Channels outside of Google and Meta have struggled to get recent headlines in the paid advertising world. But my agency’s performance marketing team has seen one under-the-radar channel — Reddit — introduce a ton of new features and ad types in 2026, making it more viable for our clients.
Between new ad types and AI-powered functions, powerful integrations for shopping, video ad enhancements from late in 2025, and Reddit’s AEO and SEO clout making marketers more aware of the need to engage on the platform, we’ve got more clients active on, or curious about, Reddit than I’ve ever seen.
That said, Reddit’s ad platform — though much further along than it was a couple of years ago — still needs more features and improvements to existing ones to draw a bigger share of advertising budgets.
My Reddit targeting and audience wishlist
A huge part of Reddit’s influence comes from the extremely candid and opinionated content its users provide — and users are enabled to be exactly that, in large part, because accounts use usernames rather than real names.
Redditors can be as anonymous as they want, and I don’t wish to change that. Instead, I’d like Reddit to offer the following.
1. Expand reach without sacrificing privacy
Better identity targeting for anonymous or logged-out users in a privacy-safe way would expand our targetable reach, which is a big deal given Reddit’s niche status compared to Google and Meta.
See exactly how your competitors win.
Uncover the keywords, ads, landing pages, and strategies driving your competitors’ paid search success—and find your next opportunity to outperform them.
Analyze your competitors
2. Bring B2B intent to Reddit
B2B and firmographic targeting that includes:
Job title, function, and seniority.
Industry and company size.
Named-company and account-list targeting.
Tech usage and/or professional-interest segments.
Decision-maker and buying-committee audiences.
Reddit offers communities, interests, keywords, customer lists, and lookalikes. Adding privacy-safe firmographic data would make the platform more competitive with LinkedIn while preserving Reddit’s differentiated behavioral signals.
3. Make purchase intent easier to target
Reddit should turn its strongest advantage — people actively researching and discussing decisions — into understandable, scalable “in-market” and journey-stage audiences, including:
Recently researched a category.
Asked a question about a product or problem.
Engaged with comparison or recommendation threads.
Evaluated alternatives.
Repeated recent engagement.
4. Make existing audience signals more reliable
As for what we can do on Reddit with the reach we have, I’d like to request stronger retargeting depth, lookalike audiences, CRM integrations, and cross-subreddit behavioral signals. Reddit introduced all those targeting options, but they’re still patchy and unreliable, further eroding scale and performance.
5. Give advertisers more control over who they reach
Reddit advertisers need greater confidence that their strategies are hitting the right people. Adding exclusions would help ensure we can eliminate some irrelevant eyeballs, so I’d like to ask for:
Negative keyword and community exclusions.
Current-customer and existing-lead suppression.
Clear control over AND versus OR targeting logic.
6. Make audience planning more predictable
Last in the audience and targeting category, advertisers need better audience planning and forecasting. Before spending budget, they need to see:
Realistic audience sizes (Reddit currently has ranges that can seem wildly unrealistic).
Demographic and device composition.
Top associated communities and topics.
Estimated CPM, conversion volume, and audience saturation based on budget.
With the releases of Brand Lift, Conversion Lift, and CAPI, Reddit measurement took a huge step forward in 2023.
Today, Reddit’s role in the user research and consideration phase is increasingly important as AI search minimizes clicks and pushes performance marketers to focus further up the funnel.
That means Reddit’s value is increasingly measured through analyses like MMM and MTA rather than through a persistent reliance on last-click attribution.
All of that is to say that it’s easier to measure Reddit ads’ impact than it was a few years ago. Yet, indicative of a platform that’s still more of a challenger than a powerhouse, it’s falling short on some basics.
There’s one specific (and persistent) gap that should be table stakes for a serious ad platform: more third-party integrations for dynamic UTM tracking. Beyond that, I’d love to see Reddit introduce the following.
7. Prove incremental value without leaving the platform
Reddit is generally considered a test channel, so self-serve incrementality testing — which larger competitors offer — would help demonstrate the incremental value it drives. Specifically, I’d like to see Reddit provide:
Conversion-lift studies.
Randomized audience holdouts.
Geo experiments.
Self-serve brand-lift studies.
Incremental reach and frequency measurement.
In aggregate, these tests would help Reddit advertisers defend upper- and mid-funnel investment when last-click reporting underrepresents its influence.
8. Connect ad delivery to revenue outcomes
Reddit has Pixel, CAPI, and omnichannel attribution capabilities. The larger opportunity is to make it easier to pull in offline data from CRMs and other sources, so advertisers can lean into down-funnel and revenue-based optimization.
Native HubSpot, Salesforce, and other CRM integrations would be particularly valuable. Advertisers need to tell Reddit which leads became valuable customers, then allow its delivery system to find more people with similar signals.
My Reddit ad formats and creatives wishlist
More than on most platforms, it’s critical to make sure your ads appear as native as possible to their context. Redditors turn up their noses at anything inauthentic.
In 2024, Reddit made some updates to Conversation ads that gave advertisers a useful option in a native setting, but there are many more native formats they could introduce. I’ll list some of my recommendations below.
9. Give advertisers more ways to fit into the conversation
Polls, product carousels, and Q&A ad units designed to fit within the Reddit environment would all be good formats for advertisers to test.
10. Bring short-form video up to speed
Another area of opportunity for Reddit is more competitive short-form video. Yes, they do have short-form video, but introducing vertical formats and optimizing for autoplay would make that format a lot more viable for advertisers (this is kind of in the “table stakes” category).
11. Put ads where high-intent Reddit research happens
Reddit Answers was integrated into the broader Reddit search experience in May 2026. Reddit Answers ads in high-intent searches and recommendation questions, with clear sponsorship labeling and strict relevance requirements, would give advertisers another way to reach users close to the purchase decision.
12. Let advertisers learn from past campaigns
The last big item on my creative list is an ad library that expands to show historical ads, much like Meta’s, where you can see both paused and active ads. That kind of archival reference would be super helpful to have on the back end.
Every click they win is a customer you lose.
See where competitors are investing, which keywords drive their results, and how to capture more of the market.
See who’s stealing your traffic
Reddit could earn more of the ad budget
If it’s not obvious, Reddit has taken some significant strides forward over the last few years, to the point where it can be surprising when it hasn’t checked some of the more basic boxes.
These requests shouldn’t be considered pipe dreams, either. The above list was compiled from situations where my team and I have butted up against limits when running real Reddit campaigns.
Given the way AI search engines have eaten into clicks and made awareness more of a paid media play, and given Google’s heavy-handed move away from control and toward mass automation, we’re very willing to divert budget to Reddit if we can make a solid business case for it.
Reddit’s unique community and research signals would be powerful if the platform figured out how to turn them into targetable audiences using ad formats that enable real conversations throughout the buying journey.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/08/12-things-Reddit-needs-to-make-its-ad-platform-more-competitive-5Nag4x.png?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-08-11 15:00:002026-08-11 15:00:0012 things Reddit needs to make its ad platform more competitive
Franchise SEO is the practice of optimizing your online presence so each franchise location ranks higher in search engine results and AI Overviews.
SEO for franchises requires balancing brand consistency across locations with the need for each branch to stand out independently in its local market.
Core tactics include targeting local keywords, creating unique location pages, setting up a Google Business Profile for every branch, and maintaining accurate name, address, phone number (NAP) citations.
AI Overviews now influence a majority of local searches, and thin or duplicate location pages tend to get filtered out. That means each page needs unique content and visible experience, expertise, authoritativeness, and trustworthiness (E-E-A-T) signals.
SEO is a long-term play. Pair it with a solid technical audit and a franchise pay-per-click (PPC) strategy to cover ground that organic search alone can’t quickly reach.
Operating a franchise is a fantastic way to start a business.
Not only are people already aware of your brand, but you get the support and backing of a well-established organization and a pre-existing customer base ready to buy.
However, consistent effort is essential for long-term success.
Franchise SEO is essential for ranking in search engine results and AI Overviews, increasing your reach to new customers. About 60 percent of searches result in zero clicks due to the growing appearance of AI Overviews. Franchisees need to understand both traditional and modern SEO tactics to truly expand their audience.
In this article, I’ll explain SEO for franchise businesses, how it differs from standard SEO, and up-to-date strategies to help your franchise stand out.
What Is Franchise SEO?
Franchise search engine optimization (SEO) is when you optimize your online presence to make your franchise business stand out in search engines like Google, Bing, and Yahoo, as well as in AI search tools like ChatGPT, Google’s AI Overviews, and Perplexity.
Key franchise SEO tips include:
Optimizing your website for local keywords
Creating website content to appeal to customers and localizing the content when relevant
Getting backlinks from relevant local websites
Encouraging customers to submit reviews of your business
Ensuring your website loads quickly and is mobile responsive (52.3 percent of global website traffic comes from mobile devices)
With franchises, there’s a lot of competition. You’re not only up against other brands but potentially competing branches in the same franchise.
Given that almost three-quarters of people use Google to find local business information, the higher you can rank in the search engine results, the better.
Whether you run one franchise or 20, implementing effective SEO for your franchise website means more organic traffic. That, in turn, leads to more store visits, sales, and profits.
Franchise SEO vs. Regular SEO: What’s the Difference?
Standard SEO focuses on keywords and content for a single website or a company as a whole.
Local SEO for franchises is a specialized form of SEO that focuses on optimizing each franchise location. Local SEO is typically a large part of this strategy. That includes making sure that each site contains the correct information, such as address and contact details, and is formatted correctly.
SEO for franchise websites can be tricky, as you need to optimize each location independently.
If you manage multiple franchise locations, you also need to consider your website’s structure and how all your location pages work together. After all, you don’t want to promote one location at the expense of another.
With franchise SEO, people tend to search for more niche, location-specific keywords. You need to consider their search intent to optimize your website properly.
Challenges With Franchise SEO
While effective franchise SEO is rewarding, it presents unique challenges.
Here’s what to watch for:
The risk of duplicate content. Franchise locations often use similar content across their websites, which can lead to duplicate content issues in the search results. It’s essential to keep duplicate content to a minimum.
Building local content. With SEO for your franchise, you need to balance standard SEO with local SEO. You have to make each location stand out for its own area while keeping every site within brand guidelines.
Brand inconsistency. Franchises need to have a cohesive look and feel. While it’s important for your stores to rank as high as possible, they also need to share a consistent brand identity.
AI Overview visibility. With AI Overviews appearing for 57 percent of local searches. To be cited in these results, each location needs unique, credible content rather than boilerplate copied across sites.
Use These SEO Strategies for Your Franchise
Now that we understand the differences between standard and franchise SEO, let’s look deeper into some strategies to help your franchise succeed.
1. Identify the Most Important Keywords Related to Your Business
The first step is to identify and research the keywords most relevant to your franchise.
Consider:
Your target audience. What keywords are they most likely to use?
Your location. You want to target local keywords and terms.
Your competitors. What keywords do they rank for?
Search intent. Are customers looking for information, or are they ready to buy?
Plenty of tools can help you find and research the right keywords for your franchise.
Ubersuggest, for instance, shows you what’s already working for others in your market, so you can borrow those ideas and refine them.
In the example below, the term “best mattresses near me” has an SEO difficulty of 55. This tells you that ranking highly is doable, but landing the top spot will take some work.
However, “best mattress stores near me” has an SEO difficulty of 75, meaning there’s much more competition and you’ll have a harder time clinching the top spot.
While it’s designed for use with Google Ads, it can suggest a wealth of relevant keywords based on a seed phrase or your website URL.
2. Localize Your Keywords
Localizing your keywords makes sure you target the right customers in the right areas.
If you have multiple locations, it is especially important to localize your keywords to reach customers in each area.
Let’s say you run a car maintenance franchise in Broward County, Florida. Strong localized keywords might include:
“Car repairs in Fort Lauderdale”
“Car servicing in Pembroke Pines”
“Car mechanic in Hollywood”
Hyperlocal SEO takes it a step further, targeting specific geographic areas, like neighborhoods or streets, to maximize visibility. For example: “Car mechanic on Ocean Drive.”
3. Localize Your Website
Localizing your website is a great way to optimize for local SEO and promote your individual franchise locations.
Create dedicated pages for each franchise location with a clear address, phone number, and other relevant information. You’ll also need to include unique content on each location page, as pages with thin content don’t compete in AI Overviews or Local Packs.
Creating a proper local page for each location helps you rank for local keywords and increases your NAP (name, address, phone number) citations, which I’ll discuss later.
It also helps to include a map of the area with each franchise’s location marked. This makes your website more search-engine-friendly and helps customers find the nearest location.
Franchises often use multiple domains. The main corporate domain serves as the central hub for the franchise brand, providing high-level information, while individual franchise locations use separate domains to target local audiences more effectively. In this case, linking all the domains together will help search engines understand that they are related.
4. Optimize Your Individual Businesses on Google Business Profile and Other Directories
One of the best ways to enhance your franchise’s search engine presence is to set up Google Business Profiles (GBPs) for each location.
A GBP provides additional context about your franchise locations, including your address, phone number, hours, and products or services you provide.
Having a Google Business Profile also increases the chance of your franchise appearing in the Local Pack map at the top of the search results.
Additionally, regularly publishing posts like offers, events, and updates, and actively managing the Q&A section, signals to Google that the profile is active and maintained. That can improve AI Overview and Local Pack eligibility.
Try to complete as many fields as possible and keep everything current, so searchers always get the most accurate information about your business.
Beyond Google, signing up for other directories can widen your franchise’s visibility, build additional NAP citations, and generate backlinks to your website. Just be sure to choose directories that are relevant and authoritative. Low-quality, spammy ones can hurt your online presence.
Sites like Yelp, Foursquare, and Yellow Pages (YP) are good starting points. I’d also suggest checking with your local chamber of commerce, which can often point you to directories worth listing on.
5. Make Sure Your Brand Is Consistent
McDonald’s is one of the largest franchised businesses in the world. It has a massive marketing “bible” that helps enforce brand consistency, telling franchisees everything from what colors to use to which words to avoid.
As a result, McDonald’s has remarkable brand uniformity, and customers can expect the same experience wherever they are in the world.
You don’t need a guide as extensive as McDonald’s, but you do need a cohesive look and brand voice. This helps customers trust you, and it helps search engines and AI systems recognize you as a legitimate business.
That means keeping your brand messaging, logo, visuals, and fonts consistent across all platforms, including your website, Google Business Profile, and social media accounts.
The easiest way to manage this is with a style guide and regular audits to keep all your digital marketing assets aligned.
6. Audit Your Technical SEO
Your franchise’s content can’t rank if search engines can’t properly access and understand your site. That’s where technical SEO comes in.
Here’s where to focus:
Mobile-friendliness: Google indexes mobile-first, so your franchise site needs to look and function well on smaller screens.
Page speed: Slow-loading pages frustrate users and can hurt rankings. Use PageSpeed Insights to help pinpoint the culprits, whether that’s oversized images or a slow server response.
Crawlability: Make sure search engines can easily navigate your site by maintaining a clean sitemap, fixing broken links, and reviewing your robots.txt file.
Once you’ve reviewed those areas, run a site crawl using a tool like Screaming Frog or Semrush to uncover hidden issues with duplicate content or missing meta tags.
For larger franchise operations managing hundreds of locations, our guide to enterprise SEO digs deeper into scaling these technical fixes.
7. Target and Remove Duplicate Content
When you have a website with multiple location pages that are very similar, you risk confusing the search engines.
If a search engine sees several pages with duplicate content, it has to decide which one to rank. It uses several factors to work this out, but there is a chance it gets the answer wrong.
Fortunately, you can see which pages are being indexed and which aren’t by using Google Search Console’s “Crawled – currently not indexed” filter. From there, you can take steps to make sure search engines aren’t showing the wrong location page to the wrong person.
Let’s say you operate a chain of restaurants and have the same menu at each. Rather than having an identical menu on each location page, use a stand-alone menu page and link to it instead.
You can also steer crawlbots toward what you want them to see and guide them away from pages you don’t. Implementing a “rel=canonical” tag makes sure you can consolidate rankings to the intended URL.
8. Have a Local Link-Building Strategy
Link building is the practice of getting other websites to link back to your site.
When you get links from relevant, high-quality sites, it shows search engines that your website is trustworthy and reliable, leading to a boost in search results.
Link building is one of the trickiest aspects of franchise SEO because the results are beyond your control. There’s no guarantee that other websites will link to you. Plus, you need backlinks for all your locations to optimize your search engine presence. However, get your strategy right, and it’s worth the effort.
Here are some ways you can get links for your franchises:
Befriend local journalists, and they’ll give you a heads-up when they need a quote for a story.
Partner with related local businesses. For example, if you run a coffee shop, join forces with a bakery.
Hold special events, sponsor local sports teams, or run a competition to get links in the local press.
Use tools like Ubersuggest to identify the backlinks your competitors have. You can then approach the same websites to see if they’ll link to your franchise as well.
9. Ensure NAP Consistency
A NAP (or NAP citation) is a mention of your franchise’s name, address, and phone number on a website.
This can be your own website, a directory, a social media profile, or another company’s site.
NAP citations are significant to local SEO success. They show the search engines that your business is active and legitimate.
It’s important to make sure your franchise name, address, and phone number are accurate and consistent across all platforms to maximize visibility. Accurate citations across the internet increase your authority. Similarly, accurate information helps potential customers find you more easily.
10. Optimize E-E-A-T on Local Pages
E-E-A-T stands for “experience, expertise, authoritativeness, and trustworthiness.”
Google wants to prioritize the most credible and reliable websites in the search engine results. It uses the E-E-A-T framework in its Search Quality Rater Guidelines to determine which content to promote.
E-E-A-T is especially important on your money or your life (YMYL) content. These are websites with subject matter that could affect someone’s future happiness, health, financial stability, or safety.
To optimize E-E-A-T on your franchise’s local pages, include a variety of content, such as customer reviews, product descriptions, contact information, FAQs, and informational articles.
Doing this will help build trust and credibility with search engines and customers. It can also help with AI visibility since Google’s AI Overviews strongly favor pages with visible E-E-A-T signals.
Another good way to enhance your franchise SEO is to showcase your credentials. For example, if you run a daycare franchise, telling customers about the qualifications and certifications of your employees can help build your credibility.
It encourages customers to stay on your website and convert while attracting backlinks and helping you rank.
The great thing about content marketing is how many forms it takes. Whatever you sell, wherever your franchise operates, and whatever your budget, you can create content that prospective customers want to engage with.
The blog above is from the Molly Maid website. It focuses on cleaning tips readers can use, so even if you end up not using their services, the content still has value.
These content types work especially well for franchise SEO, as you can focus on both local and national topics and trends:
Blog posts
Podcasts
Video content
Infographics
Social media posts
To get started with content marketing, get clear on your target market and their pain points. Then address those pain points with helpful, well-structured content. Each piece should follow an intuitive format of headings, subheadings, and short paragraphs that make the reading experience easy and informative.
From there, work in your keywords naturally, and you’ll have SEO-optimized content that answers your audience’s questions and encourages them to use your product or services.
12. Encourage Local and Online Content Reviews
More than 9 in 10 (93 percent) of customers say they read customer reviews before purchasing a product. The more positive reviews you can generate, the more likely prospective customers are to buy from you.
Having lots of reviews builds trust and entices customers to use your products or services, all while supporting local SEO. Reviews posted on platforms like Google Business Profile can include keywords customers naturally use, which helps improve your relevance in local search results.
While these keywords may not directly boost your website’s rankings, they can enhance visibility on the review platform itself, making it easier for potential customers to find your business. Additionally, positive reviews serve as powerful social proof, showing future customers that others trust and value your business, which can drive more conversions.
The easiest way to get reviews is just to ask. Send an email to customers after they use your services and ask them to leave a review, whether on your Google Business Profile, your Facebook page, or a review platform like Tripadvisor.
Be sure to respond to all reviews, whether they’re positive or negative.
According to BrightLocal, 80 percent of consumers say they’re likely to use a business that takes the time to respond to all its reviews.
13. Consider Franchise PPC to Complement Your SEO
SEO is a long game. It builds durable, compounding visibility, but it can take months for a new franchise location to start pulling meaningful organic traffic. That’s where paid advertising comes in.
Using franchise pay-per-click (PPC) tactics can put your franchise at the top of search results right away, which is especially useful when opening a new branch or trying to stay visible during high-competition seasons.
A few things to keep in mind:
Geographic targeting matters as much as it does for SEO. Structure your campaigns by location so individual branches aren’t bidding against each other and driving up costs.
Protect your branded terms. Searches like “[Franchise Name] near me” signal high intent, and competitors can bid on your brand name even when your organic rankings are solid. Running paid ads on those keywords keeps that traffic where it belongs.
Franchise SEO Tools
Running franchise SEO without the right tools gets tough fast, especially as you add locations to your portfolio. These will help keep your campaigns effective, whether you manage one location or 100:
Ubersuggest: This is my go-to for keyword research, competitor backlink analysis, and surfacing the local search terms your franchise should be ranking for. It’s especially handy when you need to compare how individual locations stack up against nearby competitors.
Google Business Profile Manager: If you’re managing more than a handful of GBP listings, the bulk location management features save serious time. You can update hours, services, and descriptions across multiple branches in one go.
Screaming Frog or Ahrefs Site Audit: Both crawl your site at scale and flag duplicate content, canonical tag issues, and thin location pages. This is critical when you have dozens (or hundreds) of near-identical templated pages.
BrightLocal or Whitespark: These are built specifically for local SEO. Use them to audit NAP citations and track rankings location by location.
Google Search Console: It’s free, and it’s still one of the most powerful tools out there. Monitor indexation, Core Web Vitals, and per-location performance straight from the source.
FAQs
What is franchise SEO?
SEO for franchises is the practice of optimizing your website (or websites) so you rank as high as possible in the search engine results for all locations you operate in.
How do start building an SEO program for a franchise?
Focus on three things: a strong corporate site, optimized local landing pages for each location, and a fully built-out Google Business Profile for every branch. Consistent NAP citations and local reviews do the heavy lifting from there.
Should franchises have separate URLs for local SEO?
Yes. Each location should live on its own URL, typically as a subfolder (yoursite.com/chicago). This lets you target city-specific keywords, build local backlinks, and rank in the right geographic SERPs without competing against your other branches.
How do you jumpstart local SEO for new franchise locations?
Claim the GBP listings, publish the location pages, and pursue local citations and reviews immediately. Prioritize getting your first 10 to 15 reviews within the opening month, since fresh listings with active engagement tend to climb the Local Pack faster.
How do local events and sponsorships boost franchise SEO?
They earn local backlinks and brand mentions from community sites. Sponsoring a youth sports team or charity 5K race often gets your franchise listed on organizer pages, news outlets, and event recaps, which are exactly the kinds of locally relevant signals Google rewards.
Plus, the best part is that it works whether you sell sandwiches, smoothies, or software solutions.
Remember that when it comes to SEO for franchises, you’ve got to be consistent.
Make sure your information appears the same across all platforms and that each of your location pages uses the same branding and tone of voice. This also means optimizing your content and online presence for AI Overviews, as they’re quickly becoming the new frontier for local visibility.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-08-10 19:00:002026-08-10 19:00:00SEO Tips and Strategies for Franchises: A Guide for 2026
SaaS SEO requires a strategy built for long sales cycles and global competition.
Map your content to every stage of the buyer journey, from awareness-stage blog posts to decision-stage case studies and comparison pages.
AI search has reshaped how buyers research software, with 51 percent of B2B software buyers now starting with an AI chatbot more often than Google. That means optimizing as much for AI citations as search rankings.
Build authority through technical SEO, high-quality content, digital PR, and a strong presence on third-party review sites like G2 and Capterra.
There are few better traffic sources for software-as-a-service (SaaS) companies than organic search.
Recent data shows that B2B SaaS companies see a 702 percent return on investment (ROI) from their SEO efforts. That’s a huge number, and it makes a ton of sense.
With the right SaaS SEO campaign, you can acquire users for free and at scale. Best of all, many of those users arrive intending to make a purchase.
So, how do you get your website to dominate the search engine results pages (SERPs)? And how do you keep those visitors on your site and convert them to customers?
SEO for SaaS companies has to account for the unique features of the industry. SaaS brands have a very different target market and customer journey than, say, a local business or a law firm. Your SaaS SEO strategy needs to reflect this.
In this SaaS SEO guide, I’ll show you how to build a strategy that checks all the right boxes. We’ll cover the benefits of generating organic traffic for SaaS businesses and walk through actionable steps you can use to improve your SEO positioning today.
What Is SaaS SEO?
SaaS SEO is search engine optimization that helps software-as-a-service brands rank higher in Google. It covers the activities that drive search engine traffic, including:
Keyword research
Content creation
On-page SEO
Link building
The difference is how you approach each one.
Google’s ranking factors are the same across industries, but SaaS brings its own opportunities and challenges.
The SaaS Industry and What Makes It Unique for SEO
The SaaS industry has its own quirks, and you’ll want to keep them in mind when building your SEO strategy.
For one, competition is fierce. Plenty of businesses offer similar services, and some are working with much larger marketing budgets. Comparison sites like Capterra and G2 pile on, too. Their domain authority is strong enough that they regularly outrank individual SaaS brands for product-related queries.
Oh, and your competition is worldwide, not local, which means global SEO considerations come into play from the start.
AI search has reshaped how SaaS buyers research and discover tools. ChatGPT, Perplexity, and Google’s AI Overviews now answer many top-of-funnel questions directly in the results.
That means fewer informational clicks reach your site. According to GrowthSRC, Google’s average click-through rate (CTR) for the No. 1 ranking position dropped 32 percent year-over-year, falling from 28 percent in 2024 to 19 percent in 2025 after the AI Overviews rollout.
For SaaS marketers, the goal is now to rank in traditional search results and be cited in the AI answers that show up in front of your buyers.
The SaaS Buyer Journey
The SaaS customer journey often involves weeks or months of research and decision-making.
That means your focus should be on long-term SEO strategies, such as content marketing and technical optimization. A “quick fix” approach won’t be as effective.
You’ll also need content for every stage of the journey. It’s not enough to create top-of-the-funnel content to attract people and hope they convert. You need product-focused content to show readers why they should choose your tool over a competitor’s.
You probably won’t get a conversion the first time someone visits your site, as the image below from Salespanel shows. Your SEO plan needs to build sustained authority in your space over time. That’s how you stay visible in search results and keep attracting potential customers to your site.
The Salespanel graphic maps the full lifecycle (awareness, consideration, acquisition, service, and loyalty). For SEO planning, though, marketers will more likely work from a simpler model focused on where content does the heavy lifting.
HubSpot’s three-stage framework is a good starting point:
Awareness: Educational blog posts and guides that answer the problem your tool solves.
Consideration: Comparison pages, alternative roundups, and integration content that helps buyers shortlist you.
Decision: Case studies, ROI calculators, and product-led landing pages that close the gap between interest and signup.
It’s not just new customers you have to think about, though.
Because SaaS brands use a subscription-based business model, you need to be constantly thinking about how to retain customers. Your B2B SaaS SEO strategy will need to reflect this by creating content that simultaneously attracts new users, educates existing customers, and showcases your authority.
Benefits of SEO for SaaS Businesses
Done well, SEO supports SaaS marketing efforts. It puts you in front of potential customers who are actively seeking what you sell.
The global SaaS market reached $315.7 billion in 2025, is projected to hit $375.6 billion in 2026, and could reach nearly $1.5 trillion by 2034. Capturing even a sliver of that growth means investing in SEO.
If you’re not sold yet, here are more benefits of SEO for SaaS brands:
Increased brand visibility and credibility. The more your content appears in the search, the more reputable your brand will become. This can be a deciding factor when potential customers are choosing between you and a competitor.
Scalable traffic. There is a huge compounding factor with SEO. The more content you create and the more links you build, the more your month-on-month traffic will grow.
Lower-cost acquisition. Putting more effort into your SEO reduces your dependence on paid marketing. As is the case in an industry with high ad costs, anything to alleviate some of that spend will be helpful for SaaS companies.
Increased product awareness. The more your product shows up in search engines, the easier it will be for your sales team to talk about your product because customers will have probably seen it before.
Helps to convert prospects from other channels. The SEO content you create is multipurpose and can be used by sales teams to convert prospects from other marketing channels, such as social media and pay-per-click (PPC) ads.
Improve your user experience. Many SEO tasks also improve user experience, such as optimizing for speed, navigation, and readability. This helps ensure that potential customers stay on your website longer and are more likely to convert.
Stronger brand health. Organic search data can flag early signs of shifting brand trust or product satisfaction, giving SaaS teams a real-time read on how the market perceives them and a chance to course-correct before issues show up in churn or pipeline.
Regular SEO work also helps you hold on to the rankings you’ve earned. Search engines constantly update their algorithms, so refreshing content and earning new links will keep you ahead of the game.
More and more buyers are searching online for products, too. More than two-thirds (67 percent) of B2B buyers now prefer a rep-free buying experience, and 45 percent say they used AI during a recent purchase. That means you need to appear in search engines and AI Overviews if you want your product on your audience’s shortlist.
SEO also plays well with the B2B industry’s long sales cycles. A single paid ad isn’t going to be enough to keep your prospects on the hook. You need a regular stream of content that showcases your authority and keeps your prospects in your funnel if you want to succeed. Only SEO can achieve that.
Best Practices for Improving Your SaaS SEO Strategy
SEO for SaaS companies takes more than publishing a few blogs and hoping for the best. It succeeds when keyword research, on-page optimization, technical SEO, and off-page authority all work together.
Here’s how to approach each one.
Best Practices for SaaS Keyword Research
Before optimizing your website for SEO, it’s important to do keyword research to identify the terms and phrases potential customers use when searching for your SaaS tool.
Start by creating a list of relevant keywords related to your business and the products or services that you offer. This can include words and phrases related to your software’s features and general topics in your industry.
You’ll also want to choose keywords that map to each phase of the buyer journey. That’s even more important now that AI search favors content that closely matches user intent.
Think beyond the keyword itself. Ask what the searcher is trying to accomplish, then create content that answers that need. The better your content matches intent, the more likely it is to rank in search results and appear in AI-generated answers.
Once you have a shortlist of keywords, use a tool like Ubersuggest to compare their search volume and competition. That will help you prioritize the opportunities with the greatest potential.
For example, searching for “customer data management platform” shows you how often people search for the term and which related keywords are worth exploring next.
Don’t worry if your initial keyword list is short. SaaS companies often operate in highly specialized, competitive markets where obvious keyword opportunities are limited.
That doesn’t mean the opportunity isn’t there.
First, don’t get hung up on search volume. A keyword that brings in even a dozen visits per month can result in a sale that delivers a return on your investment.
Second, focus on your product’s features and the problems they solve for customers. These kinds of queries may be hyper-specific, but they also tend to have strong purchase intent. You can also create vs. pages that compare your product with your competitors.
Again, these bottom-of-the-funnel keywords are great at driving high-intent traffic.
Source: https://www.rock.so/blog/asana-vs-monday
Third, look at what your competitors are ranking for. Use a tool like Ubersuggest to mine your competitors’ websites and find relevant keywords you can rank for, too.
The more you understand your competitors’ content strategies, the easier it will be to identify gaps you can fill with better, more useful content.
Strong keyword research lays the foundation for every other part of your SaaS SEO strategy.
Best Practices for On-Page SaaS SEO
On-page SEO involves optimizing the pages on your website so they’re easy for both search engines and people to understand.
To improve on-page SEO, start with the fundamentals. Write descriptive page titles and meta descriptions, organize your content with clear headings, optimize your images with descriptive alt text, and add internal links that guide readers to related resources. These elements help search engines understand your content while making it easier for visitors to navigate your site.
The more relevant information you provide on a single page, the better your chances of ranking in the SERPs.
Great on-page SEO, however, starts with the quality of your content. If your page doesn’t answer a searcher’s question better than what’s already ranking, no amount of optimization will make up the difference.
So, what separates great content from average content?
First, it fully addresses the reader’s problem. It goes beyond the basics and answers the follow-up questions they’re likely to have instead of forcing them back to the search results.
Second, it provides an exceptional user experience. It’s well-structured and easy to read, broken into sections with headings, lists, images, and other elements to help users scan and digest information quickly.
This blog post from ClickUp is a good example of clear structure and strong formatting.
Best Practices for Technical SaaS SEO
Technical SEO involves making changes to your website’s back end that are not visible to users but can still have a major impact on search engine visibility.
To optimize your technical SEO, start by optimizing site speed so your pages load quickly. Next, create an XML sitemap and submit it to search engines so they can easily crawl and index your pages.
Also, make sure to set up structured data, such as schema markup, to better understand the content on your page and help improve Google rankings. This helps search engines determine what each page is about, which in turn helps them return more relevant results for user queries.
It’s also important that your website is mobile-friendly and works properly on all devices. This will improve the user experience and enhance your chances of ranking in SERPs.
Best Practices for Off-Page SaaS SEO
Off-page SEO builds your website’s authority beyond your own domain. The biggest ranking signal is still high-quality backlinks from trusted, relevant websites that show search engines your content is worth recommending.
Below is an example of Twilio backlinking to SpotHero’s website.
One of the easiest ways to find link-building opportunities is to analyze your competitors’ backlink profiles. Tools like Ubersuggest can show you which websites already link to competing SaaS companies, giving you a shortlist of publications and blogs worth targeting.
Here’s an example using Twilio.com:
Note: If you click the orange “one link per domain” button, it will remove repeat domains and give you a much more manageable list.
You don’t have to rely on outreach alone, though. Original research, industry surveys, reports, free tools, and useful resources naturally attract links because people want to reference them. Building relationships with industry publications and influencers can amplify that effect even further.
The goal is to earn links from websites your audience already trusts. Those links strengthen your authority and drive qualified referral traffic at the same time.
Creating Your SaaS Strategy: Step By Step
Now that we’ve covered the core pillars of SaaS SEO, it’s time to put them into action. Use the framework below to build a content-based SEO strategy that attracts qualified traffic and supports long-term growth.
This process breaks down into eight steps:
1. Set Goals and KPIs
Start by defining measurable goals for your SaaS SEO strategy. SaaS companies typically focus on attracting prospective buyers, driving traffic to free demos and converting them into paid users, and educating and retaining current users.
Next, choose the key performance indicators (KPIs) you’ll use to measure success. Track metrics like organic search visibility, keyword rankings, website traffic, and lead generation rate. That way, you’ll know what’s working and where to adjust your strategy as you go.
2. Identify Your Target Audience
Next, determine your target audience. Are they engineers? Marketing operations leaders? Business owners? Knowing who you are targeting helps you create content tailored to their needs.
3. Identify Their Pain Points
Once you know your target audience, focus on their needs and the kind of content they want. Identify topics that are relevant to them and create content that offers value, solves their problems, and answers their questions.
4. Analyze Best-Fitting Keywords
Now it’s time to put your keyword research into action. Use the terms you’ve identified to build content that matches search intent and supports each stage of the buyer journey. Then optimize your titles, headings, body copy, and URLs so search engines can clearly understand what each page is about.
In the example below, Gartner targets the keyword “customer data management platform” consistently across the title, meta description, and URL, reinforcing the page’s relevance for that search.
5. Set Campaign Goals and Tracking Abilities
Now that you’re ready to start creating content, you’ll need to revisit goal-setting on the campaign level. Set measurable goals for each campaign to track performance. You should also establish a tracking system (Use my SEO templates if you’re not sure where to start) to measure the success of each piece of content you produce and make changes accordingly.
6. Produce Content
Create high-quality content that matches search intent and gives readers a reason to choose your page over the competition. Use clear headings, visuals, examples, and other formatting elements to make complex topics easier to scan and understand.
7. Distribute Content
Publishing is only the beginning. Promote your content through your website, email newsletters, social media, and other marketing channels to maximize its reach. Then monitor its performance and refine your approach based on what’s driving traffic and conversions.
8. Monitor Results and Optimize Based on Findings
Your job isn’t over once your content goes live. Regularly optimizing your content based on analytics data can significantly improve your organic search visibility and get closer to achieving your SaaS SEO goals.
Once a year is a good baseline timeframe for content review and refreshing, but ultimately, you should review your traffic and make adjustments based on what’s being read and what’s not.
Successful SaaS SEO Strategies
Looking for inspiration? Here are a few SaaS brands that have built strong organic visibility and the SEO strategies that helped them get there.
Adobe XD
To grow awareness of Adobe XD, its interface design and prototyping software, Adobe created a content-focused community targeting design professionals.
With the help of my agency, Adobe created an intent-based content strategy, backed by in-depth keyword, competitive, and content gap research. We also incorporated technical best practices, optimizing page speed, schema markup, and internal linking.
The results? A 648 percent increase in page one rankings and over 25,000 downloads in the first six months, with just under half of all traffic coming from search engines.
Canva
If you want an example of how to improve your SaaS SEO through backlinks, look no further than Canva.
The design platform has created hundreds of backlink magnets through its template pages. Covering everything from letterheads and invoices to coupons and invitations, these pages give users everything they need to create a great-looking design. And this means that websites provide a lot of value to their readers by linking to them.
As a result, Canva has acquired backlinks from highly authoritative domains such as The Next Web, Yahoo, and Thrillist.
But it doesn’t just wait for the links to roll in. Canva engages in personalized outreach to win those backlinks from target websites.
The result?
They have over 25 million backlinks from 280,000 domains and rank for over 5 million keywords, according to Ahrefs.
Smartlook
Product analytics and insights tool Smartlook used a product-focused keyword research strategy to break into the U.S. market and take on established goliaths like Hotjar.
They achieved this via several strategies.
The first was to improve existing landing pages that targeted competitor-style keywords like “Hotjar alternative.” While these pages were well-designed, they lacked sufficient depth of content to rank well on Google. Smartlook added much more information to these pages to better meet searcher intent and created new pages that comprehensively covered each topic.
Cisco has since acquired Smartlook and continues to operate as part of its portfolio, but the SEO strategy remains just as relevant today. Building comprehensive comparison pages around high-intent keywords is still an effective way for SaaS companies to capture buyers evaluating their options.
Verint
A clear example of what a strong SaaS SEO strategy can achieve is our work with Verint, a customer experience software company serving more than 9,800 brands across 175 countries.
As Verint prepared for a major website migration, including multiple blog and acquired business unit migrations, the challenge was preserving existing rankings while growing non-branded organic traffic in a highly competitive market.
We partnered with Verint’s product and marketing teams to build an SEO strategy around the migration. That included technical fixes for Core Web Vitals, mapping redirects, and content audits using our 6Rs framework to determine what content should be reformatted, repurposed, refreshed, retired, redirected, or simply remain as is.
The broader strategy also incorporated localization for global markets and a digital PR initiative to strengthen domain authority.
Source: https://www.verint.com/blog/
Within 30 days of launch, Verint achieved a:
210 percent increase in non-branded organic search clicks year over year
33 percent increase in keywords ranking in positions one through 10
32 percent increase in total organic search clicks year-over-year
The results show what’s possible with a coordinated SaaS SEO program.
SaaS and AI Search
AI search has become a fixture in the SaaS buyer journey. Prospects who used to start with a Google search are now asking ChatGPT to compare tools, prompting Perplexity for vendor shortlists or scanning Google’s AI Overviews before clicking anywhere.
According to G2’s 2026 AI Search Insight Report, 51 percent of B2B software buyers now start their software research with an AI chatbot more often than with Google, and 71 percent rely on AI chatbots somewhere in the research process.
For SaaS marketers, this changes what visibility means. Ranking on page one still matters, but getting cited inside an AI answer matters, too.
If a buyer asks ChatGPT for the best project management tool for remote teams and your product isn’t mentioned, you’ve lost the discovery moment entirely. The same G2 research found that 69 percent of buyers reported AI chatbots surfaced information that led them to choose a different vendor than expected.
To keep your audience engaged, your SaaS SEO strategy should now account for:
Clear, well-structured content that LLMs can parse and pull from with confidence.
Strong E-E-A-T signals like author bylines, original data, and expert quotes that build trust with both search engines and AI tools.
Presence on trusted third-party sites such as G2, Capterra, and respected industry publications. A recent SE Ranking study found that sites with profiles on review platforms like G2, Capterra, and Trustpilot were three times more likely to be cited by ChatGPT.
Branded content that defines your category so your name surfaces when buyers ask about the problem you solve.
FAQs
What is SaaS SEO?
SaaS SEO is the practice of optimizing a software-as-a-service company’s website to rank higher in search engines for keywords that matter to its buyers. It accounts for the industry’s specific nuances, such as long sales cycles and a subscription-based revenue model.
How important is SEO in a SaaS business?
SEO is one of the highest-leverage growth channels available to SaaS companies. It compounds over time, lowers your dependence on paid acquisition, and meets buyers where they’re already researching solutions. With B2B’s stretched-out sales cycles, a steady stream of search-visible content can keep prospects engaged from first touch to close.
How SaaS companies improve their SEO?
Start with a technical audit to fix page speed and indexing issues. Then build topic clusters that map to each stage of the buyer journey, target bottom-funnel terms like comparisons and alternatives, and earn authority through digital PR and third-party reviews on G2 and Capterra.
What’s more important, a SaaS content strategy or SEO strategy?
They aren’t competing approaches. Your content strategy defines what you publish and why, and your SEO strategy ensures that content gets found. The strongest SaaS programs treat them as a single integrated effort, where keyword research informs content topics, and content quality drives rankings and conversions.
How do I combine SEO and PPC for SaaS lead generation?
Use PPC to capture high-intent commercial queries while SEO builds long-term authority on informational and comparison terms. Share keyword data between teams to identify which paid terms convert well enough to warrant organic investment, and use retargeting to re-engage organic visitors who didn’t convert on the first visit.
Conclusion
SaaS SEO is one of the most effective ways to attract qualified buyers throughout the customer journey. The stronger your strategy, the more opportunities you’ll have to build awareness and convert prospects into long-term customers.
While your industry may be more technical than others, following the advice in this SaaS SEO guide will help you achieve better organic search visibility and get closer to your goals.
At the same time, search itself is changing.
AI tools like ChatGPT and Claude are changing how buyers discover software, while Google’s AI Overviews are reshaping what visibility looks like on the SERP.
The SaaS brands that win going forward will be the ones building content for both traditional search and AI answers, while mapping every piece to a real stage in the buyer journey
LinkedIn announced algorithm changes on May 20, 2026, targeting low-quality AI-generated posts, comments, and automation tools.
Content flagged as AI slop will not be removed but will be suppressed beyond a user’s immediate network, limiting reach significantly.
LinkedIn’s detection systems claim 94 percent accuracy in identifying generic AI content, though false-positive data has not been disclosed.
Content creation on the platform is up 14 percent year over year, driven largely by AI-assisted creation.
AI-assisted content is still welcome if it contains original perspective, expertise, or meaningful contribution.
Brands that combine AI efficiency with authentic subject matter expertise will gain a competitive distribution advantage as the algorithm matures.
LinkedIn has a problem, and it is one the platform created for itself.
After years of integrating AI writing tools directly into its product, LinkedIn is now fighting to contain the flood of low-quality AI-generated content those tools helped produce. The result is a content algorithm that is actively suppressing posts it identifies as “AI slop”: polished-sounding, generic, and hollow.
For brands and marketers who have been using AI to scale their LinkedIn presence, this is a signal worth taking seriously.
What LinkedIn Is Actually Doing
LinkedIn VP of Product Laura Lorenzetti announced the changes in a May 20 blog post. Three types of content are in scope: generic AI-generated posts that lack original perspective, bot-generated and generic AI comments, and automation tools that create AI content at scale.
Posts flagged by LinkedIn’s detection systems will not be removed, but their distribution will be suppressed. They will remain visible to a user’s direct connections and followers, but the broader recommendation engine will not amplify them. In practice, this means the platform reach that makes LinkedIn valuable for brand awareness and thought leadership becomes inaccessible to content that fails the originality check.
LinkedIn built its detection capability using an “AI solving AI” approach. Human editors annotated thousands of posts as either generic or original, those examples trained machine learning models to identify patterns at scale, and the system now runs on the feed continuously. The 94 percent accuracy figure comes from LinkedIn’s own testing, which means false positive rates are unknown. Some legitimate content will likely be caught in the net.
The business logic behind the crackdown is straightforward. LinkedIn sells premium subscriptions and advertising on the promise of a high-value professional audience. A feed filled with content nobody wrote undermines that promise, reduces engagement, pushes premium members out, and eventually costs advertisers. Protecting content quality is protecting the revenue model.
What Gets Flagged and What Does Not
LinkedIn has been specific about what it is targeting. Posts that feel generic or repetitive, even if they appear polished on the surface. Comments that simply summarize the post they are replying to without adding anything. Content created through bulk automation tools. Posts that use construction patterns associated with AI assembly, including phrasing like “it’s not X, it’s Y.”
What is explicitly not being targeted is AI-assisted content that contains original thinking. LinkedIn has been careful to draw this line. If a writer uses AI to research, structure, or polish a post built around a real professional insight, that post is welcome. What is not welcome is a post where AI is doing all of the intellectual work.
The practical distinction is whether the content contains something that only the author or their organization could provide. A post built around a first-party data point, a client case study, an executive’s direct experience, or a genuinely specific industry observation has something AI cannot fabricate. A post built around generic claims about industry trends or leadership principles, however well-written, has nothing that differentiates it from the thousands of similar posts already in the feed.
Why This Changes the Distribution Math
For teams that have been using AI to increase posting frequency on LinkedIn, the suppression mechanism changes the math significantly. Volume without quality now actively works against reach. A suppressed post still consumes the posting slot without delivering the visibility that made posting worthwhile in the first place.
The brands and individuals who will gain distribution advantage as this algorithm matures are the ones that understand what LinkedIn’s algorithm actually measures. The system does not detect AI-written text directly. It detects whether anyone cared enough to stop scrolling. Near-zero dwell time, no saves, and no meaningful comments are the behavioral signals that reduce distribution. Content without a specific professional insight at its core produces exactly those outcomes.
What Content Actually Passes LinkedIn’s Test
The behavioral signal LinkedIn’s algorithm reads is whether people engaged with the content meaningfully: dwell time, saves, substantive comments, and shares. These behaviors correlate strongly with one thing: whether the post contains something specific that only the author or their organization could provide.
A post built around a generic observation about industry trends, however well-written, will produce near-zero dwell time because it says nothing the reader has not already seen. A post built around a specific client outcome, a first-party data point, or a direct professional experience produces engagement because it contains information that does not exist elsewhere in exactly that form.
LinkedIn’s detection system cannot read AI-generated text as such. What it can detect is whether the behavioral signals suggest anyone cared enough to read past the first two lines. That is the actual test. The brands and individuals who will outperform in this environment are the ones producing posts that earn saves and real comments because they contain something worth returning to.
The practical audit for any LinkedIn content program is simple: read the last ten posts and ask, for each one, what specific information does this contain that only our brand could provide? If the answer is “nothing,” the content is at suppression risk regardless of how it was produced.
What to Do Now
Continue using AI as a production tool, not a thinking tool. Research, drafting, editing, formatting, and refinement are all appropriate uses of AI in the LinkedIn content workflow. The step AI cannot replace is identifying the specific professional insight that makes the post worth reading.
Prioritize writing LinkedIn articles with executive thought leadership and first-party perspectives. Our guide to covers how to build that kind of content at scale.
These are the content types most resistant to AI suppression because they contain information that genuinely cannot be replicated at scale. An executive’s direct experience with a business problem, a client outcome with specific context, or an internal data point with genuine industry relevance will consistently outperform generic posts on the same topic.
Audit your current LinkedIn content mix. If a meaningful percentage of recent posts would pass for content from any other company in your category, that is a suppression risk worth addressing. The fix is not less AI. It is more raw material from the people and experiences that are actually specific to your brand.
FAQs
Will LinkedIn remove AI-generated posts entirely?
No. Posts identified as AI slop will be suppressed in the recommendation feed but remain visible to a user’s direct connections and followers. Removal is not currently part of the announced changes.
How does LinkedIn detect AI slop?
LinkedIn uses a machine learning system trained on human-annotated examples of generic versus original content. The system identifies patterns in language, structure, and engagement behavior. It claims 94 percent accuracy, though false-positive data has not been disclosed.
Is AI-assisted writing still allowed?
Yes, explicitly. LinkedIn has drawn a clear line between AI-generated content that lacks original perspective and AI-assisted content that uses AI tools to support human thinking. The latter is welcome. The former is what the suppression system targets.
Does this affect LinkedIn ads as well as organic content?
The announced changes target the organic recommendation feed. LinkedIn’s paid advertising products operate separately and are not currently in scope for these content quality restrictions.
Conclusion
LinkedIn’s crackdown on AI slop is a predictable consequence of the content inflation that AI writing tools have produced on every major platform. The platforms that survive on professional audience quality will protect that quality, even when it means limiting the reach of content created using their own tools.
For brands, the competitive advantage in this environment goes to those who treat AI as a production accelerator for content that starts with genuine expertise. The brands already doing this will benefit from the suppression of lower-quality content filling the same feeds. The brands relying on AI as a substitute for original thinking face a meaningful distribution penalty.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-08-05 16:00:002026-08-05 16:00:00LinkedIn Is Cracking Down on AI Slop. Here’s What That Means for Your Strategy
AI visibility metrics help uncover pre-click influences that might not be obvious from search terms, conversion tracking, landing page analysis, or other conventional data points. They add context that helps explain why campaigns attract the right customers, the wrong customers, or no customers at all.
Performance marketers are paid to find the most valuable customers with the most efficient spend. The baseline is understanding what happened after someone searched, clicked, or converted. But focusing exclusively on cleaning up existing demand is a short-sighted goal.
AI experiences (and what goes into serving them) are just as important to performance workflows because AI can shape what customers know, which brands they consider, and the language they use before they ever reach an ad or website.
What AI visibility metrics reveal
AI visibility metrics help answer two questions:
What information did an AI system retrieve to support its response?
Was your brand part of the information that shaped that response?
Three signals are especially useful:
Grounding queries show the retrieval searches AI systems use to gather supporting information and the broader topics your brand appears in.
Citations show when your content is referenced in an AI-generated response.
Share of authority shows how much citation activity belongs to your domain compared with other cited domains for the same topic or query set.
See exactly how your competitors win.
Uncover the keywords, ads, landing pages, and strategies driving your competitors’ paid search success—and find your next opportunity to outperform them.
Analyze your competitors
How to put AI visibility to work
Grounding queries show how AI interprets human intent
Search terms show what a person typed. Grounding queries show the information an AI system retrieved to help answer that person’s question.
A single prompt can generate several grounding queries covering comparisons, pricing, reviews, product details, availability, implementation questions, or other supporting topics. Those queries show how AI translates a human need into retrievable information.
It gives you another way to evaluate whether landing pages, product descriptions, and campaign messaging communicate the right value.
If AI systems consistently associate your brand with services you don’t offer, audiences you don’t want, or use cases that don’t convert well, that mismatch may eventually show up in paid campaigns as traffic that looks relevant but performs poorly.
For example, a B2B company offering executive coaching may not want to be interpreted the same way as a company offering tactical sales training. Both ideas may be semantically similar, but they can attract different buyers, budgets, expectations, and conversion paths.
If grounding queries consistently associate the company with lower-value training searches instead of higher-value advisory or coaching needs, that’s a signal worth investigating.
The useful signal isn’t whether one phrase is universally better than another. It’s whether grounding queries, search terms, landing page behavior, and conversion quality point to the same interpretation of the same offer.
What to do with this insight
If you see a lot of relevant grounding queries, that can be a strong opportunity to test AI-powered query matching with Performance Max, AI Max, or other AI-supported campaign types.
At minimum, grounding queries can inform keyword testing, search themes, creative ideas, and landing page updates. The key is to treat grounding queries as inputs, not instructions.
A grounding query isn’t the same as a keyword. It’s a clue about how AI systems interpret intent. Before acting on it, ask:
Does this query reflect a product or service we actually want to sell?
Does it match the customers we want more of?
Do we have a landing page that supports this intent?
Would this work better as a keyword, search theme, creative test, or content update?
Can we measure whether the test improves conversion quality?
If grounding queries and search terms overlap in valuable ways, that may validate that AI systems and customers are interpreting your offer similarly
If they don’t overlap, investigate before changing bids or budgets. The issue may be messaging, landing page clarity, or content gaps rather than campaign settings.
Citations and topics help you preview how AI understands your brand
A citation doesn’t mean you won the customer, the final answer, or the conversion. It means your content helped shape the AI experience. That matters because consideration can happen before a measurable click.
If your brand is cited for topics that align with your paid campaigns, your content and campaign messaging may reinforce each other. If your brand is cited for topics that don’t match what you sell, who you serve, or where you win, that may explain why some campaign traffic looks relevant on the surface but doesn’t convert well.
Topics add another layer by showing the themes, attributes, and categories AI systems associate with your brand. That can be especially useful when you’re trying to understand whether AI systems accurately represent your brand.
For example, if a cybersecurity platform wants to be known for enterprise identity protection but AI visibility reporting consistently associates it with small-business antivirus comparisons, that mismatch could be leading to lower-quality “conversions” that translate into bad data for the ad platform. What looks like a campaign settings or targeting issue could actually be a phrasing or content problem.
This is why landing page content matters. AI-powered campaign features such as final URL expansion, asset optimization, and broader matching systems also rely on how your brand, products, services, and pages are interpreted.
If AI visibility data shows that AI systems misunderstand your brand, there’s a reasonable chance campaign automation could inherit some of that confusion.
What to do with this insight
If citations and topics are misaligned, audit your landing page content before assuming the issue is bidding, budget, or audience targeting.
Look at how your priority pages describe:
The problems you solve and whether you attempt to tackle more than one.
The proof points that support the claims made in your advertising.
The customer types you want more of based on how you describe your services.
Then compare that language with the topics where AI systems cite or associate your brand.
If AI systems are pulling your brand into the wrong topics, tighten the content. Make the product category, audience, proof points, and next step easier to understand. If the right topics appear but your citations are weak, look for places where your content may be too thin, too generic, or missing the evidence needed to be useful.
Better content can strengthen paid media campaigns because AI-powered campaign tools are increasingly tasked with interpreting pages, assets, and customer context. The clearer your landing pages and content are, the easier it is for those systems to understand where your offer is relevant.
Share of authority reveals where competitors are beating you in AI recommendations
Citations show whether your content is referenced in a given grounding query.
Share of authority shows how much citation activity belongs to your domain compared with other cited domains for the same topic or grounding query. This can be a useful competitive signal because it shows where other brands may be influencing the customer’s research journey before a search, click, or conversion happens.
If competitors are cited more often on a priority topic, they may be doing a better job answering the questions customers ask before they convert. This might mean:
More accessible landing pages.
Better proof points, such as reviews and awards.
More useful product information that aligns with their feed.
Fundamentally, ask:
Are competitors doing a better job proving their value in the places AI systems use to shape recommendations?
If the answer is yes, the issue may not be that your campaigns are broken. Your content may not be giving AI systems enough clear, useful, or verifiable information to understand why your brand belongs in the recommendation set.
What to do with this insight
Use share of authority to identify where competitors are outperforming you on topics that matter to the business. Then invest in the content and landing page experiences that prove your value.
That might mean:
Building new landing pages for high-value use cases.
Expanding product or service details to include ideas mentioned in grounding queries.
Adding clearer proof points, such as reviews and awards.
Updating creative and messaging to reflect what customers and AI systems are already asking about.
Not every share-of-authority gap deserves action. If you’re underrepresented on a topic that doesn’t matter to your business, that may be fine.
But if you’re underrepresented on a high-value category, core product, branded initiative, or use case that your paid campaigns depend on, that gap deserves attention.
The performance question isn’t: “How do we become visible everywhere?” A better question is: “Where do we need stronger evidence so qualified customers are more likely to consider us?”
Every click they win is a customer you lose.
See where competitors are investing, which keywords drive their results, and how to capture more of the market.
See who’s stealing your traffic
What AI visibility adds to PPC reporting
AI visibility reporting isn’t a replacement for conversion reports, bidding reports, placement reports, or behavioral analytics. Those tools still tell you whether campaigns are working and where performance is coming from.
AI visibility adds a different perspective. It helps explain how customers, AI systems, and competitors interact with information before traditional performance metrics exist.
Google updated the Data Manager API with new audience management tools, more flexible data ingestion and expanded support for user-provided address data, making it easier for developers to maintain Customer Match lists and improve data quality.
The release is aimed at reducing manual work while providing better visibility into data issues that don’t require an ingestion request to fail.
What’s new. The headline addition is a new RemoveAllAudienceMembers method, which allows developers to clear an entire audience list in a single operation. An optional timestamp parameter also lets advertisers remove only members added before a specified date, making full audience refreshes much easier.
Google has also introduced field-level ingestion warnings. Rather than failing an entire request when optional fields contain invalid data, the API now processes valid records while returning detailed warnings that identify the problematic fields and explain why they failed validation.
The update also expands the address information that can be sent to Google Analytics destinations. Developers can now include street address, city and state or province alongside existing fields such as name, postal code and region, while user-provided data can also satisfy identifier requirements for certain multi-source events when other identifiers aren’t available.
Google has additionally released new AI agent skills in its Google Skills GitHub repository to help developers build Data Manager API integrations more efficiently within AI-assisted coding environments.
Why we care. The update removes several friction points for developers managing first-party data. Audience refreshes become simpler, ingestion issues are easier to troubleshoot without interrupting workflows, and the expanded address fields provide more flexibility when sending user data to Google Analytics.
Bottom line. The latest Data Manager API release streamlines audience management, improves error handling and expands data collection capabilities, giving developers more efficient tools for managing customer data across Google Ads, Display & Video 360 and Google Analytics.
Every click they win is a customer you lose.
See where competitors are investing, which keywords drive their results, and how to capture more of the market.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/08/Google-Data-Manager-hAulHT.webp?fit=1672%2C941&ssl=19411672Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-08-03 18:30:582026-08-03 18:30:58Google expands Data Manager API with smarter audience management
On the latest episode of PPC Live the Podcast, Ana Kostic shared how one account restructure taught her a lesson that still shapes how she manages paid media today: successful PPC isn’t about building the perfect campaign structure—it’s about protecting the business behind it.
The mistake: A “perfect” account restructure
Early in her career, Kostic inherited a Google Ads account with a messy structure. Determined to apply PPC best practices, she rebuilt the account from the ground up, confident that cleaner campaigns and better keyword organisation would improve performance. Instead, traffic and sales dropped by around 40%.
The hidden cost of starting over
The issue wasn’t the new structure itself—it was wiping away years of historical performance data that Google’s systems had learned from. The account eventually recovered, but only after roughly two and a half months, with the full benefits taking closer to six months to materialise.
The lesson wasn’t about Google Ads
For Kostic, the biggest takeaway wasn’t technical. It was recognising that businesses can’t afford major revenue dips while platforms relearn campaign performance. “You have to think about the business first,” she said.
Ask business questions before platform questions
Today, Kostic starts every onboarding by understanding the business rather than the account. She asks about cash flow, margins, growth goals and how much short-term disruption the company can realistically absorb before recommending major structural changes.
Why slow beats perfect
Rather than replacing campaigns overnight, Kostic now introduces changes gradually, allowing new structures to learn while existing campaigns continue delivering results. Her philosophy is simple: “We like it slow and boring.”
Communication is part of optimisation
Kostic also credits her former manager for helping navigate the difficult conversations that followed. Instead of assigning blame, the agency focused on transparency, created a recovery plan and supported both the client and the team until performance stabilised.
PPC doesn’t stop inside Google Ads
One of Kostic’s biggest pieces of advice is to spend more time talking to sales teams. Those conversations often reveal the language customers actually use, helping advertisers build better campaigns than platform data alone can provide.
AI doesn’t change the fundamentals
While she’s a strong advocate for products like Performance Max, Kostic says AI shouldn’t replace good decision-making. Advertisers should still put guardrails in place, test gradually and prioritise business stability over chasing every new feature.
Bottom line
The biggest PPC mistakes aren’t always campaign mistakes—they’re business mistakes. For Kostic, rebuilding an account too aggressively became the experience that transformed her from a platform specialist into a business-first strategist.
See exactly how your competitors win.
Uncover the keywords, ads, landing pages, and strategies driving your competitors’ paid search success—and find your next opportunity to outperform them.
Incrementality and attribution are two approaches to measuring marketing performance that are frequently discussed as though they are competing lenses viewing the same data. But they’re actually designed to answer very different questions, using different forms of evidence.
Attribution asks which observed marketing touchpoints should receive credit for a conversion. Incrementality asks whether the marketing activity caused additional conversions that wouldn’t have occurred without it.
A refresher on attribution
Attribution is the favored child of marketing analytics teams everywhere, circa 2015. Marketers discovered that some conversion paths contained multiple touchpoints across the digital landscape, like this:
That raised questions about which channel should get what “credit”:
Should the display ad get the most credit for the conversion because it was the first exposure?
Or should the email, because that’s the touchpoint that finally convinced the user to buy?
And what about the social ad and the organic presence in the middle?
That’s where attribution modeling came in. Attribution modeling provided frameworks for deciding how that credit should be distributed. Some models assigned the entire conversion to a single touchpoint. Others divided it among multiple interactions.
So if the final value of the conversion is $100, an attribution model tells marketers that display can take credit for $30, email for $30, and the remaining $40 is split between paid social and organic.
Then, when you’re evaluating the success of your channels, you have a more nuanced framework for distributing revenue credit. And when you’re deciding on what channels get what budget for the next fiscal year, you have a way to compare and contrast.
Example: How a $100 conversion might be distributed across four marketing touchpoints.
Attribution model
Display
Paid Social
Organic Search
Email
How credit is assigned
First-touch
$100
$0
$0
$0
All credit goes to the first observed interaction.
Last-touch
$0
$0
$0
$100
All credit goes to the final observed interaction before purchase.
Linear
$25
$25
$25
$25
Credit is divided equally among every observed touchpoint.
Position- based
$40
$10
$10
$40
The first and last interactions receive the most credit, while the middle interactions split the remainder.
Time-decay
$10
$20
$30
$40
Touchpoints receive progressively more credit as they occur closer to the conversion.
Data-driven
$30
$20
$20
$30
Credit is distributed according to each touchpoint’s estimated contribution to the conversion.
Note: These are simplified examples. Position-based models can use different weighting rules, time-decay allocations depend on timing, and actual data-driven models vary.
See exactly how your competitors win.
Uncover the keywords, ads, landing pages, and strategies driving your competitors’ paid search success—and find your next opportunity to outperform them.
Analyze your competitors
Incrementality 101
Incrementality began to gain renewed interest from marketers around 2020.
Rather than dole out credit for a sale to different touchpoints and channels based on a mathematical equation, incrementality relies on carefully guardrailed tests of real, live sales data that attempt to prove the “true” impact of a marketing activity rather than its correlation. Incrementality tries to answer the question:
How many of these sales were actually caused by this campaign, without counting how many would have happened regardless?
The answer to that question is what marketers call lift. And through tightly controlled tests leaning on the scientific method, marketers were able to isolate the difference in sales between a group exposed to the marketing activity and an equivalent group that wasn’t exposed to marketing materials.
Incrementality is best explained through an example.
Let’s say you want to discover the lift of a given marketing campaign. So you divide your audience into two groups: a control group of folks who won’t be exposed to the campaign and an exposed group that does see the campaign.
You run your campaign for 30 days, then look at the results. While the exposed group completed 1,000 purchases, the control group completed 800 purchases. The incremental lift of the campaign would be 200 purchases.
An attribution model could associate many or all 1,000 purchases with the campaign. It would allocate the value across the platforms and touchpoints involved according to the model you choose.
Incrementality, on the other hand, would conclude that only those 200 additional purchases were actually caused by the campaign.
Where marketers go wrong is when they go all in on either framework. The two concepts can play nicely together (provided you’re using the right one to answer the right question). If you’re looking to optimize your campaigns or deep dive into the user journey of your customer, attribution is going to be your best friend, helping you evaluate platforms and touchpoints by giving you a shared success metric with which to compare them.
If, on the other hand, you’re defending your budget from a proposed cut, incrementality is going to be your strongest source of evidence regarding which channels actually create additional business with their budget, rather than capturing business that would have happened anyway.
Attribution
Incrementality
Primary question
Which observed marketing touchpoints should receive credit for a conversion?
How many additional conversions occurred because of the marketing activity?
Best use case
Ongoing campaign optimization, understanding customer journeys, and allocating credit across measurable channels.
Validating whether an investment creates additional business value and informing higher-level budget decisions.
Main blind spot
Correlation is not causation: a touchpoint may receive credit for a conversion it did not actually create.
Tests can be expensive, slow, or difficult to design, and results may not explain which individual touchpoints influenced the customer.
Most likely stakeholder
Channel managers, performance marketers, platform teams, and marketing analytics teams.
Marketing leadership, finance, data science, growth strategy, and budget owners.
Where platforms get confused by attribution and incrementality
Most often, it’s asked when a channel platform’s reported revenue or conversions differ from the numbers found in the client’s CRM, web analytics, or other source of truth. They almost never line up perfectly.
What can be tough to explain succinctly to a client is this: The fact that they differ doesn’t necessarily mean either is incorrect. Each system applies its own logic based on the interactions it can observe, which conversions should qualify for credit, and how long after an interaction credit can still be attributed. But neither is “wrong.”
An advertising platform may correctly observe and report that a customer viewed or clicked on an ad before purchasing. But evidence that an ad was seen before a purchase isn’t necessarily proof that the ad caused it, nor is it proof that the ad didn’t cause it.
This becomes particularly important with automated campaigns, especially as platforms continue to push these automated solutions on marketers. Automated systems are designed to maximize performance based on the conversion signals defined inside the platform. They’re simply not designed to maximize performance based on your carefully calculated incremental lift test results.
As a result, automated campaigns target audiences, placements, and queries already associated with users likely to convert, such as existing customers, branded searchers, and remarketing audiences. Those conversions may be entirely valid according to the platform’s attribution model, while creating less additional revenue than the campaign report implies.
In other words, automated campaigns can increase the number of conversions credited to a given campaign without actually causing an equal increase in total sales.
It’s true, platforms are increasingly offering lift studies and other incrementality-focused tools. But it’s a mistake to assume that incremental value is automatically incorporated into automated campaign optimization.
A platform like Google Ads may provide a controlled lift experiment, but unless the advertiser applies the test findings — or selects a campaign setting explicitly designed to optimize for incrementality — the measurement system and the delivery system are still going to be working toward two different definitions of success.
Some platforms are starting to address this. Meta, for example, now offers a very promising incremental attribution model intended to optimize delivery toward conversions it predicts were directly caused by advertising. For now, though, that’s a specific optimization choice that, again, requires action by the advertiser and isn’t an inherent feature of every automated campaign.
Every click they win is a customer you lose.
See where competitors are investing, which keywords drive their results, and how to capture more of the market.
See who’s stealing your traffic
Know which question you’re trying to answer
In sum, attribution and incrementality aren’t competing methods for finding one definitive metric. They’re different tools designed to answer different questions. And like most tools, they perform best when they’re doing the job they’re designed for. You wouldn’t try to use your Allen wrench as a hammer, would you?
Attribution helps us marketers understand which touchpoints contributed to a conversion and provides a shared basis for comparing channels. Incrementality helps businesses understand whether their marketing investment generated additional conversions that wouldn’t have occurred otherwise.
The best marketers need both. While attribution provides the ongoing signals needed to optimize campaigns and understand customer journeys, incrementality helps validate whether those optimizations are creating new business value or simply capturing demand that already existed.
As automated campaigns take greater control over targeting, placements, bidding, and budget allocation, understanding both sides will only become more important. A system can become exceptionally efficient at maximizing attributed conversions without becoming equally effective at producing incremental growth.
So the next time a platform report contradicts your CRM, don’t assume either number is wrong — ask which question each number was designed to answer.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/07/image-378-7F3YrD.webp?fit=1491%2C1055&ssl=110551491Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-28 15:00:002026-07-28 15:00:00Attribution vs. incrementality: Why you need both
One of AI Max’s capabilities is creating assets for you. This can take the strain off the PPC team by reducing the need to customize ads for every single ad group.
We wanted to quantify how well the text customization feature worked for various companies, so we ran tests with three different companies to understand how much we should or shouldn’t be using text customization.
As we’re a software company that helps companies manage their PPC accounts, not the agency doing the work, we worked with the companies to guide them on how to set up and analyze the tests.
We’ll start by examining the process we used to help the companies set up the tests so you can run this experiment yourself, and then we’ll examine the test results.
Text customization and messaging restrictions
Before you run this test, you need to understand the features involved.
First, you need to turn on AI Max and the text customization feature.
Since this is AI working in the background for you, it can tailor the assets in every single ad group to the keywords in that group. While this sounds nice, the assets can sometimes be used to run promotions or advertise products and services you don’t offer.
To help guide the system, you should apply messaging restrictions when using auto-created assets.
Messaging restrictions can help guide the system on what the ads should and shouldn’t say. In addition, you can give it rules around your brand guidelines.
The overall process for creating good messaging restrictions is fairly simple and only takes an hour or two:
Use a prompt in Gemini to create your initial assets.
Then use a prompt to make the ads overly promotional, create promises you don’t like, etc. Essentially, you’re having the system write ads you don’t approve of and want to ensure Google doesn’t create assets like these on your behalf.
Create messaging restrictions to stop the assets you don’t like from being generated.
Use a prompt to create new, highly promotional ads with your messaging restrictions until all created assets fit your company’s messaging guidelines.
Uncover the keywords, ads, landing pages, and strategies driving your competitors’ paid search success—and find your next opportunity to outperform them.
Analyze your competitors
Choosing the campaigns to test
We wanted to see how these assets performed across different types of companies and different levels of campaign optimization.
Therefore, we first chose three business types:
Ecommerce.
B2B lead gen.
B2C lead gen.
In each account, we only wanted campaigns that met a specific set of criteria:
Did not use brand keywords.
Spent at least $20,000 per month.
Had at least 100 ad groups.
Most companies have some campaigns that are their best performers, where the team spends a lot of time optimizing the campaigns. Then you have your campaigns that are long-tail, do well in aggregate, but receive less attention.
To understand how well text customization was going to perform, we used two campaigns that were highly watched and two that were somewhat neglected from each account.
We also wanted to see how the new assets performed, so we chose campaigns that didn’t rely heavily on pinning, which excluded many of the highest-spending campaigns, since most large and enterprise companies extensively use pinning in their top campaigns.
Finally, we wanted to test only the assets, not URL expansion, so none of the campaigns used the final URL expansion feature.
Asset review
As you run these tests, you’ll want to monitor the auto-created assets and remove any that don’t align with your brand messaging or offers.
When looking for AI-generated assets, it’s essential that you change the default filters to include the ad, as this filter isn’t chosen by default.
The companies in the test monitored these assets as they were created and removed them before they received many impressions. Ignoring the B2B results (more on that later), approximately 19% of the auto-created assets were removed.
This ecommerce company sells over 100,000 SKUs, so it has a lot of products, and many users are accustomed to visiting the website and searching again if the landing page doesn’t have the specific product they’re looking for.
At first glance, it appeared that both AI Max and text customization were incredibly successful.
However, after further analysis, we found that AI Max was poaching impressions, clicks, and conversions from other campaigns and that the overall revenue for the account declined.
The company added many search terms as keywords to help Google prioritize the correct ad group and campaign. Then it added more negative keywords and audience lists to slow cannibalization and reran the tests.
We observed that AI text customization wasn’t as effective as human management of assets for highly optimized campaigns. However, it was good at assisting the long-tail campaign.
When creating RSA assets for B2B companies, one of the most important considerations is how to properly prequalify your audience through your asset usage. You want your ads to be unattractive to B2C searchers and appeal to B2B searchers.
This company had previously used pinning quite extensively across its assets to ensure this qualification. However, it wanted to see how well Google could optimize its accounts, so it removed its pins during this test.
The nuance of prequalifying a B2B audience is one that text customization clearly doesn’t understand. The B2B accounts saw their CTRs skyrocket. However, the conversion rates declined significantly since the ads were attracting many B2C searchers.
The messaging restrictions included language to ensure the assets prequalified users as part of a B2B audience. While some of the assets met this criterion, the overall ads that were shown to users didn’t properly appeal to B2B buyers.
The other tests ran for over a month. However, after three weeks, the results were so poor that this company stopped the tests and went back to pinning its ads and removing the auto-created assets. Within a week, its results returned to their pretest levels.
B2C lead generation
Our last test was with a B2C lead generation company that localizes its ads through geographic ad copy or geographic insertion.
Its optimized campaigns had tailored ad copy for the keywords in every single ad group. Its long-tail campaign had a few headline assets in each ad group, tailored to the keywords, but most assets were reused across ad groups.
Seeing formulaic ad copy in lower-priority campaigns is quite common, and this is where we were hoping to see AI auto-created assets perform well, since there was a lot of opportunity for better ads.
AI Max auto-created assets didn’t disappoint in these low-priority campaigns. While these assets didn’t outperform the assets humans had spent a lot of time testing in their top campaigns, AI performed quite well for the long-tail campaign.
Every click they win is a customer you lose.
See where competitors are investing, which keywords drive their results, and how to capture more of the market.
See who’s stealing your traffic
Where AI Max automated assets work best
AI is a fantastic tool at your disposal. For ads where you’re spending a lot of time thinking through your messaging, it can be good at helping you generate ideas, but human-created assets still outperform AI-generated assets.
If you need specific types of assets, such as prequalifying users for B2B audiences, specific offers, or short-term promotions, then you should take control of the assets yourself and not turn them over to AI.
However, auto-created assets shine when you just don’t have enough time to fully optimize your creatives. Using AI to create your assets, assuming you have good messaging restrictions and regularly review these assets, can help your overall performance.
We’re still a long way from AI being a turn-on-and-forget setting. It needs babysitting and oversight. However, having AI do the heavy lifting in areas where you don’t have the time to fully optimize, and then spending your time reviewing and tweaking the outcomes, is the best use of AI in ad creation.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/07/Google-Ads-AI-Max-Asset-optimization-HuwqrY.webp?fit=1604%2C686&ssl=16861604Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-28 14:00:002026-07-28 14:00:00Putting Google Ads AI Max’s automated ad copy to the test
For every dollar you spend on SEO, how much do you get in return?
Impressions, clicks, rankings, and query growth can show the results of SEO activity. But they don’t tell executives what they need to understand: how much it costs to acquire a customer (CAC), how that cost changes as SEO efforts continue, and whether overall acquisition efficiency is improving.
The challenge is that SEO rarely operates within the clean boundaries that a channel-level CAC calculation implies.
SEO creates entry points across the customer journey and influences other acquisition channels along the way. Its value, then, isn’t only in the customers directly attributed to organic search. It’s also in how SEO can make the broader acquisition system more efficient.
The reality of acquiring a customer
A user may first find a company through a nonbrand search, return through paid search, compare alternatives through content found in ChatGPT, sign up for a newsletter on the website, read through guides for a week, and then finally convert through the owned channel.
The final conversion might be attributed to email. Paid search may receive some credit for the return visit. The original organic discovery may disappear from the standard report entirely.
But SEO still influenced the acquisition and may have reduced its total cost.
CAC can be measured by individual channel or across channels as blended CAC. CAC expectations vary considerably by channel:
Paid search.
Paid social.
Email.
SEO.
Be the brand customers find first.
Track, grow, and measure your visibility across Google, AI search, social, local, and every channel that influences buying decisions.
Start your free trial
Paid search captures high-intent demand
Paid search may have the cleanest attribution. Users search for solutions to their problems, product terms, categories, or other related queries. You pay for the click, and a percentage of those clicks convert.
From there, you get spend / customers acquired = paid search CAC.
A user who clicks a search ad may already know the brand through social campaigns, podcast appearances, how-to guides, recommendations, or competitor research. The demand is high-intent, and paid search captures its final expression.
Paid social influences demand earlier
Paid social’s impact on CAC is often indirect because its primary strengths are creating awareness, warming audiences, building retargeting or feeder pools, and connecting users with problems they may want to solve before they’re ready to buy.
It’s unlikely that your users are scrolling on Instagram thinking, “I would like to spend some money right now.”
But seeing a product address their problem during their free time may make the brand more familiar when they later search for a solution at work, improving blended CAC efficiency.
If you looked only at paid social as spend / customers acquired = paid social CAC, you’d probably cut the budget. But then, if you look at what it does to paid or branded search CAC through holdout tests, you’d potentially restart the social budget after seeing the overall acquisition engine decline.
It’s a form of incrementality. Experiments compare exposed and unexposed groups to estimate how much additional activity a marketing investment produces instead of simply assigning credit to the last recorded touchpoint.
Email depends on other acquisition channels
Lifecycle channels like email work differently. If you own an audience through email capture and you look at converting them into paid users or continuous purchasers, you can think of email CAC along the lines of cost of email program / converted customer value.
But then you still have to run paid to capture the emails in the first place, or you need a strong SEO presence to do so. The apparent efficiency is highly dependent on other channels.
SEO touches all of these channels, and all of these channels can influence SEO in return. For example, a paid social campaign could generate 100 brand mentions that benefit your overall organic visibility.
Together, they create a connected acquisition system.
It sounds like the solution is a better attribution model, and then SEOs can speak about CAC more effectively and get more budget. But there are still limitations.
Last-click attribution would just give credit to the final measurable source. First-click would just give credit to the initial source. Linear or position-based models distribute credit, and data-driven attribution would observe data to estimate what contributed most.
Data-driven attribution may improve reporting, but it remains a model rather than a complete record of the customer journey.
Because how do you evaluate interactions that aren’t observed, identified, or connected to a user’s journey? There are deleted cookies, consent restrictions, cross-device behavior, long sales cycles depending on your niche, offline conversions, and that list of limitations could go on for a while.
So, regardless of your attribution model, it doesn’t always provide a complete record of causality and shouldn’t be treated as such. This further reinforces that acquisition is a system, not an isolated channel.
As the search ecosystem changes, even more of SEO’s influence is becoming difficult to observe.
Measuring CAC for SEO as an isolated channel is becoming increasingly difficult.
SparkToro’s analysis of Similarweb clickstream data found that 68.01% of U.S. Google searches ended without a click during the first four months of 2026. In 2024, the figure was 60.45%, representing an increase of roughly 7.6 percentage points in two years.
Users can still see a company in an AI Overview, read a search snippet, or engage in other behaviors, but fewer and fewer are measured through impression → click → conversion.
SEO still influences these interactions, but its impact may appear smaller in a dashboard.
There’s also significant overlap between SEO efforts and AI visibility, depending on which agency or existential hill you’re standing on.
SEO leans out blended CAC
SEO’s biggest advantage is that its financial returns can compound. A paid campaign stops sending traffic to the website when the budget stops, but a strong organic presence can continue creating entry points long after the initial investment.
If you build topical authority across a category with meaningful demand, the cost to maintain that visibility, including the costs of keeping up with competitors, is often lower than continuously buying the same demand through paid search.
That could include technical improvements, content production, digital PR, product pages, and ongoing optimization.
During the first few months, the program may appear inefficient from a CAC perspective because the investment occurs before returns materialize. Then, as visibility grows, that same work starts to increase customer volume while spend stabilizes at maintenance levels, and CAC decreases.
That’s one way SEO leans out blended CAC. It does this by:
Creating nonpaid entry points into the funnel.
Capturing demand that paid would otherwise have to buy.
Supporting paid search and paid social conversion.
Increasing branded and direct demand over time.
Educating buyers before sales conversations.
Improving conversion through comparison, use-case, and objection-handling content.
Feeding owned channels like email.
Reducing support and retention friction through product and help content.
When considering the impact, it can be true that SEO is among the more efficient levers for reducing a business’s blended CAC.
See where your brand appears, where it doesn’t, and exactly how to win more visibility across search, AI, local, social, and every channel that matters.
Start your free trial
Reframe the SEO investment conversation
The attribution model can point to various channels as the highest-performing, but the organic infrastructure may be contributing to that performance.
That’s where SEOs should point when reframing the conversation.
Instead of answering how much you get in return for every dollar spent on SEO, consider how much more money you’ll have to spend on other channels for every dollar not spent on SEO.
If SEO is doing its job, it’s part of a cohesive system, and its role is to increase volume while reducing blended costs.
SEO teams should still report channel CAC when the data allows, but executives should evaluate it alongside influenced pipeline, replacement costs, and changes in blended acquisition efficiency.
