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Best Backlink Analysis Tools: Compare Free and Paid Options

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Key Takeaways

  • The right backlink analysis tool depends on the job: Ahrefs or Semrush for deep data, Pitchbox or BuzzStream for outreach, Linkody for monitoring, and Whitespark for local SEO.  
  • Referring domains carry more weight than total backlink count. One link from 100 different sites beats 100 links from a single source. Backlink tools help you target the most impactful ones. 
  • Every tool in this guide includes a competitor link gap report, which surfaces sites linking to your competitors but not to you.  
  • A quarterly link audit catches toxic links, broken backlinks pointing to your site, and outreach wins worth replicating.  
  • Monthly pricing spans $14.90 to over $500. Match the tool to the scale of your work, not to what an enterprise SEO team would buy. 

What comes to mind when you think about creating a “good” link profile? 

Search “What are backlinks,” and Google returns plenty of information, including the  steps  you can take today to improve your link profile and boost your rankings. 

In the past, I’ve provided a lot of advice on building quality links, where to find the best links, and tools that can help. 

Those tips can get you far, but there’s something else you need to do first: Examine your link profile. 

If your website is brand new, this won’t always be a big deal. That’s because you probably don’t have links pointing to your website yet. 

Conversely, if your website’s been around for a few months or longer, there’s a good chance you have some links pointing to it. Some may be good. Some may be bad. Others may not move the needle in either direction. 

It’s important to understand your link profile, as this will give you a clear idea of whether you’re on the right track. 

In a perfect world, you’d see nothing but high-quality, relevant links  pointing to your site. That’s rarely the case in the real world, though. 

Need help conducting a link audit and reviewing the results? If so, this post is for you. Below, you’ll find 14 backlink tools packed with features that shed light on your link profile.  

Backlink Analysis Tools: The Basic Comparison

Pages with backlinks get more organic traffic than pages without, according to Ahrefs research.  

A good backlink analysis tool tells you who links to you, who links to your competitors, and which links are helping your rankings. That way, you know where to target your outreach efforts next. 

The 14 tools I cover in this guide differ in several ways. Some are all-in-one SEO suites with strong backlink modules. Others focus narrowly on a single job, such as outreach or local citations. The right pick depends on your linking strategy and your budget. 

Here’s a side-by-side look at every tool covered below. Pricing reflects entry-level paid plans at the time of writing and may have shifted, so check the vendor’s site before committing. 

Tool  Best For  Standout Feature  Starting Price (Monthly) 
Ubersuggest  Small businesses and solopreneurs  Affordable lifetime plans  $29 
Semrush  Agencies and in-house marketing teams  Backlink gap analysis  $139 
Ahrefs  SEO professionals who need deep link data  Largest live backlink index  $129 
BuzzSumo  Content marketers and PR teams  Content + influencer discovery  $199 
AIOSEO  WordPress users running on-site SEO  Native WordPress integration  $49.50/year 
Linkody  Solo SEOs monitoring a few sites  Real-time disavow management  $14.90 
Cognitive SEO  Mid-sized teams cleaning toxic links  Unnatural link detection  $129.99 
Majestic SEO  Researchers focused purely on link metrics  Trust flow and citation Flow  $49.99 
SEOptimer  Agencies producing white-label audits  Customer-facing reports  $29 
Moz Link Explorer  Marketers who rely on domain authority (DA)  DA, page authority (PA), and spam score  $99 
Pitchbox  Outreach-heavy link-building teams  Automated outreach sequences  $300 
Whitespark  Local SEO specialists  Local citation discovery  $39 
Linkstant  Real-time backlink alerts (legacy)  Instant new-link notifications  $7 
BuzzStream  Outreach and digital PR teams  Built-in customer relationship management (CRM) for prospects  $24 

1. Ubersuggest 

A screenshot of the Ubersuggest homepage telling brands they can get mentioned in Google and Gemini. 

Ubersuggest is my own tool, and I’ve designed it to make serious backlink analysis accessible without an enterprise budget.  

You get a full backlink overview for any domain, including new and lost links, referring domains, anchor text breakdowns, and a domain authority (DA) score.  

The Backlink Opportunity feature is the one I use most. Plug in two or three competitor URLs, and Ubersuggest gives you every site linking to them but not to you. That’s a ready-made outreach list. 

  • Pricing: Plans start at $29 per month for individuals, $49 for small teams, and $99 for agencies. Unlike most competitors, Ubersuggest offers lifetime plans. 
  • Best for: Solopreneurs, small business owners, and in-house marketers who want a solid backlink workflow without paying enterprise rates. If you’re new to SEO, the interface is easier to navigate than that of Ahrefs or Semrush. 
  • Considerations: The link index isn’t as deep as Ahrefs or Majestic, but that won’t matter as much for most small to midsizedsites, that won’t matter. 

2. Semrush 

A screenshot of Semrush’s backlink checker homepage tells readers they can win backlinks that move rankings and offers a free trial.  

Semrush says it runs one of the largest backlink databases in the industry, with more than 43 trillion backlinks indexed. It earns its place on this list for that data depth alone. 

The free Backlink Checker is good for quick checks. You can see a site’s top backlinks, Authority Score, total backlinks, referring domains, dofollow percentage, anchor text, link attributes, and whether links are new or lost. That’s useful if you just want a snapshot of your site or a competitor. 

The paid tools are where Semrush gets more useful for serious backlink work.  

Backlink Analytics gives you fuller backlink and referring domain data, more reports, filters, and tracking. Backlink Gap lets you compare your link profile against up to four competitors in a single view.  

Semrush also includes Backlink Audit, which scores toxic links and flags candidates for the disavow file.  

Both pair well with Semrush’s keyword and traffic data, which is why many agencies consolidate on this platform. 

  • Pricing: Semrush’s SEO + AI Search plans start with the SEO plan at $139. The Pro+ plan is $299. The Business plan ($549) adds AI visibility tools and other functionality. Discounts for annual billing are available, and Semrush also offers a seven-day free trial. 
  • Best for: Marketing agencies and in-house teams that need backlink data alongside full SEO, PPC, and competitive intelligence. If backlinks are one of five or six things you analyze regularly, Semrush is hard to beat. 
  • Considerations: Semrush’s free backlink tools are fine for quick checks. You’ll need a paid plan for serious backlink work, though. 

3. Ahrefs 

Ahrefs Backlink Checker homepage offering a free trial version. 

Ahrefs is one of the strongest backlink tools for SEOs who need more than a quick link count. Its free Backlink Checker is useful for spot checks, but its Site Explorer tool is where serious backlink work happens.  

Ahrefs says its backlink index updates with fresh data every 15 minutes and includes 35 trillion external backlinks in historical records.  

Site Explorer shows referring domains, backlinks, domain rating, anchor text, followed vs. nofollowed links, backlink growth, and “best by links,” which helps you find the pages attracting the most links.  

Content Explorer helps surface link-worthy content ideas, while Link Intersect finds sites linking to competitors but not to you.  

Ahrefs does have free access, but there’s a catch. The free account gives verified site owners limited Site Explorer access for their own websites, including backlinks, referring domains, anchors, and “best by links.” Competitor research, larger reports, Content Explorer, and more advanced link-building workflows require a paid plan.  

  • Pricing: Lite is $129 per month, Standard is $249, Advanced is $449, and Enterprise starts at $1,499 per month. Discounts for annual billing are available. 
  • Best for: SEO consultants, agencies, and in-house specialists who live on backlink data daily. Ahrefs may be overkill for someone publishing one blog post a month but invaluable for anyone running active outreach or technical SEO audits. 
  • Considerations: Lite works for basic backlink monitoring, but Standard is the better fit for most serious users. 

4. BuzzSumo

A screenshot of the BuzzSumo homepage. 

BuzzSumo started as a content discovery tool, and that’s still its strongest trait. Its backlink data is built around content, not just domains, so you can see which articles in your niche earned the most links and where they came from. 

The Content Analyzer pulls the top-shared and top-linked content for any keyword. Pair it with the influencer search, and you have a workflow for finding the writers and publications most likely to link to a similar piece on your site. The link-building use case here is digital PR, not technical backlink audits. 

  • Pricing: Content Creation starts at $199 per month; PR & Comms at $299; Suite at $499; and Enterprise at $999. Annual billing knocks roughly 20 percent off. 
  • Best for: Content marketers and PR teams who build links through earned media rather than direct outreach. If you want to know what’s working in your space and who to pitch, this is the tool. 
  • Considerations: It’s not a replacement for Ahrefs or Semrush on raw backlink data. 

5. AIOSEO

A screenshot of AIOSEO’s homepage. 

AIOSEO (All in One SEO) is a WordPress plugin first and an SEO suite second. It’s not a dedicated backlink checker, but it can help WordPress users manage the links they control inside their own site. 

Its Link Assistant shows internal links, external links, affiliate links, orphaned posts, and top domains you link to. That makes it useful for improving internal linking and cleaning up outbound links, but it won’t replace a backlink database like Ahrefs or Semrush. 

Broken Link Checker is another useful add-on. It scans your content for broken links and images, then lets you address issues. The free version includes 250 internal and external link checks per month. 

  • Pricing: Annual plans range from $49.50 for Basic to $299.50 for Elite. 
  • Best for: WordPress site owners who want on-page SEO, sitemaps, and lightweight backlink data in one plugin. If you already pay for a dedicated backlink tool, AIOSEO is more of a complement than a replacement. 
  • Considerations: AIOSEO is not a true backlink analysis tool. Use it to manage links on your WordPress site. 

6. Linkody

A screenshot of Linkody’s homepage. 

Linkody is a tool built for backlink monitoring. Add your domain, and Linkody tracks discovered links and alerts you when links go live or drop. The platform even handles marking links for disavow with a built-in file generator. 

The dashboard provides backlink status, anchor text, follow/nofollow data, landing pages, Moz DA, spam score, majestic trust flow and citation flow, and other link-quality signals.  

The disavow workflow is what sets Linkody apart, though. You can flag toxic links inside the dashboard and export the file for Google Search Console in a couple of clicks. 

  • Pricing: Webmaster starts at $14.90 per month, Advanced at $24.90, Pro at $49.90, Agency at $99.90, and Agency XL at $153.90. Free trials and discounts for annual billing are available. 
  • Best for: Solo SEOs, freelancers, and small agencies who want backlink monitoring and disavow management without paying for a full SEO suite. The price-to-feature ratio is the main draw. 
  • Considerations: Linkody is great for monitoring and managing backlinks, but it’s not as deep as some of the other tools on this list for large-scale backlink research. 

7. CognitiveSEO

A screenshot of CognitiveSEO’s homepage. 

cognitiveSEO gets its reputation from the Unnatural Link Detection tool, which scores links in your profile for risk and flags candidates for disavow. If you’ve inherited a site with a messy link history or recovered from a penalty, this is the platform for you. 

Beyond toxic link cleanup, you get rank tracking, content optimization, and competitive backlink analysis.  

The main draw is the link-quality workflow. cognitiveSEO aggregates backlink data from trusted link databases, then crawls and analyzes links on demand, so it’s better framed as an audit and recovery tool than a pure backlink index play. 

  • Pricing: Starter is $129.99 per month, Premium is $199, and Elite is $499. Annual billing offers a discount. A free trial is available. 
  • Best for: Mid-sized teams and consultants who handle penalty recovery, link audits, or sites with risky historical link profiles. 
  • Considerations: cognitiveSEO is strongest for backlink cleanup and risk review. It’s less compelling if you only need everyday backlink discovery or broad SEO reporting 

8. Majestic SEO

A screenshot of Majestic SEO’s homepage suggesting they’re specialists in backlink analysis. 

Majestic predates most of the tools on this list and remains a go-to for pure link metrics. Its proprietary metrics, trust flow and citation flow, are highly regarded across the SEO industry and shown inside other tools, like Linkody. Trust flow estimates link quality, while citation flow reflects link quantity. 

Majrestic’s Site Explorer report shows referring domains, anchor text, and a topical trust flow that breaks down which niches link to you. The Link Context feature displays the surrounding paragraph for any backlink, helping you judge link quality at a glance. 

  • Pricing: Lite is $49.99 per month, Pro is $99.99 per month, and API access is $399.99 per month. Annual billing is available at a discount. 
  • Best for: SEO researchers, link prospectors, and analysts who care more about link metrics than the full SEO suite experience. Trust flow and citation flow are the reasons most people sign up. 
  • Considerations: Majestic is strong for backlink analysis, but it does not replace all-in-one SEO tools. 

9. SEOptimer 

A screenshot of SEOptimer’s homepage. 

SEOptimer is best known for white-label site audits, and its backlink research module fits into that broader reporting workflow. The dashboard pulls referring domains, anchor text, and link quality scores you can drop straight into client-facing reports. 

The embeddable audit tool is a nice touch for agencies. You can install a lead-generation form on your site that runs a free backlink and SEO audit for prospects, capturing the lead. 

  • Pricing: DIY SEO starts at $29 per month, White Label at $39, and White Label & Embedding at $59. Annual billing discounts are available, as is a free trial. 
  • Best for: Small agencies and consultants who need affordable, brandable reports for clients. If you’ve been using a tool called Monitor Backlinks, that product has now been merged into the SEOptimer platform. 
  • Considerations: SEOptimer is stronger for audits and lead generation than deep backlink research. 

10. Moz Link Explorer

A screenshot of the landing page for Moz Link Explorer. 

Moz Link Explorer is built around domain authority, the metric many SEOs still use as a quick read on site strength. The tool provides DA, page authority (PA), spam score, and a full backlink profile with link-quality filters. 

The Link Intersect report shows pages linking to your competitors but not to you, similar to Ahrefs and Semrush. Moz also tracks new and lost links over time, which helps spot outreach wins or drops in your link profile. 

  • Pricing: Standard starts at $99 per month, Medium at $179, and Large at $299. Annual billing is discounted, and a free trial is available. Free Moz access is useful for occasional DA checks and light backlink research, but it’s limited. 
  • Best for: Marketers and content teams who already work with DA and want a familiar backlink dashboard alongside keyword and rank tracking tools. 
  • Considerations: The link index is smaller than Ahrefs, but Moz’s data quality and reporting are reliable for everyday work. 

11. Pitchbox

A screenshot of Pitchbox’s homepage also showing a snapshot of their SEO campaign dashboard. 

Pitchbox is an outreach platform with backlink prospecting built in. You can create prospect lists, find contacts, run outreach sequences, manage follow-ups, track replies, and monitor links from one dashboard. 

Integrations with Moz, Ahrefs, Semrush, and Majestic let you filter prospects by link metrics before reaching out, shortening the time from finding a prospect to sending a personalized pitch. 

  • Pricing: Pro is $300 per month, Advanced is $600 monthly, and Scale is $1,200 monthly. Enterprise pricing is custom. Annual billing discounts are available, and a free trial is available. 
  • Best for: Agencies and in-house teams running serious outreach programs. If link building is a primary channel and you’re sending hundreds of pitches a month, Pitchbox pays for itself quickly.  
  • Considerations: For occasional outreach, Pitchbox is probably overkill. 

12. Whitespark

A screenshot of Whitespark’s homepage. 

Whitespark focuses on local SEO, and its backlink-adjacent work centers on citations—the mentions of your business name, address, and phone number across local directories and review sites. 

The Local Citation Finder identifies high-value citation opportunities for any business or competitor. Whitespark also offers done-for-you citation building and cleanup, as well as a local rank tracker. The toolset is deliberately narrow. It’s built for local businesses, not enterprise SEO. 

  • Pricing: Local Citation Finder has a free starter plan. Paid plans start at $39 per month for Small Business, $49 for Specialist, $59 for Agency, and $149 for Enterprise. Annual billing is available at a discount. 
  • Best for: Local businesses and agencies serving multi-location clients. If you serve a geographic market and rely on Google Business Profile rankings, Whitespark is the tool for you. 
  • Considerations: Whitespark is not a backlink research tool in the Ahrefs or Semrush sense. Use it for citations, listings, Google Business Profile visibility, and local rank tracking. 

13. Linkstant

A screenshot of Linkstant’s homepage explaining why it’s powerful to discover your new backlinks instantly.  

Linkstant carved out a niche around one promise: instant alerts when a new link points to your site. While most tools poll for new backlinks once a day or once a week, Linkstant ran on near-real-time detection, enabling users to thank the linker, share the content, or correct a broken link within minutes. 

  • Pricing: Linkstant’s small business package is $7 per month, and its enterprise pricing is $27 per month.  
  • Best for: Anyone building outreach workflows around instant backlink notifications.  
  • Considerations: Linkstant is not a replacement for a backlink analysis platform. 

14. BuzzStream 

A screenshot of BuzzStream’s homepage. 

BuzzStream is an outreach customer relationship management (CRM) platform with link research baked in. You research prospects, find their contact info, send personalized pitches, and track every conversation from a single dashboard. The backlink piece comes from integration with Moz and built-in link metrics that help you qualify prospects. 

The list-building features make it easy to import prospects from a Google Sheet or scrape them directly from search results. From there, the CRM handles the rest of the outreach cycle. 

  • Pricing: Starter is $49 per month, Growth is $174, Professional is $424, and Custom starts at $999. A free trial is available. 
  • Best for: Digital PR and link-building teams that prioritize relationship management over raw link data.  
  • Considerations: BuzzStream is best paired with a dedicated backlink tool if you need deep competitor link research or large-scale backlink audits. 

Finding the Right Backlink Tool for You (and Getting the Most Out of It)

The right backlink analysis tool depends on three things: what you do with backlink data most often, how many sites you manage, and what you can spend. A solopreneur running one blog doesn’t need the same setup as a large agency. 

Start with your goals and the task at hand.  

  • If you mostly research competitors and prospect for new links, Ahrefs or Semrush makes sense.  
  • If outreach is the bottleneck, Pitchbox or BuzzStream pays for itself.  
  • For budget-friendly monitoring and disavow management, choose Linkody or CognitiveSEO.  
  • Local businesses should look at Whitespark first. 

Once you’ve picked a tool, get the most out of it by following a few rules. 

  • Audit your link profile quarterly. Look for new toxic links, broken backlinks pointing to your site, and outreach wins worth replicating. 
  • Track referring domains, not just total backlinks. One link from 100 domains beats 100 links from one domain. If the difference between referring domains and backlinks is fuzzy, start there. 
  • Pay attention to link attributes. A lot of people get hung up on dofollow vs. nofollow backlinks when creating their strategy, but these attributes don’t change much. A toxic dofollow link can hurt your rankings, while a high-quality nofollow link still drives referral traffic. Your energy is better spent on routinely auditing and maintaining your profile. 
  • Use competitor gap reports. Every tool in this guide offers some version of a competitor link intersect. That report alone justifies the subscription for most users. 

If picking and running a backlink strategy still feels like a lot, my team handles this work for businesses every day. NP Digital builds custom link strategies, and I offer SEO consulting for businesses that want a more hands-on approach. 

FAQs

What is a backlink profile?

A backlink profile is the full picture of external links pointing to your site, including referring domains, anchor text, dofollow or nofollow attributes, linking-site authority, and link velocity. A healthy profile draws from varied, authoritative sources. Backlink tools like Ubersuggest, Ahrefs, and Moz can help you pull yours in seconds. 

What is a backlink analysis tool?

Backlink analysis tools help website owners analyze their website’s backlink profile. It provides information on the links pointing to their website from external sources, including the number, quality, and relevance of the links. You can use this information to identify areas for improvement in the website’s link-building strategy and improve its search engine rankings. 

How to check backlinks of a website?

Open Ubersuggest, Ahrefs, Moz Link Explorer, or Semrush and run the report for your target site’s root domain. You’ll see total backlinks, referring domains, anchor distribution, top-linked pages, and a domain authority score. Focus on referring domains rather than raw link count, and watch for unnatural anchor patterns to weed out bad links. You should also study top-linked pages for you and your competitors to see which content is working for specific keywords in your industry.  

How to check competitor backlinks?

Run three to five competitor domains through a tool with a link gap or link intersect report (most, if not all, of the tools in our list offer one). The report surfaces sites linking to your competitors but not to you. Sort by domain authority, then prioritize relevant high-authority targets for outreach.  

Why use a backlink monitor tool?

Manual tracking can’t keep up. The average site gains and loses dozens of links each month, and Search Console won’t catch a toxic link spike or a competitor pulling ahead. A monitoring tool automatically runs alerts for new links, lost-link notifications, toxic scoring, and trend data.  

Conclusion

Your link profile is one of the strongest signals Google uses to rank your site, making picking the right backlink tool an important decision for your business. 

The 14 options above cover virtually every budget and use case, so the right one for you is the one that fits the work you actually do and teaches you how to build backlinks correctly. Pick the tool that matches your goals, then commit to using it regularly. 

A tool you check once is wasted money. A tool you check weekly drives real results. 

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Foodblogger Q&A: Google updates, AI search, and what actually matters for your blog in 2026

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Foodblogger Q&A: Google updates, AI search, and what actually matters for your blog in 2026



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Carolyn Shelby

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Bjork Ostrom

Co-Founder of Pinch of Yum and Food Blogger Pro

Search is changing fast – and if you’re a food blogger trying to keep up with Google updates, AI Overviews, and what actually moves the needle for your site in 2026, this Q&A is for you. On June 17, we’re sitting down live with Carolyn Shelby, SEO expert at Yoast, to break down the latest Google updates and what the rise of AI-driven search means for your content strategy. Bring your biggest SEO questions and get answers straight from one of the most knowledgeable people in the space – register now to save your spot.

Carolyn helps shape SEO strategy for millions of WordPress users around the world. With over 20 years of experience in technical SEO, site architecture, and digital growth, she’s known for translating complex SEO concepts into actionable strategies that actually make a difference – especially for creators who want to grow sustainable, long-term traffic.

P.S. Can’t attend the Q&A live? Register anyway and we’ll send you the replay!

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The SEO Update by Yoast – June 2026
30 June 2026

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The post Foodblogger Q&A: Google updates, AI search, and what actually matters for your blog in 2026 appeared first on Yoast.

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Shopify launches AI-powered marketing automation tool

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Shopify is introducing Campaign Autopilot, a new AI-powered marketing tool that automatically creates, manages and optimizes campaigns across multiple channels, reducing the need for merchants to manually manage advertising and email marketing.

The feature is launching in early access and is available directly within the Shopify admin.

What’s happening. Campaign Autopilot uses AI to plan and run marketing campaigns on behalf of merchants across channels including Meta, Shop Campaigns and email.

Additional channels are already on the roadmap, including ChatGPT Ads, Microsoft Advertising and Snapchat.

Rather than requiring merchants to build campaigns manually, the system handles campaign creation, budget allocation and ongoing optimization automatically.

Why we care. Campaign Autopilot lowers the barrier to running multi-channel marketing campaigns by automating much of the work traditionally handled by agencies or in-house specialists. Instead of managing separate campaigns across Meta, email and other channels, merchants can set a budget and goals while Shopify handles campaign creation, optimization and budget allocation.

How it works. Merchants set a monthly budget, choose which channels to connect and define approval rules and guardrails.

From there, Campaign Autopilot:

  • Creates and launches campaigns.
  • Allocates budget across channels.
  • Adjusts spending based on performance.
  • Recommends email automations.
  • Monitors results and makes ongoing optimizations.

Merchants can approve campaigns before launch, modify budgets or pause activity at any time.

What’s different. Shopify is positioning Campaign Autopilot as an alternative to traditional campaign management tools and agency-led marketing.

The company says the system leverages performance data and patterns across millions of Shopify stores to inform recommendations and budget decisions.

Campaign Autopilot also operates separately from existing campaigns, meaning merchants already running Meta or Shop ads won’t see those campaigns altered.

The bigger picture. Shopify is increasingly embedding AI into merchant workflows, moving beyond ecommerce infrastructure and into growth and customer acquisition.

The launch reflects a broader industry trend toward autonomous marketing systems that can execute campaigns with limited human involvement while continuously optimizing performance.

What to watch. Shopify plans to expand channel support in the coming months, including integrations with ChatGPT Ads, Microsoft Advertising and Snapchat.

The company also says merchants can use its AI assistant, Sidekick, to review recommendations, trigger actions and monitor campaign performance.

Dig deeper. Introducing Campaign Autopilot: AI-powered Marketing Built into Shopify

First spotted. The update was spotted by Digital Marketing Consultant Susan Richards-Benson who suggested this feature for smaller ecommerce brands on Linkedin.

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Straight from the source: 2026 Search Engine Land Awards judges reveal what makes an application award-worthy

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search engine land awards winners
search engine land awards winners

Since its inception in 2015, the Search Engine Land Awards have recognized exceptional marketers on an annual basis — showcasing outstanding work, providing well-earned exposure in coverage and interviews, and bestowing upon them the highest honor in search.

While there’s no single formula for creating a winning entry, our judges have seen enough submissions over the years to know what separates the truly exceptional from the merely good. The strongest applications don’t just share results, they tell a story. They provide context, demonstrate strategic thinking, and clearly communicate why the work mattered.

And because great advice shouldn’t be gatekept, we thought we’d bring some of those insights directly to you.

We asked several of this year’s Search Engine Land Awards judges to share their best advice for prospective entrants. From common mistakes to avoid to the elements that consistently stand out, their insights offer a valuable look inside the judging process and can help you build a stronger, more compelling submission.

Keep reading for a roundup of fresh insights from some of our judges. (And see the complete list of 2026 judges here!)

“A great entry is a story with a goal, an action, and a measurable outcome that ties back to it. Tell that story as well as you can, and include a deck that makes it easy to see exactly what you accomplished.”

– Amy Hebdon, Founder, Paid Search Magic

“Explain your tactics. Many entries just say “we used best practices”. Everyone’s best practices and tactics differ. Explaining the process that lead to your results will highlight your creative thinking, problem solving, and uniqueness. Showing your insights and thought processes helps your entry standout and showcase your company’s competitive edge.”

– Brad Geddes, Co-Founder, Adalysis

Brad Geddes

“I look for SAY which stands for: Situation, Action and Yield.
Applicants should write a clear example of the situation, what they did and the result achieved over the time period,”

– Jo Juliana Turnbull, Growth Marketing Senior Manager, Holafly

“Show me the humans behind the metrics. We’re in a time where AI is reshaping search at a pace none of us have seen, and that shift matters…but the applications that rise to the top will lead with empathy, not just analytics. I want stories where I can see how your work built genuine trust with real people, not simply visibility in search and AI engines. I’m especially drawn to entries that embrace a wellness-based approach to their craft, and to teams who pair their quantitative wins with qualitative insight: the quote, the aha moment, the change in how someone felt about a brand or experience. Tell me how you held the human at the center – as strategy. If your project made people feel seen, understood, or genuinely helped, lean into that. Those are the stories I’ll be looking for.”

– Danita Smith, Founder & CEO/Chief Innovation Strategist, Adanis Design

“Clearly state the challenge you solved, and back it up with data. Explain the strategy behind the tactics you used and the results they drove. Tell me not just what happened, but what impact did it have on your campaigns? What did you do differently as a result?”

– Melissa Mackey, Director of Paid Search, Compound Growth Marketing

“Evidence: charts, analytics, screenshots. Be detailed, specific, and share data.”

– Barry Schwartz, Editor, Search Engine Land

“Tell a story. Numbers get you in the room, but the story is what stays with the judges. I want to know what the problem was, why it was hard, what you tried, and what finally worked. That arc, the messiness of real work, is what separates a memorable entry from a forgettable one. The submissions that stick with me are the ones where I can feel the thinking behind the decisions, not just the outcome. You did great work this year; now, make the judges feel the weight of what you solved before you show them the numbers.”

– Ameet Khabra, Founder, Hop Skip Media

“The two main things all award-winning entries share are that they explain the whys behind the hows, and they bring receipts (data to back up claims). If you can’t share the data behind your entry (budgets, revenue, etc.), you are putting yourself at a distinct disadvantage and may end up wasting the entry fee. A lot of people submit the same practices – if you can distinguish yourself by showing innovative thinking, you’ll do well!”

– Navah Hopkins, Product Liaison, Microsoft

“Give me all the data you can. Show the numbers and the real impact of whatever you did; conversions, ROI, and whatever monetary increases you were able to cause.”

– Celeste Gonzalez, Content Implementation and Product Specialist, Lastmile Retail

“Show me something I haven’t seen before, then prove it worked. The applications that land are the ones with a genuinely unexpected approach backed by numbers that make the result undeniable.”

– Adam Tanguay, Head of Growth, Jordan Digital Marketing

“I am looking for an approach or strategy that challenges the norm of SEM. A unique approach that focuses on achieving the business goals by way of campaign structure across Brand, Non-Brand, Performance Max, Conquesting, and general upper-funnel tactics. An advanced way of thinking about the target audience, messaging, conversion goals, etc. that helps show a sophisticated way of managing the campaigns & overall strategy to exceed business goals.”

– Matt Devinney, Director, Client Partner, Tinuiti

“I am looking for projects that break new ground with innovative takes on SEO, and are backed up by data and numbers-driven insights every step of the way.”

– Olya Ianovskaia, Founder and Lead Consultant, MycoMinds SEO

“Make your entry easily readable. We are going to need to go through several entries – I know the entries could be quite technical (and the quality of that will take precedent), but I am more likely to vote for you if I enjoyed reading your entry.”

– Anu Adegbola, Paid Media Editor, Search Engine Land

“My #1 piece of advice is to showcase strategy that truly breaks new ground. Award-winning applications demonstrate innovation that anticipates where SEM is heading, whether that’s leveraging AI in novel ways, pioneering audience-targeting approaches, or developing unique cross-channel integration. But innovation alone isn’t enough. The most compelling entries connect these forward-thinking strategies directly to measurable business outcomes, providing clear evidence of how your work translated to client growth metrics that matter. We’re looking for that perfect balance: creative execution that pushes boundaries while delivering documented ROI that proves your approach wasn’t just innovative—it was transformative.”

– Joseph Kerschbaum, Senior Vice President, Search & Growth Labs, DEPT

And there you have it! Submit your entry today to be considered by this year’s esteemed judges. Early Bird rates expire July 10… so get a move on!

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YouTube rolls out new Gemini-powered insights tools

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YouTube is expanding its suite of creator marketing and campaign intelligence tools with new Gemini-powered features designed to help brands identify trends, understand creator audiences and improve campaign performance.

What’s happening. Google is introducing several new insights and optimization tools across YouTube and Google Ads that give marketers more visibility into trends, creator performance and audience behavior.

The company says the new capabilities are intended to help advertisers make better creative and media planning decisions in an increasingly AI-driven marketing landscape.

Why we care. These updates provide deeper visibility into what’s trending on YouTube, which creators are resonating with audiences, and how their brand is performing across both paid and organic content. That can help marketers make smarter decisions about creator partnerships, campaign planning and creative strategy.

What’s new:

More detailed trend insights.

Google Ads’ Insights Finder is gaining expanded trending insights in the U.S., providing advertisers with a more granular view of what’s gaining traction on YouTube.

Brand Pulse data comes to Insights Finder.

Select Brand Pulse metrics are now being integrated into Insights Finder, allowing brands to evaluate both their paid and organic presence in a single location.

New creator insights API.

The new Content & Creator Insights API gives agencies and partners deeper information about YouTube creators and their audiences, helping improve media planning and creator selection.

Gemini-powered creative recommendations.

Google says Gemini will soon provide creative optimization tips for Demand Gen campaigns, including recommendations on visuals and creative elements that may improve performance.

The bigger picture. As creator-led content becomes increasingly influential in purchase decisions and brand discovery, advertisers are looking for better ways to identify emerging trends and measure creator impact.

Google is betting that AI can help marketers surface those insights faster and make campaign planning more efficient.

Bottom line. YouTube is giving brands and agencies more data on trends, creators and campaign performance, while using Gemini to help turn those insights into stronger creative and media decisions.

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How to make Performance Max focus on net new customers

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How to make Performance Max focus on net new customers

There’s a trap door waiting for DTC brands that invest in Google Ads that makes your dashboards look amazing, but absolutely wrecks your P&L.

It’s the danger of recycling traffic from Meta.

Thanks to the overlap between paid search and paid social traffic, running Google as a standalone channel is incredibly difficult if you don’t know how to set it up. Ad platforms refuse to share data with one another, and they love to claim credit for the same conversion — even if those sales would’ve happened without the influence of ads.

The DTC brands I speak to are often proud to show off their new customer numbers: month-over-month growth, a steady upward trend, and a fantastic dashboard. But when we go deeper into the data, we often find that a big chunk of those “new” customers are:

  • Conversions that would’ve happened because of brand or content efforts.
  • Customers who aren’t truly incremental because they consumed ads on multiple platforms.
  • The same people signing up with multiple email addresses.

You could argue that these overlapping sales still count as revenue, and they do. But when you look at the contribution margin from those sales, they cost far more than they should and erode actual profit.

In other words, you lose money when you run ads on both platforms without guardrails.

But that doesn’t mean you need to stop or limit yourself to one channel. Instead, you need a better system for measuring actual customer acquisition.

Why the new exclusions matter

If you’re spending five figures or more on Meta, TikTok, AppLovin, or any other top-of-funnel channel, you’ll want to minimize overlap with other channels to drive actual new customer acquisition.

Here’s what that looks like:

  • Someone sees your ad on Facebook or Instagram.
  • They visit your site, browse, and leave without buying.
  • A while later, they search for your brand on Google or get retargeted on YouTube.
  • Performance Max swoops in, grabs the conversion, and reports strong ROAS.
  • You may have won that order anyway, but now Google and Meta both want credit for it.

Now you’re paying two or more platforms to recycle a conversion that you might have earned with just one.

Ever since Performance Max launched, there wasn’t much you could do about this. It’s been a bit of a black box that automatically goes after the warmest traffic it can find: branded search, site visits, email subscriptions, and existing customers.

It lets you bid more for new customers, but you can’t really stop the campaign from defaulting to easy mode.

A while ago, Google began letting you exclude people searching for your brand on Search and Shopping. Performance Max still targeted warm audiences through YouTube, Gmail, and the Display Network.

The latest round of updates from Google has finally addressed this problem. You can now force Performance Max to focus on net new customer acquisition through a combination of brand exclusions, audience exclusions, and Customer Match data. 

First-party audience exclusions, announced in March, are the final piece that makes this possible (though not foolproof – customer list matching is never perfect).

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A four-step framework for net new customer acquisition

Here’s a four-step framework we’re using at my agency to help clients maximize incrementality.

Step 1: Exclude your brand

This one has been around for a while, but it’s the foundation, so we have to start here.

For smaller brands, brand exclusions usually aren’t necessary. But once you’re spending real money and seeing more than 15% to 20% of your cost or revenue coming from brand searches, it’s time to take action. 

There are two parts to this.

Go into your campaign settings and add a brand exclusion. If your brand isn’t already on the list, click New brand list, create one, and add your brand. Google will do its best to block branded queries from this list.

Because brand exclusions aren’t foolproof, go to the Keywords tab inside the campaign and add your brand name as a phrase match negative keyword. Add a few common variations, too. This catches anything the brand list misses.

If you’re excluding brand terms from Performance Max, you need a dedicated brand Search campaign and a brand Shopping campaign to capture those searches. Otherwise, you’re just leaving money on the table for competitors.

Step 2: Exclude website visitors and email subscribers

Even if you blocked brand searches, Performance Max would still retarget people who visited your website, opened your emails, or interacted with your brand on YouTube, Gmail, Discover, and Display. So even with brand exclusions in place, a big chunk of your spend still went to warm traffic.

Now you can change that. Go to your campaign settings and find the new audience exclusions option. Then build a few remarketing lists:

  • All website visitors: Set this up through the Google Ads pixel or Google Analytics. It captures anyone who has visited your site.
  • Email subscribers: Connect Klaviyo (or whatever ESP you’re using) directly to Google Ads. The benefit of the Klaviyo integration is that the audience updates in real time, so new subscribers are added automatically.

Once you exclude these audiences, Performance Max can only go after people who haven’t interacted with your brand in any meaningful way. What we typically do, and what I recommend, is to come up with an engagement metric that fits each account’s business goal, such as cart adds rather than visitors from the past seven days.

What a change from how this campaign type used to work.

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Step 3: Exclude existing purchasers

Same idea as Step 2, but specifically for people who have already bought from you. You can do this two ways.

  • Through a pixel-based audience that captures anyone who has triggered the purchase event. 
  • By uploading your customer list directly. Shopify now lets you set up Customer Match lists right inside the Google Shopping app, and Klaviyo can do this, too.

Add these audiences to the exclusions section of your campaign, and you’re done.

A small caveat to keep in mind: audience matching is never 100%. If you upload a customer list of 1,000 people, Google might only match 900 of them. So you’ll still see some level of bleed. But going from “the campaign is targeting all my existing customers” to “the campaign is targeting maybe 10% of them” is still a huge win.

Step 4: Use ‘New Customer Bidding’ in campaign settings

The last piece is to tell the campaign explicitly that you want new customers.

In your campaign settings under customer acquisition, you’ll see two options: bid only for new customers, or bid higher for new customers. Both require you to connect a customer list (which you’ve probably already done by Step 3).

The “only new customers” option is the most aggressive setting. The campaign simply won’t bid on existing customers. Combined with the audience exclusions from Steps 2 and 3, this gets you as close to pure new customer acquisition as Performance Max will allow.

The “bid higher for new customers” option is more flexible. You set a dollar value that represents the additional value of a new customer, and the system bids more aggressively when it thinks an auction will result in one.

Here’s where you need to be careful. If you tell Google a new customer is worth an extra $100, and you get a $200 sale from a new customer, Google will report it as $300 in revenue. That extra $100 is a fictional reporting value, not real revenue. It will inflate your ROAS numbers and distort your target ROAS bidding.

Our recommendation is to use a small placeholder value, such as a penny or a dollar, when you want to nudge the system toward new customers without distorting your reporting. Or use a number that genuinely reflects the lifetime value premium of a new customer to your business.

What to expect from this approach

It’s still early, so we can’t draw firm conclusions yet. But based on my experience managing PPC for ecommerce brands, here’s what I expect to happen.

Many advertisers who walked away from Performance Max did so because it was simply recycling Meta traffic. By splitting it out, you force it to go after net new traffic.

This will likely benefit brands that don’t have a ton of video creative for YouTube, which is another platform where brands try to drive net new acquisition at the awareness stage.

One of the big differences between Performance Max and Demand Gen is that the former is much more conversion-focused. Any brand considering excluding branded Search and Shopping from Performance Max should also consider this tactic, as it tends to over-index on hot traffic.

In terms of outcomes, I expect the reported ROAS attributed to Performance Max to be lower than what you may have seen in the past.

But when you look at the breakdown of new versus returning customers, it should align much more closely with new customer acquisition. Without advanced configuration, it might be a 60/40 split, even in the best situations.

Limitations and realistic expectations

Nothing about this is foolproof. Audience exclusions don’t match perfectly. Brand exclusions don’t catch every variation. Customer Match has its gaps. So even with all four steps in place, some percentage of your spend will still hit warm audiences.

But for the first time, you actually have the levers to push Performance Max into upper-funnel territory. You can make it work like a real prospecting channel instead of a retargeting channel that takes credit for demand created elsewhere.

This matters most for brands spending heavily on Meta, TikTok, or other channels and wanting Google to actually grow the customer base rather than recycle the traffic those channels generate. If you’re seeing strong ROAS in Performance Max but flat new customer numbers month over month, this framework is for you.

If you’re a smaller brand still trying to find product-market fit or build initial momentum, this is probably overkill. Let Performance Max do its thing and pick up conversions without too many restrictions.

But once you’re scaling and the question is no longer “Can we be profitable?” but “Can we be profitable while growing the customer base?” these settings become some of the most important levers you have.

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Google’s giving you more control over PMax. Use it.

The conversation around brand versus non-brand is everywhere. You can’t throw a dart at a paid media conference without hitting someone with a strong opinion on it. But for some reason, almost no one seems to be testing this new option.

I just finished auditing an account spending $100,000 a month on Search with no Performance Max or Shopping, so they get purely new customer acquisition. We looked at their numbers and said maybe now’s the time to try this, exclude all these segments, and let it rip.

So here’s when I recommend implementing this test: if your ad spend is high enough (it doesn’t need to be $100,000 a month or anywhere near it), or you’re revisiting Performance Max. Your hypothesis should be that this approach increases the proportion of actual new customer conversions.

I think you’ll find that the needle moves further than you think.

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How to approach build-versus-buy decisions for SEO

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How to approach build-versus-buy decisions for SEO

AI has made SEO teams ambitious about what they can automate. Tasks that previously required engineering support can now be solved with the help of Claude or ChatGPT.

That’s exciting, but it also creates a new problem: thinking you can automate everything. In modern language, that often comes down to one question: Should we build or buy this new tool?

This build-versus-buy dilemma has never been simple, and AI has made it even more complicated. The challenge goes beyond cost. It involves security, maintenance, data access, internal capabilities, workflow fit, and whether a custom solution will remain maintainable, reliable, and useful six months from now.

How AI lowers the barrier to building

AI has lowered the barrier to experimentation. Even without technical knowledge, you can now create a custom GPT, build a workflow, connect data sources, or create an internal AI assistant.

But that doesn’t mean the same person can build and maintain a tool that will remain reliable over the next few years.

In most cases, AI can help SEO teams analyze data, identify patterns, summarize information, and recommend actions. It can save a lot of time, and teams that ignore AI are clearly falling behind.

But, at least for now, AI isn’t doing truly creative work in the same way humans do. It works from existing patterns and predicts likely outputs. That may change in the future.

AI also comes with hidden costs. Internally built tools are often treated as free because the invoice usually doesn’t sit with the SEO team. But that doesn’t mean token usage, API calls, infrastructure, engineering time, security reviews, and maintenance don’t cost money.

We are already seeing this effect. Reuters has described it as “corporate AI sticker shock,” with companies struggling to forecast usage-based AI costs. TechCrunch also reported that Uber introduced AI spending caps after blowing through its annual AI budget in four months.

Today, marketing teams aren’t the heaviest AI users, especially compared with engineering teams. But that can change quickly.

And when usage grows, the bills will grow too. That will naturally make companies ask which AI tools and AI-powered workflows create value and which ones only consume budget.

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Start by defining what you need

Before deciding whether to build or buy, SEO teams need to define what they really need.

Different ways to use AI and automation

Many teams group these solutions together, but they vary significantly in cost, complexity, and maintenance requirements.

  • A custom tool: A more complex internal system that usually needs engineering support. It is often more about automation, but it can have an artificial intelligence aspect.
  • A custom workflow: A repeatable process built with different tools, such as a custom GPT, Claude project, spreadsheet, reporting template, and so on. It often includes automation, for example, a scheduled task in an AI tool, and it usually has an artificial intelligence layer.
  • A custom layer on top of SaaS: Using data from existing tools and shaping it into your own reporting, prioritization, or recommendation workflow.
  • A true AI agent: A system that can take more autonomous actions. For example, it can scan your Slack and follow up with people you are still waiting on.

These aren’t the same, but people often label them incorrectly. Calling everything an “AI agent” creates confusion and can lead to wrong estimates about cost and complexity.

Look for repetitive, context-rich tasks

We’re still experimenting. Most of what our team has built focuses on daily tasks that require a lot of manual work.

For example, we’ve created a custom GPT that evaluates whether our content matches our personas and their pain points. The goal is not to replace the human copywriter or reviewer. It is to determine whether a piece remains generic and whether a few additions can make it more relevant.

We are also using AI for translations, monthly reporting, and a weekly summary that combines meeting notes, Slack, and Jira, and helps me see whether I have missed adding a task to Jira or where I still need to follow up.

One of our latest workflows transforms recorded internal meetings into organized landing page briefs.

These types of tasks are good candidates for AI-powered custom workflows because they rely on internal context, repeatable processes, and company-specific knowledge.

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Not everything should be built

One example from our team was a prompt tracking tool that my colleague vibe-coded. It worked well as a starting point. But the data presentation was not perfect, and it was hard to create a trend graph without additional manual steps.

Soon, it became a maintenance burden because every external change in any of the LLM tools required fixes, for which we needed engineering help.

The real issue was reliability. For AI visibility and prompt tracking, we needed consistent data in one place, presented in a way we could analyze over time. That is why we moved to a specialized platform like Peec AI instead of continuing to maintain our own version.

That experiment was still valuable. It helped us understand the problem, the complexity, and the features we actually needed from an external vendor.

And this is one of my pieces of advice: whether you want to build a tool internally or buy one, always test what is already available on the market. Only then will you really understand what you actually need. You may think you need 10 features, only to realize you use only three.

For business-critical tools such as rank and AI visibility tracking, and website crawling, small SEO teams without dedicated technical support should usually be careful about building from scratch. If the data is fundamental to decision-making, reliability should be your main decision factor.

Use AI where your data already lives

Buy the crawler, rank tracker, or AI visibility platform. Then focus your internal efforts on connecting data from these tools to custom information, such as your GA and GSC accounts or even CRM data. Once connected, create reports that combine all these sources and enable you to analyze everything in one place.

MCP connections are also worth considering. The Model Context Protocol is an open standard for connecting AI applications to external systems, data sources, tools, and workflows. With MCP servers, you can analyze data from your primary tools directly using AI, taking your current workflows to the next level.

This doesn’t mean you’re required to learn how to code. But they need to know enough to ask the right questions.

If a tool connects to an internal knowledge base, customer data, or proprietary research, you should be aware that this could pose a security risk. And it might turn out that it is better for the company to dedicate an engineer to support you rather than risk exposing sensitive information.

You should also understand what the final cost will be for your company when you decide to go with a custom tool. Custom tools aren’t free just because the invoice doesn’t sit with SEO. Engineering time, security reviews, AI tokens, and API usage are all part of the cost.

Before asking leadership for a tool, SEO teams should be able to explain the workflow problem, the expected value, the cost of buying compared with the estimated cost of building, and what might happen if nothing is done.

The best requests don’t start with: “We need this tool.”

They start with: “Here is the problem, here is why it matters, here is what we’ve tested, and here is the best way we think we can solve it.”

How to prioritize what to build first

There’s no single prioritization matrix that will work for every situation.

A website crawler, a content evaluation tool, a report builder, or a competitive intelligence system can’t be judged by the same criteria.

If you are in a situation where you think you need more than one tool, start by mapping your current workflow and what your ideal situation looks like.

Once you do that, the patterns will be clear. Often, your strongest priorities will fall into two groups.

The first are tools that can support revenue creation. SEO teams are usually part of the marketing organization, and marketing is expected to bring visibility or leads. If a tool can help identify content opportunities, improve conversion rates, increase AI visibility, or surface gaps versus competitors, it can be seen as a priority.

The second group is workflows and tools that can help you minimize repetitive manual work. This category may not create revenue, but it will give your team time back to focus on more strategic work.

Don’t forget that quick wins also matter. Stakeholders don’t want to wait three months before seeing results. A smaller project that can bring value in three weeks will help you build trust and make it easier to get support for bigger initiatives.

Cross-team value should also be part of your decision.

SEO problems are often not just problems for your team. Competitive intelligence, for example, matters to PPC, ABM, content, product marketing, and sales, too. If several teams share the same pain, the business case becomes stronger.

So don’t be afraid to act as a cross-team synchronization layer when needed. Talk to the same teams you have already worked with, and try to understand their workflows and pain points, and where your needs overlap.

And remember, the best tool is not always the most ambitious one. Starting with something small is often the smartest move.

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Good decisions start with proper scoping

AI has made it easier to build, but that doesn’t mean you don’t need to think about what really needs to be built.

Before deciding whether to build, buy, or customize, take the time to properly scope the work.

  • Understand the problem, the value you expect, who will use the solution, and who will maintain it after launch.
  • Talk to your team and other teams. Determine whether this is only an SEO problem or a wider business problem.
  • Don’t build because AI makes it possible. Don’t buy because a demo looks impressive.

Without proper scoping, you can end up with an expensive SaaS tool that doesn’t fit your workflow or an internal tool your team can’t maintain.

Always think first. Dedicate enough time to scope properly. Then decide whether to build, buy, or customize.

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Why some channels reward breadth and others require commitment

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Why some channels reward breadth and others require commitment

Many budget allocation strategies assume that every channel follows the same pattern: the first dollar is the most productive, and each additional dollar yields a slightly lower return.

The charts below show what that pattern looks like.

The log shape means that the first dollar is the most productive, and each subsequent dollar is worth a little less. When every channel looks like that, the game plan is to spread the budget to as many channels as possible and equalize the marginal CPAs to maximize profit.

But not every channel looks like that. Some have a warm-up region where the early spend is the least efficient, not the most. On those channels, the logic above breaks, and so does the “test small, scale the winners” playbook that most of the industry runs on autopilot. 

The difference comes down to one question about the channel: Is the response curve C-shaped or S-shaped?

The answer can change how you approach channel testing and channel measurement, including any MMM analysis. Moreover, Google has been incorporating more S-shaped campaign types, and after its Google Marketing Live announcements, this trend seems set to continue.

The two shapes — and the only part that matters

The response curve plots output (conversions, revenue) against input (spend). This generally results in two types of curves in marketing.

  • C-shaped (concave): Diminishing returns from the very first dollar. A log or power curve. Picture the top-left quarter of a circle: steep at the start, flattening as you go.
  • S-shaped (sigmoid): A slow, inefficient start, then an inflection point where it gets steep, followed by a flattening into saturation. A logistic curve.

The response curve itself isn’t what you allocate against. You allocate against the marginal curve, the derivative, which answers the question: “What did the next dollar buy me?” That’s where the shapes diverge in a way that matters.

  • For a C-curve, marginal return is highest at the first dollar and falls in only one direction. Marginal CPA rises from the first dollar onward. If conversions are a*ln(s), marginal conversions per dollar are a/s, so marginal CPA is s/a, climbing in a straight line as you scale. There’s no warm-up. The cheapest conversion you’ll ever buy is the first one.
  • For an S-curve, marginal return starts low, rises to a peak at the inflection point, then falls. Marginal CPA is U-shaped. It’s expensive at the start, bottoms out around the inflection point, then climbs into saturation.

That region of increasing marginal returns is the whole story. It’s the difference between a channel where small budgets are productive and one where they are wasted.

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How this looks in a marketing campaign

Say your CPA goal is $50. Here is an S-shaped channel, modeled as Conversions = 1000 / (1 + e^(-0.25(s – 20))), with spend in the thousands and the inflection at $20,000/month:

Run the $10,000 test that a sane person runs before committing real budget. Average CPA comes back at $132, marginal around $94. If those two metrics are all you look at, you conclude that this channel can’t hit $50, so let’s kill it.

That verdict is wrong. At $20,000 to $25,000, the channel is running at an average of $32 to $40, and the marginal dollar in the $15,000 to $25,000 band costs $18. That’s not “barely viable.” In that band, it’s the best marginal buy you have. The small test fell within the warm-up and reversed the conclusion.

In a C-shaped channel, the small test would have shown you the best the channel can do. On an S-shaped channel, it shows you the worst.

This is the trap. The standard playbook is “test small, scale what works.” On S-curves, small tests systematically condemn channels that would’ve worked at scale because the test is structurally stuck in the inefficient region.

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The allocation logic, restated

C-shaped channels, go wide

The optimization is convex. There’s one global optimum, the equimarginal rule from the marginal-CPA post applies cleanly, and the solution is usually interior, meaning lots of channels get funded.

Even a small allocation is productive because the first dollar is the best dollar. Run many channels lean, reallocate continuously at the margin, and pull back the instant marginal CPA crosses your goal.

S-shaped channels, go deep or skip

The optimization is non-convex. A small allocation can be strictly worse than zero because below the inflection your marginal return sits under your target, and you’ve sunk money to get nowhere.

The decision isn’t “how much.” It is binary: commit past the threshold, or don’t fund it at all. There’s a real minimum viable budget, and it’s often above normal test budgets. You can’t sprinkle an S-curve and expect efficiency, and you can’t evaluate one on an underfunded test.

Those two rules can look like they fight each other, but that’s only true to a certain point. Past the inflection, an S-curve is concave, so the equimarginal rule governs it exactly as it governs a true C. The S-specific instruction — commit a block instead of sprinkling — is only about the trip from zero to past the inflection.

Shape is therefore mostly a launch-and-evaluation problem. Getting a new prospecting channel into its efficient range requires a committed block and patience with ugly early numbers. Once it clears the inflection, you manage it at the margin like everything else, right up until you consider cutting it hard, where shape matters again because the downside is a cliff, not a ramp.

This is the part that’s genuinely counterintuitive, and it echoes the original marginal-return point: The right move isn’t always the one that looks most efficient at a small scale.

Which channels are which?

The historical default was concave. Simon and Arndt reviewed more than 100 studies and concluded that advertising follows the law of diminishing returns, a concave response. 

The dissent came later: Vakratsas, Feinberg, Bass, and Kalyanaram found that threshold effects do exist and that response is not necessarily globally concave. Their explanation for why thresholds were so hard to find is the useful part. Mature accounts already operate inside the effective range, so the warm-up never shows up in the data, and most studies fit a concave model (the double-log) that can’t reject an S-curve even when one is present.

The platform shift has made the threshold visible again. Here is a fuller map, ordered roughly from C to S. The shape column is an inference from how each system targets and learns, not a measured constant, and the right shape for your account still has to be measured.

Two rows do most of the work.

AI Max is the live example of a channel migrating from C toward S. Swapping explicit keywords for broad and keywordless matching means it needs conversion volume to learn which queries convert, so below a data threshold, it explores badly.

The mixed independent results fit that: Google reports about 14% more conversions on average and up to 27% for exact-match-heavy campaigns, while independent testing reports 84% of advertisers seeing neutral or negative results. Much of that spread is accounts that turned it on without the conversion volume to clear the learning region.

Performance Max is the trap, because its curve is a composite. It blends a harvesting layer (branded, retargeting, Shopping against existing intent) with a prospecting layer (keywordless expansion across surfaces). The harvesting layer is a cheap C that pays off on the first dollar. The prospecting layer is the S underneath.

Blended, the early efficiency looks great, because you are mostly skimming demand you already had, and the average hides the prospecting warm-up entirely. That is also why the platform is glad to optimize it for you: the blend flatters the headline number. You can’t read PMax or run the shape analysis on it until you split the harvesting from the prospecting.

The throughline runs in two layers. Rules-based auctions capture the best inventory first, which yields concavity; machine-learning systems must be fed before they are efficient, which introduces a threshold. Underneath both, harvesting existing demand is concave and mostly non-incremental, while creating new demand is the S-shaped part where the real growth and the real warm-up cost both sit.

Average versus marginal: total over spend, or the slope where you stand.

What you allocate against is marginal incremental return, the slope of the incremental curve at your operating point. A holdout fixes the first axis only. Time-sliced marginal CPA on attributed data fixes the second only. A multi-cell scaling test gets both, at a cost. 

MMM (method 1) estimates the whole curve from aggregate data and sidesteps click attribution entirely, but pays in identifiability and modeling assumptions instead. Most arguments about ‘what is working’ are two people standing on different axes.

There are two major cautions, and I would flag both as genuinely unsettled rather than settled facts. 

  • Separating a true S-curve from “concave with a high half-saturation point” is hard, because a concave model will fit S-shaped data well enough to hide the inflection (this is the Vakratsas point, and it applies to your own dashboards as much as to academic studies). 
  • The learning phase may be a one-time fixed cost to train the model rather than a permanent feature of the steady-state curve. If it is transient, the channel may behave concavely at the margin once it is trained, and the S you measured was a startup artifact. The truth is probably a mix: a one-time training cost, plus an ongoing minimum-volume requirement to stay efficient. Treat every shape call as provisional and re-check it.

One more failure mode, and this one is not unsettled science but a matter of where you are standing on the curve. An S only looks like an S if your data spans the inflection. 

Above the inflection, an S is concave, mathematically identical to a C. Look at only the $20,000-and-up rows of the table above: marginal CPA rises monotonically from $18, a textbook C-curve, and the convex warm-up is invisible because you are no longer operating in it. 

Established accounts usually sit past the inflection, which is exactly why Vakratsas found thresholds so hard to detect, and why you can run an S-shaped channel for years, correctly, while believing it is concave. The tell arrives the day you cut hard and fall off the inflection instead of easing down a slope.

When to go wide and when to go deep

The marginal-return post told you to equalize marginal CPAs across the program. That rule is still correct, but the shape of the curve tells you how you’re allowed to get there. 

  • On C-shaped channels, you can get there by sprinkling, because every dollar is productive and breadth is the natural answer. 
  • On S-shaped channels, you have to commit a block of budget past the inflection before the channel earns its place, and then concentrate rather than spread.

Lay the harvest-versus-create cut on top. Harvesting channels (branded, retargeting, non-brand search) are your C-curves: fund the first dollars, then cap them early, because they saturate fast and most of the tail isn’t incremental, no matter how strong the attributed ROAS looks. 

Prospecting channels (Meta, YouTube, LinkedIn, the expansion half of PMax) are your S-curves and your only real source of incremental growth: commit past the warm-up or don’t start, and judge them on incremental lift rather than attributed CPA, or you’ll kill the thing that was working.

Classic search rewards going wide. PMax, AI Max, and Meta prospecting reward going deep on fewer bets and giving each enough volume to clear the warm-up. Run an S-curve like a C-curve and you’ll starve it, read the underfunded result, and kill a channel that would’ve been one of your best.

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Bing Webmaster Tools updates AI reporting with Intents, Topics, Citation Share and Compare

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Microsoft is now officially releasing a preview of the new AI performance report within Bing Webmaster Tools that now includes Intents, Topics, Citation Share, and Compare. We saw Microsoft demo these features in late April but now it is actually starting to roll out to users.

As a reminder, Bing officially rolled out its AI performance report in February. Google didn’t roll out its AI reporting in Search Console until June, and it seemed forced.

What is new. “These new capabilities build on that foundation by helping publishers better understand why their content is being surfaced, which broader subject areas they are gaining visibility in, how their presence evolves relative to other cited sources, and how citation patterns change over time,” wrote Krishna Madhavan from Microsoft.

Intent: The new Intents feature in Bing Webmaster Tools now classifies the grounding queries in the AI Performance Report in broader categories, such as Informational, Commercial, Navigational, Learn and Solve, Research, Creation, Local, and more. This in a sense helps you understand the intent behind the prompt or query. “This helps publishers move beyond simply seeing which queries triggered citations and begin understanding the broader query context our systems associate with those citation appearances,” Krishna Madhavan wrote.

The example provided was that an e-commerce publisher may discover strong visibility in comparison-oriented or shopping-focused AI experiences, while an educational publisher may find that their content is frequently surfaced in research or learning-oriented interactions. These insights can help publishers better align content structure and depth with the types of experiences where AI systems are surfacing their content.

Topics: The Topics in the AI performance reports group related grounding queries into broader thematic clusters. AI systems reason across concepts and themes rather than isolated keywords, Microsoft explained. So by having topics, it will help publishers understand visibility in the same thematic structure that modern AI systems use to organize information.

So for example, queries such as “solar panels,” “solar energy efficiency,” and “residential solar installation,” for example, may all map into a broader topic cluster like Solar Energy.  “This creates a more natural way to analyze AI visibility. Content teams and publishers often think in terms of themes, editorial areas, and audience interests rather than isolated keywords. Topics help bridge that gap by turning grounding query data into a more thematic view of AI engagement,” Microsoft wrote.

One note, “during the preview phase, some labels may still be broad – especially for highly specialized or niche domains – but the system is already beginning to reveal meaningful thematic patterns,” Microsoft wrote.

Citations. Microsoft also added citation share, which shows how much of the citation space your site receives for a specific grounding query. Citation share is calculated as the percentage of citations attributed to your site out of all citations shown across all sites for that same grounding query. “This helps publishers understand not just whether they were cited, but how much visibility they received within the full set of cited sources for that query,” Microsoft explained.

Microsoft added these points:

  • “This can provide a more directional view into how visibility is evolving over time. Publishers may begin to identify areas where their content has strong and growing representation in AI-generated experiences, as well as areas where visibility may be more fragmented across many sources.”
  • “Importantly, Citation Share is designed as an observational metric – not a ranking system or a competitive scoreboard. It does not expose competitor domains, represent traffic share, or assign quality scores to content.”
  • “AI citation ecosystems are inherently dynamic. Citation patterns can shift due to changes in user behavior, evolving models, freshness signals, partner refresh cycles, and broader changes across the web itself.”

Compare. With all of that, you can also compare the changes over time. The compare feature allows you to overlay a previous time period directly onto the current reporting view. 

“Compare is designed to help publishers observe changes over time. Citation activity can be influenced by many factors including evolving AI models, competing content, freshness signals, and shifts in user demand,” Microsoft wrote.

Here is what it looks like:

Why we care. While we still do not have click and click-through rate data, Microsoft keeps adding more and more to its AI performance reports.

I am hopful that one day we will get click data, but I am still not expecting to see that from Google or Microsoft any time soon.

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Google penalties: Why prevention is cheaper than recovery

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Google penalties- Why prevention is cheaper than recovery

Google penalties, also known as manual spam actions, are among the few events in search that can disrupt an otherwise healthy online business overnight. 

For companies heavily dependent on organic traffic, the consequences often extend far beyond lost rankings. Revenue drops, customer acquisition costs rise, expansion plans stall, and the effects can linger long after the original policy violations have been remedied.

With a steady 90% market share, Google remains the primary traffic source for many publishers, ecommerce platforms, retailers, travel brands, affiliates, and lead generation businesses. 

Direct traffic rarely compensates for a major visibility loss, and Bing seldom offsets the difference. As a result, a manual spam action carries serious operational implications, not merely SEO risks.

Manual actions aren’t algorithm updates

One point still misunderstood throughout the industry deserves clarification. Manual spam actions differ from algorithmic updates. They aren’t fluctuations caused by changes in relevance calculations or ranking system adjustments. 

Google’s manual penalties involve direct enforcement after suspected violations against Google Search Essentials, formerly Google Webmaster Guidelines, have been identified and confirmed. The distinction matters because the response required is completely different.

A website affected by changing ranking systems requires analysis, adaptation, and recrawling. A website affected by a manual spam action requires remediation and applying for reconsideration. Those are separate situations entirely.

Google doesn’t issue manual spam actions casually. The process involves internal senior employee review cycles. Suspected violations must be investigated and confirmed first.

Google states clearly that manual actions are the consequence of proven policy transgressions. Despite frequent cries of foul, false positives are exceptionally rare. Once a manual action appears in Google Search Console, the enforcement is already in the production pipeline.

The operational problem is that many businesses fail to recognize how much unresolved policy exposure their web platforms have accumulated over time.

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How penalties develop

The initial steps that ultimately lead to a manual penalty and a website’s drop in search visibility often begin inconspicuously, gradually eroding policy compliance. 

  • An ecommerce business launches an aggressive link acquisition campaign during an early growth phase. Over the years, PageRank-passing spam links accumulate unchecked until eventually nobody remembers where thousands of exact-match backlinks originate. 
  • A publisher enters into commercial partnerships involving sponsored content or affiliate sections, which gradually become structurally integrated into the editorial architecture of the website. 
  • A SaaS company creates large numbers of low-quality location pages while expanding into new markets. 
  • A lead generation business scales supplemental SEO content through low-cost LLM production systems with limited editorial oversight because that appears to be what most competitors are doing.

The underlying patterns are remarkably similar across industries. In many cases, organic search visibility initially improves and may even generate measurable revenue gains attributable to the SEO initiative. 

The short-term results reinforce the perception that the approach is working. However, as time passes, nobody revisits whether those earlier decisions remain aligned with evolving search quality standards and webmaster policies.

Why historical violations still matter

One reason manual spam actions create so much disruption is that policy violations often persist quietly for years before review. Many organizations incorrectly assume that questionable SEO tactics of the past lose their relevance over time. 

Yet Google Search systems don’t forget historical footprints. Archived URLs remain crawlable. Legacy sections continue contributing content quality signals long after internal ownership was abandoned. 

Most persistently, backlink patterns remain visible for decades. Large numbers of websites remain affected by backlinks generated through manipulative campaigns dating back many years. 

Paid placements, article syndication networks, private blog networks, commercial keyword-heavy guest posting campaigns, expired domain backlinks, directory spam, and widget distribution schemes that once formed part of mainstream SEO activity are today’s liabilities. 

Some of these practices continue to operate more or less openly for years, while enforcement may appear erratic or inconsistent. When left unaddressed, they represent an incalculable risk to the website publisher.

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Time doesn’t eliminate the risk

This becomes particularly important during acquisitions. Businesses purchasing established domains frequently inherit unresolved compliance exposure alongside rankings and traffic. Google evaluates the website’s condition, not which employee, agency, or previous owner introduced the violations.

Traffic growth alone doesn’t confirm compliance health. A domain generating millions of clicks may still carry unresolved risks tied to old link schemes, expired sponsorship arrangements, deceptive user-agent cloaking, manipulative redirects, or scaled low-quality content sections. Those issues often go unnoticed until they’re brought to the surface by a Google manual spam action notification.

A common sign of a manual spam action: rapid loss of visibility
A common sign of an algorithmic adjustment: Gradual loss of visibility

Reputation abuse and publisher liability

The mechanics behind reputation abuse are straightforward. A trusted brand with an established web platform allows third parties to publish unrelated, often unsupervised content under the same domain name. In many cases, publishers integrated discount coupon sections, casino reviews, affiliate content, or commercially motivated informational pages directly into existing editorial systems. 

The problem frequently worsened significantly because the content wasn’t properly segmented. The consequence is that the distinction between trusted editorial work and commercially motivated material became blurred.

Once confronted with a site-wide penalty, affected publishers experience broad visibility declines across the entire platform, not merely within the originally offending sections of the website. The damage to a brand that lends its reputation to a disreputable third party is often substantial. 

Recovery efforts frequently prove time-consuming and costly. Removing isolated pages rarely resolves the problem. Many organizations require broader structural changes, including archive cleanup, internal link reviews, crawl management adjustments, sponsorship governance reforms, the removal of spammy redirects, stronger editorial oversight, and stricter technical segmentation.

In short, recovering from such a penalty takes time, costs significant amounts of money, and is often a painful process.

A common sign of a manual spam action: Rapid loss of visibility
A common sign of a manual spam action: Rapid loss of visibility

The risks of scaled content

Google increasingly scrutinizes large-scale publishing systems that produce repetitive, low-value content without a unique selling proposition. 

The issue isn’t maintaining many websites simultaneously. Large website portfolios have thrived in Google Search for years and continue to do so. The underlying problem involves quality control, editorial oversight, originality, and informational value.

  • Affiliate networks produce near-identical product comparison pages across thousands of long-tail keywords. 
  • Local SEO operations deploy templated service pages across hundreds of regions with minimal differentiation. 
  • AI-assisted workflows publish large numbers of informational pages without factual oversight or genuine expertise to support them. 
  • Travel websites generate mass-produced destination pages through repetitive, generic content systems.

Most organizations don’t cross into problematic territory intentionally. The transition usually occurs gradually, often unbeknownst to the decision-makers who rely on outdated or misleading recommendations. 

The resulting manual spam action in Google Search Console, followed by a sharp decline in rankings, frequently occurs after a prolonged period of spam signal accumulation rather than during the apparent growth phase.

Incomplete remediation prolongs penalties

Many site owners approach reconsideration requests as if they were negotiating with Google. That puts them at a significant disadvantage from the outset. 

The reconsideration process exists for one purpose only: to demonstrate that the website has been restored to full compliance with Google’s guidelines. It’s important to note that Google expects complete compliance before lifting a manual spam action.

This means the requirement extends beyond the specific violation highlighted in Google Search Console. A site owner who addresses only one known spam issue while leaving unrelated policy violations unresolved elsewhere on the website will typically face rejection.

A common testing approach, such as a publisher removing some problematic sponsored content while retaining similar affiliate arrangements elsewhere, will fail. Likewise, a business that disavows recent manipulative backlinks while ignoring historical paid link schemes is unlikely to convince Google of its genuine commitment to complying with Google’s policies going forward.

Similarly, a website network that cleans up one property while continuing identical publishing practices across related domains signals incomplete remediation rather than meaningful operational reform. As a result, it stands little chance of regaining Google’s trust.

Why repeated rejections make recovery harder

Effective website recovery requires a comprehensive review rather than selective cleanup. Technical infrastructure, content quality, sponsorship structures, redirect behavior, link acquisition history, indexing patterns, archive sections, and ownership transparency all require examination during serious compliance recovery efforts.

The Google Search team expects compelling documentation detailing what has changed and how future violations will be prevented. Temporary cosmetic adjustments rarely persuade reviewers to lift a manual spam action.

Making matters worse, each rejection typically requires an even more comprehensive review and cleanup effort. At the same time, every reconsideration request that Google deems disingenuous further erodes Google’s trust in the publisher.

The cost of uncertainty

There’s no guaranteed turnaround time for reconsideration processing. Some reviews are completed within days. Others take weeks or months.

At the same time, large websites with extensive SEO legacies accumulated over many years often require longer assessment periods due to the substantial volumes of data that must be crawled and analyzed before changes can be evaluated.

For businesses that rely primarily on Google traffic, this uncertainty creates a potentially existential threat.

  • An ecommerce business approaching a peak seasonal period with an unresolved manual spam action can face cash flow problems quickly.
  • Publishers dependent on advertising revenue experience ranking losses that translate directly into declining commercial performance.
  • Lead generation businesses often encounter immediate pipeline contraction once visibility declines significantly.

The operational risk becomes even greater when companies fail to build a strong brand capable of partially offsetting organic traffic declines through direct navigation or alternative revenue-generating channels. In this context, paid traffic is a poor substitute due to its associated costs.

In short, some online businesses can’t afford to be penalized in the first place.

Penalties can cripple operations

The issue extends beyond SEO performance. Search visibility directly affects commercial expansion, investor confidence, company valuation, partnership negotiations, and revenue stability.

Penalty expiration represents another commonly misunderstood aspect. Google manual spam actions may expire after prolonged periods, often years. However, this is rarely a viable strategy for an affected business. 

Waiting passively through an extended period of declining visibility seldom aligns with commercial realities. More importantly, expiration alone doesn’t guarantee recovery or renewed growth, as the penalty could be reapplied not too long after it expired.

Google’s search systems continue evaluating overall site quality independently of manual enforcement status. A website carrying unresolved spam signals across its content, technical infrastructure, or off-page profile may continue to struggle long after the manual action itself has been lifted.

Compliance requires ongoing oversight

Compliance reviews can’t be considered optional or a luxury. Organizations heavily dependent on organic Google visibility require ongoing operational review cycles focused specifically on comprehensive policy compliance.

These reviews shouldn’t be conducted internally. Even the most talented in-house SEO teams are often hard-pressed to diligently identify shortcomings that may reflect on their own work or that of their colleagues. Policy compliance requires external expertise, sufficient authority, and a proven track record.

Purely technical SEO audits, while indispensable, are insufficient if commercial partnerships bypass oversight. Editorial standards alone won’t suffice if historical link manipulation remains unresolved. Planned growth initiatives require evaluation against established compliance frameworks before deployment, not after traffic has become dependent on questionable practices.

Mature organizations increasingly integrate compliance reviews into their operational governance. Sponsorship structures undergo search compliance review before launch. Scaled publishing systems are assessed for quality before expansion. Historical content is evaluated on a recurring basis. Acquisition due diligence includes policy exposure analysis alongside financial review.

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Compliance is a business imperative

This level of discipline and vigilance matters because manual spam actions rarely arrive at convenient moments. More often than not, undesirable Google scrutiny coincides with critical periods: just before a long-planned commercial expansion, in the run-up to a migration project, ahead of an acquisition, as the peak retail season begins, or shortly before investor reporting deadlines.

This is hardly intentional. It’s simply a matter of unfortunate timing. Google doesn’t align search quality enforcement with business planning calendars. Google cares primarily about user experience. For every website that loses its top position, there is usually another capable of providing users with a similarly compelling experience.

Businesses that ignore unresolved policy exposure often discover the problem the hard way, only after search visibility has collapsed and online sales have followed suit. At that point, recovery becomes a far more prolonged, expensive, and operationally disruptive undertaking than ongoing compliance reviews would have been prior to penalization.

Nevertheless, the work must be done. The one silver lining is that, in many cases, the process proves cathartic. Once the penalty has been resolved and the website’s SEO signals have become more consistent, the removal of legacy issues often allows rankings not merely to recover, but to exceed their previous highs.

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