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Google vs. Microsoft AI Max: What’s the same and what’s different

September 1, 2026/0 Comments/in Search Engine Land, Web Design News/by Dubado Solutions

With the global launch of Microsoft AI Max for Search campaigns, it’s worth spending some time unpacking where the features align and differ from Google’s version.

Much of the core functionality is the same:

Search term matching expanding your reach beyond static keyword lists. AI Max uses your keywords, ads, and landing pages, along with intent and contextual signals, to uncover relevant searches you may not qualify for with keywords alone. This is especially helpful for complex conversational queries.
Text customization allowing ads to adapt to high-value placements and prospects. AI Max uses your existing assets and website content to generate and test additional messaging variations. It then selects the most appropriate combinations at auction time, helping you to deliver more relevant ad creative, including in AI-native experiences.
Final URL expansion that routes people to the page on your site that best matches their intent. Instead of always sending traffic to a static landing page, AI Max can route people to the page that best matches what they’re looking for. This ensures a more consistent user experience across query, creative, and website.

However, there are some key nuanced differences. We’ll dive into:

Where Google and Microsoft AI Max are the same.
Key points of differentiation between Google and Microsoft AI Max.
How to leverage AI Max successfully in new and existing account structure.

(Note: I am a Microsoft Advertising employee and wrote this post as platform-agnostically as possible. Features discussed in this post are based on publicly available help documentation as of September 2026.)

What’s the same between Google and Microsoft AI Max?

Aside from the core functionality, the following AI Max mechanics remain the same across both Google and Microsoft.

AI Max is a setting, not a campaign type.

Unlike Performance Max (PMax), Demand Gen, Audience ads, and other unique campaign types, AI Max represents optional settings within Search campaigns. These settings are designed to work better together, and advertisers tend to see the most benefit when they opt into all three.

Here’s how advertisers benefit when they leverage all three core AI Max features across Google and Microsoft:

Search term matching allows for net new queries and that can bring the advertiser into auctions their static ad might not accurately reflect. By allowing the text to adapt in the moment, you’ll ensure your ads are relevant for those new queries.
To deliver the most relevant landing page for unique queries, Final URL expansion can be really helpful. Final URL expansion is linked with Text asset generation as platforms need to be able to adapt creative to make a promise the dynamic landing page can deliver on.
If you’re going to allow for text generation, you’ll get the most bang for your optimization buck by also allowing for intelligent matching of URLs and relevant queries.

That said, if advertisers want to begin with a more conservative test, they absolutely can.

For example, an ecommerce brand selling products with similar margins might feel more comfortable testing URL expansion because it allows them to get fuller coverage of their products without building out unique ad groups, but don’t want to leverage search term matching. While Brand Controls exist and can help ensure certain brands aren’t included (more on that later), it’s fair for the advertiser to start with just Final URL Expansion and Text customization.

Whether an advertiser tests just one, two, or all three parts of AI Max, these tests can be done safely with experiments. While both platforms support controlled testing, Google’s AI Max experiments are designed to run within the existing campaign by diverting traffic, whereas Microsoft Advertising’s Search Experiments compare a standard campaign against a cloned test version with AI Max enabled.

Here is guidance on how to experiment with AI Max:

Start with your strongest campaign. Choose a campaign with stable performance and enough volume to generate results. Experiments work best when there’s enough traffic to detect meaningful differences.
Create a 50/50 split. Split traffic evenly between the control and experiment. Keeping traffic balanced makes it easier to determine whether performance differences are due to the change you’re testing.
Let the experiment learn. One common mistake is ending a test too quickly. New bidding strategies and AI-powered features need time to learn. If you’re testing AI Max specifically, go directly into an A/B test and allow sufficient learning time before drawing conclusions.
Measure business outcomes, not just clicks. Focus on:
- Conversion rate
- Cost per acquisition (CPA)
- Return on ad spend (ROAS)
- Changes in revenue or conversion value

Conversion-based bidding is a core part of search term matching

AI Max’s search term matching relies heavily on conversion data in order to be successful. This is why both Google and Microsoft AI Max require conversion-based bidding when enabling this part of AI Max.

Conversion-based bidding works when there’s accurate conversion data flowing into the platform. It is ideal to have at least 15-30 conversions in a 30-day period before working with conversion-based bidding.

If your brand likely won’t hit the threshold, it makes sense to wait on trying AI Max until you can or to use micro-conversions with strategic conversion values. This means setting conversion values aligned to each stage of the journey and TROAS that reflects the focus on important steps.

For example, if a brand wanted to secure applications for their financial product, they might include conversion goals reflecting different completion milestones (beginning, mid-way, completed, and accepted). These conversion goals would get a profit-related conversion value associated:

Beginning application: $10
Mid-way: $20
Completed: $50
Accepted: actual conversion value using offline conversion uploads.

Brand controls exist on both, though there are some differences in what’s available.

Google and Microsoft both understood advertisers need messaging and branding controls to ensure ad creative stays within style guides. That’s why both AI Max variants allow advertisers to set brand inclusions, exclusions, term exclusions, and message constraints.

To make it easier to understand the mechanics for each, here’s the breakdown:

Google:

Brand inclusions: 10 brand lists per campaign, with up to 5,000 brands per list
Brand exclusions: 10 brand lists per campaign, with up to 5,000 brands per list
Term exclusions: 25 per campaign
Message constraints: 40 per campaign

Microsoft:

Brand inclusions: 20 brand lists per campaign, with up to 100 brands per list
Brand exclusions: 20 brand lists per campaign, with up to 100 brands per list
Term exclusions: 25 per campaign
Message constraints: 40 per campaign

Microsoft offers disclaimers that do not take up ad real estate, and work with AI Max. Google is piloting disclaimers as of this writing that would take up description line #2.

What’s unique between Google and Microsoft AI Max?

While most of the core AI Max functionality remains the same, there are a few key differences to account for when moving between ad platforms.

Consolidation of settings vs being able to pick and choose all three.

AI Max features work better together, so it makes sense to opt into all three. However, if you want to test individual settings before committing to the full suite, Microsoft makes this easier by offering all three features as opt-in toggles.

Google opts all advertisers who turn on AI Max at the campaign level into search term matching, which can be turned off at the ad group level.

While Microsoft maintains all existing ad group targeting settings (including location targeting, scheduling, and time zone selection), there are no AI Max-specific ad group settings.

Google also supports Locations of interest, URL inclusions, and Brand inclusions at the ad group level.

This means Google’s AI Max management asks advertisers to make more decisions at the ad group level, while Microsoft focuses AI Max settings at the campaign level.

Matching mechanics and search term transparency are different.

Both Google and Microsoft recommend leaning away from syntax-oriented keywords as they can get in the way of being eligible to serve for complex and longer queries, especially in AI experiences.

However, Google and Microsoft take different approaches to matching and search term reporting. Microsoft offers full search term reporting for any query resulting in a click for AI Max, PMax, and traditional Search and Shopping campaigns. These can be found in the reporting templates, Search term and Search term landing page.

Google hides some search terms for privacy reasons, which also means there isn’t full transparency on whether the query is relevant. To mitigate this, Google allows for more close variant mechanics in negative keywords.

When it comes to matching, due to different ecosystems, there are inherently different signals about how Google and Microsoft will match user queries to advertiser campaigns. Here’s a breakdown of the main signals by each ad platform:

Google:

YouTube data
Previous search behavior
Conversion data
Landing page
Other keywords in the ad group
Audiences: In-market, demographics, and other 1P audiences like Customer Match).

Microsoft:

LinkedIn data
Previous search behavior
Conversion data
Landing page
Other keywords in the ad group
Audiences: Impression-based remarketing, In-market, demographics, and other 1P audiences like Customer Match

How to leverage AI Max in existing and new account structures?

AI Max brings the best of AI functionality to search campaigns, and it’s understandable that advertisers will want to test some or all of the AI Max feature suite. However, there are some key considerations for accounts bringing AI Max into their structures.

Here are the top five considerations:

Do you trust your conversions measurement and do you meet conversion thresholds?
Will your landing pages be a help or a hindrance in conveying what your brand offers to AI systems as they address human questions?
Are your existing ad assets on brand or do they have serious deviation?
Is PMax already part of your account structure?
Have you budgeted for the targets you’re setting?

Let’s dig deeper into each one.

Do you trust your conversion measurement and do you meet conversion thresholds?

AI Max and conversion-based bidding are joined at the hip because conversions are a critical signal AI Max uses to help advertisers connect with the right customers at the best ROI. If your campaigns don’t have accurate conversions feeding into the system, or don’t have enough conversion data (15-30 conversions in a 30-day period is the minimum), it will be very hard for AI Max to make intelligent matching choices.

If you have an existing account with at least 90 days of accurate conversion data, AI Max is a no-brainer.

Newer accounts should work on building conversion data so they can leverage AI Max and conversion-based bidding once the account ramps up.

Will your landing pages be a help or a hindrance in conveying who you are to the ad platform?

Landing pages are a critical signal that ad platforms use to understand your utility to a potential user. If your landing page is accessible in phrasing and visuals, this translates to easier content consumption for AI and humans alike.

A really great example of this is including alt-text on images and videos on the landing page, which ensures there can be no doubt about the subject matter. On the flip side, if you don’t make it clear what you’re offering and why your customers enjoy working with you, it can be hard to translate those messages to AI creative and matching.

A common mistake brands will make is disallowing all bots from crawling their landing pages, which deprives AI from critical insights into who you are and how you can help your customers.

A good way to understand whether your landing pages are AI Max compatible is to plug them into the PMax campaign creation flow. If you strongly disagree with the assets Google or Microsoft generated for you, that can be a sign to adjust landing page copy/mechanics before turning on AI Max.

Are your existing ad assets on brand or do they have serious deviation?

Both Google and Microsoft AI Max rely on existing text assets to inform potential new ad creative. Beyond adding in Brand Controls (including term exclusions and message constraints), it’s really important that your ad assets reflect the guidance you share.

For example, if you give a style guide note that all headlines should be sentence-cased, but your existing headlines are title-cased, that can cause confusion in the system. It’s important to audit your ad creative and landing pages for phrasing choices that might not align or were included unintentionally.

Is PMax already part of your account structure?

AI Max takes the best parts of PMax AI and layers them as optional boosts to Search campaigns. That means there are inherently fewer net-new opportunities for AI Max to unlock in accounts already running mature PMax campaigns.

However, AI Max can still add value when PMax is focused primarily on Shopping, is budget-constrained, or isn’t fully capturing your search opportunity. Rather than evaluating AI Max and PMax in isolation, focus on whether the combination is driving incremental account-level growth in conversions, revenue, or efficiency

PMax, by its nature, is cross-channel, and has a strong affinity for ecommerce. It can be useful to have AI Max as a search specific tool to go after parts of your business you don’t want exposed to non-search inventory.

In short, running AI Max and PMax in the same account isn’t inherently good or bad. If you want the full AI performance lift, Performance Max will have an easier time delivering that because it’s not restricted to search only surfaces. AI Max represents the useful AI gains of PMax in choice oriented and search-specific experience.

Have you budgeted for the targets your setting?

AI Max requires leaning into conversion-based bidding (“Smart” on Google and “Auto” on Microsoft). One of the biggest reasons any campaign can fail is it’s asked to go after too many targets for the budget.

For example, if you’re targeting customers for your plumbing business, it won’t be useful to ask the same campaign to go after minor repairs and a burst pipe. This is because the services have different costs, levels of urgency, and service capacities.

As a general rule, all services/products within a campaign should be within 20-30% of each other. If there’s too much variance, make sure you’ve set up accurate conversion values/TROAS goals, URL exclusions, and budgeted enough to cover the spread.

Final takeaways

Ultimately, Google and Microsoft’s AI Max are fairly similar. The differences have more to do with platform-specific mechanics.

Both agree that you’ll see the best results when you opt into all three core AI Max features.

Google puts more AI Max functionality at the ad group level, while Microsoft makes it a campaign-level choice.

Both platforms actively take on advertiser feedback, so if there is a preference for one style of management vs another, it’s worth sharing through support.

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Anthropic AI watermarking: What it means for content and SEO

September 1, 2026/0 Comments/in Search Engine Land, Web Design News/by Dubado Solutions

On Aug. 11, Anthropic announced it would begin adding machine-readable watermarks to Claude’s outputs. The reaction was immediate and predictable. LinkedIn and X filled with the usual takes:

“All AI writing is now fully traceable!”
“This is the death knell for AI content farms!”
“SEO is dead. Again.”

Sensing the uproar, Anthropic quickly followed up with a blog post, FAQs, and a technical demo showing that the watermark had no practical effect on output quality.

A few days later, Dario Amodei posted on X about AI’s broader crisis of trust, arguing that the public’s skepticism runs deeper than any one company’s messaging.

The technical explanations were clear. The demo was impressive. Yet public reaction remained largely negative.

In this article, I want to separate the hype from the reality and explore why what appeared to be a straightforward regulatory compliance announcement may instead become a flash point dividing the Eloi who embrace AI from the Morlocks who oppose it.

A quick history of watermarking

Craftspeople have marked their work for centuries.

In 1266, the English Parliament required bakers to use distinctive marks on their bread. By 1282, papermakers in Fabriano, Italy, were creating translucent watermarks with wire molds embedded in the paper.

The principle was simple: this is someone’s work, and the maker should be identifiable.

In the digital era, stock image libraries adopted the same idea. You’ve seen Shutterstock’s repeating patterns and Getty Images’ overlays stamped across preview images. The goal was the same: identify the original creator and discourage unauthorized use.

The EU rule Anthropic is answering

Anthropic’s decision is a direct response to Article 50(2) of the EU AI Act (Regulation 2024/1689). The provision requires providers of systems that generate synthetic text, images, audio, or video to mark those outputs in a machine-readable format so they can be detected as artificially generated or manipulated. The technical measures must be effective, interoperable, robust, and reliable, “as far as this is technically feasible.”

That final phrase carries significant weight. It’s not a precise legal standard.

To give companies a practical compliance path, the EU published a Voluntary Code of Practice on Transparency of AI-Generated Content. Most major providers (Anthropic, OpenAI, Google, Meta, Microsoft, Mistral, Cohere) signed it. xAI did not.

What ‘text watermarking’ actually means here

The term itself is causing confusion, so it’s worth being precise.

Traditional text watermarking typically relied on orthographic steganography: inserting hidden characters, zero-width spaces, or other invisible markers into finished text. These methods alter the form of the text. Once you know what to look for, they’re relatively easy to detect and remove.

Anthropic is using a different approach: statistical, or generative, watermarking.

When a language model generates text, it doesn’t always choose the single most likely next word. Instead, it samples from a range of plausible candidates. That controlled randomness helps keep the writing from becoming flat and repetitive. Statistical watermarking replaces some of that randomness with choices guided by a secret key. To the user, the output still appears natural. To the provider, the sequence of choices creates a detectable statistical signature.

Anthropic has said the method doesn’t insert hidden characters, identify individual users, or have any practical effect on output quality. A developer also released a demonstration tool based on the SynthID-Text approach. The engineering is sound.

Yet public reaction remained largely negative, even after Anthropic’s explanations.

That’s because the company answered the technical objections while largely missing the concerns that matter most to the people who use these tools every day — or who still need convincing to use them.

The real problems

1. It treats AI use itself as the problem

Imagine buying a set of kitchen knives and having the government assign someone to monitor you around the clock to make sure you don’t stab anyone. Don’t worry, they say. As long as you only use the knives to cut vegetables, you’ll be fine.

That’s the logic behind this approach.

Historically, watermarking existed to protect creators. Here, it’s meant to protect the potential victims of people who use AI.

Yes, scammers will use AI for fraud. Yes, people will be misled by synthetic content.

Those risks are real. But this policy rests on the assumption that the default use of AI is suspect, so the tool itself must bear a permanent mark.

Anyone who’s worked in SEO has seen this pattern before: white text on white backgrounds in the 1990s, paid links in the 2000s, private blog networks in the 2010s. The tactics worked for a while, then the market and the platforms adapted.

We didn’t need a special regulatory regime treating every form of content creation as potentially fraudulent. Existing fraud and consumer protection laws, along with Google’s incentive to protect the quality of its search results, were enough.

AI is a tool. It can be used well or poorly. Building the system on the assumption that users can’t be trusted isn’t a good way to earn their trust.

2. A positive detection becomes a Scarlet Letter

This is the practical issue that matters most to people doing the work.

Statistical watermarking can’t distinguish between high-value and low-value uses. If Claude performs light editing, rewriting, translation, or tone adjustment, the output can still carry a watermark. The watermark indicates the text was processed by Claude, not that Claude was the original author.

That distinction will be lost on most people. In practice, a detected watermark is likely to become a negative signal — a sign that the work is somehow less legitimate. Ironically, the people producing the lowest-value content will have the strongest incentive to strip or evade the watermark. Its absence will prove almost nothing.

The technique also isn’t especially durable. Just when we thought we were past the endless “we cracked Google’s algorithm” cycle, we’re about to start the same cat-and-mouse game again. Once reliable detectors exist, people will test how much paraphrasing, human editing, or multi-model processing it takes to weaken the signal.

3. It treats writing like a math problem to be optimized

I studied both computer science and English. When I read Anthropic’s explanations, the computer scientist in me was intrigued. The description of the sampling process was clear, and the demonstration tool was genuinely instructive.

The English major in me cringed.

Read these three sentences and see if you can spot the difference:

Four score and seven years ago our fathers brought forth on this continent, a new nation, conceived in Liberty, and dedicated to the proposition that all men are created equal.
Eighty-seven years ago, our forefathers established upon this continent a new nation, born in liberty and devoted to the principle that all men are created equal.
Fourscore and seven years past, those who came before us brought into being on this continent a new nation, conceived in freedom and committed to the truth that all men are created equal.

From a narrow technical perspective, all three are grammatical, coherent, and “high quality.” From the perspective of someone who values good writing, only one is doing the work of literature. The other two are competent paraphrases.

An engineer or computer scientist might not even notice the difference. Readers will.

AI writing already has recognizable patterns: a heavy reliance on em dashes, the familiar “It’s not X, it’s Y” construction, overuse of words like “delve,” “leverage,” and “underscore” where simpler language would do, neatly balanced but empty phrasing, and a lack of specific, independently verifiable details that could only come from real experience.

Adding a statistical bias on top of those tendencies introduces another artificial constraint on the output. The stronger the required signal, the more constrained — and less human — the writing is likely to feel.

4. It applies a regional rule globally

Anthropic didn’t write the EU regulation; it’s simply responding to it. Still, the decision to apply the watermark worldwide at launch, rather than limiting it to the jurisdictions where the law applies, was deliberate and speaks volumes.

The company’s stated reason was the “lack of a durable way to scope the feature by region.” That may be technically inconvenient, but it’s hardly impossible.

Companies routinely adapt product behavior to local legal requirements. Choosing not to do so here — especially for a user base that extends well beyond the EU — suggests a surprising disconnect from its users, many of whom are sophisticated enough to switch to open-weight or non-watermarked models when they want maximum flexibility.

The deeper problem

On the surface, the past week looks like a tech company solving a technical problem to meet a regulatory requirement. To Anthropic’s credit, it moved first and was transparent about the change.

Where it went wrong was the audience it seemed to be addressing. Its explanations were clear to people who already understand how language models work. They did little to address the broader crisis of trust.

A few days after the announcement, Dario Amodei posted on X that the public’s negative view of AI is fundamentally a crisis of trust.

“I do agree that the public has a negative view of AI (and that this is a big problem), but I don’t think it is primarily caused by me or any other AI leader warning about AI’s risks. I think it is fundamentally a crisis of trust.”

He has the diagnosis right. What’s less convincing is the cure.

He went on to argue, correctly, that glitzy marketing won’t fix the problem, and neither will simply claiming AI will cure cancer. The real solution, he suggested, is actually curing cancer.

That framing misses the point. It’s a blind spot shared by many AI executives.

AI won’t cure cancer. Humans will.

AI can surface connections, identify patterns, and accelerate parts of the work. But it’s still a tool. Behind every meaningful result is human judgment and human responsibility.

The same gap appears at a more ordinary level.

Outside of work, AI has improved my life. I’ve already shared how it helped me improve my health. I’ve also used it to plan vacations, adapt recipes, repair my car, and research my family history.

None of those uses will change the world. But they changed mine. Not because I picked the right model, but because I knew how to use it.

I’ve found the same is true for many long-time SEOs. Good SEOs know how to ask questions. We know how to challenge what a computer gives us, refine our prompts, and decide when to accept an answer and when to push back.

Most people haven’t had that experience. Their exposure to AI is largely limited to viral videos and a steady stream of horror stories: mass layoffs, data centers straining local resources, and executives accumulating fortunes that would make the old robber barons blush. With all due respect to Amodei, actually curing cancer won’t change any of that.

Talking as though the technology itself will deliver the breakthrough turns people into spectators instead of participants. Worse, some hear that message and conclude the companies quietly share Agent Smith’s view in “The Matrix”: humans are the problem, and AI is the solution.

What will close the gap is the same force that drove mainstream internet adoption in the 1990s: people discovering tangible benefits in their own lives. That happened because the early internet was built in a spirit of openness rather than control.

The internet scaled because its architects favored open protocols and worked in a culture that was skeptical of concentrated power, whether in government or corporations. Vint Cerf, Bob Kahn, Tim Berners-Lee, Jon Postel, Linus Torvalds, Richard Stallman, Paul Mockapetris, and many others still aren’t household names. Most never became multimillionaires or sought public recognition, yet their contributions to daily life are immeasurable. The political class’s greatest contribution was restraint.

Today, the major AI labs are responding to pressure by adding constraints and tightening control. Too often, the visible motivation seems to be who can produce the biggest exit. That’s a very different spirit from the one that built the early internet.

What actually matters

There’s a useful parallel here for SEOs. You’ve always been able to distinguish between using a technique to create real value and using it to game the system.

This article is a good example. I wrote it the old-fashioned way, drafting it myself and using AI only for research.

Once I had a draft, I used AI to organize, prune, and refine it. I didn’t blindly accept every suggestion. I pushed back and, in some cases, overrode it.

A good example is the H.G. Wells “The Time Machine” analogy above. AI kept urging me to expand that paragraph and explain the reference. I said no. I think enough of this audience will get it immediately. The rest of you can spend five seconds Googling it (or, better yet, check the book out from your local library).

The difference between quality work and slop isn’t whether it passes a detection tool. It’s whether people engage with it, share it, and convert. Everything else is secondary.

It’s also telling which tool I chose. I’ve been using Claude all month for real work. For this piece, I switched to Grok precisely because it doesn’t fingerprint its output.

Part of that decision was rational. Part was emotional. Companies ignore that mix at their own risk.

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What’s new in Yoast SEO 28.4: find, fix, and generate at scale

September 1, 2026/0 Comments/in Web Design News, Yoast SEO/by Dubado Solutions

The Yoast SEO Bulk Editor already helps you update titles, meta descriptions, and focus keyphrases across your whole site, filter by content type, and let Yoast AI draft metadata you approve before it saves. This update builds on that: it’s now easier to spot exactly which posts need attention, catch AI suggestions that need a second look, and get started without leaving your regular post list.

Here’s what’s new.

Find weak fields in seconds

The Filters dropdown now includes “needs improvement” options for SEO title, meta description, social title, and social description. Turn one on, and the bulk editor shows only the posts where that field is empty or scoring poorly, using the same scoring your post editor already relies on.

Once you’ve found them, you don’t have to select each one by hand. New quality-based options in the Select menu let you select every post on the page that needs improvement in a single click, using that same score, so what you filter and what you select always agree.

Prefer working row by row? Shift+click in the Bulk Editor now selects a range at once, the way you’d expect from any table.

Yoast SEO Bulk editor Products screen with the Select menu open, showing Social titles and Social descriptions quality-based selection options. — Use the Select menu to select every product with a missing or weak social title or description in one click.

Catch weak suggestions before you publish

When you generate SEO titles, meta descriptions, or social copy in bulk, the AI does its best work with posts that already have some content to draw from. Previously, posts with very little written content could still generate suggestions, even though the input was thin, so you had no way to know which ones needed a closer look.

That’s why the Bulk Editor now gives you a built-in guardrail: after a generation run, it flags exactly which generated posts had limited content, so you know which ones are worth a second look. Add a bit more content, regenerate, and you’re set.

Start bulk editing from your regular post list

You no longer need to open the bulk editor separately to get started. Select posts, pages, or products from their regular overview screens, choose “Bulk edit” from the bulk actions dropdown, and your selection carries straight into the bulk editor, ready to go.

WordPress Posts screen with the bulk actions dropdown open, showing the Yoast SEO Bulk edit option. — Select your posts, pages, or products, then choose Bulk edit from the Yoast SEO bulk actions menu.

Keep working, even when your site is hard to reach (rolling out)

If Yoast AI can’t reach your site, for example because it’s behind a firewall, offline, or has the REST API disabled, you’ll now see an option to connect your site to MyYoast right from the error message, instead of a dead end. Once connected, Yoast AI keeps working even on trickier hosting setups. This is rolling out gradually, so you may not see it yet. You don’t need to do anything to prepare, you’ll be invited to connect right when it’s useful to you.

The post What’s new in Yoast SEO 28.4: find, fix, and generate at scale appeared first on Yoast.

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Calls and clicks keep falling as Google Maps becomes the destination

August 31, 2026/0 Comments/in Search Engine Land, Web Design News/by Dubado Solutions

In February, we wrote that local rankings were holding steady while calls and website clicks quietly disappeared. By April, our data pointed to something much more dramatic, enough that we declared “local SEO” was dying on stage at BrightonSEO.

We were wrong about the scale.

Q1 data was revised retrospectively. After testing the corrected numbers against a full second quarter, we found a clearer, more specific story than either version suggested.

Why Q1 looked like a crisis

Sterling Sky and Jepto’s analysis of 179 Google Business Profiles found that AI-powered local packs often show just two businesses instead of three, frequently without a click-to-call button, and surface only 32% as many unique businesses as the traditional Map Pack. Most rank trackers couldn’t see any of it.

Joy Hawkins, Claudia Tomina, and Matt McGee were independently reporting the same pattern: rankings held steady while performance declined.

Our initial reading of the Q1 data, presented at BrightonSEO in April, looked even more severe. Actions and impressions appeared to have fallen by roughly half across our U.S. portfolio. The drop was dramatic enough that we advised some agency customers to invest in paid search.

It didn’t hold up.

The corrected Q1 number, and what Q2 adds

Rechecking Q1 revealed a much less dramatic picture. Year over year:

U.S. website clicks and calls each fell 15.8%, while direction requests rose 31.3%.
Desktop Search impressions increased 12.3%.
Mobile Search impressions fell 20.6%.
Desktop Maps impressions declined 17.9%.

We don’t put much weight on the mobile Maps figure alone. A small number of large advertisers can significantly shift that metric.

*Q1 2026 vs. Q1 2025, corrected year-over-year Google Business Profile performance. (Source: GMBapi data)*

Q2 data tests that corrected the baseline.

U.S. calls and website clicks are still falling, down 11.9% and 12.5%, and direction requests are still growing, up 21.1%, all three decelerating against Q1’s sharper moves.

The standout change is desktop Maps: down 17.9% in Q1, up 3.2% in Q2, a genuine reversal, specific to the US. Mobile Maps impressions are up 30.4%, desktop search is up 13.9%, and mobile search is down 20.1%, enough on its own to erase the other gains.

*Q2 2026 vs. Q2 2025, year-over-year Google Business Profile performance across the US, EU and UK. (Source: GMBapi data)*

Our read is that U.S. customers are increasingly completing the entire journey within Google Maps rather than starting on a search results page. The reversal in desktop Maps impressions suggests that the shift is no longer limited to mobile.

Two ranking systems and reviews as the connective tissue

Traditional Maps rankings still rely on proximity, relevance, engagement, and prominence. The signals behind AI Mode and Gemini differ: web context, entity matching, brand authority, and review sentiment are layered on top of the Google Business Profile.

One system determines whether you appear on the map. The other determines whether Google’s AI trusts what it knows about your business enough to answer a customer’s question directly, without a click.

Reviews are becoming the raw material for that second system. Google now prompts reviewers with structured tags such as atmosphere, price, and cleanliness, rather than relying solely on free text, and also encourages customers to review businesses they’ve visited.

It’s building cleaner data so its AI can answer questions without sending users to a website first — a plausible explanation for direction requests rising while calls and website clicks decline.

The EU and UK aren’t a slower version of the US

The picture outside the U.S. is different. In the EU, desktop Maps impressions fell faster in Q2, dropping from -16.5% in Q1 to -34.7%, even as direction requests continued to grow, slowing from +21.7% to +13.1%.

The U.K.’s smaller dataset showed the opposite reversal. Mobile Maps impressions swung from +22.4% in Q1 to -70.8% in Q2, while direction requests and website clicks continued to rise.

Neither market appears to be simply trailing the U.S. They’re following different paths.

Rank tracking alone won’t tell you this

SOCi’s 2026 Local Visibility Index found AI platforms recommend far fewer locations than Google’s 3-pack: 1.2% on ChatGPT, 7.4% on Perplexity, and 35.9% on Google. A key reason is profile accuracy, which averages 68% on ChatGPT and Perplexity versus 100% on Gemini, which pulls directly from Google Maps data. Search Engine Land covered the report in detail.

Reviews matter here, too. Locations recommended by ChatGPT average 4.3 stars, while those recommended by Perplexity average 4.2, suggesting review quality is becoming a gate for AI recommendations, not just a ranking signal.

What this means for your reporting

If your reporting still leads with call volume, you’re measuring only part of the customer journey. Direction requests deserve equal weight. Businesses with the most complete, accurate Google Business Profile data capture more of the remaining clicks, calls, and directions.

For multi-location brands, agencies, and SMBs focused on the bottom of the funnel, local SEO has grown up. It’s starting to look a lot more like SEO.

More detail on GMBapi’s Q2 Local SEO trend data is available here.

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Best PPC Companies of 2026

August 31, 2026/0 Comments/in Web Design/by Dubado Solutions

Key Takeaways

The best pay-per-click (PPC) agencies specialize in specific verticals. NP Digital leads in data-driven omnichannel campaigns, while many agencies focus on B2B, SaaS, or ecommerce.
PPC agencies typically charge 10 to 20 percent of monthly ad spend, a flat retainer of $1,500 to $25,000 per month, or a hybrid of both. Setup fees run from $2,500 to $10,000.
Google’s AI-powered tools like Smart Bidding and Performance Max are now baseline competencies for competitive PPC. Ask any agency you’re evaluating how they configure target return on ad spend (tROAS) and monitor AI-driven campaigns.
Define your goals and success metrics before the discovery call. If you show up without clear key performance indicators, you’ll waste their time and money. You could even have less leverage at the negotiating table. Check agencies against third-party review platforms like Clutch and G2. Then pressure-test their case studies against your own industry.

Pay-per-click (PPC) advertising delivers an average return on investment (ROI) of 200 percent.

Marketers are taking notice. About 93 percent of them view PPC advertising as effective or highly effective, second only to content marketing.

A lot of that success depends on the PPC agency running your campaigns. Agencies aren’t interchangeable, though. The wrong partner can burn through your budget and compromise your results before you even launch.

This article breaks down the best PPC agencies of 2026. Each one is highlighted for their expertise and specific strengths, helping you find the perfect partner who aligns with your goals and drives real results.

Define Your Goals Before Hiring a PPC Agency

PPC campaigns can target drastically different outcomes, so you need to set clear goals and expectations before you start looking for an agency. Without them, you’ll have a hard time making an educated decision and could waste your time and money.

Negotiating services with your agency of choice may also be a struggle without a clear target in mind. Scoping and pricing conversations generally fall to the agency if you don’t bring clear goals to the table, putting you in a weaker position at the signing table.

So, before you get started, sit down with your team and decide what success looks like for your PPC campaigns. 

Your goals may include:

Adding more qualified leads to your automated sales funnel
Selling more of a specific product or category of products
Increasing the number of visitors to a landing page
Building awareness for your brand

Clear campaign goals matter more when you’re building a paid media strategy around AI-driven campaign types like Performance Max. Campaigns like these rely on inputs from your business, including target return on ad spend (tROAS), target cost per action (CPA), or conversion value. Only you know what those numbers should be.

If you can’t articulate what a successful conversion looks like to your business, the AI will optimize toward the wrong outcome faster than a human team ever could.

Best PPC Companies of 2026

Each of the top PPC agencies below earned its spot by delivering measurable results across a specific business type, budget range, or channel focus. Some specialize in enterprise omnichannel work, while others lean into niches such as B2B lead generation or paid social. The right fit depends on the goals you defined above, so keep them in mind as you browse.

NP Digital — Best Data-Driven Omnichannel PPC Agency

Running a successful PPC campaign involves more than placing ads on one platform.

At  NP Digital, we create  omnichannel campaigns that connect with your audience wherever they are, whether they’re searching on Google, scrolling social media, or shopping online. This approach strengthens  brand awareness and drives engagement, turning leads into customers while maximizing ROI.

Our track record speaks volumes. 

One client, ZAGG, needed to boost paid search revenue while reducing costs. Its existing campaigns had inefficient conversion rates from warranty offerings, requiring a strategic pivot to improve profitability.

As part of our process, we:

Restructured campaigns and consolidated ad groups for clearer tracking and better attribution.
Implemented tROAS bidding and Performance Max campaigns for growth while managing underperforming categories with standard shopping campaigns.
Enhanced product categorization to focus on smaller product groups, increasing visibility and profitability.
Tested ad copy in smaller environments, scaling proven combinations to maximize impact.
Expanded broad match adoption, capturing untapped long-tail queries and driving efficiency at scale.

Results:

Winner of the 2024 Online Media, Marketing, and Advertising (OMMA) Best SEM Campaign award
28 percent increase in revenue from paid search
38 percent boost in media spend efficiency, reducing cost-per-conversion by 18 percent
17 percent lift in return on ad spend (ROAS) for power products
13 percent decrease in cost-per-click (CPC)
23 percent boost in ROAS and 26 percent increase in sales per click (SPC) for the Keyboards and More product category
6 percent lift in campaign efficiency and 30 percent boost in ROAS month-over-month from broad match testing

Here’s what ZAGG Digital Marketing Director Brayden Martin praised the team’s hands-on approach and the ROI results NP Digital delivered on ZAGG’s PPC campaigns.

Brayden Martin, Digital Marketing Director at ZAGG, praises Neil Patel Digital for their hands-on service and impressive ROI results handling ZAGG’s PPC advertising.

Our client  ConnectWise needed to shift from marketing qualified lead (MQL) volume to a strategy focused on sales qualified opportunity (SQO) and pipeline growth. The challenge was a lack of visibility into campaign-level ROI.

As part of our process, we:

Mapped Power BI data to paid search campaigns, aligning investment with mid-funnel key performance indicators (KPIs) and pipeline contributions.
Identified and reallocated $180,000 per month in savings from low-quality campaigns to high-performing campaigns.
Focused budgets on campaigns driving the highest SQO conversion rates, improving cost efficiency.

Results:

166 percent increase in SQO conversion rates across regions
100 percent improvement in ROAS, with a 68 percent pipeline growth
25 percent reduction in cost-per-SQO, driving higher-quality leads at a lower cost

ConnectWise’s results came from paid search, but our reach extends further. We hold premier or select partner certifications from Google, Meta, Microsoft Advertising, and Amazon Ads, so you get certified expertise and direct platform support regardless of where your campaigns run.

NP Digital holds a 4.5 rating on Clutch, with reviewers frequently citing the team’s professionalism and project management.

Directive Consulting — Best for B2B and SaaS Businesses

Directive Consulting’s homepage invites prospective clients to “Rethink the Potential of Your B2B Agency.”

Directive Consulting is the top choice for B2B, SaaS, and enterprise businesses aiming to convert ad spend into predictable revenue. It focuses on connecting with high-intent buyers through precise targeting based on deep total addressable market (TAM) analysis and detailed ideal customer profile (ICP) modeling that factors in the fragmented buyers’ journey AI answer platforms are creating.

Directive’s expertise spans paid search, social, account-based marketing (ABM), and programmatic advertising. The agency prioritizes metrics like  customer lifetime value (CLV), customer acquisition costs (CAC), and net sales margin to optimize campaigns for revenue growth.

On Clutch, Directive carries a 4.8 rating across more than 50 reviews, with B2B clients consistently highlighting the team’s proactive approach and strategic depth.

For businesses looking to scale PPC campaigns efficiently and grow their sales pipeline, Directive Consulting provides a results-driven approach tailored to your industry and goals.

Stryde — Best for Ecommerce Businesses

Stryde’s homepage touts their expertise in scaling online visibility and marketing performance for ecommerce brands.

Ecommerce brands need PPC campaigns that bring in and convert traffic to build long-term revenue.  Stryde focuses on helping ecommerce businesses stand out and grow through well-executed paid search and social strategies.

Stryde’s team specializes in crafting PPC strategies on Google and organic SEO or generative experience optimization (GEO) strategies geared toward ecommerce brands. It creates campaigns that guide potential customers from initial awareness to the final purchase, ensuring each step of the process is intentional and impactful.

Stryde carries a 4.5 rating on Clutch, with ecommerce clients praising the team’s flexibility and responsiveness.

For ecommerce businesses looking to grow their audience and revenue, Stryde offers a proven strategy that’s designed to drive results while building customer loyalty.

Disruptive Advertising — Best for Data-Driven Google and Meta Campaigns

Disruptive Advertising’s homepage lists their marketing results and logos of their bigger accounts, such as Guitar Center and PennyMac.

Disruptive Advertising is one of the most-reviewed PPC agencies on Clutch, with more than 360 client reviews and a 4.8-star rating spanning a decade and a half of work across Google Ads, paid social, and search engine marketing (SEM).

It’s hard for any agency to stay in the business that long without providing real results for their clients, which is why Disruptive’s Premier Verified status on Clutch comes as no surprise.

Its model emphasizes personalized strategy and transparent ROI reporting, backed by a performance guarantee and no long-term contract requirement. Disruptive is best suited for mid-market and growth-stage businesses that want an agency with high review volume and documented results across multiple industries.

KlientBoost — Best for Landing Page Design

KlientBoost’s homepage bills the company as The Outcome Marketing Agency That Hits Bigger & Bigger Goals.

KlientBoost takes PPC campaigns further by focusing on what happens after the click. Its 400-plus verified reviews and 4.9-star rating on Clutch are proof that its expertise in PPC management and landing page design turns traffic into measurable results.

The agency’s in-house team of developers and designers creates landing pages that drive conversions. It focuses on A/B testing and advanced analytics to continually improve performance, ensuring campaigns deliver maximum value.

For businesses that want PPC campaigns paired with high-performing landing pages, KlientBoost delivers solutions that turn clicks into meaningful revenue growth.

AdVenture Media— Best for CRO

AdVenture Media’s homepage positions it as a digital marketing strategist for service-based businesses.

Campaigns start with an account audit and competitor research to establish where quick wins exist. From there, the team builds out targeted ads on Google, Meta, and LinkedIn, backed by continuous A/B testing on landing pages and creative. Every campaign gets monitored closely, with reporting centered on ROAS rather than vanity metrics.

AdVenture Media pairs experienced strategists with its proprietary AI platform, SHERPA, to sharpen conversion rate optimization (CRO) across every campaign. The platform pulls in data from Google, Meta, Shopify, and CRM tools, then flags the patterns and opportunities a team would otherwise spend days finding. This combination lets AdVenture Media catch high-intent leads early and turn traffic into real, trackable outcomes for both ecommerce brands and lead generation businesses.

AdVenture Media has built a reputation on this data-driven approach to CRO, earning recognition from Google and a spot on the Clutch 1000 list of global B2B leaders. With offices across New York, Philadelphia, and Fort Lauderdale, the agency works with businesses ranging from fast-growing ecommerce brands to established lead gen operations looking to get more out of their ad spend.

Ignite Visibility — Best for Paid Social

Ignite Visibility’s homepage says they’re the Trusted Digital Marketing Agency for More Traffic, Leads, & Revenue. They also offer a free audit to potential clients.

Ignite Visibility specializes in paid social advertising, using platforms like Meta, TikTok, and LinkedIn to drive growth. Its Premier Verified status and five-star rating, based on more than 170 reviews on Clutch, prove it understands what gets its clients results.

The agency’s approach goes beyond running ads. Ignite Visibility builds funnel-based strategies that engage target audiences on the right platforms. The team runs branded challenges on TikTok and connects B2B brands with decision-makers on LinkedIn. A/B testing and ongoing adjustments help their campaigns consistently perform.

With over a decade of experience and awards for its work, Ignite Visibility has become a trusted partner for businesses looking to grow through paid social advertising.

SmartSites — Best for Small and Mid-Sized Businesses

SmartSites advertises itself as an Award-Winning Digital Marketing Agency and showcases its premier partnerships with Google, Microsoft, Meta, and Amazon. The page also displays SmartSites’ 9-year run in the Inc. 5000 and A+ rating with the Better Business Bureau (BBB).

SmartSites maintains a strong presence on Clutch, with more than 350 verified client reviews and Premier Verified status. Much of that stems from small businesses and local advertisers who emphatically back their services with a 4.9-star rating.

Founded in 2011 by brothers Alex and Michael Melen, the agency runs PPC as a core service alongside web design and other marketing channels. Client reviews cite responsiveness, transparent billing, and measurable gains in lead generation as consistent upsides to working with SmartSites.

How Much Do PPC Agencies Charge in 2026?

By now, you’ve probably spotted a few agencies that match what you’re looking for. The next question is what they’ll cost. The answer can be a little tough to find without some digging.

Many agencies quote on discovery calls rather than post rates online. Pricing transparency seems hard to come by in the industry, so here’s a general breakdown of what to expect.

Generally, three pricing models dominate the market in 2026:

Percentage of ad spend. Agencies charge 10 to 20 percent of monthly ad spend, typically landing at 15 percent. This model dominates accounts spending $15,000 or more per month.
Flat monthly retainer. Retainers typically range from $1,500 to $10,000 per month for small- and mid-market accounts, with enterprise retainers reaching $25,000 or more.
Performance-based or hybrid. Pure performance pricing is rare in 2026 due to attribution challenges stemming from iOS privacy changes and cookie deprecation. Hybrid structures that combine a base retainer with a percentage layer above a spend threshold are more common.

The ballpark monthly ranges by tier are:

Small businesses: $1,500 to $3,000
Mid-market: $3,000 to $7,500
Enterprise: $7,500 to $25,000 or more, or a percentage of large ad spend

Clutch’s PPC Pricing Guide shows the average monthly PPC project cost across their verified client reviews at about $7,165, with most PPC projects falling between $10,000 and $49,999 in total. You’ll also need to account for setup fees, which Solid Marketing estimates at anywhere from $2,500 to $10,000, depending on the agency.

Pricing shouldn’t be the deciding factor, though. An agency that misfires on strategy could easily burn through more in wasted ad spend than you’d save on a lower fee.

What Makes a Great PPC Agency?

The path to finding the best PPC marketing agency isn’t a straight line. Your research will take some twists and turns. Some excel at specific advertising types, and others specialize in creating excellent customer experiences across every platform. 

One isn’t necessarily better than the other, but it ultimately depends on what you’re looking for. Use these characteristics as a baseline for creating a list of viable options:

Extensive industry knowledge: A top-tier PPC agency understands the specifics of your industry. It knows how to target the right audiences with the right keywords based on real-world experience and proven results. Check case studies to see if the agencies you’re considering have delivered outcomes for businesses like yours. NP Digital does a great job of this, as seen in the examples above.
Advanced analytics and reporting: Clear reporting is key to understanding the success of your campaigns. The best agencies provide detailed performance data, showing where your money goes and what it brings back. They use this information to fine-tune campaigns and cut waste.
Intent-driven keyword strategy: Keyword strategy can make or break a PPC campaign. Effective campaigns target transactional keywords, the terms people search for when they’re ready to take action. Great agencies avoid keywords that generate traffic without meaningful results and use intent-based research to guide their strategy.
First-party data and partnerships: Agencies with direct access to platforms like Google and Meta, along with first-party data, deliver stronger campaigns. These connections allow them to stay ahead of trends and tap into audience behaviors others might miss.
Mobile optimization: With mobile devices accounting for 58.3 percent of paid clicks, agencies must prioritize mobile-friendly campaigns. This means creating ads and landing pages that look great and perform well on smaller screens.
Multi-channel PPC services: Running ads across multiple platforms helps you reach a wider audience. The ideal scenario is to have the agency managing your Google Ads campaigns also manage social media, programmatic advertising, and more, so your campaigns stay consistent across channels. NP Digital offers a wide range of services and makes it super easy for anyone to find what they need.
AI bidding and automation expertise: Smart Bidding, Performance Max, and tROAS configuration are now baseline competencies for competitive PPC. Ask how the agency structures Performance Max campaigns, sets bidding targets, and monitors AI-driven optimization to catch drift before it burns through budget. NP Digital’s ZAGG campaign above shows what tROAS bidding and Performance Max produce when applied with proper human oversight.
Well-versed in all things digital marketing: The best PPC agencies work to create effective campaigns and PPC strategies to support your overall business goals. The strategies they use should fit nicely into your broader digital marketing strategy. Great agencies understand the big picture and can often help improve other parts of your digital marketing system because they’re great marketers themselves.

How AI Is Changing PPC in 2026

Campaign management has shifted from keyword-by-keyword control to setting inputs for machine learning systems. Three shifts define how AI is reshaping paid search in 2026:

Consolidated campaign management. Performance Max rolls multiple Google platforms into one campaign optimized by AI. You provide goals and creative assets, and the algorithm decides where each ad serves and to whom.
Automated bidding. Google’s bidding framework now runs on AI. Strategies like Target CPA, Target ROAS, Maximize conversions, and Maximize conversion value optimize bids in every auction based on your conversion signals.
Machine-assembled creative. Responsive Search Ads and Performance Max asset groups both feed headlines, descriptions, images, and video into machine-learning systems that assemble and test combinations in real time.

As you interview agencies, ask specifically how they structure Performance Max campaigns and monitor AI-driven optimization. It’s critical to set up Google Ads Manager with clean conversion tracking and make sure there’s human oversight. That’s what will keep your ad spend from going to waste.

NP Digital’s ZAGG results earlier show what AI advertising can do when the inputs are set correctly.

How to Work With a PPC Agency

Working with a PPC agency can reshape how you approach paid advertising. Here’s what to expect at every step of the process:

Discovery and Onboarding: Your agency will start by learning everything about your business. You’ll discuss your budget, goals, target audience, and what sets you apart. Sharing details about your audience, like their habits and preferences, helps your agency develop a strategy tailored to your needs.

Planning and Testing: Next, your agency creates a roadmap and identifies key performance indicators (KPIs). Pilot campaigns often follow, testing audience segments, ad creatives, and copy. These tests provide valuable data to guide full-scale campaigns and improve targeting.

Execution and Monitoring: Once the campaign launches, the agency tracks performance and makes adjustments. Expect regular updates on key metrics like impressions, click-through rates (CTR), conversion rates, and CPA. This feedback keeps you informed and helps refine the strategy.

Measuring Results and Next Steps: At the campaign’s end, the agency reviews the data with you. They’ll highlight successes, explain what could be better, and suggest ideas for future campaigns. This phase is about learning from the results to achieve even better outcomes next time.

To avoid confusion, designate a single point of contact for communications from your agency. This ensures there are no miscommunications or wasted time from several people managing that relationship.

  The best PPC agencies are experts in their space, and their advice usually reflects patterns they’ve seen work across dozens of accounts. This could be advice on improving your home page for conversions or redesigning landing pages to increase sales.  Perhaps it’s a suggestion to improve your ad copy or headline. 

Always remember they’re experts, and you hired them for a reason. Take the time to listen and keep an open mind throughout the process. Working with a PPC agency should feel like a partnership, with clear communication and results-driven strategies leading the way.

How to Choose the Right PPC Agency for Your Business

There are hundreds (if not more) of PPC agencies to choose from. Choosing the right one is often the hardest part of getting started. 

But the best PPC agencies for you specialize in the types of campaigns you’re interested in. They should also have in-depth knowledge of your specific industry and be proficient in the AI paid advertising focus areas I mentioned earlier.

An agency that’s been around for a while and uses savvier tactics, such as analyzing your competitors’ paid ads, also helps. Ultimately, every brand’s needs are unique, but some combination of these characteristics should provide your best results. 

It may also help to make a list of your expectations and requirements before starting your search. 

From there, list the companies you’re considering. Be sure to include:

Their specialty areas
What makes them stand out to you
Why they seem like a good fit
Pricing if it’s available online
Any negatives about their business

Then, when you hire a PPC manager, you can use your requirements and expectations to cross off agencies that don’t match what you need. Client reviews on the top PPC agencies of 2026 from platforms like Clutch or G2 can help you narrow the field even further.

Once you’ve narrowed down your short list, schedule calls with those agencies. This is your chance to interview them just as much as it’s their chance to interview you. Ask all your questions and take notes throughout the meeting so you have all the information in front of you when making your final decision. 

FAQs

What should I expect from a PPC management agency?

A PPC management agency handles paid campaigns all the way from strategy to post-campaign reporting. The process should start with a discovery call, with ongoing communication about ad performance and creative testing throughout.

How much do PPC agencies charge?

PPC agencies typically charge 10 to 20 percent of monthly ad spend, a flat retainer of $1,500 to $25,000 per month, or a hybrid of both. Pricing will vary by agency and depend on your business size or the complexity of your campaign.

How much is PPC with a marketing agency?

Monthly PPC costs range from $1,500 for small businesses to $25,000 or more for enterprise accounts, plus setup fees of $2,500 to $10,000. You’ll typically have to speak to the agency you’re interviewing about their pricing model for your business size and campaign goals.

How do I avoid wasting money hiring a PPC agency?

Define your goals and KPIs before signing, and require a monthly reporting cadence tied to business outcomes rather than vanity metrics. It’s also smart to check the agency’s case studies against your industry and avoid long-term contracts without performance clauses.

What should a transparent PPC agency monthly report include?

A transparent monthly report should include spend by campaign and channel, conversions and conversion value, cost per acquisition or return on ad spend, click-through and quality score benchmarks, and specific optimization actions taken during the reporting period.

Conclusion

Hiring a top PPC company is a smart choice if you’re looking to save time, strategize with experts in your industry, and get short-term results (when compared to something like SEO).

However, choosing a PPC agency you can trust is harder than it sounds. If you’re looking for someone to manage your PPC campaigns, use the tips and recommendations in this guide.

Of course, you can always reach out to my team at NP Digital for a conversation. We have a proven track record of building impactful paid media strategies for our clients and can’t wait to show you what our omnichannel approach to PPC can do for you.

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The Practical Guide to Google Analytics 4

August 26, 2026/0 Comments/in Web Design/by Dubado Solutions

Key Takeaways

Google renamed “Conversions” to “Key Events” in 2024. The terms mean different things in GA4 and Google Ads, so getting the terminology right matters when comparing numbers across platforms.
Every interaction in GA4 is event-based, with parameters providing the context behind what happened.
Data-driven attribution is GA4’s default model, using machine learning to spread credit across all the touchpoints that led to a conversion.
BigQuery export is free for standard properties and unlocks analysis that the standard reports can’t handle.
DebugView is the fastest way to catch broken tracking before it becomes weeks of unusable data.

Are you getting the most out of Google Analytics 4 (GA4)?

GA4 has been Google’s standard analytics platform for a few years now. The move from Universal Analytics (UA) was a major shift, but Google has continued updating GA4 itself along the way. One of the biggest changes came in 2024, when Google replaced “Conversions” with “Key Events” in 2024.

While some marketers might miss UA, there’s no going back.

This GA4 guide skips the history lesson and focuses on what you need to do in GA4 day to day, from proper setup to tracking key events (not conversions) and other impactful metrics.

What Is Google Analytics 4?

Google Analytics 4 is the current version of Google Analytics, and it provides web analytics on a level that allows marketers to effectively analyze important customer usage metrics, not just track traffic.

Before we get into the details, here’s a quick Google Analytics 4 overview.

Google Analytics 4 tracks the entire customer path across multiple platforms and leverages AI and machine learning to provide more detailed insights into how users interact with your website and app.

GA4 combines cross-channel measurement and AI-driven predictive analytics. It also operates under a privacy-first tracking framework designed for today’s privacy laws, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

These features combine to give marketers a more complete view of how users interact with their digital properties than what UA offered.

How to Set Up Google Analytics 4

Many marketers ask me how to use GA4. Your setup process will depend on whether you’re tracking a website or an app (or both). I’ve broken down each option below.

Google Analytics for Firebase

Firebase is Google’s app development platform that helps you build and deploy apps and games. If you enable Google Analytics when creating a Firebase project, Firebase automatically links your app to a GA4 property.

If you’re working with an older Firebase project, you may need to link it to a GA4 property or complete Google’s upgrade process.

To upgrade your Firebase account to Google Analytics 4, follow these steps:

Log in to the Firebase console.
Go to Analytics > Dashboard on the left panel.
Follow any prompts to enable or upgrade GA for the project.

Once linked, you can find app analytics in both the Firebase console and Google Analytics.

Setting up Google Analytics 4 from Scratch

If you’ve never used Google Analytics before or you want to set up a brand-new website, you’ll need to start from scratch:

Create an Analytics account if you haven’t already by visiting https://analytics.google.com and opening Admin. Then click Create, and then Account (skip if you already have an account).
In the Property column, click Create > Property.
Enter a name for your new property, and select a time zone and currency.
Select your industry category and business size.
Select your business objectives (e.g., “Get baseline reports,” “Generate leads”).
Click Create and accept the Terms of Service.
Add a data stream (Web, iOS, or Android) to start collecting data.

Source: https://www.eridesignstudio.com/insights/set-up-a-google-analytics-4-property/

A data stream is a source of information that feeds your GA4 analytics property. It could be your website or your app. Google will guide you through adding each data stream.

Install With Google Tag Manager

For most marketers, Google Tag Manager (GTM) is the recommended installation method. GTM lets you deploy and update the GA4 tag (along with other marketing tags) without touching the site code whenever something changes.

To set up, create a free GTM account and add a container for your site. Google will give you two snippets of code to paste on every page (one in the <head>, one right after the opening <body> tag).

Once the container is live, create a Google tag inside GTM using your GA4 Measurement ID and set it to fire on all pages. Publish the container to push your changes live.

Verify With DebugView

After setup, use DebugView (found under Admin) to confirm your events are firing correctly. It shows real-time event data from a single device with debug mode enabled, which beats waiting for reports to populate. Skipping this step is a common way for broken tracking to go unnoticed for weeks.

The DebugView interface showing real-time events firing on the left panel (the event stream).

Now that your setup is verified, let’s look at what really makes GA4 tick.

Understanding the GA4 Data Model

Understanding GA4 comes down to one core concept: Any custom action you can define (clicks, scrolls, and purchases) is viewed as an event.

While it may seem confusing, classifying events this way enables GA4 to provide a single, flexible structure for all your important metrics.

Each event carries parameters that add context. A purchase event, for example, includes parameters like revenue, item name, and transaction ID. Parameters are where the useful details live, and you can attach up to 25 parameters to any event.

GA4 groups events into four categories:

Automatically collected: Things GA4 tracks the moment you install it
Enhanced measurement: Extra web interactions you toggle on in the data stream settings
Recommended: Predefined names Google suggests for common actions like sign_up or login
Custom: Anything you define yourself

A single event expanded in DebugView or Realtime, showing the parameters underneath.

Sessions still exist in GA4, but they’re derived from events, like a page view or opening your app, rather than the other way around.

A session starts when a session_start event fires and ends after 30 minutes of user inactivity, but there’s no limit to how long they can last while active.

Key Events in GA4 (Formerly Conversions)

As I mentioned earlier, Google renamed Conversions to Key Events throughout GA4 in March 2024.

A Key Event is any event you mark as important to your business, whether that’s a purchase, a form submission, a newsletter signup, or a demo request. This terminology change may be one of the biggest changes introduced by GA4 and could trip you up if you’re not careful.

The rename wasn’t cosmetic. Google separated the terms to reduce confusion between platforms.

Key Events now lives inside GA4, and Conversions is reserved for Google Ads. The two are related (Key Events can be imported into Google Ads and become conversions there), but they’re counted and reported separately. Overlook this distinction, and you could end up misreading your marketing data.

On most web properties, purchase is the primary event that GA4 automatically marks as a key event. Most other events will need a manual toggle. However, if you connect an app property, you should see all pre-defined Key Events.

To mark an existing event as key, go to Admin > Data display > Events. Find the event in the list and click the star icon next to it. To create a new event and mark it as key in one flow, navigate to the same Events menu. Click Create event, and enter the new event’s name, then click the toggle next to it to mark it as a key event.

After that, select your Default key event value and Counting method, and click Create.

The Events page in Admin > Data display > Events, with the "Mark as key event" toggle visible next to one or two events.

Source

You’ll find key event data in the standard reports (Reports > Engagement > Key events) and as a column in most acquisition reports.

One thing to watch for: The Advertising workspace still uses Conversions language because that section pulls data from linked Google Ads accounts, where the term still applies. It’s the same underlying action, but a different label depending on where you are in the interface.

It’s important to keep this language straight as you begin to pull reports.

The GA4 Reports That Marketers Actually Use

GA4 has more reports than most marketers will ever need, but don’t feel overwhelmed. I’ll cut through the noise in this next section of our GA4 guide and give you four foundational reports for the bulk of your day-to-day analysis.

Acquisition Reports

The Acquisition reports show where your traffic comes from, broken down by default channel groupings like Organic and Paid Search, Direct, Referral, and Organic Social.

I think your two most important reports here are User Acquisition (how users first found you) and Traffic Acquisition (session-level sources). Both give insight into which channels are bringing your audience to you and deserve more attention.

The Traffic Acquisition report showing the channel breakdown table (Organic Search, Direct, Paid Search, etc.)

The data-driven attribution model may change how channels are compared in your report. You can switch models in Admin > Data display > Attribution settings if the default doesn’t fit how your team makes decisions.

Engagement Reports

Engagement reports are where most day-to-day content analysis lives. To find your top-performing pages, open Reports > Engagement > Pages and screens, and sort by views or engagement rate.

The Pages and screens report showing the sortable table with Page path, Views, Users, Views per user, and Average engagement time columns.

To truly understand this report, you have to understand that Google’s benchmark of an engaged session is any session that lasts more than 10 seconds, includes at least one key event, or has two or more page views. GA4’s engagement rate is built from this count, and bounce rate in GA4 is calculated as its inverse.

Monetization Reports

Monetization reports cover revenue data, such as product performance and which promotions are bringing in new customers.

The report comes with pre-made summary cards that can break down revenue and purchaser figures, and even provide insights by cross-referencing revenue per purchaser.

You can also drill down further with views like Ecommerce Purchases (transactions, revenue, and top items) and In-App Purchases (for mobile apps).

Ecommerce events aren’t collected automatically. You (or your developer) have to implement events such as add_to_cart, begin_checkout, and purchase with the correct parameters before any data shows up in these reports. If you’re not seeing any data in your Monetization dashboard, skipping this implementation step is almost always why.

Explorations

Explorations are for questions that the standard reports can’t answer. Three versions of these reports that may be useful are:

Funnel exploration: Where users drop off in a defined marketing funnel
Path exploration: What users do before or after a specific event
Segment overlap: How two or three audiences intersect

A Segment overlap report in GA4 showing the convergence point of all Conversions, New Users, and Mobile Traffic tracked within the dashboard.

Use standard reports for recurring monitoring and Explorations for one-off analysis. When you want an answer to a question the team’s never seen before, it’s time to dig into Explorations reports.

Building Audiences in GA4

Audiences in GA4 are user segments grouped by demographics or Key Events. You can use them for analysis in reports or push them to linked ad platforms for targeting and exclusion.

There are two main types:

Predictive audiences: These use GA4’s machine learning to group users based on future behavior using predictive metrics, such as purchase probability and predicted revenue. Your property needs enough historical data to train the models before these audiences appear. You may start to see predictive tools like these popping up across platforms. Used well, they can guide both your traditional SEO and AI SEO strategy.
Custom audiences: These let you define the criteria yourself using the audience builder at Admin > Audiences > New audience. Conditions can be based on events, event parameters, user properties, dimensions, or metrics, with a limit of 10 conditions per audience.

Any audience can be published to a linked Google Ads account for targeting or exclusion. Just remember that GA4 audiences don’t populate in real time, so a user may not appear for 24 to 48 hours after initially meeting your audience criteria.

Attribution in GA4

An attribution model is a rule (or a data-driven algorithm) that determines how conversion credit gets distributed across the touchpoints a user encounters before triggering a key event. If someone clicks a paid search ad on Monday and then converts through organic search on Friday, the attribution model decides which channels get credit and how much.

GA4’s default model for event-scoped dimensions is data-driven attribution, which uses machine learning to distribute credit fractionally based on which touchpoints influenced the conversion.

The other two options Google gives you are “Paid and organic last-click” and “Google paid channels last-click.” You can change your attribution model by going to Admin > Data display > Attribution settings. Switching recalculates how credit is spread across channels, so it may change what your reports say.

Beyond the settings, two attribution reports are worth exploring. Both live in the Advertising section: Attribution paths shows the full sequence of touchpoints leading to a Key Event and Attribution Models compares how credit gets distributed across different models.

BigQuery Export: GA4’s Most Underused Feature

This Google Analytics 4 guide wouldn’t be complete without covering BigQuery export. This feature lets you export your raw GA4 event data to Google Cloud Data Warehouse, where you can query it using structured query language (SQL).

Standard GA4 properties can export up to 1 million events per day for free to a BigQuery sandbox, and most marketers should be taking advantage.

BigQuery is powerful because you can join your raw GA4 data with customer relationship management (CRM) data, ad spend, or anything else you can put into a table, which opens up analyses that aren’t possible in the standard reports. It also stores every event and parameter from your GA4 account for as long as you keep the database.

Any time you need custom attribution windows or multi-source reporting is a good time to use BigQuery. An example use case might be joining BigQuery event data with your CRM to see which acquisition channels bring in customers with the highest lifetime value.

Common GA4 Mistakes to Avoid

Even a well-configured GA4 property can lead you to bad decisions if the data is read incorrectly.

Here are some common missteps to guard against as you’re gathering and interpreting your data:

Confusing Conversions and Key Events. They’re reported and used differently in GA4 and Google Ads, so using the terms interchangeably could lead to confusion when the numbers don’t match across platforms.
Reading acquisition reports without checking attribution settings. Attribution models each distribute credit differently, so the model you’re using shapes how channel performance appears.
Skipping DebugView after making changes. Any new event or setup change should be verified before you rely on it. DebugView lets you see if the proper triggers are firing, catching problems before they become weeks of broken data.
Mixing up sessions and engaged sessions. Engaged sessions are a subset defined by 10-plus seconds, a Key Event, or two or more page views. Treating them as interchangeable will misrepresent your engagement picture.
Using standard reports for questions that need Explorations. Standard reports are pre-aggregated. Deeper analysis with custom segments or path exploration belongs in Explorations.
Not linking Search Console. Without the link, organic search query data and landing page metrics aren’t available in GA4’s Search Console reports.

FAQs

What is GA4 in marketing?

GA4 is Google’s current analytics platform for tracking user behavior on websites and apps. In marketing, it’s what most teams use to measure traffic sources, campaign performance, and on-site behavior.

When did GA4 launch?

GA4 launched in October 2020, when Google announced it as the successor to Universal Analytics.

Does GA4 use cookies?

Yes. GA4 uses first-party cookies (like _ga) to identify unique users and sessions. Cookie behavior can be controlled through Consent Mode.

Is GA4 correctly installed?

Use DebugView (under Admin > Data display) to verify events are firing after setup. Google’s Realtime report also helps confirm data is flowing.

Conclusion

My goal with this GA4 guide was to cover more than theory or technical instruction. I wanted to show you the mechanics of how GA4 works for marketers in 2026.

GA4’s event-based data model, paired with predictive audiences, gives you a level of insight Universal Analytics never really offered.

The catch is that neither one delivers value automatically. You have to configure things correctly and know which reports to pull for which questions.

If you need help with custom reporting or more complex functions, such as using GA for SEO measurement or keyword research, contact my team at NP Digital. They’re some of the best in the business when it comes to using GA4, and they’re more than happy to help.

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AI visibility has two jobs: Execute SEO and mobilize the organization

August 25, 2026/0 Comments/in Search Engine Land, Web Design News/by Dubado Solutions

For most of search marketing’s history, action items stayed close to what SEO and website teams owned: technical issues, content, links, authority, and related work. Fixes often required developers, writers, or subject-matter experts, but SEO and website teams could usually diagnose the problems and influence the outcome on their own.

AI visibility doesn’t fit as neatly into that SEO box.

A company can have a technically sound website that AI crawlers can access and understand, including what the company sells and who it serves. AI may even mention and cite the brand regularly in informational responses.

Yet when a buyer asks what to purchase, those same brands can be left out. That’s because AI uses a different set of criteria when it shifts from providing information to making recommendations. As a result, AI visibility plans require action from teams across the organization, far beyond SEO and AI search.

This is why I believe AI visibility now has two jobs:

Optimize what SEO and development control.
Mobilize the organization for everything else.

Mobilizing teams across the organization will become one of the most important capabilities for in-house teams responsible for AI visibility.

This is something I’ll be addressing in my AI Brand Visibility SMX Master Class on Oct. 5.

Being visible and being recommended aren’t the same problem

Much of the industry’s GEO conversation today focuses on getting found and mentioned:

Can AI crawlers access our content?
Are we mentioned?
Are we cited?
Which sources influence AI responses?
How often do we appear compared with competitors?

This is a job in itself.

However, when a buyer asks:

“I need a compressed air system for a food manufacturing facility that maintains consistent pressure during variable production demand without introducing oil contamination into the process. What should I consider?”

This isn’t simply a request for information about compressed air systems. The buyer has provided a specific set of requirements and asked AI to help make a decision. AI shifts from providing information to giving advice.

To answer well, AI must determine which solutions are appropriate for food manufacturing, which can handle variable demand, which address contamination concerns, what tradeoffs the buyer should consider, and more.

The AI system compares products using documentation, technical specifications, customer experiences, third-party sources, and its understanding of manufacturers and the buyers they serve. It also applies its own understanding of what matters most in that buying scenario to its evaluation.

Once AI moves into advising, it’s no longer simply retrieving information. It’s making recommendations, and that’s when being understood and citable is no longer the same as being recommendable.

That significantly expands the role of the SEO and AI Search (GEO) team.

Sometimes AI understands your product perfectly and that’s why you’re omitted

Consider a manufacturer with strong domain authority, extensive content, and technically sound product pages. Its products consistently appear when buyers ask informational questions about the category.

Now imagine a buyer asks:

“What equipment should I use for this application if minimizing downtime is more important than initial cost?”

The manufacturer disappears from the recommendations.

Why?

Most search teams would look for a content opportunity. Maybe the website doesn’t explain the product in the context of that application. Maybe its operational advantages aren’t documented. Maybe the information exists but isn’t easy to retrieve.

Those are fixable search and content problems.

But our analysis of leading brands is uncovering issues far beyond what SEO and AI Search (GEO) teams typically consider. We’re seeing reasons such as:

Higher maintenance requirements than competing products
Missing capabilities that matter for the buyer’s specific application
Consistent customer reports of difficult support experiences for complex issues
A component with a reputation for frequent failure
Cloud connectivity that’s reported to drop frequently

These aren’t hypothetical examples. We’ve uncovered issues like these while investigating why large, sophisticated brands with strong products are omitted from AI recommendations.

In these cases, AI wasn’t failing to find the company or its products. It understood them extremely well — in fact, too well.

AI accurately recognized the products’ limitations, what could go wrong, and where buyers were likely to face risk, frustration, higher total cost of ownership, more downtime, longer repair times, and other tradeoffs.

The gaps we’re finding come down to buyer scenarios. Specific prompts surface evidence within AI’s context window that it uses to decide whether a company is a good recommendation for that buyer.

This is a fundamentally different visibility problem than SEO or getting found by AI. It’s a recommendation problem.

When recommendations are the issue, the work extends beyond the SEO and AI search (GEO) team and into cross-functional teams across the organization. In many cases, winning in AI search requires mobilizing far more teams than SEO ever did.

Getting AI to recommend a product often exposes problems outside SEO’s jurisdiction

Now consider a SaaS company that’s a true leader in its niche but consistently loses recommendations when buyers want a native integration with a particular enterprise platform. Its leading competitors offer one. This company doesn’t.

The website could clearly explain the available workaround. It could publish implementation documentation and customer examples showing the alternative works. That may improve the company’s visibility and AI’s perception. But content can’t turn a workaround into a native integration. If that capability matters to the buyer, AI sees the product as a poorer fit or a higher-risk choice.

We saw an even more striking example while researching a complex manufacturing machine. AI understood that one component was made from a different material than its competitors, recognized the performance implications of that design choice, and surfaced both the component and its material when throughput became important to the buyer later in the conversation.

The product’s design itself becomes a factor in recommendations.

Let that sink in for a moment.

Product design has rarely influenced marketing channels beyond reviews, listicles, and ecommerce filters.

This is where AI visibility moves beyond the traditional boundaries of SEO. The SEO or AI Search (GEO) team can identify the pattern, measure how often it affects important buyer scenarios, and diagnose why the product loses recommendations. But it can’t change the material used in a product, add a native integration, or rewrite a company’s warranty policy.

SEO needs to know when and how to mobilize other teams to execute AI visibility solutions.

AI visibility creates a different kind of cross-functional challenge than SEO. Analysis may uncover recommendation problems whose solutions don’t belong to SEO at all.

If AI repeatedly excludes a product because buyers need a capability it doesn’t have, the next conversation belongs with Product.
If customer evidence causes the company to lose recommendations because of poor support for complex issues, that conversation belongs with Technical Support leadership.
If the return policy or refund timeline is blocking recommendations, that conversation belongs with Finance leadership.

The SEO and AI search (GEO) team’s expanded role is to bring cross-functional teams a business problem they may not even know exists:

“When buyers ask AI about this requirement, we lose. Here’s why. Here’s how often it happens. Here’s which products or revenue opportunities it affects. How do we fix it?”

From there, the business can decide whether anything should change.

Sometimes the answer is to change the product, policy, or process. Sometimes the answer is, “We can’t change,” and the team must rely on better positioning, stronger evidence, or clearer content to improve AI’s perception. And sometimes the company decides the buyer scenario simply isn’t important enough to justify action.

Pacesetter AI visibility programs will own the program while mobilizing cross-functional teams to own the solution. That creates two layers of ownership, unlike SEO, where responsibility typically rests with the SEO team.

The SEO and AI search (GEO) team may own monitoring recommendations, investigating losses, and diagnosing their causes. But when the cause lies in the product, customer experience, operations, finance, or another function, that team must be mobilized to own the solution.

The pacesetters in AI visibility won’t be the teams that learn to optimize everything they find. They’ll be the teams that know what SEO can fix, what it can’t, and how to mobilize the organization when the answer lies elsewhere.

Join me Oct. 5 for my all-new AI Brand Visibility SMX Master Class and discover what it really takes to earn recommendations in the AI era. This isn’t another tactical SEO workshop. It’s a strategic roadmap for understanding how AI is changing discovery, and how your organization can adapt before your competitors do.

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Google brings Gemini Omni video creation to Google Ads

August 25, 2026/0 Comments/in Search Engine Land, Web Design News/by Dubado Solutions

Google is bringing Gemini Omni into Asset Studio in Google Ads, allowing advertisers to turn creative briefs, brand guidelines and existing assets into multi-format videos that can be deployed directly into campaigns.

The new capabilities are aimed at reducing the work required to produce creative variations for Demand Gen, Performance Max and other Google and YouTube campaigns.

What’s new. Starting today, Google DeepMind’s Gemini Omni model powers new multimodal video creation tools directly within Asset Studio.

Advertisers can provide their brand guidelines and website URL, enter a creative brief or select existing static assets, and use Gemini Omni to generate video storyboards and motion scenes.

How it works. Google has built the workflow around four stages: establish the brand, generate concepts, refine the creative and deploy the finished assets.

After importing brand guidelines and a URL, advertisers can generate initial concepts from prompts or existing creative. They can then use additional prompts to adjust individual scenes, voiceovers, pacing and aspect ratios before exporting the assets directly into campaigns.

Prompt-based editing. Advertisers can refine generated videos using natural language rather than rebuilding creative from scratch.

Gemini Omni can make changes to scenes, backgrounds and styling while retaining context from previous instructions, allowing advertisers to iteratively develop the creative through a conversation-like workflow.

Brand controls. Google says Gemini Omni uses reasoning during scene generation to help AI-generated assets remain consistent with an advertiser’s visual identity and tone.

Rather than only generating realistic-looking scenes, Google says the model reasons about what should happen next while attempting to maintain the brand standards supplied by the advertiser.

Multi-format creative. One of the main goals is to make it easier to produce the creative variety needed across Google’s different advertising surfaces.

Gemini Omni can generate horizontal 16:9 and vertical 9:16 versions of video creative, reducing the need for advertisers to separately produce variations for different placements across Google and YouTube.

From creation to campaigns. Finished assets can be exported from Asset Studio directly into Demand Gen, Performance Max and other YouTube or Google campaigns.

That creates a more integrated workflow where advertisers can move from a brief to generated creative and then into campaign deployment without relying on separate video production tools for each stage.

Why we care. Producing enough video variations for Google’s increasingly creative-heavy campaign types can be expensive and time-consuming, particularly when advertisers need multiple formats. Putting generation and editing directly inside Google Ads could dramatically shorten that process. But advertisers will still need to scrutinize whether AI-generated videos genuinely meet their brand standards rather than relying solely on Gemini Omni’s interpretation of their guidelines.

Bottom line. Google is turning Asset Studio into a more complete AI video production environment, using Gemini Omni to take advertisers from creative brief to brand-aware, multi-format video assets that can be deployed directly into campaigns.

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How to turn an SEO backlog into a roadmap

August 25, 2026/0 Comments/in Search Engine Land, Web Design News/by Dubado Solutions

Your SEO roadmap needs to be more than a list of activities.

Every line in the list could be worth doing, but they should also tell you why it matters, what it’s supposed to accomplish, or what happens if it slips a quarter. A roadmap should answer “so what” for everything on it. Otherwise, you’ve built a backlog.

It’s important to note the difference because roadmaps and backlogs serve different purposes, even though they’re sometimes mistaken for interchangeable.

SEO backlogs vs. roadmaps

A backlog is where ideas, patches, or nice-to-haves sit and wait their turn, while a roadmap is where you show measurable outcomes tied to initiatives when tasked with answering what SEO will actually deliver within a set time frame and why it deserves to be continuously funded compared to other growth levers.

Treating the two as the same is how teams end up defending activity instead of outcomes, and often, where roadmaps fail.

A backlog says:

Fix this set of canonical errors.
Add schema to a template.
Update category pages.

A roadmap says:

Why this matters.
What business outcome it supports.
Who owns it.
What has to happen first, if anything.
Expected impact, direct or indirect.
What it costs in time and resources.
How you’ll know it worked by measurement.

Everything that can clear those questions should be scored and sequenced on your roadmap. Everything that can’t should stay in the backlog until it can.

Your list is the first step. A framework for building the roadmap is the qualifying layer.

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Using SCOPE for SEO roadmapping

A helpful framework to categorize between roadmap and backlog is SCOPE. It’s similar in method to other acronymic frameworks, like RICE in product or RACI in operations.

SCOPE stands for:

Strategic alignment: Does the initiative tie to business goals the executives care about?
Confidence in delivery: Will the initiative get shipped in the way it’s intended, without being derailed by dependencies?
Ownership of execution: Who will actually do the work, and what’s their capacity?
Potential impact: What’s the value of the initiative? Can you measure it honestly?
Effort and elapsed time: What does the initiative cost, and how long will it take?

Take your list of initiatives and run them through the matrix (hypotheticals within):

Initiative	Strategic alignment	Confidence in delivery	Ownership of execution	Potential impact	Effort and elapsed time
Fix canonical tag errors on product pages	High. Protects existing rankings from splitting equity.	High. No dependencies.	SEO team	Medium. Recovers lost equity but no new demand.	Low effort. 2 weeks.
Add schema to top commercial pages	Medium. Supports visibility and CTR.	High. No dependencies.	SEO team + content	Medium. Incremental changes.	Low effort. 3 weeks.
Consolidate thin category pages	Medium. Cleans up cannibalization.	Medium. Needs stakeholder alignment.	SEO team	Medium. But potentially avoids further issues later.	Medium effort. 6 weeks.
Rebuild internal linking architecture	High. Impact across the entire website.	Medium. Needs CMS support for dynamic linking.	SEO team + dev	High. Lifts authority flow across entire site.	Medium effort. 1 quarter for data-driven analysis.
Build a pSEO directory from the product database	High. Net-new organic demand captured at scale.	Low. Needs engineering bandwidth.	SEO team + engineering + QA	High. Largest net-new traffic opportunity.	High effort. Half year.

Dig deeper: SEO execution: Understanding goals, strategy, and planning

Sequence your SEO priorities on your roadmap

While placing the initiatives across your matrix tells you what matters most, sequencing tells you what happens when.

Doing so is beneficial because some initiatives are cheap and fast, while others are expensive and slow to pay off.

If you’re reporting on quarterly or half-year targets, your roadmap should include a mix of both. Otherwise, all of your wins will hit a wall in outcomes by month ~four, while long-horizon bets won’t show up until the next cycle, making it harder to justify the roadmap.

Here are some examples of quick wins:

Fix canonical errors: Low effort, ships in two weeks, fine outcome.
Add schema to top commercial pages: Low effort, ships in three weeks, fine outcome.

Here are some examples of long bets:

Rebuilding the internal linking architecture: Blocks a quarter of work before compounding effects become visible.
Building a pSEO directory off the product database: Has the highest upside to net-new traffic, but requires the most effort and time.

Good sequencing usually means quick wins that are low-effort and high-confidence, generating results while the slower initiatives run in parallel in the background.

That way, by the time quick wins are exhausted, you’ll start reaping the rewards of the bigger bets that have phased through their dependencies and are beginning to gain traction.

So you have the list, you have the qualifying layer, and now you’re sequencing appropriately. But what about limitations?

Dig deeper: How to prioritize technical SEO fixes by business impact

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Build the SEO roadmap around realistic implementation

Roadmaps only work if you’re honest about what you can do.

Programmatic SEO (pSEO) is a clean example of an initiative that could slow your progress. This isn’t about spammy implementation, as some websites have produced thousands of thin pages and been hit by spam updates. It’s about building rich, unique content into database pages with structure and value for users.

Think of a large database-driven directory, for example. These initiatives can look fantastic in a strategy deck, but querying a database to spin up well-done pages with widgets across each will typically require engineering time. Meanwhile, engineering has its own roadmap and backlog that doesn’t focus on organic traffic.

The same logic applies to factors such as CMS limitations and other technical considerations. Your SEO initiatives aren’t just competing with each other on a SCOPE matrix and sequencing. They’re competing with product and dev roadmaps.

SEOFomo’s 2026 survey found that implementation bottlenecks and development constraints were the most commonly reported reasons SEO projects didn’t meet their expectations. This includes dev backlogs, limited engineering capacity, and complex architecture.

Although it can be underwhelming to accept and label initiatives as just not practical to get completed, it’s important to set realistic expectations so your roadmap remains aligned with what you can actually deliver.

Any advances in AI tooling may lower the barrier to implementation, but they won’t eliminate dependencies overnight. Complex architecture, governance, and deployment to production, especially in regulated industries, will probably still require coordination beyond the SEO team.

Dig deeper: ‘Fix everything’ is the wrong SEO strategy

Defining measurement before implementation

Measurement is table stakes and baked into defining the potential impact of initiatives on your SEO roadmap. Revenue is usually the strongest outcome.

But SEO isn’t always cleanly attributable to revenue, as some initiatives support things like brand visibility, paid acquisition efficiency in multi-touch buyer journeys, or lifecycle enablement. Or, generally speaking, SEO can reduce blended customer acquisition costs.

And the initiatives that support that contribution, although not direct, may still deserve to be on the roadmap because indirect value is still value.

Whether it’s via blended CAC efficiency demonstrated through holdouts, direct revenue tied to first-touch attribution, or a mix, there needs to be an honest plan to measure it.

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Treat the roadmap like a real operating plan

SEO roadmaps that work typically qualify their initiatives in comparison to others, are considerate of cross-functional dependencies, sequence based on capacity and expected timelines, are honest about what’s measurable, and are revisited on a regular cadence.

Once there’s early traction and a clear business impact, that’s leverage to go back and ask for increased capacity for larger bets.

In the meantime, your roadmap should be built like someone is going to ask you to defend it. Do that well enough, and no one has to.

Dig deeper: How to build a 120-minute weekly SEO workflow that gets results

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The SEO Update by Yoast – September 2026

August 25, 2026/0 Comments/in Web Design News, Yoast SEO/by Dubado Solutions

The SEO Update by Yoast – September 2026

Will take place on
5 October 2026

4 PM CEST / 10 AM EDT

Don’t want to miss it?

Hosts

Alex Moss

Principal SEO

Carolyn Shelby

Principal SEO

Is your 2026 SEO strategy actually ready for the next wave of AI search updates?

Between AI-driven search overhauls and constant algorithm tweaks, keeping your site visible can be challenging.

The SEO Update by Yoast brings you the latest insights on algorithm updates, AI-driven search changes, and industry developments, all in one easy-to-follow session.

Join Carolyn Shelby and Alex Moss as they discuss the stories shaping SEO today and share actionable takeaways you can apply right away.