<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/content-marketing-console.png" class="attachment-large size-large wp-post-image" alt="Content marketing console" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/09/content-marketing-console.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/content-marketing-console-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/content-marketing-console-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/content-marketing-console-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI isn't hurting content performance. Dropping proven content strategies is the bigger reason content isn't delivering, survey suggests.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/content-marketing-console.png" class="attachment-large size-large wp-post-image" alt="Content marketing console" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/content-marketing-console.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/content-marketing-console-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/content-marketing-console-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/content-marketing-console-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Content marketing performance fell to its lowest level in 12 years, even as AI adoption topped 92%, according to Orbit Media&#8217;s latest annual blogging survey. </p>



<p class="wp-block-paragraph">Just 14% of marketers said their blogs delivered &#8220;strong results.&#8221; That&#8217;s six percentage points below the previous 12-year low and nearly half the 26% reported in 2022.</p>



<p class="wp-block-paragraph">The decline reflects marketers abandoning tactics historically linked to stronger results, not AI adoption itself, the report concluded</p>



<p class="wp-block-paragraph"><strong>Why the decline? </strong>The proven content marketing practices most closely linked to strong results became less common over the past year, the survey found. These included influencer collaboration, original research, keyword research, paid content promotion, formal human editing, and consistent use of analytics.</p>



<ul class="wp-block-list">
<li>Ann Handley, chief content officer at MarketingProfs, called the findings &#8220;a wake-up call.&#8221; Marketers need to be &#8220;much (MUCH!) more discerning about which effort we remove, and which we keep,&#8221; she said.</li>
</ul>



<p class="wp-block-paragraph"><strong>AI use doesn&#8217;t predict success</strong>. AI adoption reached 92.4% this year. But using AI showed no relationship with stronger results. And AI did reduce production time:</p>



<ul class="wp-block-list">
<li>The average blog post now takes 3 hours and 20 minutes to write, down from more than 4 hours in 2022.</li>



<li>This saves the typical marketer about 50 hours of writing a year, even as reported content performance continues to decline, Orbit estimated.</li>
</ul>



<p class="wp-block-paragraph"><strong>Keyword research and collaboration. </strong>The findings challenge the idea that SEO has become less relevant in the AI era. Marketers who conducted keyword research were more likely to report strong results, even though fewer respondents said they research keywords before publishing.</p>



<ul class="wp-block-list">
<li>Influencer collaboration showed the strongest relationship with success. Marketers who regularly worked with experts were 2.6 times more likely than the benchmark to report strong results. Yet adoption fell from 25% of marketers in 2017 to just 7% this year.</li>



<li>Traffic has become a less meaningful performance metric as AI changes how people discover information online. Instead, marketers who measured qualified leads, deals, and revenue were more likely to report strong results, the survey also said.</li>
</ul>



<p class="wp-block-paragraph"><strong>Why we care.</strong> Faster content production with AI didn&#8217;t lead to better results in this survey. The marketers reporting the strongest outcomes were more likely to invest in keyword research, original research, expert collaboration, and human editing.</p>



<p class="wp-block-paragraph"><strong>About the data. </strong>Orbit Media surveyed 1,042 content marketers for its 2026 Blogging Statistics report. The survey examined blogging practices, AI adoption, and self-reported content performance. None of the respondents reported using all eight strategies Orbit associated with its strongest-performing content programs.</p>



<p class="wp-block-paragraph"><strong>The report.</strong> <a href="https://www.orbitmedia.com/blog/blogging-statistics/">Blogging Statistics 2026: What 1,042 Content Marketers Told Us About What Works [New Research]</a>


Content marketing performance fell to its lowest level in 12 years, even as AI adoption topped 92%, according to Orbit Media’s latest annual blogging survey. 
Just 14% of marketers said their blogs delivered “strong results.” That’s six percentage points below the previous 12-year low and nearly half the 26% reported in 2022.
The decline reflects marketers abandoning tactics historically linked to stronger results, not AI adoption itself, the report concluded
Why the decline? The proven content marketing practices most closely linked to strong results became less common over the past year, the survey found. These included influencer collaboration, original research, keyword research, paid content promotion, formal human editing, and consistent use of analytics.
Ann Handley, chief content officer at MarketingProfs, called the findings “a wake-up call.” Marketers need to be “much (MUCH!) more discerning about which effort we remove, and which we keep,” she said.
AI use doesn’t predict success. AI adoption reached 92.4% this year. But using AI showed no relationship with stronger results. And AI did reduce production time:
The average blog post now takes 3 hours and 20 minutes to write, down from more than 4 hours in 2022.
This saves the typical marketer about 50 hours of writing a year, even as reported content performance continues to decline, Orbit estimated.
Keyword research and collaboration. The findings challenge the idea that SEO has become less relevant in the AI era. Marketers who conducted keyword research were more likely to report strong results, even though fewer respondents said they research keywords before publishing.
Influencer collaboration showed the strongest relationship with success. Marketers who regularly worked with experts were 2.6 times more likely than the benchmark to report strong results. Yet adoption fell from 25% of marketers in 2017 to just 7% this year.
Traffic has become a less meaningful performance metric as AI changes how people discover information online. Instead, marketers who measured qualified leads, deals, and revenue were more likely to report strong results, the survey also said.
Why we care. Faster content production with AI didn’t lead to better results in this survey. The marketers reporting the strongest outcomes were more likely to invest in keyword research, original research, expert collaboration, and human editing.
About the data. Orbit Media surveyed 1,042 content marketers for its 2026 Blogging Statistics report. The survey examined blogging practices, AI adoption, and self-reported content performance. None of the respondents reported using all eight strategies Orbit associated with its strongest-performing content programs.
The report. Blogging Statistics 2026: What 1,042 Content Marketers Told Us About What Works [New Research]

AI isn't hurting content performance. Dropping proven content strategies is the bigger reason content isn't delivering, survey suggests.

Content marketing success falls to 12-year low: Survey

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/09/AmazonChatGPT-ads.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/AmazonChatGPT-ads.png 1672w, https://searchengineland.com/wp-content/seloads/2026/09/AmazonChatGPT-ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/AmazonChatGPT-ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/AmazonChatGPT-ads-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Amazon is piloting access to ChatGPT Ads through Amazon DSP, letting U.S. advertisers extend campaigns into conversational ads.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/09/AmazonChatGPT-ads.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/AmazonChatGPT-ads.png 1672w, https://searchengineland.com/wp-content/seloads/2026/09/AmazonChatGPT-ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/AmazonChatGPT-ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/AmazonChatGPT-ads-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Amazon is partnering with OpenAI to let advertisers extend campaigns from Amazon DSP into ChatGPT, giving brands another way to buy into the chatbot’s growing advertising inventory.</p>



<p class="wp-block-paragraph"><strong>Driving the news</strong>. ChatGPT Ads inventory will be available through Amazon DSP as a managed service, with Amazon helping advertisers set up and optimize campaigns. Delta Vacations is among a select group of U.S. advertisers participating in the pilot.</p>



<p class="wp-block-paragraph">The integration is limited to the U.S. at launch.</p>



<p class="wp-block-paragraph"><strong>Why we care. </strong>The pilot gives Amazon advertisers a new route into ChatGPT Ads without requiring them to manage campaigns directly through OpenAI. It also connects one of the largest digital advertising platforms with an emerging conversational ad channel.</p>



<p class="wp-block-paragraph"><strong>The details</strong>. Advertisers can buy ChatGPT inventory on both a cost-per-click and CPM basis. Amazon is also offering product feed ads that automatically create ad assets using advertisers&#8217; product catalogs.</p>



<p class="wp-block-paragraph">Ads will appear as text and image units beneath organic ChatGPT responses and will carry sponsored labels.</p>



<p class="wp-block-paragraph"><strong>Zoom in</strong>. Buying through Amazon doesn&#8217;t mean Amazon decides where an advertiser appears inside ChatGPT.</p>



<p class="wp-block-paragraph">OpenAI will continue to control ad delivery and placement through its own systems.</p>



<p class="wp-block-paragraph">That creates a split between the two platforms: Amazon provides the buying and campaign-management layer, while OpenAI controls how those ads are ultimately served within ChatGPT.</p>



<p class="wp-block-paragraph"><strong>Measurement:</strong> Advertisers testing the integration will receive aggregated performance data including impressions, clicks, cost per result, CPM and CPC.</p>



<p class="wp-block-paragraph">Amazon&#8217;s shopping and streaming data could also make the integration particularly interesting for advertisers looking to combine its audience insights with ChatGPT&#8217;s conversational context.</p>



<p class="wp-block-paragraph"><strong>The big picture</strong>. The partnership gives OpenAI access to Amazon&#8217;s massive advertiser base as it works to scale ChatGPT Ads.</p>



<p class="wp-block-paragraph">It could also broaden ChatGPT&#8217;s appeal to nonendemic Amazon advertisers — companies that don&#8217;t necessarily sell products on Amazon but already use Amazon DSP to reach audiences elsewhere.</p>



<p class="wp-block-paragraph"><strong>What to watch</strong>. Whether OpenAI expands Amazon DSP access beyond the initial U.S. pilot and how much demand the partnership generates from advertisers that already use Amazon&#8217;s advertising ecosystem.</p>



<p class="wp-block-paragraph"><strong>Bottom line</strong>. <a href="https://www.marketingdive.com/news/amazon-pilots-ad-services-in-chatgpt-what-marketers-need-to-know/829945/">Amazon is becoming another gateway into ChatGPT Ads</a>, but OpenAI isn&#8217;t handing over control of its inventory. Amazon will manage the buying relationship, while OpenAI remains responsible for deciding how and where those ads appear inside ChatGPT.</p>


Amazon is partnering with OpenAI to let advertisers extend campaigns from Amazon DSP into ChatGPT, giving brands another way to buy into the chatbot’s growing advertising inventory.
Driving the news. ChatGPT Ads inventory will be available through Amazon DSP as a managed service, with Amazon helping advertisers set up and optimize campaigns. Delta Vacations is among a select group of U.S. advertisers participating in the pilot.
The integration is limited to the U.S. at launch.
Why we care. The pilot gives Amazon advertisers a new route into ChatGPT Ads without requiring them to manage campaigns directly through OpenAI. It also connects one of the largest digital advertising platforms with an emerging conversational ad channel.
The details. Advertisers can buy ChatGPT inventory on both a cost-per-click and CPM basis. Amazon is also offering product feed ads that automatically create ad assets using advertisers’ product catalogs.
Ads will appear as text and image units beneath organic ChatGPT responses and will carry sponsored labels.
Zoom in. Buying through Amazon doesn’t mean Amazon decides where an advertiser appears inside ChatGPT.
OpenAI will continue to control ad delivery and placement through its own systems.
That creates a split between the two platforms: Amazon provides the buying and campaign-management layer, while OpenAI controls how those ads are ultimately served within ChatGPT.
Measurement: Advertisers testing the integration will receive aggregated performance data including impressions, clicks, cost per result, CPM and CPC.
Amazon’s shopping and streaming data could also make the integration particularly interesting for advertisers looking to combine its audience insights with ChatGPT’s conversational context.
The big picture. The partnership gives OpenAI access to Amazon’s massive advertiser base as it works to scale ChatGPT Ads.
It could also broaden ChatGPT’s appeal to nonendemic Amazon advertisers — companies that don’t necessarily sell products on Amazon but already use Amazon DSP to reach audiences elsewhere.
What to watch. Whether OpenAI expands Amazon DSP access beyond the initial U.S. pilot and how much demand the partnership generates from advertisers that already use Amazon’s advertising ecosystem.
Bottom line. Amazon is becoming another gateway into ChatGPT Ads, but OpenAI isn’t handing over control of its inventory. Amazon will manage the buying relationship, while OpenAI remains responsible for deciding how and where those ads appear inside ChatGPT.

Amazon is piloting access to ChatGPT Ads through Amazon DSP, letting U.S. advertisers extend campaigns into conversational ads.

Amazon pilots ChatGPT Ads through its DSP

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/How-to-use-Claude-to-run-a-stronger-CRO-audit.png" class="attachment-large size-large wp-post-image" alt="How to use Claude to run a stronger CRO audit" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/How-to-use-Claude-to-run-a-stronger-CRO-audit.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/How-to-use-Claude-to-run-a-stronger-CRO-audit-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/How-to-use-Claude-to-run-a-stronger-CRO-audit-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/How-to-use-Claude-to-run-a-stronger-CRO-audit-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Claude can sort CRO data, surface patterns, and organize findings, but human judgment is still needed to validate conversion problems.

Most CRO audits begin with a straightforward goal: figure out what’s keeping more visitors from converting.
The process itself is rarely that straightforward. Before long, you have GA4 reports open in one tab, Search Console in another, landing-page screenshots scattered across a folder, and several theories about what’s causing the problem. 
Finding possible friction is usually the easy part. The harder part is gathering enough evidence to determine which issues matter and which only look convincing in a report.
This is where Claude can be useful. It can sort through exports, compare findings across sources, organize page-review notes, and turn a messy collection of evidence into a usable first draft. That gives you more time to focus on what requires judgment: validating the data, ruling out alternative explanations, and deciding what’s worth testing. 
But Claude can also produce an audit that sounds credible while getting the conversion definition, reporting period, sample size, or page behavior wrong. It may find a relationship between a page element and conversion performance, but it can’t prove from a spreadsheet and screenshot that one caused the other.
Start with the conversion definition
Before you upload a GA4 export or ask Claude to review a landing page, define the conversion the audit is meant to improve.
That sounds basic, but it determines whether the rest of the audit is useful. Claude can sort a large export, compare page performance, and surface unusual drop-off points. It can’t tell whether the event you selected represents the business outcome that matters.
In GA4, a key event is an event that measures an action the business considers important. Marking an event as a key event makes it more visible in reporting. It doesn’t confirm that the event fires correctly, that it represents a qualified outcome, or that it’s the right metric for a CRO decision.
A completed purchase may be a reasonable primary conversion for an ecommerce audit. Even then, look beyond purchase rate. Revenue per session, average order value, discount use, cancellations, refunds, and margin can change the interpretation of an apparent win.
For lead generation websites, form submissions are often an early signal rather than the final outcome. A shorter form might produce more submissions while reducing the share that sales accepts. If the CRM data is available, connect on-site behavior to the next qualified stage, such as:
Meeting booked.
Meeting attended.
Sales-accepted lead.
Opportunity created.
Closed-won revenue.
The point is to give Claude a clear definition of success and its limits. Otherwise, it may optimize around a visible metric that the client doesn’t actually value.
Dig deeper: Use Claude for SEO. Don’t let Claude do SEO.



  
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Create a one-page audit brief
Write the audit rules down before starting the analysis. Keep the brief in the Claude Project alongside the files you plan to review. Claude Projects provide a self-contained workspace with chat history, a knowledge base of uploadable material, and project-level instructions, so they’re useful for keeping the scope and source material consistent across the audit.
Include:
Primary conversion: The on-site action the audit is intended to improve.
Quality measure: The downstream CRM, revenue, retention, or margin metric that prevents you from optimizing for low-value conversions.
Measurement source: The exact GA4 event, CRM field, ecommerce transaction field, or reporting view used for each metric.
Date range: The audit period and comparison period.
Scope: The pages, templates, device categories, markets, audiences, and channels included in the review.
Recent changes: Site releases, tracking updates, campaign changes, pricing changes, consent-banner updates, promotions, or inventory issues that may affect performance.
Known limitations: Duplicate events, incomplete cross-domain tracking, consent-related measurement gaps, bot traffic, unavailable CRM matching, or small samples.
Business constraints: Qualification requirements, service locations, inventory, legal requirements, implementation capacity, and brand rules.
A B2B SaaS brief, for example, might define the primary on-site conversion as a completed demo-request form. The quality metric could be the percentage of those submissions that become sales-accepted leads within 30 days. It might also note that a consent-banner change went live midway through the reporting period.
That one detail can change the audit. A sudden drop in recorded form submissions after the banner release could be a measurement change, a real conversion change, or a mixture of both. Claude can flag that timing. An analyst needs to verify the implementation before treating it as a UX finding.
Add instructions before the analysis
Add a short set of standing rules to the Claude Project. The goal is to prevent the model from filling evidence gaps with plausible-sounding explanations.
You can start with this:
You are assisting with a CRO audit.

Treat the uploaded files and supplied audit brief as the source of truth. Don’t assume that a GA4 key event represents a qualified conversion unless the brief says it does.

Separate observed facts from hypotheses. Don’t claim causation from correlations, screenshots, or aggregate analytics data.

For each finding, provide:
  • The observation.
  • The source file, table, page, or screenshot that supports it.
  • The affected audience or page.
  • A confidence level: high, medium, or low.
  • Alternative explanations or measurement limitations.
  • The validation needed before action.
  • A suggested test or next step.

If the evidence is insufficient, say so directly.
This is a good place to be strict. A useful CRO audit doesn’t need Claude to sound certain. It needs Claude to show its work.
Build an evidence pack
Claude’s output will only be as reliable as the material behind it. Don’t start with, “Audit this website and tell me how to improve conversions.” That prompt invites generic UX advice because it gives the model little evidence about who visits the site, what they’re trying to do, or where performance is breaking down.
Instead, prepare a compact evidence pack that separates data, page observations, and business context.
Claude supports common audit inputs including CSV files, PDFs, DOCX documents, JSON, HTML, images, and, where code execution and file creation are enabled, XLSX files. Files can be attached to a conversation or stored in a Project’s Files section for reference across conversations.
Dig deeper: How to turn Claude Code into your SEO command center



Give Claude governed access to the evidence
You can provide audit data to Claude in two ways: upload curated exports or connect approved sources through a Model Context Protocol (MCP) server.
MCP is an open standard for connecting AI applications with external systems and data sources through defined tools. In practice, an MCP connection can let Claude query an approved GA4, Search Console, CRM, warehouse, or reporting source instead of relying on static files.
Exports remain a good default. They create a fixed record of the analysis period, make the data scope easy to inspect, and reduce the risk of Claude using the wrong property, date range, metric, or segment. A CSV also makes it easier to reproduce a finding after the audit is delivered.
An MCP connection can be more useful when the audit requires follow-up questions. For example, Claude may identify a lower mobile conversion rate on a group of landing pages, then need to compare that segment by channel, date, browser, or country. With a read-only connection, the analyst can ask for those cuts without repeatedly exporting new reports.
A live connection may make follow-up analysis easier, but it also makes clear access rules and human review more important.
Use MCP with clear boundaries. Before connecting Claude to analytics or business systems, define:
Read-only access: The audit workflow should retrieve data, not change tracking, dashboards, CRM records, audiences, or campaign settings.
Least-privilege permissions: Grant access only to the relevant analytics property, Search Console property, warehouse view, or CRM fields.
Approved tools and queries: Restrict the connection to reporting and retrieval functions where possible. Avoid giving a general-purpose agent administrative access to simplify the audit workflow.
Data minimization: Return aggregate metrics and anonymized records whenever possible. Avoid sending form submissions, email addresses, phone numbers, account IDs, call transcripts, or other personal data unless the client has approved that use.
Metric definitions: Document the conversion event, dimensions, filters, timezone, attribution rules, and date range Claude should use.
Human review: Validate every query and every recommendation that depends on it, especially if the model generated the query logic.
Logging and access review: Retain an audit trail of the connection, data accessed, prompts, outputs, and permission approvals.
For a GA4 audit, the ideal setup is a read-only connection scoped to one property, with Claude instructed to use a named conversion event, a specified date range, and agreed reporting dimensions. It shouldn’t be able to edit events, mark new key events, alter audiences, or modify Google Ads links.
Exports are still useful. Even with an MCP connection, save the data behind the final findings. A screenshot, CSV export, query result, or report link gives the strategist and client a stable reference point for review.
Use exports when:
The audit needs a fixed, reproducible data snapshot.
The data is sensitive, or access should remain limited.
The client doesn’t have an approved MCP or connector setup.
You need to clean, anonymize, or aggregate the data first.
The analysis involves a limited set of well-defined reports.
Use a read-only MCP connection when:
The audit needs repeated follow-up segmentation.
Data lives across several approved systems.
The analyst needs to investigate patterns interactively.
Access controls, logging, and client approval are already in place.
Whether you use exports or a live connection matters less than the rules you put around the analysis. Claude should have enough evidence to investigate the question, but no more system access than the audit requires.
Dig deeper: 6 content audit workflows to build in Claude




	
	
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Use Claude for discrete tasks
Don’t ask Claude to “run a CRO audit.” Give it a bounded task, the relevant evidence, and a required output format.
That produces better analysis and makes it easier to verify the result.
Start with data triage



Review the attached GA4 landing-page report. 

Primary conversion: qualified demo request.
Analysis period: May 1 through July 31.
Comparison period: February 1 through April 30.

Identify landing pages with enough traffic to investigate where conversion performance differs materially by device, channel, or new versus returning users.

For each finding, provide:
  • Page and affected segment
  • Sessions, conversions, and conversion rate
  • Comparison-period change
  • Evidence reference
  • Possible explanations
  • Any tracking or sample-size limitation
  • The next validation step

Don’t claim causation. If the data doesn’t support a conclusion, say “insufficient evidence.”
Then use the output to choose a small number of pages for review. Claude can quickly identify patterns worth investigating, such as a high-traffic page with weak mobile conversion or a paid landing page whose form-start rate falls well below comparable pages. It shouldn’t decide why the pattern exists.
Review the page itself



Review the attached desktop and mobile screenshots for this landing page.

The page’s purpose is to convert paid-search visitors evaluating enterprise plans into qualified demo requests.

Identify observable friction involving:
  • Message match with the stated intent
  • Information hierarchy
  • CTA visibility and clarity
  • Form burden and error prevention
  • Pricing or qualification clarity
  • Trust and proof
The difference matters. “The submit button appears below several required fields on a narrow mobile viewport” is an observation. “The low mobile conversion rate is caused by the button placement” is a hypothesis that needs validation.
Keep a findings table



Ask Claude to consolidate only the findings that have evidence behind them:
Create a CRO findings table from the validated notes and supplied data.

Include:
  • Finding
  • Evidence
  • Affected page or audience
  • Hypothesis
  • Confidence level
  • Recommended validation or test
  • Primary success metric
  • Guardrail metric
  • Implementation notes

Exclude recommendations that are generic, duplicative, unsupported by the supplied material, or impossible to measure.
This helps keep the audit focused on evidence instead of turning it into a list of subjective design preferences. It also gives you an easy human review checklist before anything reaches the client.
Always verify before you prioritize
Claude can identify patterns, organize evidence, and draft hypotheses. Before a finding becomes a client recommendation, verify that the pattern is real, the proposed explanation is plausible, and the business can measure the outcome.
This is the step that prevents a polished audit from becoming a list of confident but weak recommendations. Use a simple review gate for each finding.
Conversion definition




What to verify: The event reflects the outcome defined in the audit brief.
Why it matters: A form_submit event can include spam, duplicate submissions, or leads sales will never accept.
Tracking




What to verify: Tags fire once, key steps are measured, and consent or cross-domain behavior is understood.
Why it matters: A broken event can look exactly like funnel friction.
Sample size




What to verify: The segment has enough volume, and the pattern holds across a sensible comparison period.
Why it matters: A few conversions can create a dramatic-looking rate change.
Data quality




What to verify: GA4 sampling, thresholding, missing values, and sudden tracking changes have been checked.
Why it matters: Complex GA4 reports can use sampled data, which should change how confidently you interpret a small difference.
Actual page behavior




What to verify: The page, form, and CTA work in a real browser on relevant devices.
Why it matters: Screenshots may miss delayed scripts, overlays, personalization, validation states, and browser-specific issues.
Business context




What to verify: The recommendation fits qualification rules, pricing, inventory, sales process, legal requirements, and implementation capacity.
Why it matters: More on-site conversions can still be a poor outcome if lead quality or profit declines.
Test design




What to verify: The team has a clear success metric, guardrail metric, traffic plan, and stopping rule.
Why it matters: A recommendation without a measurement plan is difficult to learn from.
If Claude flags that mobile visitors convert at a lower rate on a high-traffic landing page, don’t immediately recommend a mobile redesign. First, check whether the difference persists by channel and date range, whether the event fires consistently on mobile, and whether the page actually works on the devices and browsers used by that audience.
Then review the page itself. You may find a clipped field label, a keyboard that obscures the submit button, a consent banner covering the CTA, or a form error that appears only after JavaScript validation. You may also find nothing wrong with the interface, which means the next investigation should focus on traffic quality, message match, offer fit, or measurement.
Dig deeper: How to scale SEO content updates with Claude Code



Prioritize the validated findings
Once a finding has passed review, Claude can help turn it into a roadmap. Keep the scoring visible rather than asking the model to assign a mysterious “priority score.”
For each recommendation, document:
The page or audience affected.
The supporting evidence.
The behavior you expect to change.
The hypothesis being tested.
The primary success metric.
A guardrail metric that protects lead quality, revenue, margin, refunds, or another downstream outcome.
The level of confidence in the diagnosis.
Implementation effort and dependencies.
Claude can draft that structure quickly. A strategist still decides whether the evidence is strong enough to test, whether another explanation deserves investigation first, and whether the proposed change is worth the implementation cost.
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Use AI to speed up the work, not skip it
Claude can make a CRO audit faster. It can sort through exports, compare segments, organize page-review notes, and help turn scattered evidence into a clearer set of findings.
What it can’t do is decide whether the data is trustworthy, understand every business constraint, or confirm that a recommendation will improve performance. That still requires someone to validate the tracking, inspect the experience, consider other explanations, and build a test that can produce a useful answer.
The best way to use Claude in a CRO audit is to give it narrow questions, reliable evidence, and clear rules for handling uncertainty. Let it reduce the repetitive work. Keep the diagnosis, prioritization, and final decisions with the strategist.

Claude can sort CRO data, surface patterns, and organize findings, but human judgment is still needed to validate conversion problems.

How to use Claude to run a stronger CRO audit

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/When-SEO-best-practices-become-SEO-dogma.png" class="attachment-large size-large wp-post-image" alt="When SEO best practices become SEO dogma" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/When-SEO-best-practices-become-SEO-dogma.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/When-SEO-best-practices-become-SEO-dogma-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/When-SEO-best-practices-become-SEO-dogma-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/When-SEO-best-practices-become-SEO-dogma-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>SEO best practices often start with good reasoning. So why do we still treat old recommendations like gospel after the context has changed?

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/When-SEO-best-practices-become-SEO-dogma.png" class="attachment-large size-large wp-post-image" alt="When SEO best practices become SEO dogma" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/When-SEO-best-practices-become-SEO-dogma.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/When-SEO-best-practices-become-SEO-dogma-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/When-SEO-best-practices-become-SEO-dogma-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/When-SEO-best-practices-become-SEO-dogma-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Ask a room full of SEOs whether something is a best practice, and there&#8217;s a good chance everyone will agree before anyone asks the obvious question: Why?</p>



<p class="wp-block-paragraph">We’ve accumulated plenty of these recommendations over the years: Use one H1. Keep title tags under a certain character count. Put your target keyword in the title, H1, and first paragraph. Improve your Core Web Vitals until the report turns green. Use schema. Create fresh content. None of these recommendations came out of thin air. </p>



<p class="wp-block-paragraph">Many were based on reasonable observations, testing, or guidance that was relevant at the time. Some still are.</p>



<p class="wp-block-paragraph">The problem comes when an idea gets repeated long enough. The reasoning disappears, the recommendation remains, and “this has historically been useful to consider” becomes “this is what you have to do.”</p>



<p class="wp-block-paragraph">That’s how <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> best practices become dogma.</p>



<h2 id="h-how-best-practices-are-born-and-why-they-stick" class="wp-block-heading">How ‘best practices’ are born (and why they stick)</h2>



<p class="wp-block-paragraph">Most SEO best practices start with a reasonable observation. Someone notices a pattern, runs a test, shares a successful tactic, or follows search engine guidance.</p>



<p class="wp-block-paragraph">The idea gets repeated in articles, conferences, tools, and audit templates. Over time, “this worked under these conditions” becomes “this is how SEO works.”</p>



<p class="wp-block-paragraph">That’s how a recommendation becomes a checklist item.</p>



<p class="wp-block-paragraph">Once it does, questioning it can feel irresponsible. A crawler flags 2,000 pages with multiple H1s, so fixing them feels productive. The issue is visible, the recommendation is familiar, and there’s even a number attached to it.</p>



<p class="wp-block-paragraph">But what problem are you actually solving?</p>


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<h2 id="h-the-difference-between-still-true-and-still-useful" class="wp-block-heading">The difference between ‘still true’ and ‘still useful’</h2>



<p class="wp-block-paragraph">Someone will question a long-standing recommendation, and someone else will immediately respond: “Well, that’s still a best practice.”</p>



<p class="wp-block-paragraph">Maybe. But something can still be technically true without being useful enough to prioritize.</p>



<p class="wp-block-paragraph">Take Core Web Vitals, for example. We all know a slow page can create a poor user experience. Nobody’s arguing otherwise. But that doesn’t mean every URL sitting outside a recommended threshold deserves an engineering project. </p>



<p class="wp-block-paragraph">If a high-traffic product or conversion page is slow enough to affect user behavior, that’s worth investigating. If a low-traffic resource page misses a benchmark by a fraction of a second, the business case may be much harder to make.</p>



<p class="wp-block-paragraph">Yes, clean HTML is generally preferable to messy HTML. Descriptive headings can help communicate page structure. Image alt text is important. Page speed matters. The question isn’t whether these things are good. The question is whether fixing a particular issue is the best use of your time right now.</p>



<p class="wp-block-paragraph">That’s a much harder question because it requires context.</p>



<p class="wp-block-paragraph">A publisher dealing with delayed JavaScript rendering may have a serious discoverability problem. An ecommerce site with thousands of duplicate URLs created by faceted navigation may be wasting significant crawl resources. And a service business with three H1 elements on a handful of pages styled differently by its CMS may have absolutely nothing to worry about.</p>



<p class="wp-block-paragraph">The recommendation is the same. The impact isn’t.</p>



<p class="wp-block-paragraph">And that’s why “best practice” has become an increasingly inadequate way to prioritize SEO work. We can’t optimize for compliance over outcomes by checking boxes instead of asking harder questions about impact.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/stop-saying-best-practice-start-bringing-proof-481792">Stop saying ‘best practice’ and start bringing proof</a></em></strong>



<h2 id="h-how-to-decide-which-seo-work-matters" class="wp-block-heading">How to decide which SEO work matters</h2>



<p class="wp-block-paragraph">A better way to escape SEO dogma is to change the questions we ask.</p>



<h3 id="h-1-what-evidence-suggests-we-should-change-what-we-re-doing" class="wp-block-heading">1. What evidence suggests we should change what we’re doing?</h3>



<p class="wp-block-paragraph">This should be the starting point for more SEO work.</p>



<p class="wp-block-paragraph">Google changes constantly. Search changes constantly. Someone will always have a new theory about what matters. That doesn’t mean every announcement requires a response.</p>



<p class="wp-block-paragraph">Before changing a strategy, rebuilding a page template, or assigning development resources, look for evidence that the issue is affecting visibility, traffic, discoverability, or business goals.</p>



<p class="wp-block-paragraph">Sometimes the right response to a change is action. Sometimes it’s observation. Occasionally, the right response is to leave things alone.</p>



<p class="wp-block-paragraph">That may be the least marketable SEO advice in history. It’s still advice more teams need to hear.</p>



<h3 id="h-2-what-problem-does-this-solve" class="wp-block-heading">2. What problem does this solve?</h3>



<p class="wp-block-paragraph">“Best practice” is often used as a substitute for explaining why something should be done.</p>



<p class="wp-block-paragraph">My favorite example is the familiar “multiple H1 warning.” A tool flags multiple H1s and labels them as a Warning of Medium-high priority. The recommendation says to fix them, so they get added to a backlog of site update requests.</p>



<p class="wp-block-paragraph">OK. But what problem are we solving?</p>



<p class="wp-block-paragraph">Is the page structure genuinely confusing? Is important content being communicated poorly? Is the HTML preventing search engines from understanding the page?</p>



<p class="wp-block-paragraph">Or does the page simply have multiple elements that happen to use an H1 because of how the CMS and design system were built?</p>



<p class="wp-block-paragraph">Those situations shouldn’t automatically receive the same response.</p>



<p class="wp-block-paragraph">Before fixing something, define the outcome you expect. If you can’t explain the potential outcome beyond “the tool says this is an error,” the issue may deserve another look.</p>



<h3 id="h-3-how-much-of-the-site-do-those-issues-actually-affect" class="wp-block-heading">3. How much of the site do those issues actually affect?</h3>



<p class="wp-block-paragraph">SEO audits are very good at making problems look large. A crawler can tell you that a site has 10,000 issues, and that can certainly sound alarming. But issue counts rarely tell you anything about impact.</p>



<p class="wp-block-paragraph">Ten thousand missing alt attributes on legacy images with little search value may have limited SEO consequences. One broken rendering implementation on a high-value page template can affect thousands of URLs. One canonicalization problem can create more damage than an audit full of warnings.</p>



<p class="wp-block-paragraph">The number of issues found isn’t a KPI. Neither is the number of issues closed. Scale matters, but only when the affected pages, templates, or user journeys matter.</p>



<h3 id="h-4-what-happens-if-we-don-t" class="wp-block-heading">4. What happens if we don’t?</h3>



<p class="wp-block-paragraph">What’s the actual risk of leaving the issue alone? </p>



<ul class="wp-block-list">
<li>Will important pages fail to get indexed? </li>



<li>Will search engines struggle to access meaningful content? </li>



<li>Will users have a worse experience? </li>



<li>Will organic visibility likely decline? </li>
</ul>



<p class="wp-block-paragraph">Or will the website continue to perform exactly as it does today?</p>



<p class="wp-block-paragraph">Imagine two recommendations. The first is adding meta descriptions across a ton of legacy blog pages that receive almost no search traffic and aren’t central to the site’s content strategy. The second is misconfigured canonical tags causing search engines to ignore the canonical directives entirely, leading to cannibalization.</p>



<p class="wp-block-paragraph">Both might show up in an audit. Both could technically be called SEO issues. Only one has an obvious connection to discoverability and a tangible consequence to inaction.</p>



<p class="wp-block-paragraph">Not every imperfect implementation represents an opportunity cost. Sometimes a fix is simply a fix looking for a problem.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/technical-seo-audit-mistakes-486318/">10 technical SEO audit mistakes that lead to bad recommendations</a></em></strong>



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<h3 id="h-5-does-the-reason-it-worked-apply-here" class="wp-block-heading">5. Does the reason it worked apply here?</h3>



<p class="wp-block-paragraph">SEO advice is often presented as universal because it&#8217;s easier to package. But websites aren’t interchangeable.</p>



<p class="wp-block-paragraph">A publisher and an ecommerce site can have the exact same technical SEO warnings and completely different priorities. For a publisher, crawl efficiency and content freshness might matter enormously. For ecommerce, duplicate URLs, faceted navigation, or product availability states can create problems across thousands of URLs. A local service business may have far fewer pages and far more to gain from fixing its information architecture or conversion paths.</p>



<p class="wp-block-paragraph">Someone else’s solution doesn’t automatically belong on your website.</p>



<h3 id="h-6-are-we-solving-the-problem-that-brought-someone-here" class="wp-block-heading">6. Are we solving the problem that brought someone here?</h3>



<p class="wp-block-paragraph">For years, SEO strategy has often started and ended with the SERP. What keyword do we want? Who ranks? What do they have that we don’t?</p>



<p class="wp-block-paragraph">That analysis still has value. The problem is what happens when competitive analysis becomes competitive cloning. If everyone looks at the page ranking above them, adds what they’re missing, and calls it optimization, every result eventually starts looking suspiciously similar.</p>



<p class="wp-block-paragraph">The better question is what the searcher is actually trying to accomplish. Sometimes:</p>



<ul class="wp-block-list">
<li>That means answering the obvious question. </li>



<li>It means giving them the comparison they haven’t found elsewhere. </li>



<li>The opportunity is a better tool, clearer explanation, or resource that helps them take the next step.</li>
</ul>



<p class="wp-block-paragraph">The goal shouldn’t be to reverse-engineer every feature of the page currently ranking above you.</p>



<p class="wp-block-paragraph">Google’s results can tell you what already exists. They don’t always tell you what users still need.</p>



<h3 id="h-7-where-is-our-audience-searching-when-they-decide-who-to-trust" class="wp-block-heading">7. Where is our audience searching when they decide who to trust?</h3>



<p class="wp-block-paragraph">For a long time, the SEO conversation has centered on convincing Google that a website deserves visibility.</p>



<p class="wp-block-paragraph">People now encounter brands through more sources than ever. They read recommendations from publications. They ask questions in communities. They watch videos. They use social platforms as search engines. They receive answers synthesized by AI systems that may draw on sources far beyond a company’s own website.</p>



<p class="wp-block-paragraph">A brand’s website remains important, but visibility increasingly depends on the broader footprint surrounding it. That should change how we think about what deserves investment.</p>



<p class="wp-block-paragraph">An expert contribution to an industry publication can put the brand in front of people who may have never found its website through Google. Original research can earn citations and create authority beyond the website where it was first published. Even social channels can provide something SEO reports often can’t: unfiltered language from people describing their problems in their own words.</p>



<p class="wp-block-paragraph">These signals are reminders that traditional search optimization was never supposed to become a substitute for understanding how people discover information.</p>



<h3 id="h-8-what-are-we-already-doing-that-isn-t-performing" class="wp-block-heading">8. What are we already doing that isn’t performing?</h3>



<p class="wp-block-paragraph">The SEO industry has a talent for accumulating tactics. A new recommendation appears, so it gets added to the process. Then another, and another.</p>



<p class="wp-block-paragraph">Eventually, teams are maintaining an enormous collection of “best practices” because removing something feels riskier than keeping it.</p>



<p class="wp-block-paragraph">But every task has a cost. Every low-impact optimization consumes time that could have gone somewhere else. Every unnecessary development ticket competes with work that may actually affect the business. Every piece of content created because a keyword exists is content that someone has to maintain.</p>



<p class="wp-block-paragraph">Sometimes the smartest SEO decision isn’t adding a new tactic. It’s retiring an old one.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/technical-seo-testing-build-stronger-experiment-484537">Technical SEO testing: How to build a stronger experiment</a></em></strong>


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<h2 id="h-maybe-the-only-real-best-practice-is-think-for-yourself" class="wp-block-heading">Maybe the only real best practice is ‘think for yourself’</h2>



<p class="wp-block-paragraph">None of this means best practices are useless. They got us here, and they still have a place. But we have to ask if they still deserve a place on the list.</p>



<p class="wp-block-paragraph">Our job is to figure out which changes actually matter. That means being willing to question the things we’ve always done. Sometimes, it means admitting that the thing we’ve always done worked before, but doesn’t anymore.</p>



<p class="wp-block-paragraph">And it means building things that have value beyond a single search engine: useful content, recognizable brands, strong products, good customer experiences, credible expertise, and relationships beyond a single platform.</p>



<p class="wp-block-paragraph">That’s not particularly revolutionary advice. It is, however, much harder to package into a neat checklist. Maybe that’s exactly why we need more of it.</p>

Ask a room full of SEOs whether something is a best practice, and there’s a good chance everyone will agree before anyone asks the obvious question: Why?
We’ve accumulated plenty of these recommendations over the years: Use one H1. Keep title tags under a certain character count. Put your target keyword in the title, H1, and first paragraph. Improve your Core Web Vitals until the report turns green. Use schema. Create fresh content. None of these recommendations came out of thin air. 
Many were based on reasonable observations, testing, or guidance that was relevant at the time. Some still are.
The problem comes when an idea gets repeated long enough. The reasoning disappears, the recommendation remains, and “this has historically been useful to consider” becomes “this is what you have to do.”
That’s how SEO best practices become dogma.
How ‘best practices’ are born (and why they stick)
Most SEO best practices start with a reasonable observation. Someone notices a pattern, runs a test, shares a successful tactic, or follows search engine guidance.
The idea gets repeated in articles, conferences, tools, and audit templates. Over time, “this worked under these conditions” becomes “this is how SEO works.”
That’s how a recommendation becomes a checklist item.
Once it does, questioning it can feel irresponsible. A crawler flags 2,000 pages with multiple H1s, so fixing them feels productive. The issue is visible, the recommendation is familiar, and there’s even a number attached to it.
But what problem are you actually solving?
customers find first.
      
Start your free trial
    
The difference between ‘still true’ and ‘still useful’
Someone will question a long-standing recommendation, and someone else will immediately respond: “Well, that’s still a best practice.”
Maybe. But something can still be technically true without being useful enough to prioritize.
Take Core Web Vitals, for example. We all know a slow page can create a poor user experience. Nobody’s arguing otherwise. But that doesn’t mean every URL sitting outside a recommended threshold deserves an engineering project. 
If a high-traffic product or conversion page is slow enough to affect user behavior, that’s worth investigating. If a low-traffic resource page misses a benchmark by a fraction of a second, the business case may be much harder to make.
Yes, clean HTML is generally preferable to messy HTML. Descriptive headings can help communicate page structure. Image alt text is important. Page speed matters. The question isn’t whether these things are good. The question is whether fixing a particular issue is the best use of your time right now.
That’s a much harder question because it requires context.
A publisher dealing with delayed JavaScript rendering may have a serious discoverability problem. An ecommerce site with thousands of duplicate URLs created by faceted navigation may be wasting significant crawl resources. And a service business with three H1 elements on a handful of pages styled differently by its CMS may have absolutely nothing to worry about.
The recommendation is the same. The impact isn’t.
And that’s why “best practice” has become an increasingly inadequate way to prioritize SEO work. We can’t optimize for compliance over outcomes by checking boxes instead of asking harder questions about impact.
Dig deeper: Stop saying ‘best practice’ and start bringing proof



How to decide which SEO work matters
A better way to escape SEO dogma is to change the questions we ask.
1. What evidence suggests we should change what we’re doing?
This should be the starting point for more SEO work.
Google changes constantly. Search changes constantly. Someone will always have a new theory about what matters. That doesn’t mean every announcement requires a response.
Before changing a strategy, rebuilding a page template, or assigning development resources, look for evidence that the issue is affecting visibility, traffic, discoverability, or business goals.
Sometimes the right response to a change is action. Sometimes it’s observation. Occasionally, the right response is to leave things alone.
That may be the least marketable SEO advice in history. It’s still advice more teams need to hear.
2. What problem does this solve?
“Best practice” is often used as a substitute for explaining why something should be done.
My favorite example is the familiar “multiple H1 warning.” A tool flags multiple H1s and labels them as a Warning of Medium-high priority. The recommendation says to fix them, so they get added to a backlog of site update requests.
OK. But what problem are we solving?
Is the page structure genuinely confusing? Is important content being communicated poorly? Is the HTML preventing search engines from understanding the page?
Or does the page simply have multiple elements that happen to use an H1 because of how the CMS and design system were built?
Those situations shouldn’t automatically receive the same response.
Before fixing something, define the outcome you expect. If you can’t explain the potential outcome beyond “the tool says this is an error,” the issue may deserve another look.
3. How much of the site do those issues actually affect?
SEO audits are very good at making problems look large. A crawler can tell you that a site has 10,000 issues, and that can certainly sound alarming. But issue counts rarely tell you anything about impact.
Ten thousand missing alt attributes on legacy images with little search value may have limited SEO consequences. One broken rendering implementation on a high-value page template can affect thousands of URLs. One canonicalization problem can create more damage than an audit full of warnings.
The number of issues found isn’t a KPI. Neither is the number of issues closed. Scale matters, but only when the affected pages, templates, or user journeys matter.
4. What happens if we don’t?
What’s the actual risk of leaving the issue alone? 
Will important pages fail to get indexed? 
Will search engines struggle to access meaningful content? 
Will users have a worse experience? 
Will organic visibility likely decline? 
Or will the website continue to perform exactly as it does today?
Imagine two recommendations. The first is adding meta descriptions across a ton of legacy blog pages that receive almost no search traffic and aren’t central to the site’s content strategy. The second is misconfigured canonical tags causing search engines to ignore the canonical directives entirely, leading to cannibalization.
Both might show up in an audit. Both could technically be called SEO issues. Only one has an obvious connection to discoverability and a tangible consequence to inaction.
Not every imperfect implementation represents an opportunity cost. Sometimes a fix is simply a fix looking for a problem.
Dig deeper: 10 technical SEO audit mistakes that lead to bad recommendations




	
	
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See terms.
		
5. Does the reason it worked apply here?
SEO advice is often presented as universal because it’s easier to package. But websites aren’t interchangeable.
A publisher and an ecommerce site can have the exact same technical SEO warnings and completely different priorities. For a publisher, crawl efficiency and content freshness might matter enormously. For ecommerce, duplicate URLs, faceted navigation, or product availability states can create problems across thousands of URLs. A local service business may have far fewer pages and far more to gain from fixing its information architecture or conversion paths.
Someone else’s solution doesn’t automatically belong on your website.
6. Are we solving the problem that brought someone here?
For years, SEO strategy has often started and ended with the SERP. What keyword do we want? Who ranks? What do they have that we don’t?
That analysis still has value. The problem is what happens when competitive analysis becomes competitive cloning. If everyone looks at the page ranking above them, adds what they’re missing, and calls it optimization, every result eventually starts looking suspiciously similar.
The better question is what the searcher is actually trying to accomplish. Sometimes:
That means answering the obvious question. 
It means giving them the comparison they haven’t found elsewhere. 
The opportunity is a better tool, clearer explanation, or resource that helps them take the next step.
The goal shouldn’t be to reverse-engineer every feature of the page currently ranking above you.
Google’s results can tell you what already exists. They don’t always tell you what users still need.
7. Where is our audience searching when they decide who to trust?
For a long time, the SEO conversation has centered on convincing Google that a website deserves visibility.
People now encounter brands through more sources than ever. They read recommendations from publications. They ask questions in communities. They watch videos. They use social platforms as search engines. They receive answers synthesized by AI systems that may draw on sources far beyond a company’s own website.
A brand’s website remains important, but visibility increasingly depends on the broader footprint surrounding it. That should change how we think about what deserves investment.
An expert contribution to an industry publication can put the brand in front of people who may have never found its website through Google. Original research can earn citations and create authority beyond the website where it was first published. Even social channels can provide something SEO reports often can’t: unfiltered language from people describing their problems in their own words.
These signals are reminders that traditional search optimization was never supposed to become a substitute for understanding how people discover information.
8. What are we already doing that isn’t performing?
The SEO industry has a talent for accumulating tactics. A new recommendation appears, so it gets added to the process. Then another, and another.
Eventually, teams are maintaining an enormous collection of “best practices” because removing something feels riskier than keeping it.
But every task has a cost. Every low-impact optimization consumes time that could have gone somewhere else. Every unnecessary development ticket competes with work that may actually affect the business. Every piece of content created because a keyword exists is content that someone has to maintain.
Sometimes the smartest SEO decision isn’t adding a new tactic. It’s retiring an old one.
Dig deeper: Technical SEO testing: How to build a stronger experiment



  
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Maybe the only real best practice is ‘think for yourself’
None of this means best practices are useless. They got us here, and they still have a place. But we have to ask if they still deserve a place on the list.
Our job is to figure out which changes actually matter. That means being willing to question the things we’ve always done. Sometimes, it means admitting that the thing we’ve always done worked before, but doesn’t anymore.
And it means building things that have value beyond a single search engine: useful content, recognizable brands, strong products, good customer experiences, credible expertise, and relationships beyond a single platform.
That’s not particularly revolutionary advice. It is, however, much harder to package into a neat checklist. Maybe that’s exactly why we need more of it.

SEO best practices often start with good reasoning. So why do we still treat old recommendations like gospel after the context has changed?

When SEO best practices become SEO dogma

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/09/OpenAI-interface-blocking-rival-ads.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/OpenAI-interface-blocking-rival-ads.png 1672w, https://searchengineland.com/wp-content/seloads/2026/09/OpenAI-interface-blocking-rival-ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/OpenAI-interface-blocking-rival-ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/OpenAI-interface-blocking-rival-ads-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>OpenAI is reportedly blocking ads for competing image and audio AI tools from running inside ChatGPT.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/09/OpenAI-interface-blocking-rival-ads.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/OpenAI-interface-blocking-rival-ads.png 1672w, https://searchengineland.com/wp-content/seloads/2026/09/OpenAI-interface-blocking-rival-ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/OpenAI-interface-blocking-rival-ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/OpenAI-interface-blocking-rival-ads-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">OpenAI is reportedly restricting some rival AI companies from advertising competing products inside ChatGPT, tightening control over which products can use its growing advertising platform to reach users.</p>



<p class="wp-block-paragraph"><strong>Driving the news</strong>. OpenAI has updated its advertising policies to prevent campaigns promoting standalone image- and audio-generation products from running in ChatGPT, according to a report from The Information cited by Investing.com.</p>



<p class="wp-block-paragraph">The change was reportedly communicated directly to advertising partners rather than publicly announced.</p>



<p class="wp-block-paragraph"><strong>Zoom in</strong>. Adobe is among the advertisers affected. Adobe participated in OpenAI’s initial advertising pilot, promoting products including Acrobat Studio and its Firefly AI image generator.</p>



<p class="wp-block-paragraph">Doug Wyatt, Adobe’s senior director for Americas media, reportedly said OpenAI informed advertising partners that campaigns for standalone image and voice generation products would no longer be approved.</p>



<p class="wp-block-paragraph"><strong>Why we care.</strong> AI companies increasingly see ChatGPT as both a distribution channel and a competitor. Restricting ads from rival products could make it harder for those companies to acquire ChatGPT users through paid campaigns — while protecting OpenAI’s own AI tools from competing ads.</p>



<p class="wp-block-paragraph"><strong>The details</strong>. The restriction reportedly comes as OpenAI expands its own competing image and voice capabilities within ChatGPT.</p>



<p class="wp-block-paragraph">Video-generation tools, however, can still advertise on the platform, according to the report.</p>



<p class="wp-block-paragraph"><strong>Yes, but.</strong> Blocking direct competitors from advertising isn&#8217;t unusual for major digital platforms and media businesses.</p>



<p class="wp-block-paragraph">The move does, however, raise questions about how OpenAI will balance protecting its own products with growing ChatGPT into a significant advertising business.</p>



<p class="wp-block-paragraph"><strong>The big picture</strong>. OpenAI is increasingly operating as both the owner of a major consumer platform and a competitor to many of the companies that want access to its users.</p>



<p class="wp-block-paragraph">The reported restrictions show how that tension could shape ChatGPT&#8217;s emerging advertising ecosystem as OpenAI decides which advertisers can — and can&#8217;t — compete for attention inside the platform.</p>



<p class="wp-block-paragraph"><strong>What to watch</strong>. Whether OpenAI extends the restrictions to additional AI categories, including video generation, as its own product capabilities and advertising business expand.</p>



<p class="wp-block-paragraph"><strong>Dig deeper.</strong> <a href="https://uk.finance.yahoo.com/news/openai-restricts-rival-ai-ad-214408329.html">OpenAI restricts rival AI ad campaigns in ChatGPT as competition heats up &#8211; report</a>


OpenAI is reportedly restricting some rival AI companies from advertising competing products inside ChatGPT, tightening control over which products can use its growing advertising platform to reach users.
Driving the news. OpenAI has updated its advertising policies to prevent campaigns promoting standalone image- and audio-generation products from running in ChatGPT, according to a report from The Information cited by Investing.com.
The change was reportedly communicated directly to advertising partners rather than publicly announced.
Zoom in. Adobe is among the advertisers affected. Adobe participated in OpenAI’s initial advertising pilot, promoting products including Acrobat Studio and its Firefly AI image generator.
Doug Wyatt, Adobe’s senior director for Americas media, reportedly said OpenAI informed advertising partners that campaigns for standalone image and voice generation products would no longer be approved.
Why we care. AI companies increasingly see ChatGPT as both a distribution channel and a competitor. Restricting ads from rival products could make it harder for those companies to acquire ChatGPT users through paid campaigns — while protecting OpenAI’s own AI tools from competing ads.
The details. The restriction reportedly comes as OpenAI expands its own competing image and voice capabilities within ChatGPT.
Video-generation tools, however, can still advertise on the platform, according to the report.
Yes, but. Blocking direct competitors from advertising isn’t unusual for major digital platforms and media businesses.
The move does, however, raise questions about how OpenAI will balance protecting its own products with growing ChatGPT into a significant advertising business.
The big picture. OpenAI is increasingly operating as both the owner of a major consumer platform and a competitor to many of the companies that want access to its users.
The reported restrictions show how that tension could shape ChatGPT’s emerging advertising ecosystem as OpenAI decides which advertisers can — and can’t — compete for attention inside the platform.
What to watch. Whether OpenAI extends the restrictions to additional AI categories, including video generation, as its own product capabilities and advertising business expand.
Dig deeper. OpenAI restricts rival AI ad campaigns in ChatGPT as competition heats up – report

OpenAI is reportedly blocking ads for competing image and audio AI tools from running inside ChatGPT.

OpenAI reportedly blocks rival AI tools from advertising in ChatGPT

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/How-to-use-multiple-MMMs-to-make-better-paid-media-decisions.png" class="attachment-large size-large wp-post-image" alt="How to use multiple MMMs to make better paid media decisions" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/How-to-use-multiple-MMMs-to-make-better-paid-media-decisions.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/How-to-use-multiple-MMMs-to-make-better-paid-media-decisions-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/How-to-use-multiple-MMMs-to-make-better-paid-media-decisions-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/How-to-use-multiple-MMMs-to-make-better-paid-media-decisions-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Compare MMM outputs to expose hidden assumptions, surface uncertainty, and identify which budget decisions need further testing.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/How-to-use-multiple-MMMs-to-make-better-paid-media-decisions.png" class="attachment-large size-large wp-post-image" alt="How to use multiple MMMs to make better paid media decisions" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/How-to-use-multiple-MMMs-to-make-better-paid-media-decisions.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/How-to-use-multiple-MMMs-to-make-better-paid-media-decisions-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/How-to-use-multiple-MMMs-to-make-better-paid-media-decisions-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/How-to-use-multiple-MMMs-to-make-better-paid-media-decisions-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">After some initial setup and tinkering, any marketing mix model (MMM) can produce a convincing chart with channel decomposition, response curves, a clean R², and a recommended reallocation. But the chart won’t show how much the data shaped the result versus how much the model’s built-in assumptions did.</p>



<p class="wp-block-paragraph">Adstock and decay windows determine how long a channel&#8217;s effect lingers. Saturation curves affect how quickly returns diminish, driving every reallocation the model recommends. Priors and regularization (Bayesian or ridge) encode beliefs about plausible effect sizes, while seasonality and control variables influence how much lift the model attributes to the calendar versus the channel.</p>



<p class="wp-block-paragraph">Change these factors, and the same history can produce a different story. </p>



<p class="wp-block-paragraph">As a result, a single model is a first opinion, not a verdict. Running multiple models against the same inputs reveals how different assumptions change the recommendation before you make a seven-figure budget decision.</p>



<p class="wp-block-paragraph">My process uses multiple MMMs to surface uncertainty, validate recommendations, and make paid media decisions more reliable.</p>



<h2 id="h-run-a-multi-model-mmm-comparison" class="wp-block-heading">Run a multi-model MMM comparison</h2>



<p class="wp-block-paragraph">Before comparing MMMs, it helps to separate model validation from experimental validation.</p>



<p class="wp-block-paragraph">Incrementality tests provide a stronger causal check. A geographic lift, holdout, or on/off test directly measures whether a channel caused additional outcomes. But it covers one channel at a time, costs more, and requires planned variation.</p>



<p class="wp-block-paragraph">MMM works at a broader level. It infers causal contributions across every channel at once, including those you can&#8217;t easily test, and it reruns cheaply to prove results within days or weeks. But it depends entirely on its own assumptions.</p>



<p class="wp-block-paragraph">Ideally, the two complement each other. Models generate and rank hypotheses, experiments confirm which ones matter, and confirmed results feed back into the models as priors.</p>



<p class="wp-block-paragraph">Because you can&#8217;t test every channel every quarter, second, and third models become the next line of defense against any one model&#8217;s blind spots. Skip this step, and a reallocation from a single model inherits that model&#8217;s blind spots. A misallocation can reach six or seven figures before anyone notices.</p>


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<h3 id="h-top-tools-for-running-mmm" class="wp-block-heading">Top tools for running MMM</h3>



<p class="wp-block-paragraph">My solution is to run MMM through three separate tools with different modeling approaches and assumptions:</p>



<ul class="wp-block-list">
<li><strong>Robyn</strong> (Meta, open source) uses ridge regression with an evolutionary search across hyperparameters. It&#8217;s fast to run and accessible to marketing teams without a Bayesian background, which makes it a strong initial baseline.</li>



<li><strong>Meridian</strong> (Google, open source) is Bayesian and geographically hierarchical, with reach, frequency, and upper-funnel effects within its scope. Regional spend variation adds statistical signals a national-only model doesn&#8217;t have, which makes it well suited to geographical data and brand channels.</li>



<li><strong>PyMC-Marketing</strong> (open source, Python, built on PyMC) is fully Bayesian with user-defined priors, model structure, and indirect-effect paths between channels. It gives you full control over assumptions, but it needs someone who can defend those assumptions.</li>
</ul>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1846" height="780" src="https://searchengineland.com/wp-content/seloads/2026/09/image-62.png" alt="" class="wp-image-487684" srcset="https://searchengineland.com/wp-content/seloads/2026/09/image-62.png 1846w, https://searchengineland.com/wp-content/seloads/2026/09/image-62-768x325.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/image-62-1536x649.png 1536w" sizes="(max-width: 1846px) 100vw, 1846px" /></figure>
</div>


<p class="wp-block-paragraph">Robyn relies on R, while Meridian and PyMC-Marketing are Python-native. If your team is R-first, I recommend keeping Robyn as the fast in-house baseline and either building light Python wrappers for the other two or budgeting time to work across both ecosystems. The data prep is shared across all three.</p>



<p class="wp-block-paragraph"><strong><em>Dig</em> <em>deeper: <a href="https://searchengineland.com/mmm-tools-explained-467284">Not all MMM tools are equal: Meridian, Robyn, Orbit, and Prophet explained</a></em></strong>



<h3 id="h-how-to-run-a-multi-model-comparison" class="wp-block-heading">How to run a multi-model comparison</h3>



<p class="wp-block-paragraph">Use this workflow to run and compare multiple MMM models, from inputs through testing:</p>



<ul class="wp-block-list">
<li><strong>Inputs: </strong>Use identical spend, outcome, and control variables for all three tools. While the initial model is expensive, every additional model reuses the same prepared inputs. As a result, the marginal cost of models two and three is small.</li>



<li><strong>Models: </strong>Run all three with defaults first and tuning second. Resist the urge to hand-tune model one before model two has even run. You want to see where the defaults disagree before you start explaining the disagreement away.</li>



<li><strong>Comparison:</strong> Compare channel decomposition and response curves across models. Fit statistics can be a distraction here. For instance, a high R² means the model fits history, not that its causal story is correct. Every MMM fits reasonably well, but they still disagree with each other. Budget recommendations depend more on where each model sees diminishing returns on the saturation curve and how consistently channels rank across models than on small differences in revenue share.</li>



<li><strong>Triage and testing:</strong> The comparison turns into a decision.</li>
</ul>



<figure class="wp-block-image size-full"><img decoding="async" width="1374" height="1034" src="https://searchengineland.com/wp-content/seloads/2026/09/image-60.png" alt="" class="wp-image-487682" srcset="https://searchengineland.com/wp-content/seloads/2026/09/image-60.png 1374w, https://searchengineland.com/wp-content/seloads/2026/09/image-60-768x578.png 768w" sizes="(max-width: 1374px) 100vw, 1374px" /></figure>



<h2 id="h-analyze-mmm-output-where-the-models-disagree" class="wp-block-heading">Analyze MMM output where the models disagree</h2>



<p class="wp-block-paragraph">Convergent results support action, while divergent results require investigation. A comparison makes any uncertainty visible before it turns into a budget mistake.</p>



<p class="wp-block-paragraph">When the models converge, the finding has held up across three different modeling approaches and their underlying assumptions. At this point, you can defend reallocation, close the debate, and use the result as a prior for the next model refresh.</p>



<p class="wp-block-paragraph">When models diverge, no winner is declared. The root cause is almost always a confound, collinearity problem, or data gap. Confidence gets downgraded, and the next experiment focuses on the biggest disparity.</p>



<h3 id="h-why-mmm-models-often-diverge" class="wp-block-heading">Why MMM models often diverge</h3>



<p class="wp-block-paragraph">Here are recurring reasons the models disagree:</p>



<ul class="wp-block-list">
<li><strong>Channel collinearity:</strong> Two channels scale together, so each model divides the credit between them differently. You can’t identify the split from observation alone. A holdout test settles it.</li>



<li><strong>Seasonal confounds:</strong> A channel that always spends into peak season will absorb calendar lift under weak controls. If its credit collapses once seasonality tightens, it’s likely the calendar (not the channel) was driving the result.</li>



<li><strong>Flat spend history:</strong> Always-on budgets have no experimental variation, so the models are extrapolating saturation from functional form rather than from data. To fix it, introduce deliberate spend variation.</li>



<li><strong>Adstock window sensitivity:</strong> Slow-building channels show near-zero contribution under a short decay window but meaningful contribution under a long one. This tells you the effect is slow-building and long-tailed.</li>



<li><strong>Data gaps and tracking breaks:</strong> Divergence localized to one region or period is often the fastest way to catch a tracking gap. Ideally, you spot it before it distorts a budget decision.</li>
</ul>



<figure class="wp-block-image size-full"><img decoding="async" width="1366" height="1038" src="https://searchengineland.com/wp-content/seloads/2026/09/image-61.png" alt="" class="wp-image-487683" srcset="https://searchengineland.com/wp-content/seloads/2026/09/image-61.png 1366w, https://searchengineland.com/wp-content/seloads/2026/09/image-61-768x584.png 768w" sizes="(max-width: 1366px) 100vw, 1366px" /></figure>



<h3 id="h-an-example-of-divergent-mmm-models" class="wp-block-heading">An example of divergent MMM models</h3>



<p class="wp-block-paragraph">As an example, using a synthetic dataset, suppose a direct-to-consumer (DTC) brand spends roughly $1.5 million a month across four channels. There are 2.5 years of weekly data to run through all three models.</p>



<figure class="wp-block-table"><table><thead><tr><th><strong>Channel</strong></th><th><strong>Robyn</strong></th><th><strong>Meridian</strong></th><th><strong>PyMC-Marketing</strong></th></tr></thead><tbody><tr><td>Paid search</td><td>41%</td><td>22%</td><td>19%</td></tr><tr><td>Meta</td><td>24%</td><td>31%</td><td>18%</td></tr><tr><td>Google Shopping</td><td>11%</td><td>9%</td><td>22%</td></tr><tr><td>TV</td><td>3%</td><td>14%</td><td>16%</td></tr><tr><td>Base + seasonality</td><td>21%</td><td>24%</td><td>25%</td></tr></tbody></table></figure>



<p class="wp-block-paragraph"><strong>Branded search was over-credited by roughly 2x</strong>



<p class="wp-block-paragraph">With no priors pulling it back, Robyn&#8217;s ridge regression assigned 41% of revenue to paid search. Ridge credits whatever correlates most tightly with conversions, which was branded search in this example.</p>



<p class="wp-block-paragraph">Both Bayesian models treated search as partly downstream of existing demand and cut that credit to 19%-22%. A geographical holdout test on branded search settled it at 17% incremental, close to the Bayesian estimates.</p>



<p class="wp-block-paragraph"><strong>The TV estimate followed the decay window</strong>



<p class="wp-block-paragraph">Robyn&#8217;s short geometric adstock left TV at 3%. An eight- to 10-week decay window in Meridian and PyMC-Marketing put it at 14%-16%. This disagreement indicates that the true effect is slow-building and long-tailed.</p>



<p class="wp-block-paragraph"><strong>The Meta/Google Shopping split isn&#8217;t discernible from this data</strong>



<p class="wp-block-paragraph">Meta and Google scale together every peak season, so each model split their combined ~40% share differently (24%/11%, 31%/9%, 18%/22%), each with its own level of confidence. Side by side, the splits show that it’s impossible to recover the allocation from observational data alone.</p>



<p class="wp-block-paragraph">The following patterns tend to recur in other datasets, too:</p>



<ul class="wp-block-list">
<li>Seasonal credit swings.</li>



<li>Small channels that flip sign between models (a sign of a fragile, noise-driven estimate).</li>



<li>Geographical heterogeneity that a national model averages away.</li>



<li>Halo paths where upper-funnel video gets credit for contributing to search performance in models that allow indirect effects.</li>



<li>Uncomfortable convergence, where all three models independently agree that a long-favored channel is underperforming.</li>
</ul>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/avoid-marketing-mix-modeling-mistakes-463576">How to avoid marketing mix modeling mistakes that derail results</a></em></strong>



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<h2 id="h-turn-triangulated-results-into-budget-decisions-leadership-will-trust" class="wp-block-heading">Turn triangulated results into budget decisions leadership will trust</h2>



<p class="wp-block-paragraph">From here, analysis becomes a framework for decision-making. A single-model finding can turn into a debate about the model itself. But a triangulated finding, with three independent methods in agreement and ideally confirmed by a geographical test, focuses on weighing evidence instead.</p>



<p class="wp-block-paragraph">Close the loop with testing, not averaging. Model disagreement is a ranked list of genuine unknowns, and every major divergence is a question the observational data can’t answer on its own.</p>



<p class="wp-block-paragraph">Rather than developing an experimentation roadmap based on intuition, rely on a prioritized list from a model comparison. One or two targeted geographic lift tests can resolve more uncertainty than a quarter of scattershot experimentation.</p>



<p class="wp-block-paragraph">In the example above, this played out three ways in one quarter. First, the branded search holdout moved the budget on a confirmed 17% incremental number instead of Robyn&#8217;s 41% estimate. The TV finding changed the media plan with longer flights and a longer evaluation window, rather than cutting spend based on an adstock artifact.</p>



<p class="wp-block-paragraph">The Meta/Google Shopping ambiguity became the target of the next experiment instead of triggering a budget cut based on unstable results. And Meridian and PyMC-Marketing can also incorporate experiment results as priors. This way, each test can improve the next model refresh.</p>



<h3 id="h-a-three-month-mmm-implementation-plan" class="wp-block-heading">A three-month MMM implementation plan</h3>



<p class="wp-block-paragraph">Follow this plan to implement MMM in three months:</p>



<ul class="wp-block-list">
<li><strong>Assemble one dataset:</strong> Gather weekly spend, outcomes, and controls, along with two or more years of history. This step is time-intensive, but all other models will reuse the dataset.</li>



<li><strong>Run Robyn as the baseline:</strong> This is the fastest path to a working decomposition. Treat it as a first opinion, not the final answer.</li>



<li><strong>Add Meridian or PyMC-Marketing as a genuinely different second opinion:</strong> Bayesian priors change what receives credit. Geographical structure adds signal to regional data.</li>



<li><strong>Compare decomposition and response curves, not fit statistics:</strong> Log the convergences and the divergences. Consider the latter to be your findings list.</li>



<li><strong>Plan one geographical test to explore the largest divergence:</strong> The result will become the prior that narrows the next model refresh. The disagreement should visibly shrink on the rerun.</li>
</ul>



<figure class="wp-block-image size-full"><img decoding="async" width="1200" height="1150" src="https://searchengineland.com/wp-content/seloads/2026/09/image-63.png" alt="" class="wp-image-487685" srcset="https://searchengineland.com/wp-content/seloads/2026/09/image-63.png 1200w, https://searchengineland.com/wp-content/seloads/2026/09/image-63-768x736.png 768w" sizes="(max-width: 1200px) 100vw, 1200px" /></figure>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/marketing-mix-modeling-get-right-481849">Why marketing mix modeling is still hard to get right</a></em></strong>


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<h2 id="h-use-multiple-models-to-expose-uncertainty" class="wp-block-heading">Use multiple models to expose uncertainty</h2>



<p class="wp-block-paragraph">A single MMM can produce a confident point estimate even in the presence of uncertainty. Running three models against the same data makes the uncertainty visible and shows where further testing is required.</p>



<p class="wp-block-paragraph">The result is an evidence-backed budget recommendation with a clear distinction between findings supported across models and those that still need validation.</p>

After some initial setup and tinkering, any marketing mix model (MMM) can produce a convincing chart with channel decomposition, response curves, a clean R², and a recommended reallocation. But the chart won’t show how much the data shaped the result versus how much the model’s built-in assumptions did.
Adstock and decay windows determine how long a channel’s effect lingers. Saturation curves affect how quickly returns diminish, driving every reallocation the model recommends. Priors and regularization (Bayesian or ridge) encode beliefs about plausible effect sizes, while seasonality and control variables influence how much lift the model attributes to the calendar versus the channel.
Change these factors, and the same history can produce a different story. 
As a result, a single model is a first opinion, not a verdict. Running multiple models against the same inputs reveals how different assumptions change the recommendation before you make a seven-figure budget decision.
My process uses multiple MMMs to surface uncertainty, validate recommendations, and make paid media decisions more reliable.
Run a multi-model MMM comparison
Before comparing MMMs, it helps to separate model validation from experimental validation.
Incrementality tests provide a stronger causal check. A geographic lift, holdout, or on/off test directly measures whether a channel caused additional outcomes. But it covers one channel at a time, costs more, and requires planned variation.
MMM works at a broader level. It infers causal contributions across every channel at once, including those you can’t easily test, and it reruns cheaply to prove results within days or weeks. But it depends entirely on its own assumptions.
Ideally, the two complement each other. Models generate and rank hypotheses, experiments confirm which ones matter, and confirmed results feed back into the models as priors.
Because you can’t test every channel every quarter, second, and third models become the next line of defense against any one model’s blind spots. Skip this step, and a reallocation from a single model inherits that model’s blind spots. A misallocation can reach six or seven figures before anyone notices.
your competitors win.
      
Analyze your competitors
    
Top tools for running MMM
My solution is to run MMM through three separate tools with different modeling approaches and assumptions:
Robyn (Meta, open source) uses ridge regression with an evolutionary search across hyperparameters. It’s fast to run and accessible to marketing teams without a Bayesian background, which makes it a strong initial baseline.
Meridian (Google, open source) is Bayesian and geographically hierarchical, with reach, frequency, and upper-funnel effects within its scope. Regional spend variation adds statistical signals a national-only model doesn’t have, which makes it well suited to geographical data and brand channels.
PyMC-Marketing (open source, Python, built on PyMC) is fully Bayesian with user-defined priors, model structure, and indirect-effect paths between channels. It gives you full control over assumptions, but it needs someone who can defend those assumptions.




Robyn relies on R, while Meridian and PyMC-Marketing are Python-native. If your team is R-first, I recommend keeping Robyn as the fast in-house baseline and either building light Python wrappers for the other two or budgeting time to work across both ecosystems. The data prep is shared across all three.
Dig deeper: Not all MMM tools are equal: Meridian, Robyn, Orbit, and Prophet explained



How to run a multi-model comparison
Use this workflow to run and compare multiple MMM models, from inputs through testing:
Inputs: Use identical spend, outcome, and control variables for all three tools. While the initial model is expensive, every additional model reuses the same prepared inputs. As a result, the marginal cost of models two and three is small.
Models: Run all three with defaults first and tuning second. Resist the urge to hand-tune model one before model two has even run. You want to see where the defaults disagree before you start explaining the disagreement away.
Comparison: Compare channel decomposition and response curves across models. Fit statistics can be a distraction here. For instance, a high R² means the model fits history, not that its causal story is correct. Every MMM fits reasonably well, but they still disagree with each other. Budget recommendations depend more on where each model sees diminishing returns on the saturation curve and how consistently channels rank across models than on small differences in revenue share.
Triage and testing: The comparison turns into a decision.




Analyze MMM output where the models disagree
Convergent results support action, while divergent results require investigation. A comparison makes any uncertainty visible before it turns into a budget mistake.
When the models converge, the finding has held up across three different modeling approaches and their underlying assumptions. At this point, you can defend reallocation, close the debate, and use the result as a prior for the next model refresh.
When models diverge, no winner is declared. The root cause is almost always a confound, collinearity problem, or data gap. Confidence gets downgraded, and the next experiment focuses on the biggest disparity.
Why MMM models often diverge
Here are recurring reasons the models disagree:
Channel collinearity: Two channels scale together, so each model divides the credit between them differently. You can’t identify the split from observation alone. A holdout test settles it.
Seasonal confounds: A channel that always spends into peak season will absorb calendar lift under weak controls. If its credit collapses once seasonality tightens, it’s likely the calendar (not the channel) was driving the result.
Flat spend history: Always-on budgets have no experimental variation, so the models are extrapolating saturation from functional form rather than from data. To fix it, introduce deliberate spend variation.
Adstock window sensitivity: Slow-building channels show near-zero contribution under a short decay window but meaningful contribution under a long one. This tells you the effect is slow-building and long-tailed.
Data gaps and tracking breaks: Divergence localized to one region or period is often the fastest way to catch a tracking gap. Ideally, you spot it before it distorts a budget decision.




An example of divergent MMM models
As an example, using a synthetic dataset, suppose a direct-to-consumer (DTC) brand spends roughly $1.5 million a month across four channels. There are 2.5 years of weekly data to run through all three models.

ChannelRobynMeridianPyMC-Marketing

Paid search41%22%19%
Meta24%31%18%
Google Shopping11%9%22%
TV3%14%16%
Base + seasonality21%24%25%





Branded search was over-credited by roughly 2x



With no priors pulling it back, Robyn’s ridge regression assigned 41% of revenue to paid search. Ridge credits whatever correlates most tightly with conversions, which was branded search in this example.
Both Bayesian models treated search as partly downstream of existing demand and cut that credit to 19%-22%. A geographical holdout test on branded search settled it at 17% incremental, close to the Bayesian estimates.
The TV estimate followed the decay window



Robyn’s short geometric adstock left TV at 3%. An eight- to 10-week decay window in Meridian and PyMC-Marketing put it at 14%-16%. This disagreement indicates that the true effect is slow-building and long-tailed.
The Meta/Google Shopping split isn’t discernible from this data



Meta and Google scale together every peak season, so each model split their combined ~40% share differently (24%/11%, 31%/9%, 18%/22%), each with its own level of confidence. Side by side, the splits show that it’s impossible to recover the allocation from observational data alone.
The following patterns tend to recur in other datasets, too:
Seasonal credit swings.
Small channels that flip sign between models (a sign of a fragile, noise-driven estimate).
Geographical heterogeneity that a national model averages away.
Halo paths where upper-funnel video gets credit for contributing to search performance in models that allow indirect effects.
Uncomfortable convergence, where all three models independently agree that a long-favored channel is underperforming.
Dig deeper: How to avoid marketing mix modeling mistakes that derail results




	
	
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Turn triangulated results into budget decisions leadership will trust
From here, analysis becomes a framework for decision-making. A single-model finding can turn into a debate about the model itself. But a triangulated finding, with three independent methods in agreement and ideally confirmed by a geographical test, focuses on weighing evidence instead.
Close the loop with testing, not averaging. Model disagreement is a ranked list of genuine unknowns, and every major divergence is a question the observational data can’t answer on its own.
Rather than developing an experimentation roadmap based on intuition, rely on a prioritized list from a model comparison. One or two targeted geographic lift tests can resolve more uncertainty than a quarter of scattershot experimentation.
In the example above, this played out three ways in one quarter. First, the branded search holdout moved the budget on a confirmed 17% incremental number instead of Robyn’s 41% estimate. The TV finding changed the media plan with longer flights and a longer evaluation window, rather than cutting spend based on an adstock artifact.
The Meta/Google Shopping ambiguity became the target of the next experiment instead of triggering a budget cut based on unstable results. And Meridian and PyMC-Marketing can also incorporate experiment results as priors. This way, each test can improve the next model refresh.
A three-month MMM implementation plan
Follow this plan to implement MMM in three months:
Assemble one dataset: Gather weekly spend, outcomes, and controls, along with two or more years of history. This step is time-intensive, but all other models will reuse the dataset.
Run Robyn as the baseline: This is the fastest path to a working decomposition. Treat it as a first opinion, not the final answer.
Add Meridian or PyMC-Marketing as a genuinely different second opinion: Bayesian priors change what receives credit. Geographical structure adds signal to regional data.
Compare decomposition and response curves, not fit statistics: Log the convergences and the divergences. Consider the latter to be your findings list.
Plan one geographical test to explore the largest divergence: The result will become the prior that narrows the next model refresh. The disagreement should visibly shrink on the rerun.




Dig deeper: Why marketing mix modeling is still hard to get right



  
a customer you lose.
      
See who’s stealing your traffic
    
Use multiple models to expose uncertainty
A single MMM can produce a confident point estimate even in the presence of uncertainty. Running three models against the same data makes the uncertainty visible and shows where further testing is required.
The result is an evidence-backed budget recommendation with a clear distinction between findings supported across models and those that still need validation.

Compare MMM outputs to expose hidden assumptions, surface uncertainty, and identify which budget decisions need further testing.

How to use multiple MMMs to make better paid media decisions

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/09/google-data-manager-MMM.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/google-data-manager-MMM.png 1672w, https://searchengineland.com/wp-content/seloads/2026/09/google-data-manager-MMM-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/google-data-manager-MMM-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/google-data-manager-MMM-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google is rolling out new data tools to help marketers better understand what drives growth and where to invest.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/09/google-data-manager-MMM.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/google-data-manager-MMM.png 1672w, https://searchengineland.com/wp-content/seloads/2026/09/google-data-manager-MMM-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/google-data-manager-MMM-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/google-data-manager-MMM-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google is rolling out a slate of measurement and data tools aimed at helping marketers better understand what’s driving growth — and where to spend next.</p>



<p class="wp-block-paragraph"><strong>What’s new.</strong> Google is expanding Data Manager beyond Google Ads by integrating it into Google Analytics and Display &amp; Video 360, giving marketers more places to manage and activate first-party data.</p>



<p class="wp-block-paragraph">The company is also making the Data Manager API universal by adopting the IAB Tech Lab’s ECAPI standard.</p>



<p class="wp-block-paragraph">And in Google Ads, a new Data Strength Uplift Metric will help advertisers quantify the value generated by their first-party data setup — potentially giving marketers a clearer business case for improving their data foundations.</p>



<p class="wp-block-paragraph"><strong>Measurement gets an AI upgrade:</strong> Google is adding agentic capabilities to Meridian, its open-source marketing mix modeling platform.</p>



<p class="wp-block-paragraph">The AI-powered features will help marketers audit data quality, troubleshoot errors and get guidance while building models, reducing some of the technical complexity associated with marketing mix modeling.</p>



<p class="wp-block-paragraph"><strong>Going beyond immediate conversions:</strong> Meridian will also let marketers incorporate brand signals, such as Branded Google Query Volume, into their models.</p>



<p class="wp-block-paragraph">That could help advertisers demonstrate how upper-funnel investments — including TV commercials and out-of-home advertising — influence brand demand and eventually contribute to sales.</p>



<p class="wp-block-paragraph"><strong>Zoom out.</strong> Google is also launching Meridian GeoX globally, following a beta introduced earlier this year.</p>



<p class="wp-block-paragraph">The open-source tool lets marketers run causal geo-experiments to measure incrementality and then feed those results into their marketing mix models.</p>



<p class="wp-block-paragraph">That combination is important because MMM can identify relationships between marketing investment and business outcomes, while experiments can provide stronger evidence of whether the advertising actually caused the lift.</p>



<p class="wp-block-paragraph"><strong>Why we care</strong>. To tackle fragmented tracking issues, Google is giving marketers more ways to connect first-party data, experimentation and modeling rather than relying solely on attribution.</p>



<p class="wp-block-paragraph">The bigger opportunity is moving from asking which channel received credit for a conversion to understanding which investments actually created incremental growth.</p>



<p class="wp-block-paragraph"><strong>The bottom line</strong>. <a href="https://blog.google/products/ads-commerce/data-strength-updates">Google is building a more connected measurement stack around Data Manager and Meridian</a> — with AI helping marketers clean up their data, understand what&#8217;s driving results and make better decisions about where the next advertising dollar should go.</p>


Google is rolling out a slate of measurement and data tools aimed at helping marketers better understand what’s driving growth — and where to spend next.
What’s new. Google is expanding Data Manager beyond Google Ads by integrating it into Google Analytics and Display & Video 360, giving marketers more places to manage and activate first-party data.
The company is also making the Data Manager API universal by adopting the IAB Tech Lab’s ECAPI standard.
And in Google Ads, a new Data Strength Uplift Metric will help advertisers quantify the value generated by their first-party data setup — potentially giving marketers a clearer business case for improving their data foundations.
Measurement gets an AI upgrade: Google is adding agentic capabilities to Meridian, its open-source marketing mix modeling platform.
The AI-powered features will help marketers audit data quality, troubleshoot errors and get guidance while building models, reducing some of the technical complexity associated with marketing mix modeling.
Going beyond immediate conversions: Meridian will also let marketers incorporate brand signals, such as Branded Google Query Volume, into their models.
That could help advertisers demonstrate how upper-funnel investments — including TV commercials and out-of-home advertising — influence brand demand and eventually contribute to sales.
Zoom out. Google is also launching Meridian GeoX globally, following a beta introduced earlier this year.
The open-source tool lets marketers run causal geo-experiments to measure incrementality and then feed those results into their marketing mix models.
That combination is important because MMM can identify relationships between marketing investment and business outcomes, while experiments can provide stronger evidence of whether the advertising actually caused the lift.
Why we care. To tackle fragmented tracking issues, Google is giving marketers more ways to connect first-party data, experimentation and modeling rather than relying solely on attribution.
The bigger opportunity is moving from asking which channel received credit for a conversion to understanding which investments actually created incremental growth.
The bottom line. Google is building a more connected measurement stack around Data Manager and Meridian — with AI helping marketers clean up their data, understand what’s driving results and make better decisions about where the next advertising dollar should go.

Google is rolling out new data tools to help marketers better understand what drives growth and where to invest.

Google expands Data Manager and Meridian measurement tools

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/seo-dehumanization.png" class="attachment-large size-large wp-post-image" alt="The dehumanization of SEO: Displaced by code, ignored by communication" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/seo-dehumanization.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/seo-dehumanization-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/seo-dehumanization-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/seo-dehumanization-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>When SEO gets reduced to keywords, audits, and content production, software will always look cheaper. That’s where the real risk begins.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/seo-dehumanization.png" class="attachment-large size-large wp-post-image" alt="The dehumanization of SEO: Displaced by code, ignored by communication" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/seo-dehumanization.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/seo-dehumanization-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/seo-dehumanization-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/seo-dehumanization-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">When you’ve spent years in SEO, you get used to losing clients.</p>



<p class="wp-block-paragraph">You lose them to budget cuts, shifting internal priorities, redesigned sites that tank traffic before you can save them, or a new CMO who “has a guy.”</p>



<p class="wp-block-paragraph">It hurts, but it’s business.</p>



<p class="wp-block-paragraph"><strong>What hits differently is the first time you get replaced not by a competitor, but by a software subscription.</strong>



<p><a href="https://www.seoforlunch.com/embed" rel="noopener noreferrer">View embedded content</a>



<h2 id="h-the-time-i-lost-a-client-to-ai-software" class="wp-block-heading">The time I lost a client to AI software</h2>



<p class="wp-block-paragraph">Earlier this year, a prospective client reached out to discuss working together.</p>



<p class="wp-block-paragraph">We hopped on a call to talk through their goals, and they checked all the boxes: strong fit, clear expectations, and zero pushback on my pricing or process.</p>



<p class="wp-block-paragraph">Before we hung up, they let me know they were speaking with a few other providers. I applauded them for doing their due diligence.</p>



<p class="wp-block-paragraph">My approach to pitching is super direct: no fluff, no games, no wild promises. That transparency makes me a great fit for some companies, but others simply prefer to be wined and dined.</p>



<p class="wp-block-paragraph">After a second call to address a few follow-up questions, I received the dreaded email: “Thanks, but we went in another direction.”</p>



<p class="wp-block-paragraph">Bummer, but it happens. That’s just business.</p>



<p class="wp-block-paragraph">Except the email did not end there.</p>



<p class="wp-block-paragraph">The “provider” they chose was not another consultant or agency. It was an AI tool that promised it all: automated keyword research, content briefs, technical audits, and execution for a fraction of my monthly retainer.</p>



<p class="wp-block-paragraph">I won’t lie. That one stung.</p>



<p class="wp-block-paragraph">But I wished them well and told them to come back if they needed further help down the road.</p>



<p class="wp-block-paragraph">It was not just a lost contract. It was a front-row seat to an industry-wide shift: we are rapidly trading human expertise, nuance, and strategic context for the illusion of <a href="https://www.seoforlunch.com/p/ai-shortcut" target="_blank" rel="noopener">easy-button efficiency</a>.</p>



<p class="wp-block-paragraph">And as I soon realized, once an organization decides human judgment is an unnecessary expense, the way it treats human relationships can change just as quickly.</p>



<h2 id="h-breaking-down-the-ai-promise" class="wp-block-heading">Breaking down the ‘AI promise’</h2>



<p class="wp-block-paragraph">To be clear, I am not anti-AI tooling. <a href="https://searchengineland.com/ai-watermark-content-issue-485848">I use it</a>, and I expect other good marketers to use it too.</p>



<p class="wp-block-paragraph">It can accelerate research, organize data, build a first-draft brief, identify patterns across many pages, and reduce the manual work everyone hates.</p>



<p class="wp-block-paragraph">That is the point: <strong>AI should make skilled people more efficient.</strong> It should not be treated as a replacement for the skilled person responsible for deciding what deserves to happen in the first place.</p>



<p class="wp-block-paragraph">The promise made to this prospective client was not entirely wrong. The platform could likely help them find keywords, generate briefs, run technical checks, and produce more content than a consultant could manually create in the same amount of time.</p>



<p class="wp-block-paragraph"><strong>But SEO has never been a race to complete the most tasks.</strong>



<p class="wp-block-paragraph">A platform can tell you a keyword has search volume. It can&#8217;t tell you whether that traffic will attract the right customer, support the company’s actual revenue goals, or divert attention from a much better opportunity.</p>



<p class="wp-block-paragraph">It can suggest creating another page because the query looks distinct in a spreadsheet. It may not recognize that the site already has several pages competing for the same intent — or that the right answer is to consolidate existing pages rather than publish one more URL nobody needs.</p>



<p class="wp-block-paragraph">It can flag 100 technical issues in an audit. It can&#8217;t walk into an engineering planning meeting and explain why five of those issues are worth prioritizing, 90 can wait, and the remaining five are not meaningful problems at all.</p>



<p class="wp-block-paragraph">And it can produce content at scale. What it can&#8217;t do is understand the political, legal, product, sales, brand, or customer-service context that may make an otherwise “SEO-friendly” recommendation a terrible business decision.</p>



<p class="wp-block-paragraph">That is where the easy-button pitch breaks down.</p>



<p class="wp-block-paragraph">The tool can create activity. It can make a dashboard look busy. It can even generate pages that rank. But none of that means it understands the business, owns the downside, or knows when to ignore the standard SEO recommendation.</p>



<h2 id="h-the-true-cost-of-commoditizing-expertise" class="wp-block-heading">The true cost of commoditizing expertise</h2>



<p class="wp-block-paragraph">The ideal setup is a skilled person using AI <a href="https://searchengineland.com/efficiency-not-enough-477703">to maximize efficiency</a>, <strong>not</strong> using AI as an excuse to eliminate the person accountable for the work.</p>



<p class="wp-block-paragraph">For whatever reason, the reasonable middle ground has become hard to discuss. Raise concerns about quality, risk, or strategy, and you are labeled “anti-AI.” Suggest automating everything possible, and you are innovative. (but you might also be out of a job in 6 months.)</p>



<p class="wp-block-paragraph"><strong>Neither label helps the P&amp;L.</strong>



<p class="wp-block-paragraph">The question is not whether AI can do work faster. Of course it can. The question is whether doing more work, more quickly, actually creates a better outcome for the business.</p>



<p class="wp-block-paragraph">Ten years ago, publishing more content may have been a viable growth strategy (long live programmatic SEO). In 2026, turning five thoughtful articles into 50 mediocre ones at half the cost is not automatically an efficiency win. It may just be a faster way to fill your site with content that does not earn attention, trust, links, qualified traffic, or revenue. (Start with getting indexed for longer than a few weeks; that will be harder than you think.)</p>



<p class="wp-block-paragraph">That is the easy-button trap: confusing lower production costs with better business results.</p>



<p class="wp-block-paragraph">When companies reduce SEO to tasks like keyword research, briefs, audits, reports, and the creation of pages per month — they make it easy to compare a consultant against software. <strong>Software will almost always win that comparison on price and volume.</strong>



<p class="wp-block-paragraph">But you were never paying the consultant to open Semrush or Ahrefs, build a spreadsheet, or hand you a technical audit PDF.</p>



<p class="wp-block-paragraph"><strong>You were paying for the judgment behind it all:</strong> what to prioritize, what to ignore, what creates risk, and what needs to change when the original plan is not working. <strong>You were paying for someone to own the recommendation</strong>, not simply check a box confirming the work was completed.</p>



<h2 id="h-the-transition-from-code-to-culture" class="wp-block-heading">The transition: From code to culture</h2>



<p class="wp-block-paragraph">Replacing a consultant with an AI tool is not automatically a bad decision. In the right situation, a good tool can absolutely help an internal team move faster.</p>



<p class="wp-block-paragraph">But when every person becomes a cost center, and every relationship becomes an input-output transaction, something much bigger gets lost.</p>



<p class="wp-block-paragraph">Once human expertise becomes just another expense to trim, it becomes a lot easier to treat the human relationship behind it as disposable, too.</p>



<p class="wp-block-paragraph">That mindset doesn&#8217;t stay contained to SEO strategy or marketing budgets. It shows up in how companies communicate with agencies, freelancers, employees, and even candidates looking to join the organization.</p>



<p class="wp-block-paragraph"><strong>Or, more often, how they avoid communicating with them at all!</strong>



<p class="wp-block-paragraph">Because apparently, basic professional courtesy is now optional.</p>



<p class="wp-block-paragraph">Turns out keeping a “human in the loop” is only important when someone needs to take the blame. Everything else, though? AI it!</p>



<p class="wp-block-paragraph"><em>This post first appeared on the author’s website and is republished here with permission.</em>


<div class="wp-block-image">
<figure class="aligncenter"><a href="https://seoforlunch.com/?utm_source=searchengineland.com&amp;utm_medium=social"><img decoding="async" width="1774" height="887" src="https://searchengineland.com/wp-content/seloads/2026/05/leroy2.jpg.webp" alt="Leroy2" class="wp-image-476987" srcset="https://searchengineland.com/wp-content/seloads/2026/05/leroy2.jpg 1774w, https://searchengineland.com/wp-content/seloads/2026/05/leroy2-768x384.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/leroy2-1536x768.jpg 1536w" sizes="(max-width: 1774px) 100vw, 1774px" /></a></figure>
</div>

When you’ve spent years in SEO, you get used to losing clients.
You lose them to budget cuts, shifting internal priorities, redesigned sites that tank traffic before you can save them, or a new CMO who “has a guy.”
It hurts, but it’s business.
What hits differently is the first time you get replaced not by a competitor, but by a software subscription.



View embedded content



The time I lost a client to AI software
Earlier this year, a prospective client reached out to discuss working together.
We hopped on a call to talk through their goals, and they checked all the boxes: strong fit, clear expectations, and zero pushback on my pricing or process.
Before we hung up, they let me know they were speaking with a few other providers. I applauded them for doing their due diligence.
My approach to pitching is super direct: no fluff, no games, no wild promises. That transparency makes me a great fit for some companies, but others simply prefer to be wined and dined.
After a second call to address a few follow-up questions, I received the dreaded email: “Thanks, but we went in another direction.”
Bummer, but it happens. That’s just business.
Except the email did not end there.
The “provider” they chose was not another consultant or agency. It was an AI tool that promised it all: automated keyword research, content briefs, technical audits, and execution for a fraction of my monthly retainer.
I won’t lie. That one stung.
But I wished them well and told them to come back if they needed further help down the road.
It was not just a lost contract. It was a front-row seat to an industry-wide shift: we are rapidly trading human expertise, nuance, and strategic context for the illusion of easy-button efficiency.
And as I soon realized, once an organization decides human judgment is an unnecessary expense, the way it treats human relationships can change just as quickly.
Breaking down the ‘AI promise’
To be clear, I am not anti-AI tooling. I use it, and I expect other good marketers to use it too.
It can accelerate research, organize data, build a first-draft brief, identify patterns across many pages, and reduce the manual work everyone hates.
That is the point: AI should make skilled people more efficient. It should not be treated as a replacement for the skilled person responsible for deciding what deserves to happen in the first place.
The promise made to this prospective client was not entirely wrong. The platform could likely help them find keywords, generate briefs, run technical checks, and produce more content than a consultant could manually create in the same amount of time.
But SEO has never been a race to complete the most tasks.



A platform can tell you a keyword has search volume. It can’t tell you whether that traffic will attract the right customer, support the company’s actual revenue goals, or divert attention from a much better opportunity.
It can suggest creating another page because the query looks distinct in a spreadsheet. It may not recognize that the site already has several pages competing for the same intent — or that the right answer is to consolidate existing pages rather than publish one more URL nobody needs.
It can flag 100 technical issues in an audit. It can’t walk into an engineering planning meeting and explain why five of those issues are worth prioritizing, 90 can wait, and the remaining five are not meaningful problems at all.
And it can produce content at scale. What it can’t do is understand the political, legal, product, sales, brand, or customer-service context that may make an otherwise “SEO-friendly” recommendation a terrible business decision.
That is where the easy-button pitch breaks down.
The tool can create activity. It can make a dashboard look busy. It can even generate pages that rank. But none of that means it understands the business, owns the downside, or knows when to ignore the standard SEO recommendation.
The true cost of commoditizing expertise
The ideal setup is a skilled person using AI to maximize efficiency, not using AI as an excuse to eliminate the person accountable for the work.
For whatever reason, the reasonable middle ground has become hard to discuss. Raise concerns about quality, risk, or strategy, and you are labeled “anti-AI.” Suggest automating everything possible, and you are innovative. (but you might also be out of a job in 6 months.)
Neither label helps the P&L.



The question is not whether AI can do work faster. Of course it can. The question is whether doing more work, more quickly, actually creates a better outcome for the business.
Ten years ago, publishing more content may have been a viable growth strategy (long live programmatic SEO). In 2026, turning five thoughtful articles into 50 mediocre ones at half the cost is not automatically an efficiency win. It may just be a faster way to fill your site with content that does not earn attention, trust, links, qualified traffic, or revenue. (Start with getting indexed for longer than a few weeks; that will be harder than you think.)
That is the easy-button trap: confusing lower production costs with better business results.
When companies reduce SEO to tasks like keyword research, briefs, audits, reports, and the creation of pages per month — they make it easy to compare a consultant against software. Software will almost always win that comparison on price and volume.



But you were never paying the consultant to open Semrush or Ahrefs, build a spreadsheet, or hand you a technical audit PDF.
You were paying for the judgment behind it all: what to prioritize, what to ignore, what creates risk, and what needs to change when the original plan is not working. You were paying for someone to own the recommendation, not simply check a box confirming the work was completed.
The transition: From code to culture
Replacing a consultant with an AI tool is not automatically a bad decision. In the right situation, a good tool can absolutely help an internal team move faster.
But when every person becomes a cost center, and every relationship becomes an input-output transaction, something much bigger gets lost.
Once human expertise becomes just another expense to trim, it becomes a lot easier to treat the human relationship behind it as disposable, too.
That mindset doesn’t stay contained to SEO strategy or marketing budgets. It shows up in how companies communicate with agencies, freelancers, employees, and even candidates looking to join the organization.
Or, more often, how they avoid communicating with them at all!



Because apparently, basic professional courtesy is now optional.
Turns out keeping a “human in the loop” is only important when someone needs to take the blame. Everything else, though? AI it!
This post first appeared on the author’s website and is republished here with permission.

When SEO gets reduced to keywords, audits, and content production, software will always look cheaper. That’s where the real risk begins.

The dehumanization of SEO: Displaced by code, ignored by communication

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/Rokt-20260910-header.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Rokt-20260910-header.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/09/Rokt-20260910-header-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/09/Rokt-20260910-header-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Rokt-20260910-header-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Agentic marketing works best when marketers can inspect the data and evidence driving an agent's recommendations.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/Rokt-20260910-header.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Rokt-20260910-header.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/09/Rokt-20260910-header-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/09/Rokt-20260910-header-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Rokt-20260910-header-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div><div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/Rokt-20260910-header.jpg" alt="" class="wp-image-486694" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Rokt-20260910-header.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/09/Rokt-20260910-header-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/09/Rokt-20260910-header-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Rokt-20260910-header-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></figure>
</div>


<p class="wp-block-paragraph">A marketer looking for a purchase signal often finds several similarly named events — such as “purchase,” “checkout success,” and “checkout completion” — with little guidance on which one represents the intended behavior. Enterprise data catalogs are frequently incomplete, implementations evolve, and event names accumulate over time.</p>



<p class="wp-block-paragraph">The promise of agentic marketing is often framed around autonomy, but the immediate opportunity is using agents as strategic partners to resolve ambiguity in enterprise data. However, an agent’s recommendation is only as trustworthy as the evidence supporting it. Marketers need clear visibility into which signals support the recommendation, how recently they were observed, how large the potential audience is, and what tradeoffs exist. Inspecting that evidence gives marketers the context to evaluate recommendations, apply business judgment, and accept, refine, or override the proposed action.</p>



<h2 id="h-closing-the-gap-between-a-goal-and-the-right-data" class="wp-block-heading">Closing the gap between a goal and the right data</h2>



<p class="wp-block-paragraph">In an ideal scenario, marketers begin with a clear business goal — such as driving high-value purchases, engaging likely converters, or re-engaging churning customers. In practice, however, historical audience-building workflows often forced them to work backward, starting from whichever events happen to be available in the data schema rather than a clean-slate business objective. Translating that goal into an audience requires knowing which events and attributes exist, what they mean and whether they are reliable enough to use.</p>



<p class="wp-block-paragraph">That is harder than it sounds. Beyond simple discovery, marketers need to know how each candidate event actually behaves: how often it fires, when it was last observed, where it comes from, and which signal best matches the business’s definition of a completed purchase.</p>



<p class="wp-block-paragraph">Volume alone may not settle the question. A high-volume “purchase” event might fire before payment is confirmed, while a lower-volume “checkout success” event may more accurately represent completed orders. The appropriate signal depends on both what the data shows and the business outcome the marketer is trying to achieve.</p>



<p class="wp-block-paragraph">As a result, marketers often reuse the events they already know. Valuable data may remain untouched because finding and interpreting it requires time or technical support.</p>



<p class="wp-block-paragraph">An agent can make that process more accessible. Starting with a broad goal, it can help the marketer explore available data and narrow the request toward the most appropriate signal, while making the supporting context available for review. The agent handles the work of navigating the data environment while the marketer supplies the business context. The result is faster audience creation and a clearer basis for review: the marketer can see what the recommendation rests on before acting on it.</p>



<h2 id="h-agents-and-marketers-should-make-decisions-together" class="wp-block-heading">Agents and marketers should make decisions together</h2>



<p class="wp-block-paragraph">That visibility creates a collaborative workflow. The agent can recommend an approach and surface the evidence and tradeoffs behind it, while the marketer brings the business context needed to decide whether it fits the campaign goal, customer strategy, and business priorities.</p>



<p class="wp-block-paragraph">That does not mean exposing every signal the agent considered or every step in its reasoning. The goal is to surface the evidence, uncertainty, and tradeoffs that could materially change the marketer’s decision.</p>



<p class="wp-block-paragraph">That visibility creates a collaborative workflow. The agent can propose an approach, but the marketer remains responsible for assessing whether it aligns with the campaign goal, customer strategy, and business priorities.</p>



<p class="wp-block-paragraph">This is particularly important when balancing reach and expected performance. A model can show how different audience thresholds may affect reach and predicted conversion, but the marketer should decide which tradeoff makes sense for the business.</p>



<h2 id="h-agents-and-interfaces-solve-different-problems" class="wp-block-heading">Agents and interfaces solve different problems</h2>



<p class="wp-block-paragraph">Inspection and override have to happen somewhere, and that is usually the interface. The rise of conversational agents does not make it obsolete; it gives it a more specific job.</p>



<p class="wp-block-paragraph">An agent is well suited to exploration, interpretation, and getting the marketer to a strong starting point. A visual interface is often better for precise adjustments that follow—moving a threshold to trade reach against predicted conversion, for example. The agent helps make the tradeoff legible; the interface gives the marketer a direct way to act on it.</p>



<p class="wp-block-paragraph">The strongest agentic products will let marketers move naturally between conversation and direct controls based on the task at hand.</p>



<h2 id="h-from-easier-decisions-to-better-performance" class="wp-block-heading">From easier decisions to better performance</h2>



<p class="wp-block-paragraph">At Rokt mParticle, this approach is taking shape through mParticle Agent, which enables natural-language audience creation and data exploration. The agent presents a proposal for review; nothing is saved until the marketer explicitly confirms it, and subsequent connection and activation happen separately. It draws on mParticle documentation and available platform context together, so a marketer can explore what an event appears to represent, how it is behaving in the available platform context, and get guidance in the same place they’re building an audience—reducing the need for tickets and tribal knowledge.</p>



<p class="wp-block-paragraph">Autonomy alone is the wrong standard for agentic marketing. A better standard is whether an agent can turn ambiguous data into an evidence-backed recommendation, surface the context and tradeoffs that matter, and make the next decision easier for the marketer. The agent does the exploration and synthesis; the marketer brings the business judgment. That balance is what makes agentic marketing genuinely useful.</p>



<p class="wp-block-paragraph">


A marketer looking for a purchase signal often finds several similarly named events — such as “purchase,” “checkout success,” and “checkout completion” — with little guidance on which one represents the intended behavior. Enterprise data catalogs are frequently incomplete, implementations evolve, and event names accumulate over time.
The promise of agentic marketing is often framed around autonomy, but the immediate opportunity is using agents as strategic partners to resolve ambiguity in enterprise data. However, an agent’s recommendation is only as trustworthy as the evidence supporting it. Marketers need clear visibility into which signals support the recommendation, how recently they were observed, how large the potential audience is, and what tradeoffs exist. Inspecting that evidence gives marketers the context to evaluate recommendations, apply business judgment, and accept, refine, or override the proposed action.
Closing the gap between a goal and the right data
In an ideal scenario, marketers begin with a clear business goal — such as driving high-value purchases, engaging likely converters, or re-engaging churning customers. In practice, however, historical audience-building workflows often forced them to work backward, starting from whichever events happen to be available in the data schema rather than a clean-slate business objective. Translating that goal into an audience requires knowing which events and attributes exist, what they mean and whether they are reliable enough to use.
That is harder than it sounds. Beyond simple discovery, marketers need to know how each candidate event actually behaves: how often it fires, when it was last observed, where it comes from, and which signal best matches the business’s definition of a completed purchase.
Volume alone may not settle the question. A high-volume “purchase” event might fire before payment is confirmed, while a lower-volume “checkout success” event may more accurately represent completed orders. The appropriate signal depends on both what the data shows and the business outcome the marketer is trying to achieve.
As a result, marketers often reuse the events they already know. Valuable data may remain untouched because finding and interpreting it requires time or technical support.
An agent can make that process more accessible. Starting with a broad goal, it can help the marketer explore available data and narrow the request toward the most appropriate signal, while making the supporting context available for review. The agent handles the work of navigating the data environment while the marketer supplies the business context. The result is faster audience creation and a clearer basis for review: the marketer can see what the recommendation rests on before acting on it.
Agents and marketers should make decisions together
That visibility creates a collaborative workflow. The agent can recommend an approach and surface the evidence and tradeoffs behind it, while the marketer brings the business context needed to decide whether it fits the campaign goal, customer strategy, and business priorities.
That does not mean exposing every signal the agent considered or every step in its reasoning. The goal is to surface the evidence, uncertainty, and tradeoffs that could materially change the marketer’s decision.
That visibility creates a collaborative workflow. The agent can propose an approach, but the marketer remains responsible for assessing whether it aligns with the campaign goal, customer strategy, and business priorities.
This is particularly important when balancing reach and expected performance. A model can show how different audience thresholds may affect reach and predicted conversion, but the marketer should decide which tradeoff makes sense for the business.
Agents and interfaces solve different problems
Inspection and override have to happen somewhere, and that is usually the interface. The rise of conversational agents does not make it obsolete; it gives it a more specific job.
An agent is well suited to exploration, interpretation, and getting the marketer to a strong starting point. A visual interface is often better for precise adjustments that follow—moving a threshold to trade reach against predicted conversion, for example. The agent helps make the tradeoff legible; the interface gives the marketer a direct way to act on it.
The strongest agentic products will let marketers move naturally between conversation and direct controls based on the task at hand.
From easier decisions to better performance
At Rokt mParticle, this approach is taking shape through mParticle Agent, which enables natural-language audience creation and data exploration. The agent presents a proposal for review; nothing is saved until the marketer explicitly confirms it, and subsequent connection and activation happen separately. It draws on mParticle documentation and available platform context together, so a marketer can explore what an event appears to represent, how it is behaving in the available platform context, and get guidance in the same place they’re building an audience—reducing the need for tickets and tribal knowledge.
Autonomy alone is the wrong standard for agentic marketing. A better standard is whether an agent can turn ambiguous data into an evidence-backed recommendation, surface the context and tradeoffs that matter, and make the next decision easier for the marketer. The agent does the exploration and synthesis; the marketer brings the business judgment. That balance is what makes agentic marketing genuinely useful.

Agentic marketing works best when marketers can inspect the data and evidence driving an agent's recommendations.

Your marketing agent is working. Can you see what informs its recommendations? by Rokt mParticle

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/google-ranking-factors-survey.png" class="attachment-large size-large wp-post-image" alt="Google ranking factors survey" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/google-ranking-factors-survey.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/google-ranking-factors-survey-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/google-ranking-factors-survey-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/google-ranking-factors-survey-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Search intent ranks highest, backlinks remain influential, and original research outperforms scaled AI content in perceived ranking impact.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/google-ranking-factors-survey.png" class="attachment-large size-large wp-post-image" alt="Google ranking factors survey" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/google-ranking-factors-survey.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/google-ranking-factors-survey-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/google-ranking-factors-survey-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/google-ranking-factors-survey-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Backlinks ranked as Google&#8217;s second most important ranking factor in a new survey of 131 SEO professionals conducted by Zyppy. Relevance ranked first, and content quality ranked third.</p>



<p class="wp-block-paragraph"><strong>Backlinks ranked No. 2.</strong> Although Google&#8217;s Gary Illyes has <a href="https://searchengineland.com/links-google-search-ranking-factor-gary-illyes-432422">said links are not among Google&#8217;s top three ranking signals</a>, 54.8% of respondents selected backlinks as one of the three most important factors. Only relevance ranked higher at 57.1%.</p>



<ul class="wp-block-list">
<li>Links from trusted, topically relevant domains and pages with real visitors received some of the highest ratings among backlink-related factors.</li>



<li>Spammy backlinks ranked among the strongest negative signals.</li>



<li>Cyrus Shepard wrote in the report that &#8220;the rumor of the death of backlinks has been greatly exaggerated.&#8221;</li>
</ul>



<p class="wp-block-paragraph"><strong>Search intent ranked first.</strong> Search intent match received the highest rating of any individual ranking factor.</p>



<ul class="wp-block-list">
<li>Many respondents said success depends on satisfying the searcher&#8217;s underlying goal, not just matching keywords.</li>



<li>Several also pointed to information gain as an important factor when multiple pages satisfy the same search intent.</li>



<li>Meta descriptions ranked near the bottom. Most respondents said they have little or no direct effect on rankings.</li>
</ul>



<p class="wp-block-paragraph"><strong>Original research ranked highly.</strong> Content quality ranked third overall. Within that category, original research and first-party data received some of the highest ratings.</p>



<ul class="wp-block-list">
<li>Respondents viewed AI-generated content more favorably when it received substantial human review and added unique value. </li>



<li>AI-generated content published at scale with little added value received negative ratings.</li>
</ul>



<p class="wp-block-paragraph"><strong>Brand and user signals.</strong> Branded search volume, online reputation, and user satisfaction received some of the highest ratings in their categories. Among user signals, satisfaction and task completion ranked highest. Quickly returning to Google&#8217;s search results received a negative rating.</p>



<p class="wp-block-paragraph"><strong>Technical SEO remained foundational.</strong> Respondents gave consistently positive ratings to technical SEO factors. They generally described crawling, indexing, and overall site health as foundational requirements. Internal linking also received high ratings.</p>



<p class="wp-block-paragraph"><strong>About the data.</strong> <a href="https://signal.zyppy.com/p/google-ranking-factors-expert-survey" target="_blank" rel="noopener">Zyppy&#8217;s 2026 Google Ranking Factors Expert Survey</a> collected 13,665 ratings across 103 potential ranking factors. Respondents rated each factor on a seven-point scale and identified what they believed were Google&#8217;s three most important ranking signals. Zyppy said it updated this year&#8217;s survey to reflect AI search, Google&#8217;s API leak, testimony from Google&#8217;s antitrust trial, and recent algorithm updates.</p>



<p class="wp-block-paragraph"><strong>Why we care.</strong> The survey offers an interesting snapshot of what 131 SEO professionals believe Google&#8217;s ranking systems value most in 2026. That said, nothing in the survey was too surprising — expect more confirmation than revelation. Search intent, backlinks, and content quality have long mattered for ranking well on Google.</p>


Backlinks ranked as Google’s second most important ranking factor in a new survey of 131 SEO professionals conducted by Zyppy. Relevance ranked first, and content quality ranked third.
Backlinks ranked No. 2. Although Google’s Gary Illyes has said links are not among Google’s top three ranking signals, 54.8% of respondents selected backlinks as one of the three most important factors. Only relevance ranked higher at 57.1%.
Links from trusted, topically relevant domains and pages with real visitors received some of the highest ratings among backlink-related factors.
Spammy backlinks ranked among the strongest negative signals.
Cyrus Shepard wrote in the report that “the rumor of the death of backlinks has been greatly exaggerated.”
Search intent ranked first. Search intent match received the highest rating of any individual ranking factor.
Many respondents said success depends on satisfying the searcher’s underlying goal, not just matching keywords.
Several also pointed to information gain as an important factor when multiple pages satisfy the same search intent.
Meta descriptions ranked near the bottom. Most respondents said they have little or no direct effect on rankings.
Original research ranked highly. Content quality ranked third overall. Within that category, original research and first-party data received some of the highest ratings.
Respondents viewed AI-generated content more favorably when it received substantial human review and added unique value. 
AI-generated content published at scale with little added value received negative ratings.
Brand and user signals. Branded search volume, online reputation, and user satisfaction received some of the highest ratings in their categories. Among user signals, satisfaction and task completion ranked highest. Quickly returning to Google’s search results received a negative rating.
Technical SEO remained foundational. Respondents gave consistently positive ratings to technical SEO factors. They generally described crawling, indexing, and overall site health as foundational requirements. Internal linking also received high ratings.
About the data. Zyppy’s 2026 Google Ranking Factors Expert Survey collected 13,665 ratings across 103 potential ranking factors. Respondents rated each factor on a seven-point scale and identified what they believed were Google’s three most important ranking signals. Zyppy said it updated this year’s survey to reflect AI search, Google’s API leak, testimony from Google’s antitrust trial, and recent algorithm updates.
Why we care. The survey offers an interesting snapshot of what 131 SEO professionals believe Google’s ranking systems value most in 2026. That said, nothing in the survey was too surprising — expect more confirmation than revelation. Search intent, backlinks, and content quality have long mattered for ranking well on Google.

Search intent ranks highest, backlinks remain influential, and original research outperforms scaled AI content in perceived ranking impact.

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Read the Latest Trends and Headlines in Digital Marketing.

Here are some of the latest headlines on digital marketing insights, search, and ecommerce:

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Reach out with any questions you might have regarding how RSO can help you with digital marketing.

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Digital Marketing