data sources like conversion tracking, qualitative research, or broader competitive analysis.
Competitive visibility outside the tracked set. You can see how competitors are performing within your prompt set. You can’t see how they’re performing in AI queries you aren’t tracking at all.
For each gap, the fix is the same: layer in additional signals. Use organic performance, GEO and AEO analysis alongside broader competitive research to paint the full picture. Writesonic works best as one input among several, not as a standalone source of truth.
Using Quick Wins to Improve AI Visibility Now
The Action Center is where the most immediately actionable reporting lives. Navigate to Action Center > Boost Content Visibility > Refresh existing content for AI visibility to find existing pages where competitors are being cited more often than you for the same prompts.
These are your quick wins. The pages themselves usually aren’t the problem; they’re just missing specific structural elements that AI models tend to pull from. Common recommendations from the platform include FAQ sections, comparison tables, and explicit key takeaway sections. These signal to large language models (LLMs) that a page directly answers a specific question and improves your chances of being cited.
Writesonic will generate draft versions of those elements for you. Use them as a starting point, not a final output. Editorial judgment still applies. Not every recommendation fits every page. A conversion-focused product page probably shouldn’t get a sprawling FAQ section that complicates the user journey, even if the data suggests it would improve citation share.
This module is particularly useful at campaign kick-off. Teams can surface concrete page improvements in the first few weeks while the broader strategy is still being developed, giving clients something tangible early.
New Content Opportunities in the Action Center
Beyond refreshing existing pages, the Action Center also identifies topics where competitors are earning citations, and you have no content covering them at all.
Navigate to Action Center > Boost Content Visibility > Create content inspired by competitors winning in AI citations for this view. The recommendations here are about where to create new pages or blog posts, not about tweaking what you have. If a competitor is consistently cited on a topic that aligns with your tracked prompt themes and your site has nothing on it, that’s a real gap in your AI visibility coverage, and a direct input for your content calendar.
Review this section at least quarterly alongside your standard keyword research. The two often point in the same direction.
FAQs
What KPIs matter for executive AI visibility reporting?
Lead with citation share trend direction over a rolling 90-day period, not raw citation counts. Raw numbers require too much context without supporting data. Showing category-level performance for priority topics, plus specific wins and gaps, lands better in executive reporting than a single number that needs a two-paragraph explanation.
How do you create reports showing brand visibility in AI platforms?
Use Writesonic’s Content Performance and Page Tracker views to pull citation data by URL and topic. Present directional trends and be explicit about what your prompt set covers.
How do you report AI search visibility to leadership?
Frame AI visibility as one signal alongside organic search, not a standalone metric. Show specific wins (pages gaining citation share) alongside gaps, and tie recommendations directly to business priorities. Explain volatility upfront so a single-period dip doesn’t derail an entire reporting session.
Where can you find AI visibility reports with sentiment analysis?
Writesonic includes sentiment indicators alongside citation data. You can dig deeper into how your brand is being discussed on LLMs by navigating to Overview, then the Sentiment dashboard under Brand Visibility.
Conclusion
Most teams that struggle with AI visibility reporting don’t have a data problem. They have an interpretation problem. The numbers look strange, the volatility is hard to explain, and it’s difficult to know what to act on.
Writesonic helps with that, but only if you come in with the right expectations. Know what your prompt set covers. Organize your portfolios from the start. Read citation data as a directional trend, not a precise scorecard. Use the Action Center to find the generative engine optimization improvements most likely to move the needle quickly. Teams that build these habits now will be ahead of the curve as AI-driven search grows and the tools mature.
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Keyword volume is one signal, not the full story. It tells you that demand exists, but not where it lives, how it’s being answered, or whether your brand is part of the conversation.
The Ubersuggest keyword tool and Answer the Public now pull data from Google, Bing, YouTube, TikTok, Instagram, and Amazon, giving you a multi-platform view of where your audience is actually searching.
AI tools like ChatGPT and Gemini generate answers, not link lists. Ubersuggest’s AI Search Visibility feature tracks whether your brand appears in those answers and how your visibility compares to competitors.
Ubersuggest’s global keyword data lets you identify regions where demand already exists for your product or service, so you can prioritize expansion instead of guessing.
The highest-value content opportunities sit at the intersection of strong multi-platform demand and low brand visibility. Knowing where that gap is tells you exactly where to focus.
Search is no longer a single-channel game. For a long time, SEO meant one thing: get found on Google. But Google’s own SVP Prabhakar Raghavan noted that roughly 40 percent of young people now turn to TikTok and Instagram for searches instead of Google, a number that’s only likely to grow over time.
Add ChatGPT, Gemini, YouTube, and other rising channels on top of that, and the picture becomes clear: keyword volume alone can’t tell you where demand actually lives, how it’s being answered, or whether your brand is part of the conversation.
The good news is that Ubersuggest is a great tool to help you adapt to this shift. I’ll cover here how Ubersuggest keyword ideas data actually surfaces, and how to layer multiple signals into a strategy built for the way search works today.
What Keyword Data Actually Tells You (And What It Doesn’t)
Keyword research is still the foundation of any solid content strategy. Search volume tells you how much interest exists around a topic. Keyword difficulty helps you gauge how competitive that space is. Search intent tells you what kind of content actually fits the query. All of that is genuinely useful, and none of it is going away.
But traditional keyword data was built for a world where Google was the only game in town. That world doesn’t exist anymore.
A user today might search “best email marketing tool” on Google, watch comparison videos on YouTube, follow threads on Reddit, scroll TikTok for creator recommendations, and then ask ChatGPT for a final opinion before choosing a product. Each of those touchpoints is a moment of demand. Most keyword research tools only capture one of them.
The practical result: you can have a well-optimized piece ranking on page one for a target keyword and still be invisible to a significant chunk of your audience. That’s not a traffic problem you can fix by adjusting your meta tags.
Two questions worth asking before you build any content plan:
Where does demand for this topic actually live across platforms?
Is my brand showing up when people ask AI tools about this subject?
Ubersuggest addresses both. Here’s how each capability works.
How the Ubersuggest Keyword Tool and Answer the Public Surface Multi-Platform Demand
If you used Answer the Public a few years ago, it was a visualization tool that pulled suggestions from Google Autocomplete. Useful, but limited to one platform.
That’s no longer what it is. Answer the Public (now integrated with the Ubersuggest keyword generator) pulls keyword and hashtag data from Google, Bing, Amazon, YouTube, TikTok, and Instagram. That’s a meaningful shift. You’re not just seeing what people type into a search bar anymore. You’re seeing what they watch, hashtag, and shop for across the platforms where they actually spend their time.
Here’s what that looks like in practice. Enter a broad keyword like “marketing” and select a platform.
Switch to Instagram and you’ll see the hashtags your audience is actively using around that topic. Switch to TikTok and you get a keyword wheel showing what creators and users are searching within the app.
You can also compare how results shift over time, which tells you whether interest in a topic is growing or fading on a specific platform. That matters for content planning. A keyword might have modest Google search volume but strong TikTok traction, which is a signal that short-form video would outperform a blog post for that topic. You’d never see that from Google data alone.
For content teams, this changes the planning conversation. Rather than asking “what should we write?” you start asking “what format and platform does this topic actually call for?” That’s a more useful question, and it leads to content that actually reaches people where they’re searching. For a closer look at using the two tools together, see how to use Answer the Public with Ubersuggest.
The AI Search Layer: What Ubersuggest’s AI Visibility Data Shows You
Multi-platform keyword data covers where demand lives across traditional and social search. AI Search Visibility covers something different: whether your brand shows up when AI tools answer questions in your category.
The distinction matters more than it might seem. When someone asks ChatGPT “what’s the best CRM for a small sales team?” they don’t get ten blue links to evaluate. They get a generated answer. Your brand is either mentioned in that answer or it isn’t. There’s no page-two for AI responses.
This is the core challenge of AI search: it’s not about ranking, it’s about being cited. And right now, most brands have no systematic way to know whether they’re being cited at all.
Ubersuggest’s AI Search Visibility feature is built to solve that. It runs repeated queries across AI platforms, aggregates the results, and gives you a clear, data-backed picture of how often your brand appears in AI-generated responses for your most important topics. One AI response is a data point. Hundreds of responses is a pattern.
The feature surfaces four key metrics:
Brand Visibility %: How often your brand is mentioned across aggregated AI responses for relevant prompts.
Industry Rank: Where you sit relative to competitors in your space.
Top Prompts table: The specific questions and prompts where your brand does and doesn’t appear in AI answers.
Competitor Visibility trend chart: How competitors’ AI presence is changing over time.
A note on variability: AI responses are inherently inconsistent. Ask the same question twice and you may get a different answer, different brand mentions, or a different level of detail. That’s normal, and it’s exactly why aggregating data across hundreds of repeated queries gives a more reliable read than spot-checking a single response on a given day.
One of the most actionable outputs from this feature is the Top Prompts table. It tells you which specific AI search prompts are driving brand visibility in your category, and which prompts your competitors are dominating without you. Those gaps are your content brief.
Ubersuggest’s AI visibility features are built to cut through that noise, aggregating responses at scale so your visibility score reflects a real pattern rather than a single snapshot. This is the piece of Ubersuggest keyword research that most marketers haven’t built into their workflow yet. The window to get ahead of competitors here is still open, but it won’t be for long.
Going Global: Using Ubersuggest Data Across Markets
Expanding into new markets is one of the highest-leverage growth moves a brand can make, and one of the most expensive to get wrong. NP Digital now operates in 19 countries, and that growth wasn’t built on guesswork. It came from identifying where demand already existed and going after the regions with the clearest signal first.
Ubersuggest’s global keyword data makes that analysis accessible without a research team. Type any keyword into the Ubersuggest keyword tool, run a search, and filter by country. You’ll see where search volume for your topic is concentrated across global markets.
The insight here is about prioritization. You don’t need to tackle every market at once. You need to find the markets where demand already exists for what you offer, because those are the ones where content and campaigns can work with the grain of existing intent rather than trying to create it from scratch.
Layer in the city-level targeting from AI Search Visibility and you get a second useful data point: not just where people are searching, but where your brand is (or isn’t) showing up in localized AI responses. A market might have strong keyword volume and competitors with high AI visibility, or it might have strong volume and very little AI presence from anyone, which is a wide-open opportunity. That combination turns global expansion strategy from a gut call into a data-backed decision.
For most brands, the low-hanging fruit is closer than it looks. Start by running your core keywords through the global filter and see which regions surface demand you’re currently not serving.
How to Put It All Together
The data points covered above aren’t meant to live in separate tabs. Here’s how to run them as a single workflow.
Step one: map where demand lives.
Use the Ubersuggest keyword tool and Answer the Public to build a multi-platform picture of your topic. Pull keyword volume from Google and Bing, but don’t stop there. Check TikTok and Instagram data for hashtag and creator trends. Check YouTube for video search volume. Check Amazon if your category has a commerce angle. You’re mapping where your audience is actively searching, not just where you’ve historically published.
Step two: audit your AI search presence.
For the topics where you’ve found strong demand, run them through AI Search Visibility. Which prompts is your brand appearing for? Which ones are competitors owning? The Top Prompts table will show you both. If your competitors are consistently cited for a topic your brand should own, that’s a content and PR gap. If nobody in your space is showing up consistently, that’s a first-mover opportunity.
Step three: close the gaps.
The highest-value content opportunities sit where demand is real and brand visibility is low. Those are the topics to build content around, earn citations for, and develop PR relationships that put your brand in front of journalists and creators who influence what AI models learn over time. Publishing more isn’t the goal. Publishing the right content, on the right platforms, on the topics where you’re currently invisible, is.
This framework is repeatable. Run it quarterly as your AI search visibility data evolves and as platform demand shifts. The brands that build this into their routine workflow will compound their advantage over time. For a broader foundation on getting the most out of the platform, the Ubersuggest guide is the right place to start.
FAQs
How accurate is Ubersuggest?
Ubersuggest pulls from multiple sources, including Google’s keyword planner data, to provide search volume estimates. Like any keyword tool, these are estimates rather than exact figures. For most strategy decisions, they’re directionally reliable. For AI Search Visibility, reliability is stronger because the tool aggregates data across hundreds of repeated AI queries rather than relying on a single response, which smooths out the inherent variability of AI-generated answers.
How does Ubersuggest work?
Ubersuggest combines keyword research, site audit tools, competitive analysis, and AI visibility tracking in one platform. For traditional keyword data, it pulls from search engine databases to surface volume, difficulty scores, and related terms. For AI visibility, it runs repeated queries across tools like ChatGPT and Gemini, aggregates the results, and shows how often your brand appears in those AI-generated responses compared to competitors.
How do I use Ubersuggest for keyword research?
Head to app.neilpatel.com, enter a keyword, and review the volume, keyword difficulty score, and related term suggestions. From there, you can filter by country for global demand data, use the Content Ideas tab to see which topics are already performing well in your space, or switch over to Answer the Public to pull platform-specific data from TikTok, Instagram, YouTube, and Amazon alongside traditional search engines.
Conclusion
To do marketing well in today’s world, you need to optimize for multiple platforms and regions.
SEO is no longer just a “Google” game. You must optimize for YouTube, Instagram, TikTok, ChatGPT, and all the other platforms your users use.
On top of that, you should look to expand globally.
Now, it’s too hard to tackle every country, but go after the low-hanging fruit first. What other countries have demand for your products and services? Those are the countries worth considering to move into next.
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Google’s 2024 API leak confirmed that click data and user engagement signals carry more weight in rankings than the search giant has publicly acknowledged. Brand authority matters more than many search engine optimization (SEO) professionals realize.
Core updates now target multiple ranking systems at once. The March 2024 update, combined with prior efforts, reduced low-quality content in search results by 45 percent.
AI Overviews (AIOs) appear in more than 25 percent of searches and have reshaped how content gets surfaced. Optimizing for AIO citations requires a different approach than traditional SEO.
Spam enforcement has intensified, with Google actively targeting manipulative link profiles, scaled AI-generated content, cloaking, and site reputation abuse.
High-quality content, link profiles built on relevance rather than volume, technically sound sites, and verifiable experience, expertise, authoritativeness, and trustworthiness (E-E-A-T) signals have held up through every major update.
Have you noticed rankings shift after a recent update?
Keeping pace with Google’s ranking algorithm can feel like chasing a moving target.
Google may tweak its algorithm thousands of times a year, but the core principle remains the same: rank sites that earn it and penalize those that game the system.
If you understand what Google targets in every major update, you can stop reacting to Google algorithm changes and start anticipating them.
This guide covers what we know about ranking factors and every major Google algorithm change worth tracking. It also gives you 11 practical tactics to protect and improve your rankings, no matter what update comes next.
Use the table of contents to jump ahead or read start to finish if you’re new to algorithm changes.
What Do We Know About Google’s Algorithmic Ranking Factors?
Google doesn’t publish a definitive list of ranking factors. But in May 2024, more than 2,500 pages of internal API documentation were leaked, giving SEOs an unprecedented look under the hood.
The biggest revelation was NavBoost, a re-ranking system that uses Chrome clickstream data to evaluate how users interact with search results.
The leaked documents reference click attributes including “goodClicks,” “badClicks,” “lastLongestClicks,” “unsquashed,” and “unicorn” clicks, all of which feed into how Google assesses page quality. Pages where users spend meaningful time send positive signals. Quick bounces do the opposite.
Rand Fishkin of SparkToro, who analyzed the leak, concluded that building a recognizable, trusted brand outside of Google search is one of the most effective things you can do for organic rankings.
The screenshot above comes straight from the leaked documentation. It catalogs the click-related fields Google tracks inside one of its page-quality modules, with attributes like goodClicks, badClicks, and lastLongestClicks listed directly.
Beyond the leak, here’s a rundown of Google’s established ranking factors:
Page speed: Core Web Vitals (CWVs) are confirmed ranking signals. Slow-loading pages create friction that hurts user experience and rankings.
Content relevance: Google rewards content that matches user intent. Use targeted keywords naturally and build relevant content around the topics those keywords represent.
Freshness: The leaked documentation confirmed how recently a page was published or updated factors into rankings. Regularly refreshing content with current data and examples sends a positive signal.
Link quality: Backlinks from authoritative, relevant sources remain a core signal. The leaked documents suggest Google classifies links into low, medium, and high-quality tiers based in part on click data, with low-tier links ignored entirely. Google also appears to favor diverse link profiles with a range of referring domains over concentrated links from a small number of sources.
HTTPS: Secure connections are a baseline ranking signal and a trust factor for users.
User engagement: Signals such as dwell time, click-through rate, and pogo-sticking feed into how NavBoost evaluates page quality.
E-E-A-T: This shapes how Google’s quality raters evaluate content, which in turn influences how ranking systems are calibrated. The leaked documentation also suggests Google can identify authors and treat them as entities in the system, reinforcing the value of publishing content under recognized, credible bylines.
Topical authority and content depth are increasingly the deciding factors for AIO citations.
How Often Does Google Release Algorithm Changes?
Google search algorithm updates happen constantly. Google may push multiple changes in a single day, and the company has confirmed making thousands of changes to Search in a single year.
Most of these updates are small. You probably won’t notice a drop in page rankings from any individual one.
The exception is core updates. Google rolls out these larger, more sweeping changes a few times per year, and they can directly impact your page performance.
Based on recent patterns, expect a core update about three to four times a year.
My Brief Timeline of Google Algorithm Updates
Below is a concise history of all Google algorithm updates that have had a lasting impact on how Google and SEOs operate, sorted by release date. Each entry links to a detailed breakdown further in this article.
March 2026 Core Update
December 2025 Core Update
August 2025 Spam Update
Site Reputation Abuse Update (May 2024, updated November 2024)
March 2024 Core Update
Search Generative Experience (May 2023, became AI Overviews May 2024)
How-To and FAQ Changes, September 2023
Product Review Update, April 2023
E-E-A-T Update, December 2022
Link Spam Update, December 2022
Helpful Content Update, August 2022
Page Experience Update, June 2021
Google RankBrain, October 2015
Google Hummingbird, September 2013
Google Penguin, April 2012
Google Panda, February 2011
The Google Algorithm Updates You Need to Know About
Here’s a closer look at each update and what it means for your SEO strategy.
The update produced ranking volatility, but this is more routine with updates than a red flag. SE Ranking data shared with Search Engine Land showed nearly 80 percent of top-three URLs shifting positions, and roughly one in four top-10 pages falling out of the top 100 entirely.
Google described it as “a regular update designed to surface relevant, satisfying content for searchers from all types of sites.”
Independent analysis by Aleyda Solis using Sistrix data showed visibility moving away from aggregators, directories, and comparison sites, and toward official sources, established brands, and specialist platforms.
Brand recognition: Is your site a known name in your niche, or could it be mistaken for a generic content site?
Original value: Are you producing data, analysis, or insights of your own, or summarizing what’s already ranking?
Destination authority: Does your site serve as a primary source or as a stop on the way to one?
E-E-A-T signals: Is it clear who wrote the content and why a reader should trust it?
The March 2026 update is harder to read on its own than most. The core update launched two days after the March 2026 spam update completed on March 25, and roughly a month after the February 2026 Discover update wrapped. That means any visibility changes from late March or early April could trace back to any of the three.
If your rankings shifted during that window, segment your data by date before deciding which update caused it.
Google described it as a regular update designed to surface relevant, satisfying content from all types of sites.
Within the first few days, significant ranking volatility was observed across industries, followed by a second spike around December 20. Some sites saw major drops in visibility, while others that had been penalized in previous updates experienced partial recoveries.
Google didn’t release update-specific guidance. Its standing advice remains consistent: there’s no single fix after a core update. If your site lost rankings, the most likely culprit is content that Google no longer considers the most helpful result for the queries you were ranking for.
If you were hit, here are a few areas to review:
Content quality: Does your content fully satisfy the user’s search intent, or does it leave questions unanswered?
Originality: Are you offering a unique perspective, or summarizing what’s already ranking?
E-E-A-T signals: Is it clear who wrote the content, what their experience is, and why a reader should trust it?
Technical health: Have CWVs, crawl errors, or mobile usability issues emerged since your last audit?
Recovery from core updates typically requires patience. Google has noted that meaningful improvements usually become visible after the next core update, though incremental gains are possible in between.
The December 2025 update came five months after the June 2025 core update, continuing a cadence of three to four core updates per year.
August 2025 Spam Update
Google’s August 2025 spam update rolled out from August 26 to September 22, running nearly four weeks. It was the first spam update since December 2024.
Spam updates use Google’s AI-powered SpamBrain system to identify and demote sites that violate Google’s spam policies, including link spam, thin content, cloaking, scraped content, keyword stuffing, and deceptive redirects.
The overall network impact was minimal, but individual sites felt it sharply. Some saw organic rankings collapse for key terms, while others penalized in earlier updates experienced recoveries.
One notable pattern is that sites with old spammy backlinks were not immune.
Case studies showed exact-match anchor text links from low-quality sources, some built five or more years ago, being retroactively devalued as SpamBrain’s pattern recognition continues to improve.
If you haven’t audited your backlink profile recently, run one through Ahrefs or Semrush and flag links with exact-match keyword anchors from irrelevant or low-authority sources. Going forward, focus new link acquisition on relevance and authority.
Site Reputation Abuse Update
Site reputation abuse, also known as “parasite SEO,” is the practice of publishing third-party content on a high-authority domain to exploit that domain’s established ranking signals. Think of a payday loan review page on a university website, or an unrelated affiliate section on a major news site.
Google announced the policy in March 2024 alongside the March 2024 core update, with enforcement beginning May 5, 2024. Initially, the policy targeted third-party content published with little or no host oversight.
In November 2024, Google closed a significant loophole: First-party involvement, including licensing agreements and partial ownership, no longer provides immunity.
Enforcement remains manual through Search Console, though Google has indicated plans to build algorithmic enforcement over time. If you host third-party content that exists primarily to rank for keywords outside your site’s core authority, remove it or noindex it.
March 2024 Core Update
The March 2024 core update was one of the most consequential algorithm updates in years. It ran from March 5 to April 19, overlapping with a simultaneous spam update, and involved changes to multiple core ranking systems at once.
Google’s goal was to reduce low-quality, unoriginal content in search results by 40 percent.
After the rollout completed, Google reported that the combined impact of the March update and previous efforts had reduced such content by 45 percent.
The update also introduced three new spam policies, including expired domain abuse, scaled content abuse (targeting mass-produced pages regardless of whether they were human-written or AI-generated), and site reputation abuse.
One of the most significant structural changes was the retirement of the standalone Helpful Content system. Google folded its function into the core ranking systems, meaning helpful content evaluation now operates as part of the broader quality assessment rather than as a separate algorithmic layer.
Sites that relied on high content volume at the expense of quality were hit hard, with some losing visibility within days of the rollout.
Search Generative Experience (SGE)
What started as SGE in May 2023 was the early prototype for what we now know as AIOs. At the time, SGE was an opt-in, U.S.-only experiment that used generative AI to produce detailed responses to search queries, complete with suggested follow-up questions and relevant links.
The experiment ran through early 2024, with Google iterating on the format and expanding access. By May 2024, SGE was officially retired and replaced by AIOs, which rolled out broadly to U.S. users and later globally.
In hindsight, SGE was the blueprint. Many of the patterns observed during testing carried over directly into AIOs, including a preference for high-authority sources, structured content that clearly answers specific questions, strong E-E-A-T signals, and topical depth across a subject area. The major behavioral shift was that SGE required users to opt in, while AIOs appear automatically.
Presence rates climb above 80 percent in informational verticals like B2B technology and education. Only about 17 percent of AIO-cited sources also rank in the organic top 10, according to the same BrightEdge analysis, reinforcing that content depth and topical authority matter more than ranking position for earning citations.
How-To and FAQ Changes
This update, initially released in August 2023 and upgraded in September 2023, changed how Google displayed rich search results, such as frequently asked questions (FAQs) and how-tos.
Specifically, Google reduced the visibility of FAQ rich results and limited the visibility of how-to rich results on both desktop and mobile devices. As of September 13, 2023, Google no longer shows How-To rich results on desktop.
There’s no need for websites to remove existing structured data that highlights FAQs and how-tos, but if they do, it won’t affect their rankings.
Product Review Update
The April 2023 Product Review Update focuses on experience. It leans heavily into E-E-A-T guidelines as a standard for content quality, prioritizing review content that goes above and beyond the formulaic results you generally see. Google says its ranking algorithm will reward these types of product reviews in search results.
So, if you’re writing product reviews, put in the extra effort to make them informative and helpful. That means enhancing experience with:
Visual evidence: Include original photos rather than stock images.
Audio experience: Add original audio to improve accessibility and depth.
Evidence of experience: Show proof that you’ve used the product.
Quantitative measurements: Track and share the product’s real-world performance.
The addition recognized that first-hand, lived experience with a topic produces meaningfully different content than expertise acquired secondhand. A product reviewer who has used a product for six months writes differently from someone summarizing a manufacturer’s spec sheet. Google wanted its guidelines to capture that distinction.
Trustworthiness remains the most important member of the E-E-A-T family, according to Google’s own documentation. You can have expertise and experience, but if readers can’t trust that the content is accurate and honest, E-E-A-T breaks down.
Link Spam Update
On December 14, 2022, Google released a link spam update targeting websites that buy and sell links. Google started leveraging its AI-powered SpamBrain system specifically to detect and neutralize link spam, including identifying sites purchasing links and sites used for passing them.
Any benefit previously given to a purchased link was nullified. Google’s John Mueller has repeatedly stated that most sites don’t need to manually disavow spammy links, as Google’s systems are designed to ignore them.
Keeping a clean link profile is essential to avoid getting hit by this update. Don’t buy links, and only use white hat techniques to earn them going forward.
Helpful Content Update
Google’s August 2022 Helpful Content Update rewarded websites that produce high-quality content for visitors. Google wanted the top search results filled with content that users find useful, which meant prioritizing depth, accuracy, and genuine value over keyword-driven fillers.
The initial update targeted English pages but was later expanded globally to all languages.
In March 2024, Google retired the standalone Helpful Content system and folded it into the core ranking systems, as covered in the March 2024 section earlier. It’s now part of how Google assesses quality across every core update, including the August 2024 update and beyond.
Page Experience Update
Google’s Page Experience update began rolling out in June 2021 and was completed in August 2021. It formalized CWVs as direct ranking signals, combining them with existing signals for mobile-friendliness and HTTPS security. Guidelines around intrusive interstitials were also part of the framework.
Largest Contentful Paint (LCP): Measures loading performance. Target: under 2.5 seconds.
Interaction to Next Paint (INP): Measures interactivity and responsiveness. Target: under 200 milliseconds. (INP replaced First Input Delay as a CWV metric in March 2024.)
Google clarified that CWVs are ranking signals, not a standalone ranking system. A perfect score won’t guarantee top rankings on its own. But for competitive queries where multiple high-quality pages are vying for the same position, page experience can be the tiebreaker.
Use PageSpeed Insights and Search Console’s CWV report to identify where your site needs attention.
Google RankBrain
In 2015, Google released a Hummingbird extension, RankBrain. It ranks pages based on whether they appear to answer a user’s search intent. In other words, it promotes the most relevant and informative content for a keyword or search phrase.
You can pass RankBrain’s scrutiny by researching the user intent behind every keyword and writing rich, quality content to meet their expectations.
Google Hummingbird
This 2013 ranking algorithm update was all about bridging the gap between what keywords people used and the type of content they wanted to find. In other words, it aimed to humanize the search engine experience and move the most informative and relevant content to the first page.
In response, marketers leveled up by including more keyword variations and relevant search phrases to improve their chances of meeting readers’ expectations.
Google Penguin
This update, introduced in 2012, directly combated “black hat” SEO tactics such as link directories and spammy backlinks. Like the Panda update, it also looked at keyword stuffing.
The goal was to shift away from emphasizing link volume to boost a page’s search ranking and instead focus on high-quality content that attracts valuable, engaging links.
Google Panda
Released in 2011, this SEO algorithm update targeted bad practices such as keyword stuffing and duplicate content. It introduced a “quality score” that ranked web pages based on how people would perceive their content rather than how many keywords they included.
To “survive” Google Panda, marketers needed to create quality content and use keywords strategically.
How Do I Know When Google Releases a New Algorithm Update?
Tracking algorithm updates doesn’t require constant monitoring. What it requires is the right setup.
Sources that tell you when updates happen:
Google Search Central on X: The official account where Google announces confirmed core updates and spam updates. This is the most reliable primary source. If a significant update is rolling out, it appears here first.
Google Search Status Dashboard: Google logs confirmed updates here with start and end dates. Bookmark it.
Google Alerts: Set up an alert for “Google algorithm update” to get notified whenever credible SEO publications cover new updates.
Industry publications: Search Engine Land and Search Engine Journal cover updates in detail. My blog does, too, so check back whenever you suspect a recent update. Subscribing to newsletters is an efficient way to stay informed without having to monitor daily.
Tools that show you when an update may have affected your site:
Google Search Console: The Performance report shows changes in impressions, clicks, and average position over time. If you see a steep, sustained drop in Search Console that coincides with a known update date, it’s a strong indicator of impact.
Google Search Central: Contains resources for diagnosing common performance problems, identifying possible algorithm penalties, and reviewing Google’s official recovery guidance after core updates.
Google Analytics 4: Monitor organic traffic at the channel level with your Google Analytics account. Sudden drops in organic sessions, particularly combined with changes in engagement rate, can signal an algorithmic shift.
MozCast: Tracks daily fluctuations in Google SERPs and displays them as a weather forecast. Mozcast’s high temperatures signal above-average ranking volatility.
Semrush Sensor: Monitors volatility across categories and device types, making it useful for determining whether a change is industry-wide or site-specific.
AccuRanker Grump: Provides volatility tracking by device and keyword category.
Is Google’s Algorithm Different from Other Search Engines?
Each search platform has its own algorithm and ranking factors. While many may overlap with Google’s ranking factors, they all take a unique approach to prioritizing internet content.
Bing
Bing (which also powers Yahoo, DuckDuckGo, and AOL Search) shares broad principles with Google, but Bing’s specific ranking factors differ. It places more emphasis on keyword prominence in title tags and opening paragraphs and has historically been more transparent about incorporating social signals like likes and shares.
In April 2025, Bing launched Copilot Search, its own AI-powered answer layer that blends generative AI with traditional search results.
ChatGPT (SearchGPT)
ChatGPT’s search function operates on fundamentally different logic than a traditional search engine. Rather than ranking pages, it synthesizes answers from multiple sources using a large language (LLM) model augmented with live web retrieval, then presents them as conversational responses with inline citations.
TikTok’s algorithm is engagement-first. The platform’s dominant signal is watch time, followed by shares, comments, and saves. Hashtags, captions, on-screen text, and spoken words all contribute to topical categorization.
The bigger takeaway for SEO is that your brand’s visibility across Google, Bing, ChatGPT, TikTok, and YouTube is increasingly interconnected. Brand mentions and citations across authoritative platforms improve your position in AI-generated answers, including Google’s own AIOs.
How to Succeed with Google’s Algorithm
Ready to tackle Google’s algorithm and boost your page rankings? Try these 11 Google search hacks.
1. Optimize for Mobile
Google uses mobile-first indexing, which means the mobile version of your site is what gets indexed and used for ranking, regardless of whether a user searches from a phone or desktop.
The primary technical drivers of mobile optimization are page speed and CLS. Responsiveness, measured by INP, rounds out the CWV picture. On the design side, tap target sizes and font readability matter most. Content should render cleanly on small screens without requiring horizontal scrolling.
Start with Google’s PageSpeed Insights, which provides a detailed audit of your mobile performance alongside specific recommendations.
For a deeper technical breakdown, use Lighthouse through Chrome DevTools. Search Console’s CWV report can then help you identify which specific pages fall below Google’s good threshold.
2. Audit Your Internal Links
Next, check your internal links. Do they all work properly, and do they link to relevant, up-to-date content? If not, fix the links and ensure they’re redirecting to useful posts to improve the user experience on your website.
Good quality internal links can improve your rankings.
Overuse is also something to look out for. A page crammed with dozens of internal links dilutes the value of each link. Aim for two to four internal links per post as a baseline, with more on longer, more comprehensive pages.
3. Boost User Engagement
Google Analytics 4 defines an engaged session as one lasting longer than 10 seconds, having a key event, or having at least two page views or screen views. A low engagement rate on key landing pages is a signal worth investigating.
Practical improvements you can make are:
Match content precisely to the query that brings users to the page.
Structure content so the most important information appears above the fold.
Use clear headings to help readers navigate.
Add internal links to keep users moving through your site.
If users leave immediately, there’s a good chance your content isn’t delivering what the query promised.
4. Decrease Site Load Time
A slow site hurts CWV scores and user experience. Two of the most common changes most sites can make are image optimization and script reduction.
Compress and convert images to WebP format. You can take it a step further by lazy loading any images that sit below the fold. Also, audit and remove JavaScript that isn’t critical to page functionality.
Google will provide a prioritized list of fixes if you run PageSpeed Insights. Start at the top and work your way through them. One well-executed fix often improves multiple metrics simultaneously.
Big or small, duplicate content on your website can attract a penalty.
To identify duplicate content, use Copyscape. You can search by URL to check if your content appears elsewhere on the web or paste in specific text to find matches. Review the results and take action if you find duplicates.
Implement canonical tags to tell Google which version is the primary page, set up 301 redirects where appropriate, or noindex pages that need to remain accessible but shouldn’t be indexed.
Helpful content fully answers the question a user searched for, ideally without them needing to click anywhere else. It provides context, accounts for follow-up questions, and comes from someone with genuine knowledge or direct experience with the topic.
The best way to do this is to write from real expertise and show your work with specific examples and data. If someone clicks on your website and stays there, Google knows you probably answered the user’s search query.
The result? Higher page rankings than if your articles are superficial or don’t target the right search intent.
7. Avoid Keyword Stuffing
Keyword stuffing means cramming the same keyword into your content multiple times just to boost your chances of ranking. This type of content is often distracting and difficult to read, and it falls foul of the Google algorithm.
Want to avoid keyword stuffing and stay on Google’s good side? Just use a keyword naturally within the text.
8. Improve Site Navigation
Clean navigation makes your site easier for users and search engines. It reduces bounce rate and supports crawlability. It also gives Google a clearer picture of your site’s hierarchy and the pages you want prioritized.
A few things worth reviewing:
Menu structure: Keep your primary navigation focused on the most important sections of your site. Burying key pages five clicks deep makes them harder for Google to prioritize.
Internal linking architecture: Pages you want to rank should be linked from multiple places. Your most authoritative content should link out to supporting pages. This creates a content cluster structure that signals topical depth to Google.
Sitemap: Submit an XML sitemap via Search Console to help Google discover your full page inventory, especially for larger sites.
Broken links: Run a site audit monthly. Broken links waste crawl budget and create dead ends for users. Fix or redirect them.
9. Increase Page Security
Hypertext Transfer Protocol Secure (HTTPS) has been a confirmed ranking signal since Google announced it in 2014. At this point, it’s a baseline. Sites still running on HTTP face trust warnings in Chrome, which affects user behavior regardless of ranking impact.
If you haven’t switched, you should be able to get a free Secure Sockets Layer (SSL) certificate from your hosting provider. Then update all internal links and references to HTTPS. Verify the redirect setup in Search Console to confirm that no ranking signals are lost during the migration.
10. Update and Refresh Old Content
Content that ranked well two years ago may not hold up today. Statistics go stale, tools change, best practices shift, and Google notices when a page stops reflecting current reality.
The leaked API documentation confirmed that freshness is a ranking factor, so regular content refreshes send a direct positive signal.
Build a review cadence for your highest-traffic pages. Update outdated statistics with current data, replace broken or irrelevant outbound links, add new sections where the topic has evolved, and verify that your target keywords still match current search intent.
Pages that have lost rankings over time are often the best candidates for a refresh, since the existing URL already carries domain authority and backlink equity.
11. Build Your E-E-A-T Signals
Strong E-E-A-T signals correlate with better rankings. Here’s how to strengthen each dimension:
Experience: Include original photos, first-person observations, and specific details that could only come from direct involvement with the topic.
Expertise: Add author bios with relevant credentials and links to professional profiles. For Your Money or Your Life (YMYL) content (think health, finance, legal, safety), have qualified experts review or co-author the material.
Authoritativeness: Earn links and mentions from credible sources in your industry. Press coverage and citations in widely-read publications carry particular weight.
Trustworthiness: Make your site transparently owned and operated. Clear About pages, accessible contact information, accurate citations, SSL security, and honest disclosure of commercial relationships all contribute.
FAQs
What is the Google algorithm?
Google’s algorithm is a system of ranking factors, signals, and machine learning models that determines which pages appear in search results for any given query. The 2024 API leak revealed over 14,014 individual attributes tracked across more than 2,500 modules, with core factors including content relevance, link quality, user engagement signals, mobile performance, and page security.
How does Google’s search engine algorithm work?
Google crawls and indexes web pages, then uses its ranking systems to evaluate which pages best match a given query. It weighs hundreds of signals, from content relevance and backlink authority to user engagement data collected through systems like NavBoost, to determine the order of results.
How often does Google change its algorithm?
Google makes minor changes daily. Core updates, which can significantly affect rankings, roll out three to four times per year, with additional spam updates in between.
How do I recover from a Google algorithm update?
Confirm the timing of your traffic drop against known update dates using the Google Search Status Dashboard or Google Search Central on X. Review which pages lost rankings, look for patterns in content quality and E-E-A-T signals, make improvements where warranted, and monitor for recovery after the next core update.
Does Google’s algorithm apply to AI Overviews (AIOs)?
AIOs draw from the same underlying ranking infrastructure as organic search. Pages with strong E-E-A-T signals, structured content, and clear answers to specific questions are most likely to be cited.
Conclusion
Google’s algorithm changes constantly, but what it rewards doesn’t. High-quality content that genuinely helps the reader, link profiles built on trust and relevance, strong E-E-A-T signals, and solid technical foundations have earned rankings through every major update from Panda to March 2026.
The newest layer is optimization for AIOs and LLMs. The fundamentals still apply there, too. Google’s AI draws from the same authoritative, well-structured sources its traditional algorithm has always favored.
The TikTok sale is complete. TikTok USDS Joint Venture LLC closed on January 22, 2026, placing majority control in the hands of American investors Oracle, Silver Lake, and MGX. The ad infrastructure and auction mechanics are still running.
User deletions spiked nearly 150 percent post-announcement, but active usage held flat. Sentiment and platform health are two different things.
Governance shifts hit auction dynamics before they touch the product. Watch CPM and conversion rate week over week, not month over month.
Pulling budget reactively during platform transitions destroys learning phase momentum and costs more to rebuild than staying in.
Platform governance is now a media planning variable. The TikTok sale set a precedent that extends to every major platform in your media mix.
On January 22, 2026, TikTok USDS Joint Venture LLC officially purchased TikTok’s U.S. operations from ByteDance, transferring control to an American-led investor group anchored by the tech giant, Oracle, and investment groups Silver Lake and MGX.
What does this mean for advertisers on the platform?
The app isn’t shutting down. This is a governance restructuring, and TikTok’s ad products and auction mechanics are still running for its 170 million U.S. users. That said, regulatory shifts like this create real volatility risks that deserve a structured response.
This guide breaks down what did and didn’t change, and how to protect your performance without abandoning one of the most powerful paid channels in your media mix.
What the TikTok U.S. Sale Actually Changes
After the sale, TikTok USDS Joint Venture LLC now owns the U.S. aspects of the platform. ByteDance still owns a 20 percent stake, but the governing majority is now American.
Here’s what that means in practical terms.
What changed
Data governance is the biggest structural shift. U.S. user data is now stored and managed under American oversight, with Oracle handling cloud infrastructure. The new joint venture is also retraining TikTok’s recommendation algorithm on U.S. user data exclusively, to keep the content feed free from outside manipulation. Users won’t notice that change immediately, but it’s significant.
The American-owned entity now sets content moderation. The transition introduced additional compliance review processes for ad targeting parameters and audience segments, requiring some targeting options to be re-approved as the platform rebuilt its ad infrastructure.
What didn’t change
The TikTok ads infrastructure is intact. TikTok Ads Manager, Smart+, TopView, and In-Feed formats are all still live. At the 2026 NewFronts, TikTok unveiled new ad formats, including Logo Takeovers and Prime Time placements, showing that new ownership isn’t slowing down on advertising anytime soon.
Creator monetization is also unchanged. The TikTok algorithm still powers discovery through the For You Page, so its rules are still critical for anyone trying to make money on the app. Per TikTok CEO Shou Chew’s internal memo, ByteDance’s global entity continues to manage the platform’s e-commerce operations and broader marketing functions on the new U.S. platform.
Early User Signals: Noise or Real Risk?
According to Sensor Tower data shared with CNBC, the daily average of U.S. users deleting TikTok jumped nearly 150 percent in the five days following the joint venture announcement, compared with the previous three months.
A drop that sharp could raise serious concerns for advertisers, but it deserves some context before we decide whether it signals real risk.
Three things fueled the spike, and none of them signal structural collapse:
A data center power outage caused failed uploads and For You feed irregularities, which TikTok publicly acknowledged.
An updated privacy policy prompted in-app backlash, though the flagged language was present in an archived August 2024 version of the same policy.
Uncertainty around the new ownership’s content moderation approach prompted some creators to hedge their distribution across other platforms.
Competing platforms saw temporary bumps. U.S. downloads for UpScrolled increased more than tenfold, and platforms like Skylight Social and Rednote climbed 919 and 53 percent week over week, respectively.
Monitor trends like these. A sustained shift in creator behavior matters far more to your campaigns than a short-term uninstall spike driven by a data center outage and a misread privacy policy.
The Real Paid Media Variable: Auction Volatility
Here’s what most advertisers miss during a major platform transition: governance changes hit auction dynamics before they touch the product.
TikTok operates on an auction system where costs fluctuate based on competition, targeting choices, and ad quality. Your cost per mille (CPM) isn’t a fixed rate. It moves with how many advertisers are competing for the same audience at any given time, which makes the post-sale period worth watching closely.
Two forces are working in opposite directions right now.
The first is upward CPM pressure from the algorithm retraining cycle. The new joint venture is retraining TikTok’s recommendation algorithm on U.S. user data exclusively. As that process plays out, ad delivery patterns can shift mid-campaign. Campaigns optimized against the previous algorithm’s behavior may see performance move before any creative or targeting change explains it.
The second force is a temporary drop in auction competition. Some marketers were already planning to scale back spending heading into the transition. That window won’t stay open long. As advertiser confidence returns and paused budgets resume, CPM pressure will rise again.
Three things to monitor right now:
Watch your week-over-week CPM movement. Any sustained spike signals a shift in auction dynamics, not just creative underperformance.
Monitor conversion rates independently of volume, since algorithm retraining can compress efficiency without changing impression counts.
Track creative fatigue aggressively. TikTok’s auction dynamics and creative decay rates punish advertisers who let assets run too long without refreshing.
Why Overreacting Hurts Performance
Pulling budget in response to platform uncertainty feels like risk management, but it’s often the riskiest move you can make in practice.
TikTok’s algorithm depends on a learning phase to optimize ad delivery. During this window, it tests bidding by evaluating your audience and creative to identify who is most likely to convert. Full optimization stability is generally reached around 50 conversions per ad group.
Any significant change, like pausing campaigns or cutting budgets sharply, pushes an ad group back into the learning phase, resetting the optimization progress already built.
The cost of underfunding is equally concrete. Campaigns that don’t meet effective spending thresholds show CPMs 40 to 60 percent higher than properly funded ones, because the algorithm cannot optimize without sufficient data volume.
The post-sale period sharpens this dynamic considerably. With the algorithm retrained on U.S. data, cost per acquisition may increase 20 to 40 percent before stabilizing. Pausing during this window causes the algorithm to stop learning from your account entirely. Advertisers who read that temporary cost-per-action (CPA) spike as a signal to exit will reset their learning phase mid-cycle, compounding the problem they were trying to solve.
There’s also a competitive angle worth considering. Brands that maintained their presence through the transition period emerged with stronger relative positioning as competitors pulled back. When auction competition drops, CPMs follow. Advertisers who stayed in captured that efficiency. Those who paused paid higher costs to re-enter a recovering auction.
Volatility creates both inefficiency and opportunity. Which one you experience depends on whether you plan for it or react to it.
How to Protect Performance Without Abandoning TikTok
Here’s the operating model to build so you can capitalize on TikTok’s volatility now, or another platform’s in the future.
1. Pre-Approve Budget Flex Scenarios
Making significant budget changes reactively can ruin campaign performance. Deciding your triggers now means you respond with a plan instead of scrambling.
Don’t wait for a performance drop to decide how you’ll respond. Define your thresholds in advance, like a sustained CPM increase of 20 percent or more week-over-week or a conversion rate drop held across two consecutive weeks.
2. Keep Meta and YouTube Shorts Warm
A channel you haven’t run in months is a cold channel. Meta and YouTube Shorts require the same data runway as TikTok to reach full optimization stability, roughly 50 conversion events per ad group. Maintain enough spend on both to keep your audiences warm and your algorithms learning, so you’re never rebuilding from zero.
3. Increase Creative Velocity
On TikTok, creative has a short shelf life. Volatile auctions accelerate that decay further. Volatile auctions accelerate that decay. Have new creative variations ready to deploy before you need them, not after performance has already dropped.
4. Tighten Weekly Reporting Cadence
Temporarily shift from monthly to weekly performance reviews. CPM movement and conversion rate shifts during algorithm retraining happen fast. Catching them early gives you time to adjust bids before small inefficiencies compound.
5. Audit Platform Dependency
You want to ensure you’re spending enough to gain traction, but not so much that one platform can make or break your marketing success. Roughly 13 percent of agencies’ social spend over the past 12 months has gone to TikTok. If TikTok represents more than 30 percent of your paid social budget, you have concentration risk that deserves a contingency plan.
Zooming Out: Governance Is Now a Media Planning Variable
The TikTok case underscores a growing tension between digital privacy and free speech in the government’s approach to technology platforms. As apps collect vast amounts of user data, governments will likely continue scrutinizing foreign-owned platforms.
That scrutiny isn’t going away, and it won’t stay limited to TikTok. If another foreign-owned platform gains popularity, Congress may revisit this model of ownership-based restrictions. The legal and regulatory architecture built around TikTok is now a template.
Meanwhile, data sovereignty pressures are intensifying globally. Governments worldwide are restricting cross-border transfers and asserting jurisdiction over data within their borders, possibly touching every major platform operating at scale in the U.S. market.
Platform risk is no longer purely a performance question. Ownership structure and data governance now belong in the same due diligence conversation as CPM benchmarks and audience sizing. A channel that delivers strong return on ad spend (ROAS) today can face structural disruption tomorrow for reasons unrelated to its ad product.
FAQs
Did TikTok Sell?
On January 22, 2026, TikTok closed a deal to divest its U.S. entity to a joint venture controlled by American investors, with Oracle, Silver Lake, and MGX collectively owning 45 percent of the new entity. ByteDance retained nearly 20 percent. The platform continues operating under U.S. majority ownership as TikTok USDS Joint Venture LLC.
How Much Did TikTok Sell For?
The deal valued TikTok U.S. at approximately $14 billion, a figure widely considered low given that TikTok’s U.S. entity generates roughly $14 billion annually in advertising revenue alone.
Analysts have noted that the $14 billion price tag gives the company a price-to-sales ratio comparable to that of mature, low-growth companies, far below the multiples commanded by Meta and Alphabet. Most independent estimates put TikTok U.S.’s true market value significantly higher.
Conclusion
TikTok remains a Tier 1 paid media channel. The U.S. market accounts for roughly 38 percent of TikTok’s entire global advertising income, a concentration that reflects genuine advertiser confidence. That doesn’t change because of a governance restructuring.
What does change is how you should think about it. Tier 1 status doesn’t mean risk-free. The TikTok sale established a precedent for how governments can intervene in platform ownership, and that precedent applies beyond TikTok. Every major platform you rely on now carries some version of this risk.
The smart move is better planning.
Stay active on TikTok while the auction competition is still recovering. Build a paid media strategy that lets you flex budgets quickly when conditions shift. Define your thresholds now so you don’t make reactive decisions under pressure, and keep your creative velocity high. Short-form content gives you a low-cost way to keep creative cycling regardless of what’s happening at the platform level.
The platforms that attract 170 million users don’t disappear overnight. Build your strategy around that reality.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-05 19:00:002026-05-05 19:00:00What Does the TikTok Sale Mean for Advertisers?
LinkedIn articles are long-form content published natively on LinkedIn. They live on your profile, get indexed by Google, and surface in LinkedIn search results long after you publish them.
Feed posts drive reach. Articles build the kind of credibility that makes someone want to hire you, work with you, or trust your expertise.
The strongest use case for articles is distribution, not creation. Adapt existing content rather than starting from scratch.
Performance starts with the headline. Specific, opinionated titles outperform vague ones every time.
Articles are increasingly picked up by AI-generated search answers, making them a quiet but growing visibility channel outside LinkedIn itself.
Most brands still treat LinkedIn like a feed-first platform. Post a thought, collect some likes, move on. That works well enough for reach. It does almost nothing for credibility.
The shift worth paying attention to is not about posting frequency. LinkedIn now surfaces content through search and AI-generated answers that reach well beyond your first-degree connections. The professionals and brands showing up in those spaces are not the ones with the most followers. They are the ones publishing LinkedIn articles.
LinkedIn articles are one of the most underused assets in organic LinkedIn marketing right now. I’ll cover what makes them different from feed posts, how to use them as a distribution channel, and how to write them in a way that actually gets read.
LinkedIn Articles Aren’t New, But Their Role Has Changed
LinkedIn launched its publishing platform more than a decade ago under the name Pulse. Most marketers filed it under “things we should probably use” and forgot about it. The format has since been rebranded simply as LinkedIn Articles, and the ones paying attention to what it has become now have a real head start.
What changed is how LinkedIn itself handles content discovery. The platform acts more like a search engine than it used to. Older articles get resurfaced to relevant audiences. Search queries on LinkedIn increasingly pull from published articles, not just profiles. And because LinkedIn articles are public and hosted on a high-authority domain, Google indexes them. A well-written piece can appear in organic search results for months after publication, reaching people who have never heard of your brand.
Most marketers make one of two mistakes here. They either ignore articles entirely, or they copy-paste from their company blog and treat it as done. Neither approach takes advantage of what the format uniquely offers. It is worth noting that articles are available to both individual profiles and company LinkedIn pages, which means the opportunity exists at every level of your presence on the platform.
The deeper issue is that articles require a different strategic mindset than feed content. They are not built for the scroll. Discoverability on LinkedIn works differently than most marketers assume, and that distinction is worth understanding before you publish your first piece.
Why Articles Play a Different Role Than Feed Posts
Feed posts are built for speed. A sharp observation, a quick take. They generate engagement quickly and lose most of it within 48 hours. That is not a flaw, just the format doing what it was designed to do.
Articles operate differently. They are not competing for attention in a scroll. A reader who finds your article through LinkedIn search or a Google result is already in a different mode. They are not skimming a feed but are likely looking for something specific, and are willing to spend time with it.
That behavioral difference is what makes articles valuable for credibility in a way feed posts aren’t. Publishing a well-structured argument on a topic you have real expertise in signals something that likes and comments cannot. It shows you can develop an idea past a single take. The people making decisions about who to hire or work with notice that and they are very much not counting your impressions.
There is also a practical career and business case that rarely gets discussed. The people who evaluate you before a hiring decision or a pitch are not reading your feed. They are searching your name. A LinkedIn profile with published articles on relevant topics sends a different signal than one without. It is the difference between someone who has opinions and someone who has a body of work.
The Real Opportunity: Articles as a Distribution Channel
The framing that kills most LinkedIn article strategies is boiling it down to: “We need to create more content.” More is not the problem. Distribution is.
Most marketing teams are already producing content that never reaches its full potential audience. Blog posts with strong insights get two weeks of traffic and fade. Bylined pieces in trade publications get shared once, then disappear. Presentations from industry events are often never seen again outside the room where they were given.
LinkedIn articles give that content a second life. Take a blog post, extract its central argument, and adapt it for LinkedIn’s format and audience. The original piece stays on your site. The article links back to it and drives qualified traffic from readers who found the piece through LinkedIn search or Google. This extends the shelf life of work you already did without doubling the workload.
The same logic applies at the individual level. An executive’s byline in an industry publication reaches that outlet’s audience once. The same argument published as a LinkedIn article reaches their network, their followers, and anyone searching that topic on the platform for months afterward.
This is the reframe that makes articles sustainable: they are a distribution layer, not a content creation obligation. If your team treats every article as a net-new piece, it will always feel like too much. If they treat it as an adaptation of something that already exists, the lift is manageable and the compounding visibility adds up over time.
How to Write LinkedIn Articles That Actually Perform
Performance starts before the first sentence. Your LinkedIn headline is the only thing most readers will see before deciding whether to click. Vague titles get scrolled past while specific, opinionated ones get clicked. “Thoughts on the Future of B2B Marketing” is invisible. “Why Most B2B Content Strategies Stall at the Awareness Stage” signals a real argument and a reason to keep reading.
Once someone is in, lead with the insight. Most articles lose readers in the first two paragraphs because the writer is still warming up, providing background, explaining what they are about to say. Skip that. Start with the argument. The context can come later (if it is needed at all) structure matters more on LinkedIn than on a traditional blog also, since readers on the platform skim before they commit. Short paragraphs and clear transitions help them orient quickly. The occasional subheading does not hurt either. A reader who skims and grasps the structure is far more likely to slow down and read closely than one who hits a wall of text and bounces.
Tone is the variable most writers underestimate. LinkedIn articles perform better when they sound like a person who has a real position, not a brand running a content calendar. Opinionated works. Specific works better. “Here is what we have seen hold up across dozens of campaigns” lands differently than “Here is what the research suggests.” Readers can tell the difference between lived experience and summarized consensus and respond accordingly.
One practical tip: write the headline last. Draft the piece, find the sharpest sentence in the whole thing, and ask whether it belongs at the top of the article or in the headline. The answer is usually both.
Close with a soft call to action. The article should be valuable on its own, but it can still point somewhere. A forward-looking question to spark discussion, a brief observation that invites a reply, or a link to a related resource all work. Hard sells do not belong here. The goal is to earn the next click, not demand it.
One step most people skip: after publishing, go into the Manage tab and set a custom title and description for your article. These fields are what search engines use in place of your on-page headline, so taking two minutes to optimize them for a target keyword meaningfully improves how the piece gets found off-platform.
Where LinkedIn Articles Fit in a Modern Content Strategy
Most content strategies have a gap between awareness and action. Social content gets attention. Your website converts it. What sits in between is often nothing, and that gap is where brands lose the consideration battle to whoever showed up with more substance.
LinkedIn articles fill that gap. They are where a reader who already knows you exist decides whether your thinking is worth trusting. That is a different job than a feed post or a homepage. It is the consideration stage, and most brands leave it completely unaddressed.
Think about how buying decisions actually get made in B2B. Someone sees a post, looks up the author, skims the profile, and then either moves on or goes deeper. Articles are what “going deeper” looks like. A series of well-argued pieces on a specific topic does more to establish authority than any amount of engagement metrics on short-form content. It is proof of thought, not just presence.
Your owned content still handles the conversion. An article should not be trying to close a deal. It should be building enough confidence that a reader wants to take the next step on their own.
The brands doing this well rarely talk about it as a content strategy. They talk about it as a sales and trust-building motion. That reframe is worth borrowing.
The Missed Opportunity: LinkedIn Articles and AI/Search Visibility
Here is something most LinkedIn content guides do not mention: your articles can show up in Google before your company website does.
LinkedIn’s domain authority is among the highest on the internet. When you publish an article there, you are borrowing that authority. A well-structured piece on a specific professional topic can surface in Google organic results, featured snippets, and AI Overviews faster than a comparable post on a newer company blog that is still building its own search presence.
For brands that are early in their SEO journey, that is a meaningful shortcut. And the opportunity is only growing: LinkedIn is now the second most cited source in AI-generated answers, trailing only Reddit.
Source: Semrush
Most marketers measure LinkedIn articles by what happens on LinkedIn. Reach and engagement matter, but they miss the larger picture. A piece that generates modest engagement on the platform can quietly pull in search traffic for months. The people finding it that way were never in your feed. They were looking for an answer, and your article was there.
AI-generated answers tend to pull from sources that are credible and publicly accessible. LinkedIn articles are both. If you are not publishing them, you are not in that conversation at all.
FAQs
How do you post an article on LinkedIn?
For individual profiles, go to your LinkedIn homepage and click “Write article” in the post creation box. For company pages, click “Create” and then “Publish an article.” Both paths take you to LinkedIn’s native publishing editor. Add a headline, body text, and a cover image, then hit Publish. After publishing, share the article to your feed with a short caption to extend its initial reach.
What do LinkedIn articles look like?
LinkedIn articles have their own URL and display with a headline, a cover image, and a full article body. They live on your profile under the “Articles and Activity” section and can be shared across the platform or externally.
How long should a LinkedIn article be?
Between 600 and 1,200 words tends to work well. That is long enough to develop a real argument, short enough to hold attention. Structure and clarity matter more than hitting a specific word count.
What is the LinkedIn article image size?
The recommended cover image size is 1200 x 627 pixels. Use a clear, high-contrast image that communicates the topic. Skip generic stock photography if you can.
Are LinkedIn articles credible?
They can be. Articles that reflect genuine expertise and specific experience carry strong credibility signals. The format does not make content credible on its own. The thinking does.
Are LinkedIn articles indexed by Google?
Yes. Public LinkedIn articles are indexed by Google and can appear in organic search results. This is one of the most underappreciated benefits of the format, since a well-written article can generate visibility long after it was published.
Conclusion
LinkedIn articles are not a volume play. The teams getting real results from them are not publishing more often. They are being more deliberate, using articles to deepen ideas that already have an audience and extend content that is already doing work elsewhere.
The format rewards a genuine point of view backed by specific experience. If you have content worth publishing, you have content worth adapting. Start with one strong piece, sharpen the argument for a LinkedIn audience, and publish it with a headline that earns the click. The discoverability of that article, both on and off the platform, will depend on how well you understand LinkedIn SEO, so that is worth getting right from the start.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-01 19:00:002026-05-01 19:00:00LinkedIn Articles: What Sets Them Apart & How to Write Them
Content agencies often specialize in certain industries or subsets of content marketing, such as technical SEO or conversion-focused content.
Our list of some of the best content marketing brands in the business covers a range of services and specialties.
Check out their client lists and portfolios to see if their work aligns with your expectations and preferences.
Knowing what to look for in a content marketing company and the right questions to ask can help you identify the ones with the abilities and capacity to help you expand and improve your content strategy and reach your marketing goals.
The content world is changing, but people still know its value. A 2023 survey from the Content Marketing Institute showed that over three-fourths of marketers indicated that content marketing generates demand and leads.
This is no surprise when you realize 70 percent of people would prefer to learn about a company through an article rather than advertising.
Content marketing can generate huge amounts of traffic, leads, and sales for your business. If you’re a company looking to get started with content marketing, it can be tough to find the resources and expertise you need.
What kind of content do your customers want from you? Is that the same kind of content that creates revenue for your business? Today we’ll take a look at the best content marketing companies in the industry to help you answer those questions and more.
Agency
Best For
Ideal For
Notable Clients
Standout Approach
NP Digital
Immediate and consistent revenue growth
Broad (B2B, e-commerce, SaaS, finance)
CNN, Adobe, Western Union, SoFi
Revenue-focused content with technical SEO built into every campaign from the start
Seer Interactive
Big data search and content
Competitive industries like finance, banking, and mortgages
Asos, Intuit, SendGrid, Terminix
12,000GB proprietary data warehouse surfaces hidden customer trends competitors can’t see
Brainlabs
Technical SEO
Not specified
Formula 1, Estée Lauder, Capital One, Polaroid
Built on a team of mathematicians, scientists, and programmers driving data-backed automation and testing
Fractl
In-depth, research-heavy content
Research-intensive industries
Porch, Fanatics, Superdrug, Healthline
Research published in Harvard Business Review, The Economist, and the NYT, with a dedicated client growth division
Entrepreneurial approach to flipping underperforming businesses through aggressive conversion optimization
The Content Bureau
B2B content marketing
Technology, venture capital, and financial sectors; global corporations
American Express, PayPal, Microsoft, Cisco
Woman-owned agency with 80 percent of staff at 10+ years tenure, offering premium, high-attention client service
Webprofits
Growing challenger brands
E-commerce, consumer, and retail brands scaling fast
Logitech, Philips, Nespresso, HP
“Fluid marketing” methodology blends digital strategy and performance marketing to find hidden growth opportunities
Siege Media
Scalable SEO content
Fortune 500 companies down to small startups
Zillow, Airbnb, TripAdvisor, Asana
Passive link generation through content, backed by a proprietary link management tool maintained monthly
Directive
Performance marketing for tech companies
Tech companies of all sizes
Amazon, Bill.com, Matillion, SentinelOne
Generated $10B+ in client revenue by acting as an embedded extension of in-house marketing teams
1. NP Digital – Best for Immediate and Consistent Revenue Growth
NP Digital is my content marketing company. We created NP Digital in 2017 to serve the millions of people who needed help with their content marketing to grow revenue.
Rankings are important, but many marketers still focus obsessively on keywords and content that doesn’t lead to revenue. I’ve always focused on helping readers build a business that generates traffic, leads, and, most importantly, revenue. So we have a big focus on developing high-quality content that ranks high and converts visitors into customers by aligning with user intent.
Today, we’re one of the top content marketing brands in the business. a powerhouse global agency with one of the top 100 blog destinations in the world.
Another thing that’s different about NP Digital is the fact that we incorporate technical SEO into our content marketing planning. SEO — technical, on-page, off-page, local, etc.— it’s always a package deal with content marketing. Our status as one of the top SEO agencies means you get the best of both worlds.
We stay on top of Google’s updates and algorithms and adjust our strategies accordingly. This means the content we create for our clients automatically performs well with Google. here’s no extra work required.
NP Digital is my way of helping everyone achieve the revenue and growth they deserve in their business.
2. Seer Interactive – Best for Big Data Search and Content
Wil Reynolds founded Seer Interactive, which got its start as a search engine optimization company. What makes Seer one of the best content marketing companies on our list is its focus and emphasis on big data.
Using a combination of in-house and third-party tools, they’ve built a massive data warehouse with almost 12,000 gigabytes of data they can analyze to identify new, hidden, and unexpected customer trends.
If you’re in a competitive or cutthroat industry (e.g., finance, banking, or mortgages), this data is what you need to stay ahead of your competitors.
With Seer Interactive, their approach is SEO-heavy. That should be an important priority for every company, whether you’re big or small, but not every company is ready for Big Data.
Brainlabs was founded by Daniel Gilbert in 2012. Understanding that marketing was becoming all about data, he took the unusual tactic of hiring mathematicians, scientists and programmers to support automation and data-driven insights.
His approach paid off: Since 2020, the agency has expanded its services by acquiring other marketing companies, including the SEO-focused Distilled, a leader in the space.
Today Brainlabs is known as one of the top content marketing agencies for technical SEO and helping companies evolve in an increasingly competitive SEO landscape. They are constantly experimenting and testing to improve conversion rates.
4. Fractl – Best for In-Depth, Research-Heavy Content
Fractl is a research-heavy, data-driven content marketing company. They’re focused on rapid, organic growth that’s driven by content marketing, data journalism, digital PR, and search engine optimization.
Research makes Fractl unique.
They’re always researching industry-related topics, and they share their understanding of the art and science behind newsworthy content. They share their research in top publications, leading market resources, scientific journals, and authoritative conferences around the world.
Their research has been published in MarketingProfs, TNW, The Economist, Time, the Harvard Business Review, the New York Times, Pub Con, and many other publications and journals.
If you’re in a research-heavy industry and you’re looking for a high-growth content marketing company, Fractl is a good choice. Aside from being one of the best content marketing brands, they’re one of the few companies that have a division dedicated to client growth.
5. Column Five – Best for Data and Content Visualization
Column Five describes itself as a creative content agency. They’re primarily focused on the visual side of content marketing — storytelling, design, data visualization, video, interactive motion graphics, even exhibition design.
They are most known for their “child of the 90s” viral video on behalf of Internet Explorer, which launched their reputation as one of the best content marketing brands out there.
As a content creation company, Column Five is focused primarily on content strategy, content creation, and content distribution. They rely on a simultaneous mix of organic and paid distribution channels to draw attention to client content.
The company mantra is “the best story wins,” showing their commitment to developing great content that delivers big results. It specializes in content that is “inherently newsworthy,” making it more likely to get traffic, links, and media attention.
6. Single Grain – Best for Conversion-Driven Content Marketing
In 2014, entrepreneur and leading marketing expert Eric Siu made a big gamble. He bought a failing SEO agency for less than the cost of a cappuccino — $2. This wasn’t the first time he’d made a seemingly risky bet — in the past he led the growth strategy for an online education company when it had just a few months of cash left in the bank.
“A month into it, the CEO pulls me aside,” Siu recalls, “and he’s like, ‘Eric, you know, 48 people, their families, they’re riding on your shoulders right now, and if you can’t hit numbers in the next month, we’re gonna have to let you go.’”
Did I mention he was just 25 years old at the time?
Eric leveraged his marketing know-how and entrepreneurial outlook to turn Single Grain around and take it to where it is today: solidly among the ranks of the best content marketing brands out there.
Eric Siu and the Single Grain team can do for your business what they do best: turn it around. They know how to turn a faltering business into a successful one with an approach of optimizing for conversions and focusing on rapid growth.
7. The Content Bureau – Best for B2B Content Marketing
The Content Bureau bills itself as a premier B2B content marketing company. This agency is woman-owned, 100 percent virtual, and their team is 90 percent female, of which a third are women of color. The Content Bureau focuses its attention on the technology, venture capital, and financial sectors, working almost exclusively with global corporations that rely on them year-round.
Many of their clients are long-term, stable clients who prefer their premium approach, exclusive attention, and veteran workforce; 80 percent of their team have been with The Content Bureau for 10+ years.
As an organization, they give their clients lots of handholding; they’re open and transparent with each of their clients, and they deliver amazing service with their extraordinary content.
Webprofits is the content marketing and advertising company that was co-founded by Sujan Patel and Alex Cleanthous. Their company focuses on challenger brands in the e-commerce, consumer, and retail space that want to grow their business fast. They’ve refined their process based on real-life, in-the-trenches experience.
In fact, Patel doesn’t think of Web Profits as an agency. He calls it a marketing “hit squad,” a team of specialists who understand your business inside and out.
What makes Web Profits one of the top content marketing companies? They use a unique “fluid marketing” approach, which combines digital strategy with performance marketing. This enables its team of experts to identify hidden correlations and connections that can point to exciting opportunities for content marketing.
This makes the Web Profits team uniquely qualified to serve challenger brands that want to make a big impact.
Siege Media prides itself on taking a “scientific approach” to scaling SEO-focused content. The agency works with a wide range of companies, from established Fortune 500 businesses to small startups.
The focus of the business is on link-building. Siege Media creates content that serves as passive link generators, a tactic they say is more effective than manual outreach. Their formula results in high-impact content that produces instant results—and it’s a cost-efficient tactic, too.
Siege’s superpower is a proprietary solution for link management. Siege maintains the tool for its clients on a monthly basis, ensuring that websites are always aligned with overall goals and updates.
This commitment to innovation and leveraging technology for content marketing makes Siege one of the best content marketing companies for the future.
10. Directive – Best for Performance Marketing for Tech Companies
CEO Garrett Mehrguth founded Directive when he was just 21, focusing on SEO. Today it works with some of the world’s most prominent tech companies, helping them become more discoverable in a dynamic and often challenging industry. Since its founding, it’s generated more than $10 billion in revenue.
The agency uses a unique data-driven methodology to generate quality leads organically across the marketing funnel. The team prefers to act as a partner rather than a vendor, serving as an extension of its clients’ in-house marketing teams.
4 Characteristics that Make a Great Content Marketing Company
A good content marketing company will have no problem demonstrating that they have the expertise and the resources they need to make your campaign a success. These are some qualities to expect in a high-quality content marketing agency.
1: A Stable Team of Content Creators
Content mills produce poorly written filler content that’s mainly written for search engines. Not only is that a short-sighted approach, but Google’s algorithm is more likely to ding sites that use it—especially now that it is incorporating AI.
The best content marketing companies have a roster of regular and consistent writers on their team. Stable writers are skilled at writing, grammar, logical consistency, and storytelling. These writers can draw your readers in, creating content that moves people towards a specific goal or objective that you have in mind.
These writers don’t need a lot of babysitting, and they’re able to figure things out, to a certain extent, on their own. They’re dependable, and they’re able to match your brand voice.
When you contact a content marketing company, you’ll want to ask them questions about how they run their business.
How many writers do you have on staff?
Are they freelance or W-2? Do you use a mix of both?
How many of your writers are full-time? Part-time?
How do you manage your team of writers?
How many years of experience does the average writer on your team have?
When you ask companies these questions, listen to their answers carefully. Look for any inconsistencies or red flags. If you spot any, bring them up immediately and ask for an answer.
2: Access to Publishers and Influencers
According to Derek Halpern, founder of Social Triggers, you should be spending 20 percent of your time on content creation and 80 percent of your time on content promotion. The content marketing companies you work with are no different. If you’re investing a significant amount of time and money in creating an amazing piece of content, you should be spending 4x as much time on promotion to make sure your target audience sees it.
When you’re working with a content marketing company, they should already have a list of influencers and publishers in their address book. They should also have strong connections and relationships with the right people, so they’re reasonably sure they can drive traffic to your content.
3: Specialized Knowledge About Your Industry
In an ideal world, your content marketing provider has a significant amount of experience in your space, or the ability to connect with experts who do. At a minimum, you’ll want to ensure that the content marketing company you choose can write credibly about the topics that are relevant to your business.
The more specialized the content, the more important these criteria are for your business.
Industries like healthcare, engineering, or finance require large amounts of specialized experience. It’s unrealistic to expect an inexperienced company to write credibly about a highly technical topic.
Specialization requires specialists. The more technical your business, the more important it is to hire a content marketing company with experience and expertise in your field.
4: Content Analysis and Measurement
When you’re investing in the services of a content marketing company, you’ll want to see the numbers. The agency should be able to provide you with a detailed breakdown that includes data outlining your performance as well as the KPIs, metrics, and sentiment surrounding your content.
This information should give you the answers to the following questions:
Does this content move us closer to our campaign goals?
Does this piece of content (e.g., blog post, whitepaper, e-book, infographic) lead to enough conversions?
How far are people reading into your content?
Where in our flywheel are we losing customers?
What do we need to change/optimize to improve our conversion rates?
Which content marketing opportunities are we missing, and where?
Creating content isn’t enough. The content marketing company you choose should provide you with the actionable data you need and a comprehensive strategy to create profitable content for your business.
What To Expect From a Great Content Marketing Company
Top content marketing agencies are able to get you up to speed on their processes and provide you with a consistent and comprehensive set of deliverables. These deliverables ensure that your content marketing campaigns stay on track and that you’re able to achieve the consistent results you need.
To do this, your content marketing provider should provide you with onboarding guidance and specific deliverables throughout the pre-launch, launch, and post-launch phases of your campaign. These should include:
Content samples demonstrating your knowledge and expertise
The information and materials (e.g., credentials, existing content) they need from you to get started
A statement of work and a list of deliverables (e.g., 14 2,500-word articles each month, edits included)
Their process (if they’re not working with you and yours)
Projected campaign milestones, timelines, and calendars
Your point-of-contact, including their name, and contact information
Hours of availability
The best way to communicate (e.g., Slack, email, phone, chat, or text)
Expectations from you
Their process, policies, and procedures
Analysis and reports, including business goals, objectives, KPIs, metrics, strategy, tactics, and risks
Content audits
Consistent updates on your campaign performance
Regular (weekly or monthly) calls to discuss performance
Consistently updated due dates and delivery timelines
Monthly debrief to discuss successes and failures
Here are some additional details you should also expect from your content marketing providers:
Good boundaries (including the ability to say no)
Prompt and clear feedback
Accurate information on various parts of your campaign, including financial, campaign, and performance data
The best content marketing companies ask a lot of questions. They make sure to provide you with the upfront information you need to vet their company and make an informed decision. Once you’ve decided to move forward, they ask you for all of the information and materials they’ll need to produce the results you want.
FAQs
What makes good content marketing?
Good content marketing is different for every business, but in general, it involves creating well-written content that provides valuable information for your target market. It also draws in qualified leads and converts them into customers at a rate that justifies your investment.
How do you track content marketing results?
Tracking content marketing results involves setting clear goals, identifying key performance indicators (KPIs) such as website traffic, inquiries, and conversion rates to use as metrics, and monitoring the results. Most content marketing agencies use analytics tools to track and measure results.
How do you optimize for content marketing?
Optimizing for content marketing involves several steps. First, research who your target audience is and their needs. This will guide you toward topics for content development that can answer their questions and provide valuable information. Incorporate SEO to ensure your content ranks high on search engine results pages and brings in organic traffic. Finally, analyze the results to refine content topics, formats, and overall strategy.
Which content marketing agency is best for B2B companies?
B2B companies should look for agencies that focus on long-form content, SEO, and lead generation. The best partners understand how to create content that nurtures prospects over time, not just drives traffic. Agencies with strong experience in SaaS or professional services tend to perform best here.
Which content marketing company is best for small businesses?
Small businesses need agencies that balance quality with cost. Look for teams that offer flexible packages or project-based work instead of large retainers. The goal is to get consistent, high-quality content without overcommitting your budget early on.
Which agency is best for SEO-driven content?
You want an agency that combines content creation with keyword research and technical SEO. Firms that focus heavily on search performance will build content designed to rank, not just read well. Check for proven results in organic traffic growth and rankings.
Should you hire a specialized content agency or a full-service marketing agency?
Specialized agencies go deeper into content strategy and production. Full-service agencies connect content to SEO, paid media, and conversion optimization, which can drive better overall results. If content is your main bottleneck, go specialized. If growth is the goal, full-service often wins.
How do you choose the right content marketing agency?
Start with their results. Look for case studies showing traffic growth, lead generation, or revenue impact. Then review their content quality and process. The best agencies have a clear system for research, creation, and optimization.
Conclusion
Content marketing produces more leads and revenue than traditional marketing methods. If you’re looking for a good content marketing company to help you get started, it can be tough. Use this list to identify the companies that are a good fit for your business.
With this post, you should have a pretty good idea of the questions to ask, what to expect, and how to select the right content marketing provider.
Invest the right amount of effort with the right company, and your content marketing will grow faster than you expect. It’s tough in the beginning, but it will take effort, push through, and keep creating really helpful content, even if it’s hard.
You’ll see consistent revenue growth once customers realize that you’re serious about helping them solve their problems. Content marketing is the best way to show them that you understand, and you can help. With this said, combining with other disciplines is the best way to unlock your content’s true potential. Check out my lists of the best CRO agencies and top social media agencies for more information.
