Yelp just unveiled its 2025 Fall Product Release, a sweeping AI-driven update that turns the local discovery platform into a more conversational, visual, and intelligent experience.
Driving the news: Yelp’s rollout includes over 35 new AI-powered features, headlined by:
Yelp Assistant, an upgraded chatbot that instantly answers customer questions about restaurants, shops, or attractions—citing reviews and photos.
Menu Vision, which lets users scan menus to see photos, reviews, and dish details in real time.
Yelp Host and Yelp Receptionist, AI-powered call solutions that handle reservations, collect leads, and answer questions with natural, customizable voices.
Natural language and voice search, allowing users to search conversationally (“best vegan sushi near me”) for smarter, more relevant results.
Popular Offerings, which highlights a business’s most-mentioned services, products, or experiences.
Why we care. Yelp’s new AI tools make it easier to capture and convert high-intent customers at the moment of discovery. With features like Yelp Assistant, AI-powered call handling, and natural language search, businesses can respond instantly, stay visible in smarter search results, and never miss a lead. The update turns Yelp from a review site into an always-on customer engagement platform—giving advertisers more efficient ways to connect, communicate, and close.
What’s next. Yelp plans to make its AI assistant the primary interface for discovery and transactions in 2026, merging instant answers, booking, and customer messaging into one seamless experience.
The bottom line. Yelp’s latest AI release gives brands smarter tools to engage customers in real time—turning everyday search and service interactions into instant connections.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/Screenshot-2025-10-21-at-19.18.04-kjKpB5.png?fit=776%2C1386&ssl=11386776http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-21 18:25:342025-10-21 18:25:34Yelp’s new tools help brands connect faster and engage customers in real time
OpenAI announced the launch of its first web browser, which they named ChatGPT Atlas. Atlas is currently available on Mac only right now and has all the features you would expect from an AI browser. But the most surprising part is that its built-in search features seem to be powered by Google and not Microsoft Bing, its early partner and one of its largest investors.
How to download Atlas. If you are on a Mac, you can download ChatGPT Atlas at chatgpt.com/atlas. From there, the web browser will download to your computer, you double click on the installer and then drag the application to your application folder.
What Atlas does. It is a web browser, first and foremost. You can go directly to web pages and browse them, but as you do that, there is ChatGPT available on the sidebar, like other AI powered web browsers. You can ask ChatGPT questions, you can have it re-write your content in Gmail and other tabs, offers personalization and memory, plus it will help you complete tasks, code and even shop using agentic features.
Search in Atlas. The interesting thing is that when you search in ChatGPT Atlas, it gives you a ChatGPT like response but also adds search vertical tabs to the top, like you have in other search engines. Like web, images, videos, news and more. Then when you go to those tabs, there is a link at the top of each set of search results to Google.
Here are screenshots:
More details. ChatGPT Atlas is launching worldwide on macOS today to Free, Plus, Pro, and Go users. Atlas is also available in beta for Business, and if enabled by their plan administrator, for Enterprise and Edu users. Experiences for Windows, iOS, and Android are coming soon.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/chatgpt-atlas-search-web-1-58f6Gh.png?fit=1862%2C1302&ssl=113021862http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-21 17:58:422025-10-21 17:58:42OpenAI launches a web browser – ChatGPT Atlas
Google Merchant Center is rolling out a new Issue Details Page (IDP) to help advertisers more easily diagnose and resolve account or product-level problems.
How it works:
Located under the “Needs attention” tab, the page provides a consolidated overview of current issues.
It surfaces recommended actions, business impact metrics, and sample affected products — giving merchants a clearer sense of what to fix first.
Why we care. Until now, identifying and fixing issues in Merchant Center often required navigating multiple sections and reports. The new Issue Details Page (IDP) in Google Merchant Center gives advertisers a single place to view and fix account or product issues.
It highlights the problem’s impact, recommends actions, and shows affected products, helping advertisers resolve issues faster and keep listings active. In short, it saves time, improves visibility, and helps prevent lost sales.
The big picture. The update is part of Google’s broader push to improve Merchant Center usability ahead of the holiday shopping season, when product accuracy and uptime are critical for advertisers.
The bottom line.Google’s new IDP could save advertisers time and guesswork by putting all issue diagnostics and solutions in one place.
First seen. The newly released help doc was spotted by PPC News Feed founder, Hana Kobzová
Noticed your traffic dropping even though your rankings look stable? You’re not alone.
AI tools like ChatGPT, Perplexity, and Google’s AI Overviews are now answering the same questions that used to send people to your site.
If your brand isn’t showing up in those AI-generated responses, you may be losing visibility. And the tough part? You won’t be able to measure that lost visibility with traditional analytics tools.
That’s where AI visibility tools come in. They tell you when your brand shows up in AI answers, which platforms mention you, and how often your content gets cited. In short, they track your presence across large language models (LLMs) and AI search engines so you know if your LLM seeding efforts are paying off.
The good news is a handful of tools are already helping brands track their AI visibility. Some existing platforms have added AI tracking to their SEO suites. Others focus exclusively on LLM citations.
Each gives a different way to see (and improve) your AI presence. Let’s look at some of the best AI visibility tools available right now.
Key Takeaways
AI visibility tools track brand mentions and content citations across LLM platforms like ChatGPT, Perplexity, Claude, and Google’s AI Overviews.
Think of these tools as the AI-era version of SEO tools. They give you hard data on whether your optimization tactics are actually working.
Most platforms are still adapting alongside AI search behavior, so look for tools that update often.
The right tool for you depends on your budget, whether you want standalone tracking or built-in SEO features, and how technical your team is.
Combining AI visibility metrics with traditional analytics gives you the complete picture of content performance across all channels.
Why Are AI Visibility Tools Important?
The way people search is fundamentally changing. Gartner predicts traditional search engine volume will drop 25 percent by 2026 due to AI platform and virtual agent usage.
People are getting answers directly from AI platforms instead of clicking through to websites. That makes knowing where your brand appears in AI responses as vital as tracking your Google rankings.
AI visibility tools solve a measurement problem. They monitor which LLM platforms cite your content and your brand mentions in AI Overviews. They measure changes over time so you can evaluate whether your LLM SEO efforts are actually working.
Think of these tools as Google Analytics for AI search. Without this data, you’re guessing about what resonates with AI platforms. With it, you see exactly what content drives citations and what gets ignored. These tools reveal patterns in what content formats, topics, and structures earn the most AI citations.
Traditional SEO metrics like page views, rankings, and backlinks still matter. But they tell only part of the story.
Don’t ignore the growing segment of your audience interacting with your content through AI platforms. They might not visit your site, but their interactions still influence visibility and authority.
Combining standard analytics with AI visibility data shows the complete picture of your content’s reach and what’s actually driving results across channels.
Top 5 AI Visibility Tools on The Market
The LLM visibility tool market is growing fast. New platforms launch regularly with different features, tracking methods, and pricing structures.
After comparing what’s out there, these five AI visibility tools stand out. They range from budget-friendly all-in-one platforms to enterprise-focused citation intelligence.
Ubersuggest
Ubersuggest has added new AI Visibility features to its SEO toolkit. The big win? You can now monitor AI citations and see how they connect to your traditional search performance, all from one dashboard.
Ubersuggest AI Visibility makes it easy to add AI visibility tracking into your marketing program. Key metrics the tool tracks include:
Brand Visibility: How often your brand gets mentioned across AI-generated answers in a given period.
Industry Rank: Your average position compared to other brands in your space.
Top Prompts: The main questions people are asking in AI platforms relevant to your industry, and how your brand appears in those answers.
Competitor Visibility: How your brand’s presence in AI visibility trends compared to competitors over time.
Along with the easy-to-navigate interface, Ubersuggest’s pricing is a major advantage. Most enterprise tools charge per project or lock you into long-term contracts. Ubersuggest takes a different route with flat monthly pricing and unlimited project tracking. That means an agency managing 20 clients pays the same as someone tracking just two sites.
You also get full access to all the traditional SEO features Ubersuggest is known for, so you don’t have to pay for two separate platforms to see the full picture.
Because Ubersuggest is built on years of SEO infrastructure, its data is consistent and reliable. And some teams might not be comfortable with other new visibility tools, many of which launched in the past year and are still working out bugs in their tracking.
Profound is a new platform specifically designed for enterprise brands that need detailed intelligence about how AI platforms discuss them. This goes beyond counting citations.
The system analyzes the context around every brand mention, including:
Sentiment: Whether AI platforms position you positively or negatively.
Competitive mentions: Which competitors get mentioned alongside your brand.
Authority: Topic clusters where you’re seen as an authority versus areas where others dominate.
Profound is built for customization. Its team builds dashboards tailored to your industry, integrates with your existing systems, and creates reporting formats that match your organization’s workflow.
Need specialized tracking for regulated industries? They configure it. Competitive intelligence can also be scaled across hundreds of queries, and alert systems can let you know if your brand suddenly drops from an AI response.
The tradeoff? Price. Annual contract costs typically start high and scale based on how many brands you track, query volume, and customization needs. This isn’t built for small businesses.
With that said, Profound’s depth and customization justify the cost for brands where AI visibility directly impacts market position and revenue.
Semrush added AI visibility tracking to its existing SEO suite. Already using the platform? The new features integrate smoothly into your workflow.
The tool monitors citations across major AI platforms and provides visibility scoring that works like domain authority, providing a single number showing how your AI presence compares to competitors over time.
The real benefit of Semrush’s functionality is that it connects AI visibility data with everything else it already tracks. You can see which pages earn both backlinks and AI citations. You can see whether content that ranks in traditional search also appears in AI responses. That integrated view helps you understand what’s working across all your marketing channels.
For teams trying to consolidate tools, this setup is efficient. You get traditional SEO and AI visibility data in one report, no platform-switching required.
The tradeoff is its agency pricing. Semrush limits how many projects you can track per account tier. Adding clients means upgrading plans or buying additional accounts. Managing 30-plus brands? Costs climb fast compared to platforms with unlimited project tracking.
Overall, this may be a smart add-on if you are already onboarded onto Semrush. But it might not be the most affordable option for smaller teams or tighter budgets.
Ahrefs made its name with backlink analysis and competitive research before becoming one of the most popular SEO tools around. Its move into AI visibility adds another layer to an already powerful platform.
This new functionality tracks citations across AI platforms and lets you filter by specific engines, monitor changes over time, and compare your visibility to competitors. Standard stuff.
Ahrefs stands out by connecting link data with AI citations. Its backlink index is one of the largest available and updates frequently. The platform shows correlations between your link profile and AI visibility, revealing which linked pages get cited most often in AI responses.
That connection offers real insight. Content earning quality backlinks tends to appear more in AI citations. Understanding that relationship helps you identify what makes content citation-worthy and apply those patterns to other pieces. Combine that with Ahrefs’ broader SEO features, and you get a well-rounded picture of your brand visibility online.
The major caveat, though, is the pricing, which follows a similar structure as Semrush. Plans limit tracked projects, so costs increase as you scale. Five clients work fine. Fifty clients get expensive.
For teams that prioritize link building alongside AI visibility, Ahrefs handles both well. Just know you’ll pay premium prices.
ScrunchAI is a newer offering that focuses exclusively on AI visibility. Already using other tools for standard optimization and just need LLM citation tracking? Scrunch’s specialized approach might fit.
The platform monitors brand appearances across ChatGPT, Claude, Perplexity, Google’s AI Overviews, Bing AI, and emerging AI search engines. Real-time tracking alerts you to citation frequency changes, new platforms surfacing your content, or sudden visibility drops.
Where ScrunchAI stands out is that it tracks both citation quality and frequency. It can tell whether AI platforms position your brand as a primary resource, secondary resource, and if any misinformation shows up alongside your name.
ScrunchAI also provides recommendations based on your data. Certain content structures get cited more often? It suggests creating similar pieces. Missing from responses where competitors appear? It flags those gaps with specific topic ideas.
Another interesting feature is query simulation. You can run industry-specific prompts to see if your brand appears and compare results across different AI engines. That gives you a clear picture of where you’re strong and where to focus your next optimization push.
In terms of pricing, Scrunch lands in the middle of our list. Monthly plans scale based on query volume and update frequency rather than limiting projects. That makes costs predictable for agencies.
The tradeoff is betting on a newer company. Established platforms have proven track records. ScrunchAI is still building its reputation, though early users report solid performance and responsive support.
Choosing the Right AI Visibility Tool for You
Selecting an AI visibility tool requires matching capabilities with your specific constraints and goals.
Start with three core questions: What’s your budget? How technical is your team? Do you need standalone AI tracking or an integrated SEO platform? Here are some key focus areas:
Budget determines realistic options. Tools like Ubersuggest provide AI visibility alongside comprehensive SEO features at accessible prices for small businesses and agencies. Enterprise platforms like Profound deliver granular intelligence but require substantial financial commitment that only makes sense at scale.
Technical capabilities matter. Some platforms assume comfort with data analysis and provide extensive export, API, and customization options. Others prioritize simplicity with clear dashboards and straightforward recommendations. Match the tool’s complexity to your team’s skills and bandwidth.
Consider your existing technology stack. Already investing in Ubersuggest, Semrush, or Ahrefs for SEO? Their AI visibility features extend current workflows. You avoid learning new interfaces and keep data centralized. If you’re starting from scratch or want laser focus on AI tracking, a specialized platform like ScrunchAI might be the better fit.
Consider your scaling needs. Requirements differ dramatically between tracking five websites versus managing 50 client accounts. Some tools charge per project or impose account limits, creating expensive scaling challenges. Others offer unlimited projects under single subscriptions, simplifying budgeting as you grow.
Data reliability should influence decisions. Newer tools might offer attractive features but lack infrastructure for consistent metrics. Established platforms benefit from years of data collection and algorithm refinement. Request demos, compare results across tools, and check user reviews before committing.
Finally, assess how tools adapt to AI search changes. AI search is changing at lightning speed, and the tools that don’t update will quickly fall behind. The best platforms have active roadmaps, regular feature updates, and expanding coverage across emerging AI engines.
FAQs
What is the best AI tool for increasing visibility?
The best AI visibility tool depends on your budget and needs. Ubersuggest offers strong value for small businesses and agencies, combining AI citation tracking with full SEO capabilities at accessible pricing. Enterprise brands might prefer Profound’s deeper analytics. Test several options to find which interface and features match your workflow best.
Are AI visibility tools better than traditional marketing methods?
AI visibility tools complement traditional marketing rather than replacing it. You still need solid content strategy, SEO fundamentals, and audience understanding. Think of them as an extension of your analytics, not a replacement. Use them alongside traditional metrics for a complete view of performance across all channels where audiences find information.
How do AI visibility tools integrate with existing SEO strategies?
<
AI visibility tools track LLM citations the same way traditional tools monitor search rankings. Platforms like Ubersuggest, Semrush, and Ahrefs combine both metrics in unified dashboards. This lets you optimize content for standard search results and AI citations simultaneously, creating strategies that cover all the ways people discover information today.
Conclusion
AI search isn’t slowing down. Platforms that answer questions before users ever click a link are expanding fast. Tracking your presence in AI-generated responses is essential now.
The tools covered here provide visibility into how AI platforms cite your content and mention your brand. Some integrate AI tracking into broader SEO platforms. Others focus exclusively on LLM citations. Your choice depends on budget, needs, and existing systems.
Start measuring your AI visibility now. The brands paying attention today will outperform the ones waiting to catch up later.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-21 16:26:262025-10-21 16:26:26Top AI Visibility Tools in 2026
Google Ads appears to be testing an automatic assignment of New Customer Value within New Customer Acquisition (NCA) campaigns — and it’s doing so without advertisers’ explicit consent.
The change, first spotted by performance marketer Bilal Yasin, has led to unexpected reporting shifts and frustration among advertisers.
“Without any heads-up, and without it being in the change history, a new customer value has suddenly been applied to a customer,” Yasin wrote on LinkedIn. “It was set to 200 DKK… One thing is that Google has assigned a value, but another is that I can’t remove it again!”
Why we care. Advertisers rely on New Customer Value settings to determine how campaigns optimize toward acquiring new users. When Google sets those values automatically, it can distort revenue reporting and campaign efficiency metrics.
Yasin noted several issues:
Google doesn’t know the true lifetime value of a new customer.
Many conversions are still classified as “unknown,” further clouding data.
What they’re saying. Google Ads Liaison Ginny Marvin confirmed the behavior is part of an experiment.
“This guidance is part of an experiment aimed at helping advertisers use settings that will improve results—specifically, to increase new customer ratios,” Marvin wrote.
She added that when the New Customer Value is too low—or not set—it can hinder campaign optimization.
What’s next. Google says it plans to roll out new customer reporting for all purchase conversion campaigns “in the next couple of quarters.”
The bottom line. While Google frames the test as a way to improve campaign performance, advertisers are raising alarms over transparency — especially when automatic value assignments alter reported revenue without clear notice or control.
It’s true that YouTube Ads perform very well for ecommerce advertising aimed at consumers. But YouTube can also help drive B2B leads.
You might be scratching your head and saying, “But I’ve tried YouTube for B2B. It doesn’t convert.” And you would be right.
YouTube Ads for B2B rarely convert directly into leads. Complex products with long sales cycles are not going to sell themselves in one video.
But YouTube campaigns definitely have a positive influence on B2B lead generation – we’ve seen it across nearly all of our B2B clients.
Here are two case studies, featuring very different advertisers, that show how YouTube Ads can be used to increase B2B conversions.
Case study 1: Enterprise B2B SaaS advertiser
One of our enterprise B2B SaaS clients offers multiple business solutions.
Paid search is a strong lead source for most of them, but two struggled to convert – traffic was steady, yet the cost per lead was high.
When we dug in, we found that users weren’t aware of these solutions or how they addressed specific business needs. The landing page content wasn’t persuasive enough.
We tested YouTube video campaigns that clearly explained each solution’s value. The impact was undeniable.
Comparing search performance from the quarter before video to the quarter during, we saw key metrics – CTR, CPC, cost per lead, and conversion rate – all improve.
Here, CTR improved significantly with the video live, which indicates that users had a better understanding of the solution after seeing the video.
This led to a lower CPC, which, combined with a slightly improved conversion rate, lowered cost per lead by 30%.
With the second solution, the results were even more dramatic.
For this solution, front-end metrics actually got worse: CTR declined, and CPC increased.
Search competition in this space was stiffer during the “after” period, which pushed CPCs up.
However, the campaigns still saw a 25% decrease in cost per lead, and conversion rates more than doubled.
In this instance, the video campaigns really helped explain how the solution can benefit users, which directly translated into better conversion rates from search.
For the first five months of 2025, this advertiser ran a small YouTube video campaign intended to drive consideration.
We had hoped the video would directly drive a few leads, and ran it on a Maximize Conversions bid strategy, but it never generated a single lead.
At the same time, CPLs across the entire account were rising, so in early June, we decided to pause YouTube and use the budget on campaigns that were directly driving leads.
That turned out to be a mistake.
CPLs on brand search campaigns rose by 47% when we stopped video.
This is a business without much seasonality, and brand is usually less impacted by seasonality anyway, so at first, we were puzzled. Then we decided to relaunch video.
Voila! Brand search CPLs returned to their previous levels.
We suspected the video campaigns were contributing to the success of the brand campaigns, so we decided to try adding a Demand Gen campaign to the mix.
Brand CPLs decreased by 47%.
Not only were we able to return brand search CPLs to their original levels, but we were also able to cut them nearly in half when combined with YouTube and Demand Gen campaigns.
During the whole nine-month period, YouTube and Demand Gen campaigns only generated two conversions directly. However, the positive impact on brand search performance was indisputable.
It’s important to stress here that we made other optimizations during the test periods for both clients, so the improvements in search are probably not 100% attributable to the addition of the video campaigns.
However, in the case of the enterprise client, the improvements for the solutions that ran video outpaced performance across the rest of the account.
And the fact that two very different advertisers saw correlated improvements in search performance lends further credence to the theory that video played an important role.
Even though these two cases involved very different clients, here are the key practices that made both video campaigns successful:
Use custom segments made up of high-performing search keywords. Don’t use broad targeting or in-market audiences unless you have a very large awareness budget.
If you have first-party audiences and want to run Demand Gen, use them for a lookalike audience. Otherwise, custom segments of strong search keywords work best.
Make your geo-targeting spot-on. Don’t waste spend on irrelevant regions. For the local B2B client, we carefully selected areas of the city that best met their needs. For the enterprise client, even though they wanted to reach a global audience, we took care with which countries we targeted.
Use short videos – no more than 15-30 seconds – and include your brand name and logo in the first few seconds.
Choose a Target CPV bid strategy. We were able to get CPV below $0.01, which got our message in front of as many users in the target audience as possible.
The more videos, the better. If you have 3, 4, 5, or more videos, use them. Even slight variations help minimize video fatigue and grab attention.
You don’t need huge budgets for this to work – in both cases, we spent less than 5% of the client’s total budget on video.
With the right targeting, you can keep costs very reasonable – and the campaigns pay for themselves in lower CPLs in search.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/Enterprise-B2B-SaaS-advertiser-Solution-1-yzX8m1.jpg?fit=375%2C131&ssl=1131375http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-21 13:00:002025-10-21 13:00:00How to use YouTube Ads to drive B2B conversions
Marketing mix modeling (MMM) is having a moment in marketing measurement.
As privacy regulations limit user-level tracking, marketers are turning to it for reliable, cross-channel measurement. (We love it at my agency – MMM analyses often lead to smarter budget allocation with significant downstream impact.)
But as adoption grows, so do execution errors and misconceptions about what MMM can and can’t do.
Despite its strategic potential, it’s often misused, misinterpreted, or oversold – leading to costly mistakes and credibility loss from unrealistic expectations.
MMM isn’t a black box. To produce meaningful insights, it demands context, strategy, iteration, and strong data.
Context is especially critical. Without it, MMM becomes what I call a mathematical echo chamber – no external inputs and little connection to reality.
This article breaks down how to approach MMM correctly, avoid common pitfalls, and turn your analysis into real business value.
Execution errors
Too often, teams fixate on the modeling technique and overlook the broader system – data quality, assumptions, and stakeholder context.
There are plenty of possible mistakes, but the ones I see most often are:
Using inconsistent, incomplete, or unvalidated spend and performance data.
Assuming immediate or linear responses to media spend, which oversimplifies reality.
Interpreting statistical relationships as proof of impact without experimentation.
Using MMM for daily campaign decisions despite its strategic design and lagging granularity.
Building models that are over-optimized in-sample but fail in the real world.
If you make any of these, your MMM efforts will be muddled and ineffective, and you will not get much buy-in for the initiative going forward.
Faulty expectations vs. reality
When run properly, MMM can offer highly valuable insights, but only within its appropriate use case.
With good modeling and inputs, you can:
Reallocate budgets based on marginal ROI and saturation.
Forecast sales impact from various budget scenarios.
Set spending caps to avoid diminishing returns.
Show long-term contributions of brand versus performance channels.
Track media effectiveness over time and support cross-functional alignment.
What you cannot expect MMM to do:
Optimize daily media buying decisions.
Attribute at the user or creative level.
Replace lift tests or experimentation (which are a necessary complement to MMM).
In other words, treat MMM as a strategic GPS that needs other inputs to work well, not a tactical turn-by-turn navigation tool.
Misreadings of output
You can give three marketers the same MMM output, and they might have three very different interpretations of what it means and what to do next.
We’ve got a handy chart of the ways people misread the data (and how to fix those mistakes):
The misinterpretation I’d like to spend a bit of time on here is the correlation/causation dynamic.
Marketers need to understand that MMM is essentially a fancy correlation analysis that needs to be supplemented by incrementality testing, such as geo lift testing, to establish causation.
MMM does involve coding, but it’s a lot more than that.
It’s a cross-functional discipline involving data science, marketing, finance, and strategy.
To get it right, you need:
1. Clean, longitudinal data
One note before I dive into the data elements you need to run MMM: data density is critical.
For businesses without a huge pool of revenue-generating events (think of big SaaS platforms or car dealerships advertising online), use strategic proxy metrics that happen earlier in the purchase journey and provide strong predictors of revenue generation.
With that in mind, here’s the data needed (or recommended) for your model:
Weekly data across 2–3 years.
Media spend by channel and campaign. (Region is recommended.)
Control variables (all recommended): Promos, pricing, and competitors.
Note: seasonality is baked into the model for Meta’s Robyn, one of my favorite MMM options.
2. Advanced modeling techniques
Adstock/lag functions to reflect delayed impact.
Saturation models (e.g., Hill curves) for diminishing returns.
Regularization or Bayesian priors to stabilize estimates.
3. Validation and iteration
Running an MMM analysis once and taking the results at face value is never going to get you the best possible insights.
If you’re serious about adopting MMM, prepare to include the following in your process:
I highly recommend running analyses more than once and using different methods/platforms to identify commonalities and differences.
In the visual comparing Robyn and Meridian’s output from a recent client analysis, both models attributed similar influence across most channels – a good sign that helps validate the model.
But there’s a wrinkle: for channel 0, Meridian showed much higher organic influence and a slight bump in paid.
That suggests we need additional testing before moving to action items.
4. Stakeholder engagement
Even with top-tier MMM analyses, how you communicate the findings – and what they enable – is critical to getting buy-in from clients or management.
Before you start, align with stakeholders on KPIs, ROI definitions, and model assumptions to prevent surprises or misunderstandings later.
When you share results, include uncertainty ranges and clear action items that flow directly from your data.
If you can’t answer the inevitable “So what?” question, you’re not ready to present your findings.
Better MMM becomes a competitive edge
Overall, the shift away from user-based tracking is healthy for the marketing industry.
Initiatives like incrementality testing and MMM are finally getting their due as core parts of campaign analysis.
As major platforms level the optimization playing field with automation, running these analyses more effectively than your competitors is one way to drive differentiated growth.
A website redesign is essential for remaining competitive, but for multi-location businesses, the risks are much higher. Stripping away the local relevance that drives traffic to location pages can cause rankings and online visibility to plummet.
Using localized content on location pages resulted in a 107% rankings lift, something businesses risk losing if a redesign hurts these pages.
To mitigate the risk of fallen local rankings and to get the most from your website redesign, you need to maintain good multi-location local SEO and take key steps for a successful redesign.
Prioritizing SEO during a location page redesign helps multi-location businesses stay competitive.
Technical SEO pre-redesign audit checklist
Before launching a new website redesign, it’s essential to perform a comprehensive audit to ensure that your SEO foundation is retained. Thorough auditing before launch can help prevent common mistakes and preserve your rankings.
Manage inventory: Document all business locations, Google Business Profile IDs, current URLs, organic rankings, and highest-converting queries.
Identify issues: Use a site crawler to uncover duplicate/thin content, poor Core Web Vitals, slow loading, mobile responsiveness, or accessibility gaps.
Conduct a technical crawl audit: Confirm crawl budget, indexing, updated sitemaps, and hreflang configuration on multinational sites.
Audit and enhance structured data: Ensure LocalBusiness schema is present and NAP is perfectly consistent. Validate canonical tags for duplicate prevention.
Expand structured data: Consider implementing review, FAQ, and service schema types for additional SEO coverage.
Set up robust tracking: Implement UTM tagging, conversion tracking, and phone call analytics to precisely measure local and national SEO performance
PageSpeed Insights can let you know how fast your website loads and indicate potential performance issues.
How to optimize site architecture and your URL strate
Once your pre-redesign audit is complete and you’ve identified areas for improvement, it’s time to shift focus to your site architecture for SEO. A solid foundation in site architecture ensures both search engines and users can navigate your website with ease.
Common structures include:
Subfolders (/locations/city)
Subdomains (city.brand.com)
Multisite frameworks
Dedicated microsites
Subfolders generally work best for centralizing authority and scalability, with a primary website that branches out into many pages, including one for each location.
Note that it’s crucial to maintain consistency with your URL structure. If the existing site already has a URL for each location within a subfolder structure, do not change it! Ensuring that the URL structure remains identical between the existing and new website design is essential for retaining your SEO value and preventing any loss in rankings.
Here are some other key considerations:
Canonical URLs: Identify canonical URLs that help mitigate the risk of duplicate content.
Sitemap strategy: Determine whether your site should implement an XML sitemap or an HTML sitemap, with XML sitemaps being more explicitly effective for site crawlers and SEO, while HTML sitemaps could help with user navigation.
URL templates: Use static URLs, they’re cleaner and more optimization-friendly design (e.g., example.com/services/dentistry/location/).
Location page content considerations
Technical SEO components are important, but so is content. When redesigning your website, it’s crucial to prioritize the content elements that impact the SEO performance of your location pages.
A successful redesign should seamlessly integrate these elements to preserve and boost your SEO efforts.
Unique H1 on each location page with city intent that targets a relevant keyword, such as “housekeeping services in [city]”
Full name, address, and phone number that’s consistent across all directory listings
Link to each location’s corresponding GBP page.
Business hours that are up-to-date and unique to each location, further aligning with directory information
Local phone number, preferably static to maintain consistency with NAP data
Service-specific content, including details about each of your offerings, with locally optimized keywords for each
Staff and team photos showing the people behind your business, potentially at each location
Local testimonials from satisfied customers, including review and schema markup for aggregate ratings
h1 optimized for location and main keyword with custom text and clear CTAs. Source
As you incorporate these essential content elements into your location page redesign, it’s critical to ensure each page is unique and tailored to its specific location.
Each location page should include a designated content block section where you can add customized details about that individual location. This will additionally help reduce duplicate content across your location pages.
Location page with h1 and copy optimization Source
Top design elements to consider for a multi-location website
Redesigning location pages can be challenging because it requires balancing brand consistency with the unique identity of each location. Achieving this balance involves the strategic use of design elements that appeal to both local audiences and the overarching brand.
Top design elements include:
Location-specific imagery: For brick-and-mortar locations, use high-quality images of the storefronts. For service-based locations, showcase custom visuals that reflect the areas they serve.
Interactive maps or location finders: Adding Google Maps or a custom location finder helps users easily find the nearest store or service center. This feature not only enhances usability but also provides a tailored experience for visitors.
Social media feed integration: Integrating a live social media feed on location pages adds dynamic content and more localized imagery. It also provides a space to showcase promotions, events, and local engagement, keeping the page fresh and relevant.
Team photos: Featuring photos of local team members helps humanize the brand and create a personal connection with your audience. It’s a great way to reinforce the idea that your business is part of the local community, building trust and authenticity.
Example of an optimized H1 with custom images on the team page. Source
Complete multi-location redesign audit checklist
Website redesigns often require several months of planning and execution. However, before you push the new site live, it’s essential to ensure it passes the following key tests if you want to retain your traffic:
Brand consistency: Ensure that branding elements, such as colors, typography, logos, and tone, are consistent across all pages and location-specific content.
URL mapping: Double-check that all important URLs are correctly mapped to the new design and are still functional, preserving the SEO value of your existing pages.
No URL structure changes: If you’re maintaining a subfolder structure, confirm that no URL structures are altered to prevent any loss of SEO rankings or broken links.
Site performance: Test the website’s speed to ensure it meets performance standards, passes Core Web Vitals, is mobile-responsive, and is free of any accessibility issues.
Clear CTAs: Ensure that each page features clear, concise calls to action (CTAs) above the fold to encourage user engagement and conversions from the moment visitors land on the page.
Analytics setup: Verify that all necessary analytics and tracking codes (e.g., Google Analytics, conversion goals, UTM parameters) are properly implemented to monitor site performance and user behavior across all locations.
Mobile optimization: Check that the site is fully optimized for mobile users, with responsive design elements that scale and display well on all devices.
SEO-friendly content: Review content for SEO optimization, ensuring that each location page is targeted with local keywords, meta descriptions, and proper header tag hierarchy.
Structured data implementation: Verify that all relevant schema markup (e.g., LocalBusiness, Service, Review) is correctly applied to each location page to support search engines in indexing your content.
Internal linking: Ensure that all location pages have strong internal linking, guiding users through the site, and boosting SEO by connecting related content.
User testing and feedback: Conduct user testing or gather feedback from stakeholders to ensure the new design is intuitive, user-friendly, and aligns with business goals.
Content uniqueness: Confirm that all location pages have unique, location-specific content to avoid any potential issues with duplicate content.
Legal and compliance checks: Ensure that the website complies with any industry-specific regulations (e.g., ADA compliance, GDPR, HIPAA) before launch.
Cross-browser compatibility: Test the website across various browsers to ensure it functions smoothly for all users, regardless of their preferred browser.
Backup and contingency plans: Create a backup of the current website before launching the redesign, and have a contingency plan in place in case issues arise post-launch.
By ensuring that these elements are in place, you can launch a multi-location website redesign that performs well across all locations and provides a seamless, user-friendly experience for your visitors.
Example of before website redesign and after a website redesign
The business case: search and revenue impact at scale
Partnering with a web design and development agency that truly understands the complexities of multi-location businesses, technical SEO, and CRO is essential.
Ignite Visibility is a prime example of this expertise. We implemented a performance-driven SEO strategy to help a home services franchise with over 60 locations across the U.S.
The team optimized city-specific landing pages, standardized keyword-rich updates across Google Business Profiles, and strategically matched high-intent keywords to local markets to maximize visibility.
The results speak for themselves. The Ignite Visibility approach doesn’t just maintain rankings – it creates massive growth opportunities.
If you’re ready to maximize the impact of your next website redesign and achieve measurable, scalable growth, reach out to Ignite Visibility. With our proven track record, we’ll help you stay ahead of the competition and deliver results that matter.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/Ignite-20251021-header-wFV29v.png?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-21 11:00:002025-10-21 11:00:00How to execute a multi-location website redesign without losing traffic by Ignite Visibility
Google is starting to roll out its new Text Guidelines feature in Google Ads, a tool first announced at the Think Retail event five weeks ago that gives advertisers more control over AI-generated ad copy.
Driving the news. The feature, now appearing in some accounts, lets marketers set campaign-level text parameters — guiding Google’s AI to stay within brand tone, language preferences, and compliance requirements when generating text assets.
Why we care. As Google Ads leans deeper into AI-powered creative, advertisers have been asking for stronger brand safety and message consistency controls. Text Guidelines offer a way to fine-tune AI output without sacrificing automation or performance.
How it works:
Found at the campaign level, Text Guidelines apply only when text customization is turned on.
Advertisers can define rules to steer AI-generated text assets toward specific brand or legal standards.
Designed to support “brand-safe creative” and improve asset quality.
The bottom line. Text Guidelines give brands a new lever to shape how Google’s AI writes for them — tightening control without slowing down automation.
First seen. This rollout was spotted by PPC Speacialist Arpan Banerjee
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/google-ads-text-guidelines-beta-1760877222-pPO2We.jpg?fit=1113%2C1058&ssl=110581113http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-20 16:31:512025-10-20 16:31:51Google Ads’ new text guidelines feature begins rolling out
Google is tightening its account retention policy — canceled Google Ads accounts will now be permanently deleted six months after cancellation, marking the end of indefinite account storage.
Driving the news. Under the new policy, Google will begin a cleanup of inactive accounts, sending a 30-day email warning before deletion. Previously, advertisers could reactivate canceled accounts at any time, preserving data and structure indefinitely.
Why we care. This change could impact advertisers who rely on historical performance data, conversion tracking, or campaign templates stored in inactive accounts. Once deleted, all account history and assets — including campaigns, reports, and settings — will be gone for good.
How it works:
Canceled accounts with no active campaigns will be deleted six months after cancellation.
A 30-day warning email will be sent before deletion.
Reactivating an account within the six-month window will prevent deletion.
Between the lines. The policy shift underscores Google’s broader effort to streamline its ad systems and purge unused data, mirroring similar moves across other Google services.
The bottom line. Advertisers who want to preserve old campaign data or structures should reactivate or export data from canceled accounts before the six-month clock runs out.
First seen. This update was spotted by PPC News Feed founder Hana Kobzová.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/Inside-Google-Ads-AI-powered-Shopping-ecosystem-Performance-Max-AI-Max-and-more-Hl9R7E.webp?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-20 16:12:482025-10-20 16:12:48Google Ads to permanently delete canceled accounts after six months
