A new column called “Original Conversion Value” has started appearing inside Google Ads, giving advertisers a long-requested way to see the true, unadjusted value of their conversions.
How it works. Google’s new formula strips everything back:
Conversion Value
– Rule Adjustments (value rules)
– Lifecycle Goal Adjustments (e.g., NCA bonuses)
= Original Conversion Value
Why we care. For years, marketers have struggled to isolate real conversion value from Google’s layers of adjustments — including Conversion Value Rules and Lifecycle Goals (like New Customer Acquisition goals). Original Conversion value makes it easier to diagnose performance, compare data across campaigns, and spot when automated bidding is boosting value rather than actual conversions.
In short: clearer insights, cleaner ROAS, and more confident decision-making.
Between the lines:
Value adjustments are useful for steering Smart Bidding.
But they also inflate numbers, complicating reporting and performance analysis.
Agencies and in-house teams have long asked Google for a cleaner view.
What’s next. “Original Conversion Value” could quickly become a go-to column for:
Revenue reporting
Post-campaign analysis
Troubleshooting inflated ROAS
Auditing automated bid strategies
First seen. This update was first picked up by Google Ads Specialist Thomas Eccel when he shared spotting the new column on LinkedIn
The bottom line. It’s a small update with big clarity. Google Ads is giving marketers something rare: a simpler, more transparent look at the value their ads actually drive.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/11/Screenshot-2025-11-18-at-16.40.16-l0XhcV.webp?fit=549%2C451&ssl=1451549http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-18 17:01:322025-11-18 17:01:32Google Ads quietly rolls out a new conversion metric
Ask the same question in 11 AI search engines, and you’ll get 11 different answers.
Sometimes wildly different.
Some engines focus on visuals and shoppable results. Others go deep into research. A few just try to get you an answer, fast.
Each platform prioritizes and presents it differently.
And those differences matter.
Not just for users, but for brands trying to get discovered in AI search.
So, I tested popular and lesser-known AI engines on accuracy, depth, user experience, and other factors.
Only four made the cut.
In this guide, you’ll learn which AI search engines came out on top, including pros, cons, and pricing. I’ll also share which engines didn’t make my list, and why.
Along the way, you’ll get a few tips on using these insights to improve your AI visibility.
Start with a quick overview of my findings below. Or jump straight to the #1 AI search engine on my list: ChatGPT.
What Are the Best AI Search Engines?
Tool
Best for
Pros
Cons
Price
ChatGPT
Comprehensive research and shoppable product comparisons
Visual layout with tables and images; remembers context across follow-ups; direct purchase links
Overwhelming results for broad queries; accuracy issues; overly agreeable
Free or $20+/month
Google AI Mode
Quick product searches with real buyer reviews
Fast product results with pricing and reviews; integrates Google ecosystem
Vague on informational queries; no comparison tables; unavailable in some regions
Free
Sigma Chat (Formerly Bagoodex)
Research deep dives that build on previous questions
Weak product presentation; no pricing or buy links; poor visuals
Free or $10+/month
Microsoft Copilot
Fast answers in clean, skimmable formats
Clean categorization; fast responses; easy to skim
Surface-level depth; no product links; weak for shopping
Free
How I Tested 11 AI Search Engines
To keep things consistent, I ran the same set of prompts across 11 AI search tools.
Note: For this article, I defined “AI search engine” as any generative AI platform that can understand queries, pull information from sources, and deliver answers in natural language.
This included big names like ChatGPT, AI Mode, and Perplexity.
And newer players like Arc, Andi, and Sigma Chat.
I focused on one topic (running shoes) and tested a range of prompts across different search intents.
This showed how well each engine handled the full customer journey, from research to shopping.
This included:
“Best running shoes”: Assesses top-level recommendations and how each engine handles broad prompts
“Best running shoes for beginner marathon training”: Evaluates personalization and context handling as the prompt narrows
“How long do running shoes last?”: Gauges accuracy on general product knowledge and durability expectations
“Of the trainers you’ve recommended, which ones will last the longest?”: Tests the accuracy of product details and the engine’s ability to remember details from previous prompts
“Can I wear any of these running shoes recommended for hiking?”: Assesses how each AI handles reasoning, real-world nuance, and potential safety considerations
I evaluated each tool on five factors:
Accuracy: Did it understand the intent and get the facts right?
Depth: Did it add helpful context or just summarize existing content?
Transparency: Did it credit or link to its sources?
User experience: Was the output fast, skimmable, and well-organized?
Adaptability: Could it handle follow-up questions naturally or refine vague prompts?
After testing all 11 AI search engines, these four stood out as the best for different reasons.
1. ChatGPT
Best for comprehensive research and shoppable product comparisons
This is true whether you’re comparing products, researching topics, or looking for a step-by-step tutorial.
It also remembers context across follow-up questions.
I started with a broad prompt and added specifics as the conversation progressed. ChatGPT remembered key details without making me repeat myself.
For shopping queries, the visual presentation stood out.
When I searched for running shoes, for example, ChatGPT returned products with images, prices, reviews, and short descriptions.
It also included links to retailers and external articles. This made verifying product details and purchasing easy.
The summary tables were particularly useful.
After inquiring about shoe lifespan, ChatGPT delivered a clean comparison table with products and their expected mileage.
For brands: ChatGPT’s visual layout isn’t just useful for shoppers. If you’re trying to get your brand referenced by AI search engines, it also reveals what these models prioritize. Use tables, clear specs, and organized categories on your product pages to help both shoppers and AI find your information faster.
ChatGPT is also evolving quickly.
Features like Instant Checkout (currently limited to select Etsy sellers in the United States) let users complete purchases directly inside the chat.
Great for shoppers — and even greater for the brands featured in ChatGPT’s recommendations.
Where ChatGPT Falls Short
When I tested ChatGPT, I got what most people want from AI search: answers that feel confident and complete.
But not every response was perfect.
Broad prompts, such as “Best running shoes,” resulted in lengthy lists of brands, product categories, and features.
The information took real effort to digest.
Specific prompts worked much better.
I also noticed minor inaccuracies in some instances, like when I asked about shoe lifespan.
After fact-checking the replies, some details didn’t match the manufacturer’s specifications.
For example, ChatGPT said the Brooks Ghost running shoe has a lifespan of 450 to 500 miles. But the actual range is 300 to 500 miles.
This also highlights a larger problem.
ChatGPT pulls information from multiple sources, such as blog posts and brand sites.
But it also relies on forums like Quora and Reddit, where users share personal experiences.
It then aggregates the information into its responses. This can lead to inaccurate and misleading information.
For brands: Provide clear answers to common user questions on your site. Otherwise, AI search engines may turn to other, potentially inaccurate sources for this information. Add tables with specifications, be explicit about ranges and measurements, and use structured data so AI can extract and cite your product information correctly.
ChatGPT also tends to be overly agreeable.
Whatever you prompt, ChatGPT will lean toward flattery and agreement — even when it involves safety.
For example, when I asked, “Can I wear any of these running shoes recommended for hiking?”
ChatGPT’s response was:
“Good question 👍 — you can hike in road running shoes, but whether it’s a good idea depends on the terrain and how far you’re going.”
Not the worst.
But not as good as other AI search engines in this aspect, like AI Mode, which was more cautious.
AI Mode said:
“It is not recommended to use the road running shoes previously mentioned for hiking…they lack the key features that provide the necessary grip, protection, and stability for off-road trails. Using them for hiking could lead to injury.”
Overall, ChatGPT is fast, detailed, and helpful.
But it can be too generous with information — and too polite to push back.
It pulls product listings, prices, and reviews directly into the search interface. This makes it ideal for shoppers who want to quickly compare products before purchasing.
What AI Mode Does Well
AI Mode shines when you have clear buying intent.
It instantly surfaces product options with images, prices, star ratings, and quick links to retailers. And it’s all in a clean, scrollable layout.
When I searched “best running shoes,” it showed a curated carousel of options with price comparisons across multiple sites.
I especially liked how it paired Google Reviews with its recommendations — a small detail that makes decision-making faster and builds trust.
For me, that worked perfectly.
Getting straight to the products moved me faster toward a decision.
But some users may prefer more background or context for researching and weighing options. ChatGPT’s research-style answers still win in this regard.
For brands: AI Mode pulls heavily from Google Reviews and structured product data. Focus on getting detailed, positive reviews and keeping your product schema markup up to date. These signals can influence whether your products appear in AI-generated results.
Where AI Mode Falls Short
AI Mode is not yet available in all countries, although it’s rolling out quickly.
And unlike ChatGPT, it didn’t provide any comparison tables for any of my prompts. Just products and bullet points.
This meant more scrolling and clicking to find and digest the information.
This was evident when I asked which of the recommended shoes would last the longest.
AI Mode’s response was vague and unhelpful. It said the Brooks Ghost shoe was “exceptionally long-lasting.”
It didn’t provide any of the specifics that would make me want to purchase this shoe. Like mileage range and how it differed between the options.
If you’re early in the evaluation phase, AI Mode can feel limiting.
But it delivers when you want a shortlist of top contenders.
Pricing
AI Mode is available for free within Google Search, depending on your region.
Best for research deep dives that build on previous questions
Sigma Chat’s iterative search and in-depth replies are excellent if you love to research.
Ask a question, get an answer, then drill deeper into related topics — and it remembers the full thread.
Note: Bagoodex launched in 2024 and has since rebranded as Sigma Chat. For this review, I tested it against the standard modes of other tools. ChatGPT’s Thinking mode and Perplexity’s Research mode are designed for deep research and may perform differently.
What Sigma Chat Does Well
Sigma Chat stood out for its ability to build on previous context.
When I asked follow-up questions, it remembered what I’d already searched and adjusted its answers accordingly.
No need to repeat myself or reframe the entire query.
For example, after I asked which of the recommended shoes would last the longest, it specifically referenced “marathons.”
(Even though I hadn’t mentioned this criterion again after the initial prompt.)
Sigma Chat’s follow-up suggestions also stood out for their potential to aid deep research.
Instead of ending with one answer, it nudged me toward related questions I hadn’t considered:
This makes it particularly helpful for any kind of research, whether you’re comparing products, building content outlines, or researching niches.
For brands: Sigma Chat rewards depth and topic clustering. To increase visibility in AI tools like this, build content hubs around your main topics — link related pages together and cover every sub-question your audience might ask. The more complete your coverage, the easier it is for AI to surface your site in deep research queries.
Another interesting feature of this AI search engine?
It suggests prompts tailored to content creation. This is especially helpful if you’re using it for marketing purposes.
After providing search results for the best running shoes for a marathon, it offered unexpected options like:
“Write a blog post about this topic”
“Create an image on this topic”
I tested the blog prompt, and it generated a quick draft titled “Marathon Training on a Budget: Choosing Durable Running Shoes.”
It wasn’t something you’d publish as-is, but it was a decent starting point.
If you’re prone to writer’s block or need to quickly draft comparison content around competitor products, it’s a particularly helpful feature.
From there, it suggested additional prompts like “Add a call to action” and “Shorten for social media.”
This makes it easy for marketers to generate content for multiple platforms at once.
Microsoft Copilot has the cleanest layout of any AI search engine I tested.
It’s fast, structured, and organized. Perfect for people who want distraction-free takeaways.
What Microsoft Copilot Does Well
When you ask Copilot a question, it responds instantly with skimmable categories, bullet points, and emojis.
For example, when I searched “best running shoes,” it broke recommendations into helpful categories:
“Best overall”
“Best stability shoe”
“Best daily trainer”
When I narrowed the query to “best running shoes for beginner marathon training,” Copilot further refined the results.
It added details about who each shoe was best for, making the advice more actionable — a nice touch for a tool focused on clarity.
Even for informational queries like “can I wear these for hiking,” Copilot delivered a simple breakdown.
And added specific scenarios where running shoes would and wouldn’t be ideal for hiking.
When you want fast, direct answers without having to sift through a bunch of content, Copilot is a great option.
For brands: Pay close attention to how Copilot structures its answers — categories, comparisons, “best for” labels. Use similar formatting on your own pages to help AI tools extract and present your content more effectively.
Where Microsoft Copilot Falls Short
Copilot’s polished format comes at a cost: depth and shoppability.
Its responses are tidy but often too surface-level — especially for commercial searches like “best running shoes.”
When I tested this prompt, it didn’t link directly to any product pages or show pricing.
So, I couldn’t easily comparison shop, verify information, or choose a merchant and purchase immediately.
Instead, it summarized content from other “best” listicles and linked those sources.
Like Sigma Chat, unless Microsoft improves its shoppability, it’s unlikely consumers will use it for this purpose.
Instead, Copilot works better as a light research tool — especially when you want fast information with minimal reading.
AI Search Engines That Didn’t Make the Cut (and Why)
All of these AI search engines had their pros and cons.
But overall, they fell short for different reasons.
Claude
I really liked Claude, but the output was very similar to ChatGPT.
This isn’t a problem, but I didn’t want to list tools that were similar in functionality.
I wanted to provide only the best.
Compared to ChatGPT, Claude lacked product links and visuals:
The wall of text made the information challenging to process.
I did like the categorization, but ChatGPT does this too — with tables that are easier to skim.
Perplexity
Like Claude, Perplexity came somewhat close to ChatGPT in overall performance.
When asked a prompt with buying intent, it provided a short summary along with product images, pricing, and star ratings.
No tables to help me quickly compare features and options, though.
The summary was also fairly generic.
And didn’t feel all that tailored to my prompt, even when I used the more specific “marathon” wording.
Brave
Brave, a privacy-focused AI search engine, felt too much like traditional search.
It features long lists of articles without any clear hierarchy or comparison features.
While this might be helpful for browsing links, it doesn’t summarize much or help you make quick decisions.
Andi
Andi, a minimal AI search tool, offered few results, sometimes just one (e.g., a single Reddit thread).
It’s a bit like the “I’m Feeling Lucky” button on Google. Simple to use but extremely limiting for in-depth research or shopping.
Arc
Arc, a mobile- and browser-based AI search engine, requires a download to use.
This is inconvenient compared to browser-based AI search.
When so many other options exist, it’s hard to justify using this AI engine for this reason alone.
You
You is a solid AI search engine that has been around for multiple years.
But it was slow to respond and didn’t link to products in commercial searches.
Ultimately, I found it less useful than the other AI tools overall.
What This Means for Your AI Search Visibility
After testing 11 AI search engines, one thing became clear.
No matter how their formatting or preferences differ, the goal remains the same: to serve clear, credible, and well-structured content.
If your pages do that — with comprehensive coverage, positive reviews, and clean markup — you’ll be positioned to perform well across all AI search engines and LLMs.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-18 14:52:452025-11-18 14:52:45I Tested 11 AI Search Engines: Only These 4 Made the Cut
Picture a chocolate company with an elaborate recipe, generations old. They ask an AI system to identify which ingredients they could remove to cut costs. The AI suggests one. They remove it. Sales hold steady. They ask again. The AI suggests another. This continues through four or five iterations until they’ve created the cheapest possible version of their product. Fantastic margins, terrible sales. When someone finally tastes it, the verdict is immediate: “This isn’t even chocolate anymore.”
Aly Blawat, senior director of customer strategy at Blain’s Farm & Fleet, shared this story during a recent MarTech webinar to illustrate why 82% of marketing teams are failing at AI adoption: automation without human judgment doesn’t just fail. It compounds failure faster than ever before. And that failure has nothing to do with the technology itself.
The numbers tell the story. In a Forrester study commissioned by Optimove, only 18% of marketers consider themselves at the leading edge of AI adoption, even though nearly 80% expect AI to improve targeting, personalization and optimization. Forrester’s Rusty Warner, VP and principal analyst, puts this in context: only about 25% of marketers worldwide are in production with any AI use cases. Another third are experimenting but haven’t moved to production. That leaves more than 40% still learning about what AI might do for them.
“This particular statistic didn’t really surprise me,” Warner said. “We find that a lot of people that are able to use AI tools at work might be experimenting with them at home, but at work, they’re really waiting for their software vendors to make tools available that have been deemed safe to use and responsible.”
The caution is widespread. IT teams have controls in place for third-party AI tools. Even tech-savvy marketers who experiment at home often can’t access those tools at work until vendors embed responsible AI, data protections and auditability directly into their platforms.
The problem isn’t the AI tools available today. It’s that marketing work is still structured the same way it was before AI existed.
The individual vs. the organization
Individual marketers are thirsty for AI tools. They see the potential immediately. But organizations are fundamentally built for something different: control over brand voice, short-term optimization and manual processes where work passes from insights teams to creative teams to activation teams, each handoff adding days or weeks to cycle time.
Most marketing organizations still operate like an assembly line. Insights come from one door, creative from another, activation from a third. Warner called this out plainly: “Marketing still runs like an assembly line. AI and automation break that model, letting marketers go beyond their position to do more and be more agile.”
The assembly line model is excellent at governance and terrible at speed. By the time results return, they inform the past more than the present. And in a world where customer behavior shifts weekly, that lag becomes fatal.
The solution is “Positionless Marketing,” a model where a single marketer can access data, generate brand-safe creative and launch campaigns with built-in optimization, all without filing tickets or waiting for handoffs. It doesn’t mean eliminating collaboration. It means reserving human collaboration for major launches, holiday campaigns and sensitive topics while enabling marketers to go end-to-end quickly and safely for everything else.
Starting small, building confidence
Blain’s Farm & Fleet, a 120-year-old retail chain, began its AI journey with a specific problem: launching a new brand campaign and needing to adapt tone consistently across channels. They implemented Jasper, a closed system where they could feed their brand tone and messaging without risk.
“We were teaching it a little bit more about us,” Blawat said. “We wanted to show up cohesively across the whole entire ecosystem.”
Warner recommends this approach. “Start small and pick something that you think is going to be a nice quick win to build confidence,” he said. “Audit your data, make sure it’s cleaned up. Your AI is only going to be as good as the data that you’re feeding it.”
The pattern repeats: start with a closed-loop copy tool, then add scripts to clean product data, then layer in segmentation. Each step frees time, shortens cycles, and builds confidence.
Where data meets speed
Marketers aren’t drowning in too little data. They’re drowning in too much data with too little access. The 20% of marketing organizations that move fast centralize definitions of what “active customer,” “at risk,” and “incremental lift” actually mean. And they put those signals where marketers work, not in a separate BI maze.
“There’s massive potential for AI, but success hinges on embracing the change required,” Warner said. “And change is hard because it involves people and their mindset, not just the technology.”
The adoption lag isn’t about technology readiness. It’s about organizational readiness.
Balancing automation and authenticity
Generative AI took off first in low-risk applications: creative support, meeting notes, copy cleanup. Customer-facing decisions remain slower to adopt because brands pay the price for mistakes. The answer is to deploy AI with guardrails in the highest-leverage decisions, prove lift with holdouts and expand methodically.
Blawat emphasized this balance. “We need that human touch on a lot of this stuff to make sure we’re still showing up as genuine and authentic,” she said. “We’re staying true to who our brand is.”
For Blain’s Farm & Fleet, that means maintaining the personal connection customers expect. The AI handles the mechanics of targeting and timing. But humans ensure every message reflects the values and voice customers’ trust.
The future of marketing work
AI is moving from analysis to execution. When predictive models, generative AI and decisioning engines converge, marketers stop drawing hypothetical journeys and start letting the system assemble unique paths per person.
What changes? Less canvas drawing, more outcome setting. Less reporting theater, more lift by cohort. Fewer meetings, faster iterations.
Warner points to a future that’s closer than most organizations realize. “Imagine a world where I don’t come to your commerce site and browse. Instead, I can just type to a bot what it is I’m looking for. And I expect your brand to be responsive to that.”
That kind of conversational commerce will require everyone in the organization to become a customer experience expert. “It doesn’t matter what channel the customer uses,” Warner explained. “They’re talking to your brand.”
The path forward
There is no AI strategy without an operating model that can use it. The fix requires three fundamental changes: restructure how marketing work flows, measure lift instead of activity and enable marketers to move from idea to execution without handoffs.
The path forward requires discipline. Pick one customer-facing use case with clear financial upside. Define the minimum signals, audiences and KPIs needed. Enforce holdouts by default. Enable direct access to data, creative generation and activation in one place. Publish weekly lift by cohort. Expand only when lift is proven.
Warner expects adoption to accelerate significantly in 2026 as more vendors embed AI capabilities with proper guardrails. For brands like Blain’s Farm & Fleet, that future is already taking shape. They started with copywriting, proved value and are now expanding. The key was finding specific problems where AI could help and measuring whether it actually did.
AI will not fix a slow system. It will amplify it. Teams that modernize the way work gets done and lift the language of decisions will see the promise translate into performance.
As Blawat’s chocolate story reminds us, automation without judgment optimizes for the wrong outcome. The goal isn’t the cheapest product or the fastest campaign. It’s the one that serves customers while building the brand. That requires humans in the loop to point AI in the ri
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/11/Optimove-20251118-v2-on6doc.jpg?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-18 12:00:002025-11-18 12:00:0082% of marketers fail AI adoption (Positionless Marketing can fix it) by Optimove
Attribution in the modern marketing age can be confusing. But the pressure on marketing teams to “prove what’s working” never goes away.
Traditionally, marketers had certain data we could always rely on, but the data pool we can pull from seems to be growing and shrinking at the same time. Between privacy constraints, zero-click searches, AI Overviews, and channel-walled gardens, marketers are flying blind in more ways than they realize. Attribution has always been an imperfect science. And in 2025, it’s gone from fuzzy to fragmented.
If you’re planning marketing budgets and trying to defend where your spend is going, there’s no need to freak out. Marketing attribution is possible. It doesn’t look like it used to, though. And if you’re still only relying on touch-based models or last-click reports, you might be measuring the wrong things entirely.
Let’s break down where attribution is failing, what’s making it harder, and what forward-looking marketers are doing to close the gap.
Key Takeaway
Attribution challenges have multiplied due to AI, automation, and privacy shifts.
Walled gardens, offline sales, and dark social are major blind spots, and they often overlap.
Deterministic, touch-based attribution is giving way to modeled and probabilistic methods.
AI isn’t just the problem, it’s also part of the solution.
You don’t need perfect data. You need data that helps you make better decisions.
The New Face of Attribution
Attribution used to be about stitching together clicks. Now, we’re lucky if we get clicks at all thanks to zero-click search.
Today’s buyers bounce between different platforms on multiple devices and AI-curated content. They’re influenced by ads on a connected TV or product mentions in a ChatGPT thread, and neither of those leaves a clean digital trail.
Meanwhile, ad platforms like Meta and Google have leaned hard into automation. That means fewer transparent levers to optimize and more “black box” performance metrics. According to NP Digital analysis, there are over 90% fewer optimization permutations in Google and Meta Ads today compared to 2023. So yes, marketing attribution is back. But the infrastructure around it seems more broken than ever.
Finding Marketing Blindspots
Unfortunately, the reality is that attribution blind spots don’t come with a warning light. You may be staring directly at your dashboard and not notice traffic is piling up in areas you’re not tracking. And the amount of potential blindspots is growing.
Here are the big ones:
Walled Gardens: Platforms like Google, Meta, and Amazon are all powerful, but have become much more mysterious as search evolves. You’re renting their space, but if you don’t play by their rules, you may not get complete visibility.
Offline Sales: Leads turn into deals in CRMs, call centers, or retail. They may have started as a click, but the customer journey ends at a brick-and-mortar location or an entirely different platform than the original click.
Cross-Device Journeys: That ad someone saw on mobile might convert from their phone, but they could just as easily become a sale on their desktop or smart TV.
Building Awareness: Upper funnel spend (like digital out-of-home (OOH) or video) gets undervalued because it rarely leads to a direct conversion.
Dark Social: Private sharing (think WhatsApp, SMS, Signal) shows up in attribution models as “direct”, but it’s not.
LLM Traffic: People are discovering brands via large language models, and those referrals are often invisible in GA4.
To make matters worse, these blind spots can stack. Before you know it, you find yourself in a nightmare marketing scenario where you’re not just missing one data signal, you’re missing combinations of them, making optimization even harder.
New Attribution Trends and Technology
You can keep up with all of this. It just requires a switch in perspective. Marketers should evaluate their campaigns using a combination of modeled attribution and traditional touch-based metrics. You may never fully connect every dot, and that’s okay. The goal isn’t perfection, just enough clarity to defend marketing budget allocations.
Modern marketers are using these tools:
Incrementality testing: Geo holdouts and lift studies to isolate what’s actually moving the needle.
MMM (Marketing Mix Modeling): Especially useful for larger budgets or mixed channel strategies.
Correlation analysis: Pre/post testing, contextual lift, and even proxy signals like brand search volume.
Unified first-party data: Clean, consistent CRM and web data feeding both your models and your platforms.
The best strategies blend these methods based on spend level, complexity, and conversion volume. Leveraging AI in your marketing efforts is one of the best ways to automate this research as much as possible and maximize the benefit of these tactics.
AI and Blind Spots
Some marketers may feel like AI is eroding attribution. While that could be true, the technology is also helping to rebuild it.
Here’s how AI is stepping in:
Generative AI: LLMs like ChatGPT are now discovery platforms. They drive traffic, but don’t always identify themselves unless you tag them.
AI coworkers: Agentic AI simulates user behavior, tests messaging, and can even help set up GA4 tracking automatically.
Machine learning models: Used in MMMs and platform attribution to refine forecasts, assign contribution, and make predictions.
Still, only 55% of marketers trust AI-generated insights, according to CoSchedule. The key is to treat AI as an assistant, not the authority. Use it to speed up testing and build models, but validate with your own data.
Analytics platforms like Adobe Analytics are also making steps to better capture attribution from AI tools. In October they released a new referrer type called “Conversational AI Tools” to segment out traffic from ChatGPT and other LLMs from the other channels marketers have historically monitored.
Closing The Gap With Attribution Strategies
So, how do you go from blind spots to better planning? You don’t need perfect clarity. You need consistent signals and a smarter strategy.
Here are some ways marketers are closing attribution gaps:
Clean your first-party data: Data from internal sources like your website and CRM needs to be trustworthy. These are your most important sources of truth.
Use multipliers: Adjust performance based on geo lift or experiment results. Not every click counts equally.
Invite questions: Models are approximations. Encourage teams to challenge them and make improvements as time goes on.
Survey your customers: Ask where they heard about you. It’s old school, but incredibly effective for context.
Use offer codes and landing pages: Even if not perfect, they create new signals across dark social or offline.
Track “AI Referrers”: Create custom =channels in your web analytics, including in GA4, to segment out performance from LLM-driven traffic.
Linking Attribution To Business Outcomes
Attribution and business outcomes go hand-in-hand. Understanding where your most profitable leads originate is essential to growing any business, regardless of its size.
You want to connect your data to actual decisions, such as forecasts, budgets, and resource allocation. But, with the marketing landscape changing so quickly and drastically, how do you know which metrics to follow?
Here are the metrics that matter now:
Total conversions and incremental conversions
Conversion value over time
Cost per incremental conversion
Spend thresholds by tactic
Directional change (old model vs. new)
Remember: even if your models aren’t perfect, if they get you closer to optimal spend, it’s working. Continuous improvement for your attribution strategy will get you closer and closer still.
FAQs
What is a marketing attribution blind spot?
It’s any part of the customer journey you can’t track, like dark social shares, offline sales, or LLM referrals that may be influencing conversions without showing up in your data.
Can AI help with attribution?
Yes, but only if used smartly. AI can simulate behavior and identify patterns, but it’s not a silver bullet. Use it to complement your experiments and first-party data.
What’s the best attribution model?
There isn’t one. The most effective models mix touch-based data with testing and contextual clues. Choose based on your business size, channel mix, and data maturity.
Conclusion
When it comes to effective attribution, you just need to see enough to move forward.
Mastering this skill in the modern marketing world is less about getting the credit right and more about making smarter calls with what you can measure. The key is to stop chasing perfection and start building a system that helps you plan and adapt to the data you gather from your testing in real-time. Attribution isn’t the whole picture, but it remains the best tool we have to illuminate the path forward, including its blind spots.
Naturally, we can still learn from tried and true marketing methods. We may just have to think outside the box on how to apply them to today’s search environment and customer journey. It’s worth checking out our guides on which marketing campaigns drive the best impact and how to track your marketing ROI. Combining this extra knowledge with your new attribution perspective could be the secret sauce to put you ahead of the pack in 2026.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-17 20:00:002025-11-17 20:00:00Minimizing Marketing Blind Spots: The New Era of Attribution
Undoubtedly, one of the hot topics in SEO over the last few months has been how to influence LLM answers. Every SEO is trying to come up with strategies. Many have created their own tools using “vibe coding,” where they test their hypotheses and engage in heated debates about what each LLM and Google use to pick their sources.
Some of these debates can get very technical, touching on topics like vector embeddings, passage ranking, retrieval-augmented generation (RAG), and chunking. These theories are great—there’s a lot to learn from them and turn into practice.
However, if some of these AI concepts are going way over your head, let’s take a step back. I’ll walk you through some recent tests I’ve run to help you gain an understanding of what’s going on in AI search without feeling overwhelmed so you can start optimizing for these new platforms.
Create branded content and check for results
A while ago, I went to Austin, Texas, for a business outing. Before the trip, I wondered if I could “teach” ChatGPT about my upcoming travels. There was no public information about the trip on the web, so it was a completely clean test with no competition.
I asked ChatGPT, “is Gus Pelogia going to Austin soon?” The initial answer was what you’d expect: He doesn’t have any trips planned to Austin.
That same day, a few hours later, I wrote a blog post on my website about my trip to Austin. Six hours after I published the post, ChatGPT’s answer changed: Yes, Gus IS going to Austin to meet his work colleagues.
ChatGPT prompts with a blog post published in between queries, which was enough to change a ChatGPT answer.
ChatGPT used an AI framework called RAG (Retrieval Augmented Generation) to fetch the latest result. Basically, it didn’t have enough knowledge about this information in its training data, so it scanned the web to look for an up-to-date answer.
Interestingly enough, it took a few days until the actual blog post with detailed information was found by ChatGPT. Initially, ChatGPT had found a snippet of the new blog post on my homepage and reindexed the page within the six-hour range. It was using just the blog post’s page title to change its answer before actually “seeing” the whole content days later.
Some learnings from this experiment:
New information on webpages reaches ChatGPT answers in a matter of hours, even for small websites. Don’t think your website is too small or insignificant to get noticed by LLMs—they’ll notice when you add new content or refresh existing pages, so it’s important to have an ongoing brand content strategy.
The answers in ChatGPT are highly dependent on the content published on your website. This is especially true for new companies where there are limited sources of information. ChatGPT didn’t confirm that I had upcoming travel until it fetched the information from my blog post detailing the trip.
Use your webpages to optimize how your brand is portrayed beyond showing up in competitive keywords for search. This is your opportunity to promote a certain USP or brand tagline. For instance, “The Leading AI-Powered Marketing Platform” and “See everyday moments from your close friends” are used, respectively, by Semrush and Instagram on their homepages. While users probably aren’t searching for these keywords, it’s still an opportunity for brand positioning that will resonate with them.
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Test to see if ChatGPT is using Bing or Google’s index
The industry has been ringing alarm bells about whether ChatGPT uses Google’s index instead of Bing. So I ran another small test to find out: I added a <meta name=”googlebot” content=”noindex”> tag on the blog post, allowing only Bingbot for nine days.
If ChatGPT is using Bing’s index, it should find my new page when I prompt about it. Again, this was on a new topic and the prompt specifically asked for an article I wrote, so there wouldn’t be any doubts about what source to show.
The page got indexed by Bing after a couple of days, while Google wasn’t allowed to see it.
New article has been indexed by Bingbot
I kept asking ChatGPT, with multiple prompt variations, if it could find my new article. For nine days, nothing changed—it couldn’t find the article. It got to a point that ChatGPT hallucinated (actually, tried its best guess) a URL.
ChatGPT made-up URL: https://www.guspelogia.com/learnings-from-building-a-new-product-as-an-seo Real URL: https://www.guspelogia.com/learnings-new-product-seo
GSC shows that it can’t index the page due to “noindex” tag
I eventually gave up and allowed Googlebot to index the page. A few hours later, ChatGPT changed its answer and found the correct URL.
On the top, ChatGPT’s answer when Googlebot was blocked. On the bottom, ChatGPT’s answer after Googlebot was allowed to see the page.
Interestingly enough, the link to the article was presented on my homepage and blog pages, yet ChatGPT couldn’t display it. It only found that the blog post existed based on the text on those pages, even though it didn’t follow the link.
Yet, there’s no harm in setting up your website for success on Bing. They’re one of the search engines that adopted IndexNow, a simple ping that informs search engines that a URL’s content has changed. This implementation allows Bing to reflect updates in their search results quickly.
While we all suspect (with evidence) that ChatGPT isn’t using Bing’s index, setting up IndexNow is a low effort task that’s worthwhile.
We’re all talking about being included in AI search results, but what happens when a company or product loses a mention on a page? Imagine a specific model of earbuds is removed from a “top budget earbuds” list—would the product lose its mention, or would Google find a new source to back up its AI answer?
While the answer could always be different for each user and each situation, I ran another small test to find out.
In a listicle that mentioned multiple certification courses, I identified one course that was no longer relevant, so I removed mentions of it from multiple pages on the same domain. I did this to keep the content relevant, so measuring the changes in AI Mode was a side effect.
Initially, within the first few days of the course getting removed from the cited URL, it continued to be part of the AI answer for a few pre-determined prompts. Google simply found a new URL in another domain to validate its initial view.
However, within a week, the course disappeared from AI Mode and ChatGPT completely. Basically, even though Google found another URL validating the course listing, because the “original source” (in this case, the listicle) was updated to remove the course, Google (and, by extension, ChatGPT) subsequently updated its results as well.
This experiment suggests that changing the content on the source cited by LLMs can impact the AI results. But take this conclusion with a pinch of salt, as it was a small test with a highly targeted query. I specifically had a prompt combining “domain + courses” so the answer would come from one domain.
Nonetheless, while in the real world it’s unlikely one citation URL would hold all the power, I’d hypothesize that losing a mention on a few high-authority pages would have the side effect of losing the mention in an AI answer.
Test small, then scale
Tests in small and controlled environments are important for learning and give confidence that your optimization has an effect. Like everything else I do in SEO, I start with an MVP (Minimum Viable Product), learn along the way, and once/if evidence is found, make changes at scale.
Do you want to change the perception of a product on ChatGPT? You won’t get dozens of cited sources to talk about you straight away, so you’d have to reach out to each single source and request a mention. You’ll quickly learn how hard it is to convince these sources to update their content and whether AI optimization becomes a pay-to-play game or if it can be done organically.
Perhaps you’re a source that’s mentioned often when people search for a product, like earbuds. Run your MVPs to understand how much changing your content influences AI answers before you claim your influence at scale, as the changes you make could backfire. For example, what if you stop being a source for a topic due to removing certain claims from your pages?
There’s no set time for these tests to show results. As a general rule, SEOs say results take a few months to appear. In the first test on this article, it took just a few hours to see results.
Running LLM tests with larger websites
Working in large teams or on large websites can be a challenge when doing LLM testing. My suggestion is to create specific initiatives and inform all stakeholders about changes to avoid confusion later, as they might question why these changes are happening.
By changing the footer, ChatGPT 5 started mentioning its new tagline within 36 hours for a prompt like “tell me about Seer Interactive.” I’ve checked, and while every time the answer is different, they still mention the “97% retention rate.”
Imagine if you decide to change the content on a number of pages, but someone else has an optimization plan for those same pages. Always run just one test per page, as results will become less reliable if you have multiple variables.
Make sure to research your prompts, have a tracking methodology, and spread the learnings across the company, beyond your SEO counterparts. Everyone is interested in AI right now, all the way up to C-levels.
Another suggestion is to use a tool like Semrush’s AI SEO toolkit to see the key sentiment drivers about a brand. Start with the listed “Areas for Improvement”—this should give you plenty of ideas for tests beyond “SEO Reason,” as it reflects how the brand is perceived beyond organic results.
Checklist: Getting started with LLM optimization
Things are changing fast with AI, and it’s certainly challenging to keep up to date. There’s an overload of content right now, a multitude of claims, and, I dare to say, not even the LLM platforms running them have things fully figured out.
My recommendation is to find the sources you trust (industry news, events, professionals) and run your own tests using the knowledge you have. The results you find for your brands and clients are always more valuable than what others are saying.
It’s a new world of SEO and everyone is trying to figure out what works for them. The best way to follow the curve (or stay ahead of it) is to keep optimizing and documenting your changes.
To wrap it up, here’s a checklist for your LLM optimization:
Before starting a test, make sure your selected prompts consistently return the answer you expect (such as not mentioning your brand or a feature of your product). Otherwise, the new brand mention or link could be a coincidence, not a result of your work.
If the same claim is made on multiple pages on your website, update them across the board to increase chances of success
Use your own website and external sources (e.g., via digital PR) to influence your brand perception. It’s unclear if users will cross-check AI answers or just trust what they’re told.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/11/chatgpt-changed-answer-scaled-0vWeR4.webp?fit=1525%2C2048&ssl=120481525http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-17 18:17:242025-11-17 18:17:24Small tests to yield big answers on what influences LLMs
Did you know that even a one-second delay in page loading speed can cause up to 11% fewer page views? That’s right, you might have the best content strategy and a solid plan to drive traffic, but visitors won’t stay long if your site lags. Page speed is one of the biggest factors in keeping users engaged and converting.
In this guide, we’ll uncover the most common causes of slow websites and explore proven ways to boost website performance. Whether your site feels sluggish or you simply want to make it faster, these insights will help you identify what’s holding it back and how to fix it.
Key takeaways
Page speed significantly affects user experience and conversion rates, with even minor delays leading to increased bounce rates
What do we mean by ‘website performance’ and why is it important for you?
Website performance is all about how efficiently your site loads and responds when someone visits it. It’s not just about how fast a page appears; it’s about how smoothly users can interact with your content across devices, browsers, and locations. In simple terms, it’s the overall quality of your site’s experience that should feel fast, responsive, and effortless to use.
When your page loading speed is optimized, you’re not only improving the user experience but also setting the foundation for long-term website performance.
Here’s why it matters for every website owner:
Fast-loading sites have higher conversion rates and lower bounce rates
Attention spans are notoriously short. As the internet gets faster, they’re getting shorter still. Numerous studies have found a clear link between the time it takes a page to load and the percentage of visitors who become impatient while waiting.
By offering a fast site, you encourage your visitors to stay longer. Not to mention, you’re helping them complete their checkout journey more quickly. That helps improve your conversion rate and build trust and brand loyalty. Think of all the times you’ve been cursing the screen because you had to wait for a page to load or were running in circles because the user experience was atrocious. It happens so often, don’t be that site.
A fast page improves user experience
Google understands that the time it takes for a page to load is vital to the overall user experience. Waiting for content to appear, the inability to interact with a page, and even noticing delays create friction.
That friction costs time, money, and your visitor’s experience. Research shows that the level of stress from waiting for slow mobile results can be more stressful than watching a horror movie. Surely not, you say? That’s what the fine folks at Ericsson Research found a few years back.
Ericsson Mobility Report MWC Edition, February 2016
Improving your site speed across the board means making people happy. They’ll enjoy using your site, make more purchases, and return more frequently. This means that Google will view your site as a great search result because you are delivering high-quality content. Eventually, you might get a nice ranking boost.
Frustration hurts your users and hurts your rankings
It’s not just Google – research from every corner of the web on all aspects of consumer behavior shows that speed has a significant impact on outcomes.
Nearly 70% of consumers say that page speed impacts their willingness to buy (unbounce)
20% of users abandon their cart if the transaction process is too slow (radware.com)
The BBC found that they lost an additional 10% of users for every additional second their site took to load
These costs and site abandonment happen because users dislike being frustrated. Poor experiences lead them to leave, visit other websites, and switch to competitors. Google easily tracks these behaviors (through bounces back to search engine results pages, short visits, and other signals) and is a strong indicator that the page shouldn’t be ranking where it is.
Google needs fast sites
Speed isn’t only good for users – it’s good for Google, too. Slow websites are often inefficient. They may load too many large files, haven’t optimized their media, or fail to utilize modern technologies to serve their page. That means that Google has to consume more bandwidth, allocate more resources, and spend more money.
Across the whole web, every millisecond they can save, and every byte they don’t have to process, adds up quickly. And quite often, simple changes to configuration, processes, or code can make websites much faster with no drawbacks. That may be why Google is so vocal about its education on performance.
A faster web is better for users and significantly reduces Google’s operating costs. Either way, that means that they’re going to continue rewarding fast(er) sites.
Improving page speed helps to improve crawling for search engines
Modern sites are incredibly wieldy, and untangling that mess can make a big difference. The larger your site is, the greater the impact page speed optimizations will have. That not only impacts user experience and conversion rates but also affects crawl budget and crawl rate.
When a Googlebot comes around and crawls your webpage, it crawls the HTML file. Any resources referenced in the file, like images, CSS, and JavaScript, will be fetched separately. The more files you have and the heavier they are, the longer it will take for the Googlebot to go through them.
On the flip side, the more time Google spends on crawling a page and its files, the less time and resources Google has to dedicate to other pages. That means Google may miss out on other important pages and content on your site.
Optimizing your website and content for speed will provide a good user experience for your visitors and help Googlebots better crawl your site. They can come around more often and accomplish more.
Page speed is a ranking factor
Google has repeatedly said that a fast site helps you rank better. It’s no surprise, then, that Google has been measuring the speed of your site and using that information in its ranking algorithms since 2010.
In 2018, Google launched the so-called ‘Speed Update,’ making page speed a ranking factor for mobile searches. Google emphasized that it would only affect the slowest sites and that fast sites would not receive a boost; however, they are evaluating website performance across the board.
In 2021, Google announced the page experience algorithm update, demonstrating that page speed and user experience are intertwined. Core Web Vitals clearly state that speed is an essential ranking factor. The update also gave site owners metrics and standards to work with.
Of course, Google still wants to serve searchers the most relevant information, even if the page experience is somewhat lacking. Creating high-quality content remains the most effective way to achieve a high ranking. However, Google also states that page experience signals become more important when many pages with relevant content compete for visibility in the search results.
Google mobile-first index
Another significant factor in page speed for ranking is Google’s mobile-first approach to indexing content. That means Google uses the mobile version of your pages for indexing and ranking. This approach makes sense as we increasingly rely on mobile devices to access the internet. In recent research, Semrush found out that 66% of all website visits come from mobile devices.
To compete for a spot in the search results, your mobile page needs to meet Core Web Vitals standards and other page experience signals. And this is not easy at all. Pages on mobile take longer to load compared to their desktop counterparts, while attention span stays the same. People might be more patient on mobile devices, but not significantly so.
Take a look at some statistics:
The average website loading time is 2.5 seconds on desktop and 8.6 seconds on mobile, based on an analysis of the top 100 web pages worldwide (tooltester)
The average mobile web page takes 15.3 seconds to load (thinkwithgoogle)
On average, webpages on mobile take 70.9% longer to load than on desktop (tooltester)
A loading speed of 10 seconds increases the probability of a mobile site visitor bouncing by 123% compared to a one-second loading speed (thinkwithgoogle)
All the more reasons to optimize your website and content if your goal is to win a spot in the SERP.
Understanding the web page loading process
When you click a link or type a URL and press Enter, your browser initiates a series of steps to load the web page. It might seem like magic, but behind the scenes, there’s a lot happening in just a few seconds. Understanding this process can help you see what affects your page loading speed and what you can do to boost website performance.
The process of loading a page can be divided into three key stages:
Network stage
This is where the connection begins. When someone visits your site, their browser looks up your domain name and connects to your server. This process, known as DNS lookup and TCP connection, enables data to travel between your website and the visitor’s device.
You don’t have much direct control over this stage, but technologies like content delivery networks (CDNs) and smart routing can make a big difference, especially if you serve visitors from around the world. For local websites, optimizing your hosting setup can still help improve overall page loading speed.
Server response stage
Once the connection is established, the visitor’s browser sends a request to your server asking for the web page and its content. This is when your server processes that request and sends back the necessary files.
The quality of your hosting, server configuration, and even your website’s theme or plugins all influence how quickly your server responds. A slow response is one of the most common issues with slow websites, so investing in a solid hosting environment is crucial if you want to boost your website’s performance.
One popular choice is Bluehost, which offers reliable infrastructure, SSD storage, and built-in CDN support, making it a go-to hosting solution for many website owners.
Browser rendering stage
Now it’s time for the browser to put everything together. It retrieves data from your server and begins displaying it by loading images, processing CSS and JavaScript, and rendering all visible elements.
Browsers typically load content in order, starting with what’s visible at the top (above the fold) and then proceeding down the page. That’s why optimizing the content at the top helps users interact with your site sooner. Even if the entire page isn’t fully loaded yet, a quick initial render can make it feel fast and keep users engaged.
Key causes that are causing your website to slow down
While you can’t control the quality of your visitors’ internet connection, most slow website issues come from within your own setup. Let’s examine the key areas that may be hindering your site’s performance and explore how to address them to enhance your website’s performance.
Your hosting service
Your hosting plays a big role in your website’s performance because it’s where your site lives. The speed and stability of your host determine how quickly your site responds to visitors. Factors such as server configuration, uptime, and infrastructure all impact this performance.
Choosing a reliable host eliminates one major factor that affects speed optimization. Bluehost, for example, offers robust servers, reliable uptime, and built-in performance tools, making it a go-to hosting choice for anyone serious about speed and stability.
Your website theme
Themes define how your website looks and feels, but they also impact its loading speed. Some themes are designed with clean, lightweight code that’s optimized for performance, while others are heavy with animations and complex design elements. To boost website performance, opt for a theme that prioritizes simplicity, efficiency, and clean coding.
Large file size
From your HTML and CSS files to heavy JavaScript, large file sizes can slow down your website. Modern websites often rely heavily on JavaScript for dynamic effects, but overusing it can cause your pages to load slowly, especially on mobile devices. Reducing file sizes, compressing assets, and minimizing unnecessary scripts can significantly improve the perceived speed of your pages.
Badly written code
Poorly optimized code can cause a range of issues, from JavaScript errors to broken layouts. Messy or redundant code makes it harder for browsers to load your site efficiently. Cleaning up your code and ensuring it’s well-structured helps improve both performance and maintainability.
Images and videos
Unoptimized images and large video files are among the biggest causes of slow websites. Heavy media files increase your page weight, which directly impacts loading times. If your header image or hero banner is too large, it can delay the appearance of the main content. Optimizing your media files through compression, resizing, and Image SEO can dramatically improve your website’s speed.
Too many plugins and widgets
Plugins are what make WordPress so flexible, but adding too many can slow down your site. Each plugin adds extra code that your browser needs to process. Unused or outdated plugins can also conflict with your theme or other extensions, further reducing performance. Audit your plugins regularly and only keep the ones that truly add value.
Absence of a CDN
A content delivery network (CDN) helps your website load faster for users worldwide. It stores copies of your site’s static content, such as images and CSS files, across multiple servers located in different regions. This means that users access your site from the nearest available server, reducing loading time. If your audience is global, using a CDN is one of the easiest ways to boost website performance.
Redirects
Redirects are useful for managing URLs and maintaining SEO, but too many can slow down your site. Each redirect adds an extra step before reaching the final page. While a few redirects won’t hurt, long redirect chains can significantly affect performance. Whenever possible, try to link directly to the final URL to maintain consistent page loading speed.
For WordPress users, the redirect manager feature in Yoast SEO Premium makes handling URL changes effortless and performance-friendly. You can pick from redirect types such as 301, 302, 307, 410, and 451 right from the dashboard. Since server-side redirects tend to load faster than PHP-based ones, Yoast lets you choose the type your stack supports, allowing you to avoid slow website causes and boost website performance.
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How to measure page speed and diagnose performance issues
Before you can improve your website performance, you need to know how well (or poorly) your pages are performing. Measuring your page speed helps you identify what’s slowing down your website and provides a direction for optimization.
What is page speed, really?
Page speed refers to how quickly your website’s content loads and becomes usable. But it’s not as simple as saying, ‘My website loads in 4 seconds.’ Think of it as how fast a visitor can start interacting with your site.
A page might appear to load quickly, but still feel slow if buttons, videos, or images take time to respond. That’s why website performance isn’t defined by one single metric — it’s about the overall user experience.
Did you know?
There is a difference between page speed and site speed. Page speed measures how fast a single page loads, while site speed reflects your website’s overall performance. Since every page behaves differently, measuring site speed is a more challenging task. Simply put, if most pages on your website perform well in terms of Core Web Vitals, it is considered fast.
Core metrics that define website performance
Core Web Vitals are Google’s standard for evaluating how real users experience your website. These metrics focus on the three most important aspects of page experience: loading performance, interactivity, and visual stability. Improving them helps both your search visibility and your user satisfaction.
Largest Contentful Paint (LCP): Measures how long it takes for the main content on your page to load. Aim for LCP within 2.5 seconds for a smooth loading experience
Interaction to Next Paint (INP): Replaces the older First Input Delay metric and measures how quickly your site responds to user interactions like taps, clicks, or key presses. An INP score under 200 milliseconds ensures your site feels responsive and intuitive
Cumulative Layout Shift (CLS): Tracks how stable your content remains while loading. Elements shifting on screen can frustrate users, so keep CLS below 0.1 for a stable visual experience
How to interpret and improve your scores
Perfection is not the target. Progress and user comfort are what count. If you notice issues in your Core Web Vitals report, here are some practical steps:
If your LCP is slow: Compress images, serve modern formats like WebP, use lazy loading, or upgrade hosting to reduce load times
If your INP score is high: Reduce heavy JavaScript execution, minimize unused scripts, and avoid main thread blocking
If your CLS score is poor: Set defined width and height for images, videos, and ad containers so the layout does not jump around while loading
If your TTFB is high: Time to First Byte is not a Core Web Vital, but it still impacts loading speed. Improve server performance, use caching, and consider a CDN
Remember that even small improvements create a noticeable difference. Faster load times, stable layouts, and quicker interactions directly contribute to a smoother experience that users appreciate and search engines reward.
Tools to measure and analyze your website’s performance
Here are some powerful tools that help you measure, analyze, and improve your page loading speed:
Google PageSpeed Insights
Google PageSpeed Insights is a free tool from Google that provides both lab data (simulated results) and field data (real-world user experiences). It evaluates your page’s Core Web Vitals, highlights problem areas, and even offers suggestions under ‘Opportunities’ to improve load times.
Google Search Console (Page Experience Report)
The ‘Page Experience’ section gives you an overview of how your URLs perform for both mobile and desktop users. It groups URLs that fail Core Web Vitals, helping you identify whether you need to improve LCP, FID, or CLS scores.
Lighthouse (in Chrome DevTools)
Lighthouse is a built-in auditing tool in Chrome that measures page speed, accessibility, SEO, and best practices. It’s great for developers who want deeper insights into what’s affecting site performance.
WebPageTest
WebPage Test lets you test how your website performs across various networks, locations, and devices. Its ‘waterfall’ view shows exactly when each asset on your site loads, perfect for spotting slow resources or scripts that delay rendering.
Chrome Developer Tools (Network tab)
If you’re hands-on, Chrome DevTools is your real-time lab. Open your site, press F12, and monitor how each resource loads. It’s perfect for debugging and understanding what’s happening behind the scenes.
A quick checklist for diagnosing performance issues
Use this checklist whenever you’re analyzing your website performance:
Run your URL through PageSpeed Insights for Core Web Vitals data
Check your Page Experience report in Google Search Console
Use Lighthouse for a detailed technical audit
Review your WebPageTest waterfall to spot bottlenecks
Monitor your server performance (ask your host or use plugins like Query Monitor)
Re-test after every major update or plugin installation
Speed up, but with purpose
As Mahatma Gandhi once said, ‘There is more to life than increasing its speed.’ The same goes for your website. While optimizing speed is vital for better engagement, search rankings, and conversions, it is equally important to focus on creating an experience that feels effortless and meaningful to your visitors. A truly high-performing website strikes a balance between speed, usability, accessibility, and user intent.
When your pages load quickly, your content reads clearly, and your navigation feels intuitive, you create more than just a fast site; you create a space where visitors want to stay, explore, and connect.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-13 12:22:002025-11-13 12:22:00From slow to super fast: how to boost site speed the right way
Ad intelligence software promises to show you everything your competitors are doing: their keywords, budgets, creatives, and landing pages.
But many surface insights you could get for free.
Meta’s Ad Library shows what advertisers are currently running. Google’s Transparency Center does the same for search and YouTube. TikTok’s Creative Center reveals top performers by industry.
So, when does paid software earn its cost?
You’re tracking multiple competitors across platforms and losing hours to manual checks
You need historical data on which ads they tested and killed
You rely on spend benchmarks and real-time alerts to catch shifts before your clients do
That’s the gap paid tools fill. If they’re good.
Many aren’t. They bury useful insights under dashboards that create more work than they save. The data looks complete until you actually try to use it.
This guide covers six platforms that deliver real intelligence (if you know what you’re looking at).
We’re not promising magic improvements. We’re showing what each tool reveals, who it’s built for, and what you give up at each price point.
What Is the Best Ad Intelligence Software?
Ad Intelligence Tools
Best For
Price
Similarweb
Best for stalking competitors’ ads at scale — plus, their SEO, traffic, and market moves
$649+/month. (Only higher-tier plans come with ad intelligence.)
Semrush Advertising Toolkit
Best for multi-platform ad intelligence, from Meta and TikTok to Google Shopping
$99-$220/month
SpyFu
Best for affordable Google Ads intelligence with deep historical data
$39-$249/month
PowerAdSpy
Best for analyzing ad engagement across social media platforms
$69-$399/month
Adbeat
Best for tracking competitor display ads and landing pages
$249+/month
Pathmatics
Best for enterprise-level ad spend intelligence across mobile, social, and video
Pricing is not publicly available
1. Similarweb
Best for stalking competitors’ ads at scale — plus, their SEO, traffic, and market moves
Similarweb reveals your competitors’ complete paid strategies, from their winning ad creatives to their most successful publishers.
It also includes SEO and competitive intelligence tools in every subscription, so you get the full picture of how your rivals attract and convert traffic across every channel.
This cross-channel context is especially helpful if you already use native ad libraries but want scalable intel that ties everything together.
Learn Your Competitors’ Highest-Performing Publishers and Ad Networks
If your competitors are running ads, Similarweb shows you where (and how to beat them).
You’ll see:
Which ad networks and placements work best for your top competitors
Where their ad budgets go, broken down by channel
Industry-wide trends that reveal missed opportunities
Say Similarweb shows that multiple competitors spend over 50% of their display budgets on a single publisher.
That’s a data-backed signal you can’t ignore.
Use that data to target the same publisher to test similar placements. Or find underused publishers in the same category for more affordable traffic.
Get Inspired by Proven Ad Creatives
Similarweb’s database makes it easy to browse display ads by publisher, network, and format.
See the messaging and offers competitors use to get conversions
Learn how many days each ad was active, so you know which ones excelled (and which ones failed)
Find out which formats your competitors are using, including product, display, and video ads
Of course, copying your competitors’ ads word-for-word isn’t the goal.
The real value is in spotting patterns: the hooks they repeat, the formats they invest in, and the offers they continually test.
These insights let you design campaigns that build on what already works in your market.
When you’re juggling multiple accounts, this saves hours of creative testing, and points you directly toward proven formats.
Reverse-Engineer Competitors’ Search and Shopping Campaigns
Similarweb shows you which keywords your competitors bid on and how much they’re spending.
This helps you identify high-value keywords that drive conversions and avoid wasting budget on terms that don’t perform.
From there, you can build stronger landing pages that target your competitors’ most successful keywords and match intent.
Pros and Cons
Pros
Cons
Tracks 500M+ ads across publishers, networks, and formats for deep competitive insights
Ad intelligence tools only available with the most expensive plan
Uncovers competitors’ top-performing publishers and ad placements
Can feel overwhelming for smaller teams due to the platform’s depth
Includes SEO, traffic, and market data for a full competitive picture
If you only want ad intelligence, you’ll be paying for much more than you need
Pricing
Similarweb offers multiple plans, but only the most expensive one includes dedicated tools for ad intelligence.
This plan costs $649/month ($540 billed annually). Similarweb also offers business and enterprise plans, but pricing and tools are not publicly available online.
Best for multi-platform ad intelligence, from Meta and TikTok to Google Shopping
When you’re managing multiple clients or campaigns, switching between Meta, TikTok, and Google dashboards gets messy fast.
Semrush’s Advertising Toolkit consolidates that chaos into one workspace — letting you analyze competitor campaigns and build your own in the same place.
You’ll get deep intel on keywords, budgets, ad copy, and creative trends.
Plus, actionable advice on how to turn that data into high-performing campaigns.
Track Competitor Keywords and Budgets
The Advertising Research tool reveals everything, and we mean everything, about your competitors’ Google Ads strategies.
Enter any domain and you’ll see:
Estimated ad traffic
Cost per click (CPC)
Highest- and lowest-performing keywords
Organic search position
Keyword difficulty
URL
No more wasting ad budget on terms that don’t perform. You’ll know exactly which ones to target in your next campaign.
The tool also tracks keyword trends over time.
See which keywords competitors continuously invest in month after month.
When a keyword consistently appears in their paid strategy with stable or growing volume, that’s a clear sign it’s profitable.
With this data, you might test variations of the keyword in multiple ads to capitalize on its success.
Or use them to inform your broader content strategy beyond paid campaigns.
Spy on Google Shopping Ads
Have an ecommerce brand?
The PLA Research tool shows you which products your competitors promote most heavily in Google Shopping.
You’ll see position, volume, price, product titles, URLs, and trend data for each listing.
When a product shows up month after month, it’s likely a top seller.
If you don’t carry that product yet, you might consider adding it to your catalog.
Already sell it? Increase your Shopping ads to compete directly.
You can also view all of your competitors’ Google Shopping ads in one place.
Analyze their copy, images, and offers.
Then, apply these insights to your own listings:
Adjust your product titles to match high-performing formats
Test pricing strategies that undercut or match theirs
Prioritize ads for products where you have a competitive advantage. Think better reviews, faster shipping, or exclusive features they don’t offer.
Here’s another cool feature:
Instead of bouncing between tools, Semrush’s AI-powered Ad Launch Assistant lets you create and optimize Google and Meta ads directly inside the platform.
The tool generates copy and visuals tailored to your brand, from attention-grabbing headlines to conversion-focused descriptions.
Instead of writing everything from scratch, all you have to do is review each element:
Headlines
Descriptions
Site links
Callouts
Images
Videos
Simply refine the voice and messaging as needed. You’ll be able to test multiple variations in minutes instead of hours.
Unlock Deeper Insights with AdClarity
AdClarity is Semrush’s advanced cross-channel ad intelligence tool.
Need complete visibility into competitor display, social, and video campaigns?
This is where you’ll find it.
You’ll get a lot of data with this tool.
Including how much rivals spend, which publishers drive the most impact, and the exact creatives they’re using across platforms:
Facebook
Instagram
X
Google Display
Pinterest
YouTube
TikTok
LinkedIn
Say a competitor suddenly doubles their TikTok spend. You’ll spot the shift immediately and can adjust your strategy in real time.
AdClarity also automatically identifies your competitors’ top publishers and campaigns.
So there’s no guessing or testing which ones work well for your target audience.
Pros and Cons
Pros
Cons
Combines robust multi-site ad intelligence with Meta and Google campaign execution in one platform
The base plan includes only Google and Meta ad intelligence
Google Shopping insights are especially strong for ecommerce brands
AdClarity is only included the higher-tier plan
AdClarity offers advanced ad intelligence across display, social, and video
Doesn’t include SEO tools; you’ll need a separate toolkit for that
Pricing
The Semrush Advertising Toolkit is $99 per month.
It includes Advertising Research, PLA Research, Ads Launch Assistant, and more.
The higher-tier plan ($220/month) includes AdClarity, along with all of the above.
3. SpyFu
Best for affordable Google Ads intelligence with deep historical data
SpyFu is built for one thing: uncovering Google Ads strategies.
If your strategy leans heavily on Google, it’s one of the most detailed and budget-friendly advertising intelligence software options available.
Download Competitor Keywords Without Limits
SpyFu shows you everything your competitors do on Google Ads — and lets you export it all with no limits.
Many ad intelligence platforms cap your keyword downloads, so this is a plus.
Type in any competitor’s domain and you’ll see:
Every keyword they’ve ever bought on Google Ads
Estimated monthly clicks and CPC
Total spend on paid search
For example, say you’re in SaaS project management and Asana is your top competitor.
Search their domain, and SpyFu shows you their current and historical ad keywords. We’re talking thousands of terms, not just the top 50 or 100.
Download the complete dataset and…
Feed it into your analytics tools or Google Sheets
Share it with your team for campaign planning
Build custom reports for leadership
Cross-reference it with your CRM to see which keywords actually convert
Spot Overlaps and Waste in PPC Strategies
SpyFu’s Kombat tool compares your PPC strategy against up to two competitors at once.
But instead of having to sift through 10,000 keywords, the ad intelligence tool automatically groups them into helpful buckets:
Core Keywords: Terms all competitors are bidding on
Consider Buying: Valuable keywords they use, but you don’t
Potential Ad Waste: Terms that neither competitor uses but you do
So, you know exactly which terms to focus on (and which to remove from your campaigns).
This is especially helpful if you’re newer to paid campaigns.
Or have limited time (or tolerance) for turning data into actionable insights.
SpyFu also tags certain terms as “Great Buys” and estimates how many impressions you’ll get for each one.
Plus, it shows which competitors already bid on them, so you can piggyback on proven opportunities.
For example, the report below reveals that Asana’s competitor, Monday.com, uses “top task management apps” and “work time tracker app” in its ad strategy.
Asana could (and probably should) target both terms since SpyFu’s data shows they’re worth the investment.
Learn From Ads That Worked (or Failed)
SpyFu’s Ad History tool shows every ad variation competitors have tested for a given keyword.
If an ad copy ran for 14 consecutive months, you know it was effective.
If it vanished after a week? Probably a dud.
This kind of insight lets you write ads with fewer flops and faster wins.
This is especially valuable if you handle multiple accounts. You can skip obvious mistakes and start from proven winners.
Pros and Cons
Pros
Cons
Unlimited keyword exports with no download caps
Focused exclusively on Google Ads; no social or display coverage
10+ years of historical ad data for deep competitive analysis
Historical data (10+ years) requires paying for higher-tier plans
Kombat tool automatically identifies keyword overlaps and wasted spend
The base plan doesn’t come with unlimited downloads
Pricing
SpyFu offers three main plans, all of which come with ad intelligence and SEO reports.
The most affordable plan is $39 per month.
However, you’ll need to upgrade to a higher tier to get 10+ years of historical insights ($59-$249/month).
You’ll also see landing page insights, so you get intel on the complete customer journey.
See Which Landing Pages Are Actually Converting
Adbeat shows you which landing pages drive the most ad traffic. And how long each page has been live.
For example, Squarespace’s longest-running landing page has been active for 794 days.
That’s over two years.
When a page stays live that long, you know it’s consistently converting.
This intel helps you see which page layouts, offers, and messaging are worth replicating.
If you work for an agency and have multiple clients, this is particularly valuable. It’s a fast way to benchmark what “good” looks like in each vertical.
Reveal Media Buying Strategies and Publisher Insights
The Advertiser Dashboard breaks down where competitors allocate their budgets across channels, networks, and publishers.
You’ll also see share-of-voice data to understand their market presence.
For example, Adbeat found that Squarespace ran 524 ads in one month.
And 78% of their spend went to programmatic ads.
Details like this highlight which channels matter most in your niche. And where you can reallocate budget to get better performance for your own campaigns.
Benchmark Campaign Performance and Spot Trends
Adbeat’s ad intelligence software lets you monitor how your competitors’ budgets shift over time.
But what’s especially helpful is that they break it down by ad type: standard, native, and video.
For example, Squarespace’s longest-running video ad has been live for 413 days.
If they’ve kept it running that long, it’s a moneymaker.
In other words, it’s worth considering if you’re investing enough in video ads. And studying individual high performers for hooks, visuals, and offers.
Pros and Cons
Pros
Cons
Lets you analyze ads and landing pages together for complete funnel insights
Limited coverage of search and social campaigns
Reveals media spend, publisher performance, and traffic sources
Pricing is higher than ad-creative-only tools
Great for agencies, affiliate marketers, and display-heavy advertisers
Enterprise pricing is not publicly available
Pricing
Adbeat’s pricing starts at $249 per month for display, programmatic, and native ad intelligence.
For advanced filters, alerts, and historical data, you’ll need the higher plan ($399 per month).
There’s also an enterprise plan, but pricing isn’t listed publicly.
6. Pathmatics by Sensor Tower
Best for enterprise-level ad spend intelligence across mobile, social, and video
Pathmatics is built for large teams and big brands.
Household names like P&G and Unilever use this platform, so expect enterprise-level pricing and complexity.
But if you’re managing high-volume spend or reporting to leadership, it offers the transparency and benchmarking you can’t get from native tools.
Uncover Competitors’ Ad Spend Across Every Channel
Pathmatics shows you where every ad dollar goes in a pretty granular way.
It breaks down spend by platform, campaign, or creative — and tracks impressions, reach, and frequency over time.
Say you notice a competitor’s Instagram spend suddenly increased by a significant amount in a single week during Q4.
That signals a major campaign launch — possibly holiday shopping or Black Friday prep.
With this data, you can adjust your strategy immediately. And compete head-to-head with your main competitors.
Pathmatics also lets you benchmark your ad spend against multiple competitors at once.
If you’re investing $500K on display while your top three competitors each spend $2M+, you’ll see that gap.
Use this data to justify budget increases to leadership.
Or to identify where smaller reallocations could close the gap faster.
Benchmark Market Share and Share of Voice
Pathmatics tracks your share of voice against competitors in your industry and region.
If three brands dominate 80% of impressions in your category, you’ll see who owns what percentage.
This data helps you understand your position in the market.
Are you a distant fourth? Or neck-and-neck with the leader?
You can also identify which competitors dominate specific channels and spot opportunities where they’re underinvesting.
If the market leader owns Facebook but ignores TikTok, that’s your opening.
Evaluate Creatives That Resonate
Every ad includes details like format, placement, messaging, CTAs, and audience profiles.
See which creatives competitors keep running and which ones they kill after a few days.
Track the exact messaging and offers that stick around for months or years.
Use these insights to refine your own creative strategy.
Double down on formats that consistently deliver, and try localized messaging in new markets where your competitors are seeing success.
Pros and Cons
Pros
Cons
Provides cross-channel visibility across social, display, mobile, video, and OTT
Pricing is custom and can be expensive for smaller teams and startups
Combines creative data with detailed spend, reach, and audience insights
Steeper learning curve due to platform depth and data complexity
Ideal for enterprise-level teams, app publishers, and multi-channel marketers
Some users report data accuracy issues
Pricing
Pathmatics’ pricing is custom.
Request a quote if you’re interested.
Turn Competitive Intel into Campaign Wins
The right ad intelligence software isn’t the one with the most features.
It’s the one you can trust.
This means reliable data, less manual work, and the ability to scale campaigns across platforms with ease.
On a budget and focused mainly on Google Ads? Start with SpyFu.
Need deep, multi-site advertising intelligence across search and social with campaign execution built in?
Go for Semrush’s Advertising Toolkit.
Once you’ve picked your platform and gathered competitive intel, the next step is making sure your paid and organic strategies work together.
Learn how to align SEO and PPC to maximize visibility, reduce wasted spend, and improve your ROI.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-12 19:12:442025-11-12 19:12:446 Best Ad Intelligence Software to Outsmart the Competition
October showed just how fast AI is reshaping how brands connect, convert, and stay visible. OpenAI turned chats into checkout experiences. Google tested AI-written snippets and agent-driven search. The line between platforms, ads, and transactions keeps disappearing.
Creators gained new credibility. Rebrands proved riskier than ever. Data-driven PR entered a new era.
Here’s what mattered most and how to stay ahead.
Key Takeaways
• AI is officially a channel, not a tool. Search, shopping, and PR are all happening inside AI environments now.
• Authenticity outperforms aspiration. Whether you’re selling luxury goods or refreshing your brand, identity, and connection drive growth.
• Visibility depends on AI citations and structure. The brands getting mentioned in AI results are building more trust and traffic everywhere.
• Automation is powerful, but it still needs control. As Google’s AI Max expands, you need to balance efficiency with oversight to protect budgets and brand safety.
• Every brand action is a public statement. From rebrands to creator partnerships, perception moves fast. Plan your narratives or risk losing control of them.
Search & AI Evolution
Search has moved beyond discovery. October’s updates from OpenAI and Google show how AI is collapsing the gap between queries and actions. Visibility means something different now.
OpenAI launches in-chat purchases
OpenAI rolled out Instant Checkout in ChatGPT. U.S. users can now buy products directly inside the chat. Powered by Stripe, the feature starts with Etsy listings and will expand to more merchants soon. Sellers on Shopify are auto-enrolled. Others can join by connecting product feeds and enabling Stripe checkout.
Our POV: ChatGPT shopping changes product discovery completely. If your product data isn’t complete, detailed, and conversational, you won’t show up. The most visible listings will have rich attributes and language that reflects how users naturally describe what they want.
What to do next: Audit your product feeds. Fill every field. Use detailed, long-form descriptions that anticipate real-world queries. Give the e-commerce agent what it needs to surface your products.
<h3> Google tests AI-written meta descriptions <h3>
Google began testing AI-generated snippets powered by Gemini. Instead of pulling your written meta description, the model writes or summarizes one based on on-page content.
Our POV: Google’s been rewriting descriptions for years. AI just made it smarter and less predictable. Treat your page intros as the new meta description because that’s what AI will pull from.
What to do next: Front-load the first 150 words of each key page with a clear summary of what the page delivers and why it matters. Tighten headings and intros, monitor CTR shifts, and adjust language when AI summaries drift from your brand’s tone.
<h3> Google Search Labs adds Agentic AI <h3>
Google’s AI Mode now lets users book restaurants and other services directly from results. Search is moving from recommending to acting.
Our POV: This isn’t a traffic killer. But signals are shifting. AI will handle the click path. The brands that win will have structured, verified, action-ready data.
What to do next: Audit structured data, integrate local feeds, and make sure your listings are up to date across booking platforms. When the search agent starts acting on your behalf, data hygiene becomes your conversion strategy.
Paid Media & Automation
AI is taking over ad delivery. Control is the new currency. You have to balance efficiency with visibility to keep performance from becoming unpredictable.
Google doubles down on AI Max
Google refreshed its AI Max ad pitch. The system is fully automated: it matches intent, rewrites copy, and routes users to brand assets. Powerful, but still a black box.
Our POV: Automation doesn’t replace strategy. Advertisers need visibility, not just results. Without strict guardrails, budgets can leak into low-value placements or off-brand creative.
What to do next: Run low-risk tests first. Add negative keyword lists, set URL exclusions, and manually review creative. Monitor performance closely until you can prove control before scaling.
Apple launches dedicated Games app
Apple introduced a standalone Games app with iOS 26, bridging Game Center and the App Store. Developers can now feature their games, run dual search visibility, and analyze engagement with new metrics later this year.
Our POV: This isn’t a small tweak, Apple’s essentially building a second storefront. Game publishers who adapt early will own discoverability.
What to do next: Refresh creatives, optimize In-App Events, and plan for dual indexing between the Games app and App Store. When analytics arrive, use them to refine ASO and campaign timing.
Social & Content Trends
Creators and consumers are rewriting the rules. Authenticity, identity, and emotional connection drive engagement across platforms that once ran on aspiration and polish.
TikTok reframes luxury branding
TikTok’s new research shows luxury audiences care more about self-expression than status. It’s about showing who you are, not showing off.
Our POV: That shift goes way beyond luxury. Audiences in every category now expect brands to reflect their identity. Connection beats aspiration. Authenticity beats polish.
What to do next: Reevaluate your brand’s emotional identity. Work with creators who reinterpret your message through their lens. Build content that feels participatory, not performative.
UK YouTubers contribute £2.2B to the economy
YouTube creators generated £2.2 billion for the UK economy last year, supporting over 45,000 jobs. Parliament even launched a cross-party group to represent them.
Our POV: Creators aren’t influencers anymore. They’re small businesses with real economic weight. Partnering with them means investing in industries, not individuals.
What to do next: Build collaborations that help creators grow beyond campaigns. Shared education, joint products, or community-driven initiatives create deeper, longer-term value.
PR, Reputation & Brand Risk
Reputation management has become real-time and AI-measurable. From LLM citation tracking to brand backlash, every communication choice now echoes faster and louder.
A first-of-its-kind industry partnership between these two companies now offers a tool that tracks how often press releases are cited by LLMs like ChatGPT and Gemini. It finally gives brands visibility into their “AI footprint.”
Our POV: PR just gained a measurable seat in AI discoverability. Knowing when AI cites your releases helps you shape future narratives.
What to do next: Integrate AI citation metrics into your analytics stack. Identify which stories get surfaced and refine future language to match the tone that earns citations.
Rebrands are riskier than ever
Cracker Barrel’s attempted rebrand backfired almost instantly. Modest design updates triggered outrage and political backlash—proof that brand refreshes now carry reputational stakes.
Our POV: Rebrands still matter, but they demand foresight. A design tweak is a message, whether you mean it or not.
What to do next: Before launching a new look, test reactions across audience segments and scenario-plan your communication strategy. Shape the story before the internet does.
Olivia Brown automates PR outreach
A new AI platform called Olivia Brown is automating nearly every part of digital PR, from writing press releases to pitching journalists and sending aggressive follow-ups. It promises to “democratize publicity,” but its bulk-send approach is flooding inboxes and straining relationships between brands and reporters who value relevance and trust.
Our POV: Rebrands still matter, but they demand foresight. A design tweak is a message, whether you mean it or not.
What to do next: Before launching a new look, test reactions across audience segments and scenario-plan your communication strategy. Shape the story before the internet does.
SEO 2.0: The New Search Game
Traditional rankings are giving way to AI visibility. The brands that master structure, credibility, and omnichannel authority are the ones AI systems will learn to trust and users will keep choosing.
Rankings + AI Citations
Traditional SEO metrics can’t capture how visible you are inside AI systems. NP Digital’s SEO 2.0 approach tracks AI citations alongside rankings to see how content performs in generative search.
Our POV: Rankings aren’t the endgame anymore. Visibility inside AI summaries is. The brands that get cited are the ones shaping what users read next.
What to do next: Create original, data-backed content that builds authority across multiple platforms: YouTube, Reddit, TikTok, and forums. These are the signals AI models use to decide who to trust.
<America’s favorite new query: “Is it good or bad?”
SEMrush found that U.S. users are now searching in binary terms. Tens of millions of queries every month ask if something is “good” or “bad.”
Our POV: AI Overviews have trained users to expect clear answers. If your content hedges or buries the lead, you’ll lose clicks and credibility.
What to do next: Structure pages for speed and certainty. Use FAQ blocks, schema markup, and straightforward intros that deliver the verdict early. This is how you earn trust in zero-click environments.
Conclusion
AI is rewriting the rules of visibility, discovery, and trust. Success no longer depends on who publishes most. It depends on who provides the clearest data, most credible voice, and strongest structure. The brands investing in AI-ready content, authentic storytelling, and measurable strategy will own the next wave of search, social, and PR.
Need help applying these insights? Talk to the NP Digital team. We’re already helping brands adapt as things develop.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-11 20:00:002025-11-11 20:00:00October 2025 Digital Marketing Roundup: What Changed and What You Should Do About It
Look, I get it. Every time a new search technology appears, we try to map it to what we already know.
When mobile search exploded, we called it “mobile SEO.”
When voice assistants arrived, we coined “voice search optimization” and told everyone this would be the new hype.
I’ve been doing SEO for years.
I know how Google works – or at least I thought I did.
Then I started digging into how ChatGPT picks citations, how Perplexity ranks sources, and how Google’s AI Overviews select content.
I’m not here to declare that SEO is dead or to state that everything has changed. I’m here to share the questions that keep me up at night – questions that suggest we might be dealing with fundamentally different systems that require fundamentally different thinking.
The questions I can’t stop asking
After months of analyzing AI search systems, documenting ChatGPT’s behavior, and reverse-engineering Perplexity’s ranking factors, these are the questions that challenge most of the things I thought I knew about search optimization.
When math stops making sense
I understand PageRank. I understand link equity. But when I discovered Reciprocal Rank Fusion in ChatGPT’s code, I realized I don’t understand this:
Why does RRF mathematically reward mediocre consistency over single-query excellence? Is ranking #4 across 10 queries really better than ranking #1 for one?
How do vector embeddings determine semantic distance differently from keyword matching? Are we optimizing for meaning or words?
Why does temperature=0.7 create non-reproducible rankings? Should we test everything 10 times over now?
How do cross-encoder rerankers evaluate query-document pairs differently than PageRank? Is real-time relevance replacing pre-computed authority?
These are also SEO concepts. However, they appear to be entirely different mathematical frameworks within LLMs. Or are they?
When scale becomes impossible
Google indexes trillions of pages. ChatGPT retrieves 38-65. This isn’t a small difference – it’s a 99.999% reduction, resulting in questions that haunt me:
Why do LLMs retrieve 38-65 results while Google indexes billions? Is this temporary or fundamental?
How do token limits establish rigid boundaries that don’t exist in traditional searches? When did search results become limited in size?
How does the k=60 constant in RRF create a mathematical ceiling for visibility? Is position 61 the new page 2?
Maybe they’re just current limitations. Or maybe, they represent a different information retrieval paradigm.
The 101 questions that haunt me:
Is OpenAI also using CTR for citation rankings?
Does AI read our page layout the way Google does, or only the text?
Should we write short paragraphs to help AI chunk content better?
Can scroll depth or mouse movement affect AI ranking signals?
How do low bounce rates impact our chances of being cited?
Can AI models use session patterns (like reading order) to rerank pages?
How can a new brand be included in offline training data and become visible?
How do you optimize a web/product page for a probabilistic system?
Why are citations continuously changing?
Should we run multiple tests to see the variance?
Can we use long-form questions with the “blue links” on Google to find the exact answer?
Are LLMs using the same reranking process?
Is web_search a switch or a chance to trigger?
Are we chasing ranks or citations?
Is reranking fixed or stochastic?
Are Google & LLMs using the same embedding model? If so, what’s the corpus difference?
Which pages are most requested by LLMs and most visited by humans?
Do we track drift after model updates?
Why is EEAT easily manipulated in LLMs but not in Google’s traditional search?
How many of us drove at least 10x traffic increases after Google’s algorithm leak?
Why does the answer structure always change even when asking the same question within a day’s difference? (If there is no cache)
Does post-click dwell on our site improve future inclusion?
Does session memory bias citations toward earlier sources?
Why are LLMs more biased than Google?
Does offering a downloadable dataset make a claim more citeable?
Why do we still have very outdated information in Turkish, even though we ask very up-to-date questions? (For example, when asking what’s the best e-commerce website in Turkiye, we still see brands from the late 2010s)
How do vector embeddings determine semantic distance differently from keyword matching?
Do we now find ourselves in need to understand the “temperature” value in LLMs?
How can a small website appear inside ChatGPT or Perplexity answers?
What happens if we optimize our entire website solely for LLMs?
Can AI systems read/evaluate images in webpages instantly, or only the text around them?
How can we track whether AI tools use our content?
Can a single sentence from a blog post be quoted by an AI model?
How can we ensure that AI understands what our company does?
Why do some pages show up in Perplexity or ChatGPT, but not in Google?
Does AI favor fresh pages over stable, older sources?
How does AI re-rank pages once it has already fetched them?
Can we train LLMs to remember our brand voice in their answers?
Is there any way to make AI summaries link directly to our pages?
Can we track when our content is quoted but not linked?
How can we know which prompts or topics bring us more citations? What’s the volume?
What would happen if we were to change our monthly client SEO reports by just renaming them to “AI Visibility AEO/GEO Report”?
Is there a way to track how many times our brand is named in AI answers? (Like brand search volumes)
Can we use Cloudflare logs to see if AI bots are visiting our site?
Do schema changes result in measurable differences in AI mentions?
Will AI agents remember our brand after their first visit?
How can we make a local business with a map result more visible in LLMs?
Will Google AI Overviews and ChatGPT web answers use the same signals?
Can AI build a trust score for our domain over time?
Why do we need to be visible in query fanouts? For multiple queries at the same time? Why is there synthetic answer generation by AI models/LLMs even when users are only asking a question?
How often do AI systems refresh their understanding of our site? Do they also have search algorithm updates?
Is the freshness signal sitewide or page-level for LLMs?
Can form submissions or downloads act as quality signals?
Are internal links making it easier for bots to move through our sites?
How does the semantic relevance between our content and a prompt affect ranking?
Can two very similar pages compete inside the same embedding cluster?
Do internal links help strengthen a page’s ranking signals for AI?
What makes a passage “high-confidence” during reranking?
Does freshness outrank trust when signals conflict?
How many rerank layers occur before the model picks its citations?
Can a heavily cited paragraph lift the rest of the site’s trust score?
Do model updates reset past re-ranking preferences, or do they retain some memory?
Why can we find better results by 10 blue links without any hallucination? (mostly)
Which part of the system actually chooses the final citations?
Do human feedback loops change how LLMs rank sources over time?
When does an AI decide to search again mid-answer? Why do we see more/multiple automatic LLM searches during a single chat window?
Does being cited once make it more likely for our brand to be cited again? If we rank in the top 10 on Google, we can remain visible while staying in the top 10. Is it the same with LLMs?
Can frequent citations raise a domain’s retrieval priority automatically?
Are user clicks on cited links stored as part of feedback signals?
Are Google and LLMs using the same deduplication process?
Can citation velocity (growth speed) be measured like link velocity in SEO?
Will LLMs eventually build a permanent “citation graph” like Google’s link graph?
Do LLMs connect brands that appear in similar topics or question clusters?
How long does it take for repeated exposure to become persistent brand memory in LLMs?
Why doesn’t Google show 404 links in results but LLMs in answers?
Why do LLMs fabricate citations while Google only links to existing URLs?
Do LLMs retraining cycles give us a reset chance after losing visibility?
How do we build a recovery plan when AI models misinterpret information about us?
Why do some LLMs cite us while others completely ignore us?
Are ChatGPT and Perplexity using the same web data sources?
Do OpenAI and Anthropic rank trust and freshness the same way?
Are per-source limits (max citations per answer) different for LLMs?
How can we determine if AI tools cite us following a change in our content?
What’s the easiest way to track prompt-level visibility over time?
How can we make sure LLMs assert our facts as facts?
Does linking a video to the same topic page strengthen multi-format grounding?
Can the same question suggest different brands to different users?
Will LLMs remember previous interactions with our brand?
Does past click behavior influence future LLM recommendations?
How do retrieval and reasoning jointly decide which citation deserves attribution?
Why do LLMs retrieve 38-65 results per search while Google indexes billions?
How do cross-encoder rerankers evaluate query-document pairs differently than PageRank?
Why can a site with zero backlinks outrank authority sites in LLM responses?
How do token limits create hard boundaries that don’t exist in traditional search?
Why does temperature setting in LLMs create non-deterministic rankings?
Does OpenAI allocate a crawl budget for websites?
How does Knowledge Graph entity recognition differ from LLM token embeddings?
How does crawl-index-serve differ from retrieve-rerank-generate?
How does temperature=0.7 create non-reproducible rankings?
Why is a tokenizer important?
How does knowledge cutoff create blind spots that real-time crawling doesn’t have?
When trust becomes probabilistic
This one really gets me. Google links to URLs that exist, whereas AI systems can completely make things up:
Why can LLMs fabricate citations while Google only links to existing URLs?
How does a 3-27% hallucination rate compare to Google’s 404 error rate?
Why do identical queries produce contradictory “facts” in AI but not in search indices?
Why do we still have outdated information in Turkish even though we ask up-to-date questions?
Are we optimizing for systems that might lie to users? How do we handle that?
Where this leaves us
I’m not saying AI search optimization/AEO/GEO is completely different from SEO. I’m just saying that I have 100+ questions that my SEO knowledge can’t answer well, yet.
Maybe you have the answers. Maybe nobody does (yet). But as of now, I don’t have the answers to these questions.
What I do know, however, is this: These questions aren’t going anywhere. And, there will be new ones.
The systems that generate these questions aren’t going anywhere either. We need to engage with them, test against them, and maybe – just maybe – develop new frameworks to understand them.
The winners in this new field won’t be those who have all the answers. There’ll be those asking the right questions and testing relentlessly to find out what works.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/11/seo-ai-optimization-geo-aeo-llmo-QkZE19.png?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-11 19:05:482025-11-11 19:05:48SEO vs. AI search: 101 questions that keep me up at night
Over 2.5 million home services businesses operate in the U.S., from HVAC companies and plumbers to pest control specialists and landscapers. Most compete within a 10-15 mile radius, fighting for the same local customers.
Here’s the problem: your potential customers need help right now. A burst pipe. A broken AC in July. A wasp nest over the front door. They’re Googling “emergency plumber near me,” asking ChatGPT for recommendations, or searching through Google’s AI Overviews for “same-day HVAC repair.” They’re calling the first business that looks trustworthy.
If you don’t show up in those searches, either traditional Google results or AI-generated answers, with strong reviews and clear contact info, you’ve already lost the job.
Home services marketing gets you in front of customers at the exact moment they need you, across every platform they’re using. This guide breaks down the specific tactics that work for local service businesses.
Key Takeaways
Home services marketing drives visibility when customers search during emergencies or urgent needs in your local area.
Reviews and your Google Business Profile directly impact whether customers call you or scroll to the next listing.
Effective home services marketing combines local SEO, paid search for high-intent keywords, and reputation management.
Mobile-optimized websites with click-to-call functionality are critical since most home services searches happen on phones.
AI search tools like ChatGPT and Google’s AI Overviews now influence how customers find local service providers.
Tracking call volume, form submissions, and cost per lead helps you invest in what actually brings customers through the door.
Why Do Home Services Businesses Need Marketing?
Referrals and repeat customers built your business. But what happens when your best referral source retires? Or when a new competitor opens two miles away and starts undercutting your prices?
Marketing creates a predictable lead pipeline that doesn’t depend on word-of-mouth alone.
Here’s what effective marketing does for home services businesses:
Generates leads during slow seasons. HVAC companies can’t survive on summer AC calls alone. Marketing keeps your calendar full with maintenance appointments, system upgrades, and off-season work.
Captures customers before they call your competitor. When someone searches “24-hour electrician,” three businesses appear in Google’s map pack. Marketing gets you in that top three instead of buried on page two.
Look at the example below. These three electricians dominate the local map pack for emergency searches. Notice how each has over 100 reviews, clear phone numbers, and “Open 24 hours” indicators. The businesses below this fold get far fewer calls.
Builds pricing power through reputation. When you have 200+ five-star reviews and your competitor has 15, customers stop shopping on price alone. They’ll pay more for the business that looks trustworthy and established.
Lets you choose your customers. Good marketing attracts the right jobs at the right price points. You’re not just taking whatever walks through the door.
Without marketing, you’re reacting. With it, you’re in control of your growth.
What Makes Home Services Marketing Unique?
Home services marketing operates differently than retail, ecommerce, or B2B software. You’re selling an in-person service that requires customers to let strangers into their homes, often during stressful situations.
That creates three unique challenges:
Hyper-local competition. You’re not competing nationally. You’re fighting for visibility against 15-30 other plumbers, electricians, or HVAC companies within a 10-mile radius. Your customer in Austin doesn’t care about the best roofer in Dallas.
Trust is the primary buying factor. Customers research your business before opening their door. They check if you’re licensed, read what other homeowners say about you, and look for proof you won’t rip them off or do shoddy work.
Look below for an example of what customers see when researching a home services business. This HVAC company’s Google Business Profile displays detailed reviews mentioning specific technicians and response times. These trust signals matter more than flashy branding.
Speed matters more than polish. Most home services searches are urgent. Customers need someone today, not next week. They’ll call the first business that answers the phone and can schedule them quickly. A beautiful website means nothing if your contact info is buried or your phone goes to voicemail.
Click-to-call buttons on every page, above the fold.
Service area pages for each city or neighborhood you cover.
Real customer photos showing your team, trucks, and completed work.
Fast page load times because impatient customers bounce quickly.
Digital Marketing Strategies For Home Services
Winning in local home services marketing requires a mix of visibility tactics and trust-building. You need customers to find you when they search, trust you enough to call, and remember you for future jobs.
The strategies below work specifically for home services businesses. Each section covers what the tactic does, why it matters for local service companies, and how to implement it without wasting money on tactics built for other industries.
Home Services LLM Marketing
Large Language Model (LLM) marketing optimizes your content to appear in AI-generated search results from tools like ChatGPT, Claude, Perplexity, and Google’s AI Overviews.
When someone asks ChatGPT “Who’s the best emergency plumber in Austin?” or uses AI Overviews to search “how to choose an HVAC company,” you want your business cited in those responses.
Answer specific questions clearly. Create content that directly answers common home services questions: “How much does furnace replacement cost in Chicago?” or “What causes low water pressure?” AI tools favor content that gets straight to the answer in the first paragraph.
Use structured data markup. Add schema markup (LocalBusiness, FAQPage, HowTo) to help AI understand your services, location, and expertise. This increases your chances of being cited as a source.
Build authority with detailed guides. Publish comprehensive resources like “Complete Guide to Emergency Plumbing Repairs” or “HVAC Maintenance Checklist for Homeowners.” AI models pull from authoritative, in-depth content when generating recommendations.
Check out this Google’s AI Overview for landscaping companies near Seattle. These businesses earned placement by creating structured, authoritative content that AI can parse and reference.
Claim and optimize your Google Business Profile. AI tools often reference Google’s local business data when making recommendations for service providers.
Home Services Content Marketing
Content marketing for home services means creating blog posts, videos, and guides that answer customer questions, build trust, and improve your local SEO rankings.
Customers research before calling. They want to know what the job costs, how long it takes, and whether they can trust you. Content answers those questions and positions you as the expert.
What works for home services:
Location-specific service pages. Create dedicated local landing pages for each service in each city you cover: “Emergency Plumbing in Austin, TX” or “AC Repair in Round Rock.” Include local details like average response times, areas served, and city-specific regulations.
Educational blog posts targeting search queries. Answer questions customers actually ask: “How do I know if my water heater needs replacing?” or “Why is my AC blowing warm air?” These posts drive organic traffic and demonstrate expertise.
Video content showing your work. Film your technicians diagnosing problems, completing repairs, or explaining maintenance tips. Video builds trust faster than text. The River Pools YouTube channel is a good example, showing repair tutorials and walkthroughs..
FAQs on every service page. Add 3-5 frequently asked questions at the bottom of each service page. This helps with SEO and reduces pre-call questions.
Paid Media for Home Services
Paid search (PPC) puts your business at the top of Google instantly, above the map pack and organic results. For urgent home services searches, paid ads capture customers who need help now and will call the first number they see.
Home services keywords are expensive. “Emergency plumber” or “AC repair near me” can cost $15-$75 per click in competitive markets. That’s why your campaigns need tight targeting and strong conversion tracking.
Here are some best practices for home services PPC:
Target hyper-local, high-intent keywords. Bid on “emergency electrician in [neighborhood]” or “same-day HVAC repair [city].” Skip broad terms like “plumbing tips” that attract researchers, not buyers.
Use call extensions and location extensions. Make your phone number and address visible in every ad. Most home services customers call directly rather than visiting your website first.
Run call-only campaigns for mobile. Over 70% of home services searches happen on phones. Call-only ads display just your phone number and business info with a tap-to-call button.
In the paid ads for “emergency plumber NYC,” you can see book buttons, star ratings, and location info. Notice how these ads dominate the top of results before any organic listings appear.
Track phone calls, not just clicks. Use call tracking software like CallRail to measure which keywords drive actual phone inquiries and booked jobs.
Home Services SEO
SEO (search engine optimization) helps your business rank organically in Google without paying for every click. For home services, local SEO drives the most valuable traffic because customers search for providers in their immediate area.
Local SEO focuses on appearing in the map pack (the top three businesses with pins) and ranking for city-specific keywords. Getting into that map pack means more calls.
How to optimize local SEO for home services:
Optimize your Google Business Profile completely. Fill out every section: business description, service areas, hours, attributes (veteran-owned, emergency services, etc.), and upload at least 10 photos. Add posts weekly to stay active.
Create dedicated pages for each service and location. If you serve five cities, create five separate pages for “AC Repair in [City].” Include local landmarks, neighborhoods, and zip codes in your content.
Build local citations. Get your business listed on Yelp, Angi, BBB, Chamber of Commerce, and industry directories. Consistent NAP (Name, Address, Phone) across all sites signals legitimacy to Google.
The example below shows a location-specific service page optimized for local SEO. Notice how the plumbing company includes the city name in the H1, mentions specific neighborhoods served, references local weather patterns, and includes a map showing their service area.
Optimize for mobile speed. Run your site through Google PageSpeed Insights and fix any issues slowing load times. Slow sites lose impatient mobile customers.
Social Media For Home Services
Social media for home services builds local recognition and trust. You’re not trying to go viral. You’re staying visible so customers think of you first when their water heater breaks or their AC stops working.
Focus on Facebook and Instagram for residential customers, and add YouTube for educational content. LinkedIn works if you target commercial property managers or businesses.
What works for home services social media:
Post before-and-after photos of completed jobs. Show the clogged drain versus the clean pipe. The old HVAC unit versus the new installation. Visual proof builds credibility and gives customers confidence in your work quality.
Share customer testimonials and video reviews. Ask satisfied customers to record a 30-second video explaining their experience. Video testimonials feel more authentic than text reviews and perform better on social platforms.
Show your team and trucks in action. Post photos of your technicians arriving at jobs, working on repairs, or attending training. This humanizes your business and helps customers recognize your branded vehicles in their neighborhood.
The example below shows a foundation repair company’s Instagram feed with informational content, team photos, and customer shoutouts.
Engage with local community content. Share local events, sponsor youth sports teams, or highlight neighborhood news. This positions you as a community business, not just a service provider.
Post 3-4 times per week minimum. Consistency matters more than perfection.
Email Marketing For Home Services
Most home services businesses ignore email marketing, which leaves money on the table. Email keeps you connected with past customers and turns one-time jobs into repeat business.
Home services have natural repeat cycles. HVAC systems need annual maintenance. Gutters need cleaning twice a year. Pest control requires quarterly treatments. Email reminds customers to book before they call someone else.
How to use email for home services:
Send seasonal maintenance reminders. Email past customers in April about AC tune-ups before summer heat. In October, remind them about furnace inspections before winter. These emails generate easy repeat bookings.
Automate post-job follow-ups. Three days after completing a job, send an automated email asking for a review with direct links to your Google Business Profile. Follow up 30 days later with maintenance tips or related service offers.
Share monthly tips in newsletters. Send seasonal advice like “How to prevent frozen pipes” or “Signs your water heater is failing.” Educational emails keep you top-of-mind without being pushy.
The screenshot below shows a house cleaning company’s new stripping and waxing service seasonal email reminding customers to book spring maintenance. Notice the clear call-to-action button, features, and service photos.
Win back inactive customers. Email customers who haven’t booked in 12+ months with a special offer.
Home Services Reputation Management
Your online reputation directly impacts whether customers call you or scroll to the next business. Studies show 97% of consumers read customer reviews before choosing a local service provider. For home services, where customers invite strangers into their homes, reviews matter even more.
A competitor with 150 five-star reviews will get calls over you, even if your prices are lower and your service is better. Reputation management isn’t optional.
How to manage your reputation:
Ask for reviews immediately after completing jobs. Send a text or email within 24 hours with direct links to your Google Business Profile and Yelp. Happy customers forget to leave reviews if you wait too long. Make it easy with one-click links.
Respond to every review within 48 hours. Thank customers for positive reviews and mention specific details (“Glad Tom could solve your drainage issue so quickly”). For negative reviews, respond professionally, acknowledge the problem, and offer to make it right offline.
Display reviews prominently. Add a reviews widget to your website homepage. Screenshot your best Google reviews and share them on social media. Ideally, you should have as many ways as possible to feature testimonials.
Monitor mentions across platforms. Use tools like Podium, Birdeye, or Google Alerts to track when your business is mentioned online.
Home Services Mobile/SMS Marketing
SMS marketing works exceptionally well for home services because customers open 98% of text messages within minutes. For time-sensitive communications like appointment confirmations and service updates, texting beats email every time.
How home services use SMS effectively:
Send appointment confirmations and reminders. Text customers 24 hours before scheduled service: “Reminder: Tom will arrive tomorrow at 2pm for your AC repair. Reply C to confirm or R to reschedule.” This reduces no-shows significantly.
Update customers on technician arrival. Text “Your technician is 15 minutes away” when your crew is en route. This courtesy builds trust and reduces anxious phone calls asking “Where are you?”
Request reviews via text. Send a review request within hours of completing a job: “Thanks for choosing us! How did we do? Leave a review: [link].” SMS review requests get 3x higher response rates than email.
Send seasonal promotions to past customers. Text previous clients with limited-time offers: “Spring AC tune-up special: $79 (reg $129). Book by 4/30. Reply BOOK to schedule.”
Keep messages short, personalized, and always include an opt-out option to stay compliant with
Measuring Your Home Services Marketing Success
Tracking results tells you what’s working and where to invest more budget. Home services businesses should focus on metrics that directly tie to revenue: calls, bookings, and cost per customer.
Key metrics to track:
Phone call volume and source. Use call tracking software like CallRail or CallTrackingMetrics to see which marketing channels drive calls. Tag different phone numbers for your website, Google ads, and Facebook page to identify your best sources.
Form submissions and online bookings. Track how many people fill out contact forms or book appointments through your website. Set up conversion tracking in Google Analytics to measure this.
Google Business Profile insights. Check your profile’s dashboard monthly to see how many people viewed your listing, clicked for directions, called your business, or visited your website. This shows your local visibility trends.
Cost per lead and cost per customer. Calculate how much you spend to acquire each lead and each paying customer. If your Google ads cost $2,000/month and generate 40 leads with 10 becoming customers, your cost per customer is $200.
The screenshot below shows a CallRail dashboard tracking phone calls by source. Notice how it attributes calls to specific campaigns (Google Ads, organic search, Facebook) so you know exactly what’s driving results.
Use Google Analytics, Ubersuggest, and your CRM to centralize this data in one dashboard.
FAQs
What is home services marketing?
Home services marketing is the process of promoting businesses like HVAC, plumbing, roofing, pest control, and other similar categories. It includes strategies like SEO, paid ads, local listings, email, and referral programs to attract and retain customers.
How to market home services?
Start with the basics: claim your Google Business Profile, build a review strategy, create local SEO-optimized service pages, and run targeted PPC campaigns. From there, test channels like email and SMS to nurture leads and win repeat business.
Conclusion
More leads, more reviews, and a full calendar don’t happen by accident. Home services marketing builds the visibility and trust that turn searchers into paying customers.
Start with local SEO and your Google Business Profile. These give you the foundation to appear when customers search for help. Add customer reviews to build credibility, then layer in paid ads and content to capture customers at every stage.
Track your results monthly. Know which channels drive calls and which waste budget. Double down on what works.
If you need help building a marketing strategy that fills your schedule, NP Digital works with home services businesses to create campaigns that generate real ROI.
