Microsoft Clarity is a behaviour analytics tool which allows users to gain insights into how their website is being viewed. In this article, we will discuss everything you need to know about Microsoft Clarity, including what different tools and metrics you can use and why you should be using these to improve website performance.

Why is Clarity important?

While raw data tools such as Google Analytics 4 are good for letting you see how users are interacting with your site, Clarity helps you see beyond raw data into why people are interacting with pages in certain ways.

For example, while GA4 lets you see what your most popular pages are and how long people are visiting them, it doesn’t explain why people are engaging with certain content and not others. What is causing them to click on a page and leave immediately? Is it a lack of understanding of the content? Is the content not engaging enough? This is where Clarity becomes important for a deeper understanding. Clarity allows you to use tools such as heatmaps and session recordings to understand why your content is performing how it is, combined with Clarity’s behavioural metrics, which highlight pain points for users.

Behaviour metrics

There are a few different behavioural metrics you can track with Clarity that show different pain points that users are having with your site,

• Rage clicks – Users rapidly clicked or tapped in the same small area
• Dead clicks – Users clicked or tapped on the page with no effect
• Excessive scrolling – User scrolled through a page more than expected
• Quick backs – User navigated to a page, then quickly returned to the previous one

These metrics allow you to see exactly which areas of the page are causing your users frustration and update accordingly to improve the user experience. You can also see, using some of the tools available, what points of your page are having a positive impact on users, causing them to remain on your site and potentially convert.

Heatmap, Session Recording and Insight Tools

Heatmaps incorporate the behavioural metrics we’ve spoken about while also showing an overall click map of the site, as we mentioned, this is important for understanding how users interact with the content because it shows both the good and the bad of how users view your content, allowing you to adjust accordingly.

Session recording tools are slightly different as they are live videos that allow you to watch how users are interacting with the pages in real time, including how quickly they abandon certain pages or see how they get confused by certain pages and content.

Clarity has an AI feature tool that collates data points from your heatmaps and session recordings to highlight different successes and failures on your website and allow for improvements.

Using Microsoft Clarity gives a deeper understanding of user behaviour and is an important tool for SEOs wanting to make considered improvements to their website’s content for optimisation purposes.

New research from the University of Cambridge has revealed something that should concern anyone involved in digital advertising, not just gambling brands. Social media platforms are delivering gambling ads to young men at more than double the rate of women, even when those ads were not specifically set up to target men. It is a finding that raises serious questions about how algorithmic ad delivery works, and what responsibility brands and platforms carry for the audiences their campaigns ultimately reach.

What the research found

The Cambridge-led study analysed over 400 advertisements from 88 licensed gambling operators running campaigns in Ireland, using data made available through Meta’s Ad Library under EU transparency rules. The results were stark. Across Facebook and Instagram, men were reached 2.3 times more than women. The most exposed age group was 25 to 34-year-olds, who accounted for over a third of all accounts reached. One single ad from Betfair alone reached more than 1.3 million unique accounts, equivalent to roughly a quarter of Ireland’s entire population. Crucially, the skew towards men was not simply the result of advertisers choosing to target them. Even ads set to reach all genders defaulted towards young men, suggesting the platforms’ own delivery systems were driving the imbalance.

Why this matters beyond gambling

The significance here goes beyond one industry. This research demonstrates that social media ad delivery does not simply reflect the audience you set up in your campaign. The algorithm makes its own decisions about who sees your content, and those decisions are not always visible or predictable. For any brand advertising on Meta, it is a reminder that the audience you target and the audience you actually reach can be very different things. Understanding that gap matters, both for campaign performance and for the growing regulatory scrutiny around responsible advertising.

What advertisers should take from this

Regulators are paying attention. Ireland has already introduced restrictions requiring users to actively opt in before seeing gambling ads on social media. The UK and other markets are likely to follow. But the broader lesson for advertisers is this: knowing who your ads reach is not optional. Demographic delivery data is available within Meta’s reporting tools, and reviewing it regularly should be standard practice. If your campaigns are reaching audiences you did not intend, or missing audiences you did, that is both a compliance risk and a wasted budget. The algorithm works for you when you understand it, and against you when you do not.

Google has quietly shipped one of the more useful additions to Search Console in recent memory. Platform properties, launched earlier this month, allow you to connect your Instagram, TikTok, X and YouTube accounts directly to Search Console and see how your content on those platforms is performing within Google Search. Not on those platforms. Within Google Search itself.

It sounds like a subtle distinction but it is a meaningful one. For any business that posts content on social or video platforms without knowing whether it is generating search visibility, this gives you the first direct window into that question. And given that recognition across multiple channels now drives downstream search behaviour in ways that standard analytics have never been able to capture, platform properties is a feature worth setting up properly.

What platform properties actually shows you

Once you have verified a platform property in Search Console, you get access to three types of reporting, each showing a different layer of how your social and video content surfaces in Google Search.

• Performance report. Total clicks, impressions and engagement metrics from your social content appearing in Google Search. You can filter by specific posts and sort by the search queries that are driving traffic to your accounts or content. The data is also exportable if you want to pull it into a separate reporting dashboard.
• Insights report. A higher-level view showing traffic trends, your top-performing posts and the mechanisms by which people discover your accounts in Google Search. Useful for a quick overview without drilling into individual query data.
• Achievements. Milestone tracking for growth in clicks from Google Search, measured in 28-day windows. Less analytical, but useful for demonstrating momentum to clients or stakeholders.

The rollout is gradual, so not every account will see platform properties immediately. Google has confirmed it is expanding access over the coming weeks. If you do not see the option yet, it is worth checking back rather than assuming it has been skipped.

How to set it up

Setting up platform properties requires you to verify each social account separately within Search Console. The process is the same for all four supported platforms and takes a few minutes per account.

• Open Search Console and go to the property selector dropdown, or navigate to the verification page directly.
• Click ‘Add property’ and select one of the four available platforms: Instagram, TikTok, X or YouTube.
• Follow the on-screen verification steps to authorise the connection. Google uses a secure authorisation flow rather than asking for login credentials.
• Once verified, the platform property appears alongside your standard web properties in Search Console and data begins populating, typically showing historical data going back several weeks.

For businesses managing multiple clients, you will need to verify each platform account separately. There is currently no bulk verification option, and each platform property sits in its own reporting view rather than being rolled up into a combined dashboard.

Why this matters more than it might initially appear

The reason this is genuinely useful rather than just a nice-to-have is that it closes a specific and long-standing measurement gap. Until now, if a business posted a video on YouTube or a set of stories on Instagram, there was no reliable way to know whether that content was generating impressions or clicks within Google Search. You could track traffic from Google to your social profiles in some analytics tools, but you could not see what search queries were triggering those results or which pieces of content were responsible.

Platform properties flips that. You are now looking at the data from the Google Search side rather than from the social platform side. That means you can see which search terms are surfacing your YouTube videos, which Instagram posts are appearing in Google Image Search, and which queries are driving traffic to your X profile from within Google. That is a different and more useful frame than social platform analytics provides on its own.

The timing of this feature is also worth noting. Recent data from Similarweb confirmed that more than half of the downstream traffic generated by AI recommendations arrives via branded search rather than direct AI referral clicks. Branded search lifts are increasingly a downstream signal of visibility across multiple channels, not just traditional SEO. Platform properties gives you the first standardised tool for measuring whether your social content is contributing to that broader search signal.

The practical implications for content and reporting

For most marketing teams, the immediate value of platform properties falls into three areas:

• Proving the SEO value of social content. This has historically been difficult to demonstrate in a way that satisfies sceptical stakeholders. Platform properties gives you direct click and impression data from Google Search for content posted on social platforms. That is a credible, source-backed number rather than an inferred relationship.
• Understanding which queries drive social discovery. Seeing the specific search terms that are surfacing your YouTube videos or Instagram posts in Google Search tells you something useful about the intent of people finding you through that route. If a dental clinic’s treatment videos are appearing for ‘how long does teeth whitening last’ in Google Search, that is content worth making more of. Without platform properties, you would not know those searches were leading to your video.
• Informing content decisions with actual search data. The performance report shows which specific posts are generating clicks and impressions in Google Search. That gives you a direct signal about which content formats, topics and posting approaches are earning Google search visibility rather than just social engagement. The two do not always correlate.

What to check once you have connected your accounts

Once the platform property data starts populating, the most useful starting point is the performance report filtered by query. Look for search terms that are driving impressions to your social content that you are not yet targeting with your main website. These represent keyword opportunities where Google is already surfacing your brand through social content, and where a corresponding page or article on your website could strengthen that visibility further.

The insights report is worth reviewing monthly rather than weekly – it is a trend indicator rather than a granular data source. The most commercially useful data is in the performance report, particularly for video content on YouTube where the correlation between Google Search impressions and watch time can help inform both SEO and content production decisions.

For businesses running YouTube channels alongside a website, the combination of YouTube platform property data and standard Search Console data in the same interface starts to give a more complete picture of how their content performs across Google’s various surfaces. That has never been possible before within a single tool, and it is worth taking the time to set it up properly rather than treating it as a nice-to-have.

What is brand recognition?

Brand recognition is a step beyond brand awareness; it is about when people can easily identify a brand based on elements like brand colours, jingles, logos and campaigns.

Achieving brand recognition is no easy feat, so when customers and non-customers alike can recognise your brand, it shows the effectiveness and impact your brand-awareness campaigns have had.

A key example of brand recognition is when your business is the first thought brand for that particular product or service. Leading against competitors and demonstrating the overall commercial impact that’s been had.

What is the value of brand recognition?

Brand recognition is incredibly valuable because it tends to make people feel more comfortable investing in a brand. When trust and familiarity are established through recognition, it can help reduce the perceived risk around trying new services or products. This is favourable, especially when weighing up the risk, because it is an expensive and important investment on the customer’s part.

As brand recognition is strengthened, it can help build customer loyalty. Customers with a pre-existing positive experience are more likely to return to a brand. Rather than seeking out potential alternatives. Customer recognition also increases the chances of recommendations and referrals.

Another strategic advantage of becoming a recognised brand is the ability to improve the effectiveness of marketing campaigns. The more familiar a brand is, the less effort is required to convey that.

Above all, brand recognition offers a competitive advantage, allowing brands to stand out in over-saturated markets. Increasing the difficulty for competitors to capture their audience.

Building brand recognition

The most effective way for businesses to increase their brand awareness is to repeatedly and consistently create opportunities for exposure. The more a brand creates instances to familiarise audiences, the more memorable they become.

Some of the keyways to build up brand recognition include:

• Creating content that adds value to prospects and existing customers. This can come in several forms, whether that be publishing blogs, videos, social media content, podcasts or helpful guides. The aim is to entertain or educate whilst ensuring visibility remains consistent, all while demonstrating expertise.
• Developing a consistent brand identity. This means using fixed logos, colour palettes, typography, design styles, imagery, social media profiles and marketing materials.
• Developing a strong online presence can be a particularly effective method. Regular activity, SEO and well-optimised websites help keep the brand relevant and useful. This can also help with visibility while reinforcing recognition over time.
• Maintaining a clear brand voice from the moment of establishing a brand is essential; consistent communication creates moments of familiarity and reinforces cues on brand personality.
• Promoting customers’ stories, whether that’s in reviews, testimonials, UGC or referrals.

Overall consistency with the recommended methods is the key. Brand recognition isn’t established in one day; it takes time and repeated interactions from customers to reinforce this. Every aspect of a strategy should reinforce how people remember the brand, which eventually results in getting new and returning customers.

Brand recognition is far more than simply being recognised by name or logo. It reflects the trust, familiarity and credibility a business has built with its audience over time. When customers can instantly identify a brand and associate it with positive experiences, they’re more likely to choose it over competitors and recommend it to others.

While building brand recognition requires patience and consistency, the long-term rewards make the investment worthwhile. By delivering valuable content, maintaining a cohesive brand identity and creating meaningful customer experiences, businesses can establish a recognisable presence that supports sustainable growth, strengthens customer loyalty and helps them remain competitive in an increasingly crowded marketplace.

If you have been paying attention to AI search over the past year, you will have noticed Reddit appearing everywhere. It is among the top five most-cited sources across ChatGPT, Perplexity, Claude and Google AI Overviews. Perplexity pulls close to a quarter of all its citations from Reddit alone. A study covering 30 million sources across major AI platforms found Reddit appearing in 68% of AI-generated answers. The number is striking enough that an entire cottage industry has emerged to capitalise on it: aged accounts, paid upvotes, ghostwritten threads, all packaged and sold under the banner of answer engine optimisation.

It works, for now. And it will end the same way every manufactured signal in search has ended before. If you have clients asking about Reddit as part of their AI visibility strategy, this is worth reading carefully.

Why Reddit became the most cited source in AI search

The reason Reddit is so heavily weighted by AI systems is not arbitrary. It is structural. Reddit represents something that almost no other source on the open web can provide at scale: authentic, unsponsored, experience-based discussion from people with no commercial incentive to say what they are saying.

AI systems are specifically trying to surface that kind of content. When someone asks ChatGPT which dental clinic to visit, or whether a particular care home software platform is any good, or which culinary school produces the best graduates, the most useful answers are not the ones on brand websites. They are the ones in forum threads where real people share real experiences. Reddit is the largest such corpus on the open web. Google’s $60 million annual licensing deal with Reddit for AI training access confirms that this is not a temporary arrangement. It is a structural bet on user-generated discussion as a foundational input for AI-generated answers.

The data reinforces how significant this has become. Brands with meaningful community presence on Reddit and similar platforms have roughly four times the chance of being cited by AI systems compared to brands with minimal community activity, according to SE Ranking research. That multiplier is large enough to have grabbed the attention of marketers looking for the shortest path between zero and cited.

The shortcut being sold right now

The manufactured version of Reddit presence looks like this: aged accounts with accumulated karma, upvoted threads created specifically to position a brand, ghostwritten responses seeded into relevant subreddits, and paid placements dressed up as organic community discussion. Vendors selling this approach position it as answer engine optimisation. What they are actually selling is a new flavour of link farm logic.

The parallel with the early 2000s link-building era is exact. A new signal becomes the thing that determines visibility. An industry forms to manufacture that signal. It works for a period. Then the platform whose signal is being gamed has both the incentive and the capability to filter it out, and the sites and brands that leaned on manufactured signals find themselves worse off than if they had never started.

Google shipped Penguin in 2012 because the link signal had been so heavily gamed that it was becoming unreliable. Many of the sites that had built their rankings on bought links never recovered. The principle that applies here is identical. A citation surface you can purchase is a citation surface that will be filtered. The only question is timing.

Why the filter is coming faster than people expect

Reddit has commercial reasons to protect its signal. The platform licenses its data to Google and to AI companies. If that data becomes contaminated with manufactured content at scale, the value of the licence diminishes. Reddit’s own moderation systems already catch new accounts posting promotional content, remove posts from accounts with insufficient karma, and flag domains appearing in spam reports. These are the early-stage defences of a platform that is aware its content is being weaponised.

The AI engines themselves have additional layers of detection. Generative systems can identify unnatural patterns: repetition across threads, unusual phrasing, inconsistent account histories, manufactured consensus. Manufactured mentions do not just fail to help in this environment. They actively distort the consensus formation models that AI systems use, creating contradictory signals that weaken a brand’s informational footprint rather than strengthening it.

There is also a second cost that the link-farm era did not carry at this scale. Every manufactured thread degrades Reddit as a source for the next person who uses it genuinely, including your own potential customers. The more the platform fills with astroturfed discussion, the less useful it becomes as a research tool, and the more likely AI systems are to reduce the weight they assign to it.

What genuine Reddit presence actually looks like

The distinction between a vendor selling manufactured citations and a sustainable community strategy is straightforward. The tell, as one industry observer put it, is whether you are being sold accounts, upvotes or placements rather than helped to do something real in a community. Anything priced by the account or by the upvote is link-farm logic in new clothes. Recognition compounds when it is real. It does not compound when it is rented.

For businesses that want to build genuine Reddit presence that holds up when filters arrive, the approach is slower and more deliberate:

• Identify the subreddits where your customers are already asking questions. For a dental clinic, this might be r/askdentists or local city subreddits. For a care home software company, it might be r/caregiving or sector-specific communities. For a culinary school, r/culinaryschool or r/chef. Start with one community, not ten.
• Build karma through genuine contribution before any commercial mention. A reasonable threshold is 500 karma earned through unrelated participation. Most major subreddits require this before links or branded content are permitted. A consistent 60-day schedule of real engagement typically clears this threshold.
• Answer questions that people are actually asking. The threads most likely to be cited by AI systems are those that answer a real question specifically and completely. Buyer-intent threads, comparison threads and troubleshooting threads are the highest-value targets. The language in those threads is often query-shaped, which means it matches what AI systems retrieve.
• Disclose commercial affiliations. Reddit’s site-wide rules and FTC disclosure requirements both require transparency. Undisclosed commercial posting is both a moderation risk and an ethical problem. Transparent participation builds trust in ways that anonymous promotion cannot.

Reddit as one layer, not the whole strategy

The broader point worth making is that Reddit is a signal source, not a complete AI visibility strategy. Research from Muck Rack across 25 million links found that 84% of AI citations come from earned media sources: third-party publications, press coverage, industry recognition. Reddit discussion can surface a question and provide initial credibility. Earned media validates it. Owned content preserves it durably. Together, those three layers form what some practitioners are calling a citation architecture: the retrieval stack that AI systems draw from when assembling answers.

For businesses with limited time and budget, the priority order is clear. Owned content that answers real questions specifically and completely. Earned media that provides third-party validation. Authentic community participation where it exists naturally. Reddit is part of that last category. It is not a replacement for the first two, and treating it as a shortcut to AI visibility without the underlying substance is the mistake that will cost the most when the filter runs.

The businesses standing when the cleanup lands will be the ones that were doing something genuinely useful in communities where their customers already gather. That is not a new principle. It is the same one that separated the sites that survived Penguin from the ones that did not. The surface has changed. The logic has not.

If your Google Ads campaigns use Dynamic Search Ads, there is an important date you need in your diary. Google has confirmed that all remaining DSA campaigns will be automatically upgraded to its newer AI Max format starting February 2027, giving advertisers several more months than originally planned to prepare. What looked like a race against a September 2026 deadline, right in the middle of the crucial pre-Christmas advertising period, has been extended following widespread feedback from the advertising community. That breathing room is welcome, but it should not be mistaken for an indefinite stay of execution.

What Is Actually Changing and When

Google is retiring Dynamic Search Ads entirely and replacing them with AI Max, its newer AI-driven approach to search campaign management. Rather than relying on keywords alone, DSA campaigns have long worked by automatically matching ads to relevant searches based on your website content, making them particularly useful for large or frequently changing product catalogues. The transition away from this format happens in two stages: from January 2027, you will no longer be able to create new DSA campaigns. From February 2027, any campaigns still running will be automatically migrated to AI Max on your behalf, whether you have prepared for it or not.

Why the Extension Was Granted

The delay was not granted out of generosity. It came directly in response to advertiser pushback. One recurring concern is that AI Max routes specific product searches to generic landing pages, something DSA handled more precisely by matching searchers to the most relevant page within your site. That distinction matters commercially, particularly for retailers with large or structured product ranges. Independent analysis has also shown AI Max delivering lower returns on ad spend in some campaign types, which is why treating this migration as a routine update would be a mistake.

What You Should Do Now

The February deadline may feel distant, but the window for proper preparation is shorter than it appears, especially for businesses with Q4 peak trading periods where any campaign disruption carries real commercial risk. The time to review your DSA campaigns is now, not in December. Start by auditing which campaigns are still running on DSA and how they are currently performing. Then build in time to test AI Max before the forced migration kicks in, so you can identify and resolve any issues on your own terms rather than Google’s. If you are unsure where to begin, this is exactly the kind of transition where working with a specialist pays for itself.

AI overviews, FAQ blocks and AI mode are each increasingly being displayed across the search engine results page. We have to ask the question: is it diluting the quality of content, and is it dominating the SERP, making organic content increasingly harder to find?

We see a plethora of AI uses, ranging from AI-generated search experiences (where AI creates or summarises information) and AI-assisted search features (where AI helps rank, organise, or present content).

Places AI is currently used on search engines

Google, Bing and Yahoo

Google is known for leading the race with a staggering 91.27% of traffic in comparison to other well-recognised search engines. Recognised for its cutting-edge updates, its fine-tuned algorithm and vast catalogue of websites and content to serve searchers’ queries.

In its journey to be the best and largest of search engines, AI has become a key and defining feature. With a strong presence across various search results features, it’s absolutely reshaping the way Google works.

We’ve observed the same evolution across other search engines, including Bing and Yahoo. Those features include:

The Impact of AI on organic content

AI-generated content doesn’t need to fight to be spotted; now, the first positions across the SERP are completely dominated by AI results, pushing organic content further down and reducing the chance of discoverability and clicks.

The AI Zero-clickopalypse

One of the most harmful impacts of AI-generated results is the zero-click epidemic, where AI content steals the valuable clicks from organic content on the SERP. When users don’t need to click into content, because AI has borrowed and displayed it at the top of the SERP, it directly steals traffic from those cited sites.

Ultimately, AI features are pushing the higher-ranking organic pieces of content below the fold, shrinking visible organic listings.

The consequence for a zero-click search varies. For more established brands and sites, reduced clicks may be inconsequential, while smaller brands building their content portfolio are more likely to suffer as a result, seeing the following metrics affected:

• Organic traffic  
• Click-through rates
• Page sessions
• Average duration of engagement

Shifting approaches from SEO to AEO

The entire strategy for SEO needs to adapt to AI Engine Optimisation (AEO). This changes the style, nature and focus of content, as well as the perspectives marketers use to produce it. The impact then falls on human users, who are increasingly likely to encounter more generic, factual content that doesn’t necessarily offer a human experience or perspective.

Rather than allowing users to research and draw their own conclusions, AI is serving cookie-cutter, generic content that reinforces confirmation bias, reduces searchers’ critical thinking, and stunts what was once curiosity, replacing it with quick, low-value information retrieval.

Algorithms creating personalised SERP’s

 Previously in SEO, when your content ranked, it would be a specific keyword it ranked for, and that was applied to all other sites using the same keyword. Now, personalisation breaks down that method of ranking, with results based on contextual clues like gender and location.

Content now tends to be displayed based on location, not a national or global ranking. Google also takes context from search history, inferring intent and resulting in more tailored responses, with historical searches influencing the type of results shown. Ranking has transitioned from a fixed metric to essentially a range in which you can get a general idea of performance.

So, is AI diluting the quality of content on the SERP?

The short answer is yes, but it’s not quite that simple. AI is reshaping the SERP in ways that make organic content harder to find, harder to click, and harder to rank consistently. The zero-click reality, the shift from SEO to AEO, and the fragmentation of personalised results all point in the same direction: the traditional organic search landscape is shrinking.

But dilution isn’t the same as destruction. The opportunity now sits with content that AI can’t easily replicate original research, genuine human perspective, authoritative expertise, and experiences that feel lived rather than generated. If AI serves the generic, the specific becomes more valuable.

The question isn’t whether AI is diluting quality. It’s whether we’re willing to create content that stands apart from what AI can summarise.

At Cannes Lions 2026, the world’s most influential festival of creativity, Meta used the spotlight to announce a significant leap forward in how businesses can advertise, connect with creators, and engage customers. The announcements span three areas: AI-powered creative tools, a unified creator marketing platform, and smarter customer engagement through AI. Together, they represent a clear signal of where digital advertising is heading, and why brands that move with these changes stand to gain the most. To put the opportunity in context, Meta shared that its own analysis of over one million campaigns found advertisers were generating an average of $4.13 in revenue for every dollar spent on Meta, a 25% improvement since 2022.

AI That Learns Your Brand and Builds Your Ads

The most substantial announcement for advertisers is a new end-to-end creative solution that allows marketing teams to generate, test and refine ads far more quickly than before. At the heart of it is a feature called Brand Memory, which allows Meta’s AI to absorb the look, tone and identity of your brand from your existing advertising history, so that new content it creates feels consistent rather than generic. Rather than starting from scratch every campaign, your team gets a system that already understands what your brand looks and sounds like. Meta is building this with agencies in mind from day one, with early integration through WPP Open, meaning the process slots into existing workflows rather than replacing them. Additional improvements include better AI-written ad copy, multilingual capabilities across image and video formats, and built-in approval tools that reduce the back-and-forth involved in getting campaigns signed off.

Finding and Working With Creators Just Got Simpler

For brands using influencer or creator partnerships, Meta is consolidating its previously separate tools into a single destination called Meta Creator Marketing Hub, due to launch later this year. Rather than switching between platforms to find creators, review content and activate ads, businesses will be able to do all of this from one place, covering both Instagram and Facebook. New discovery features will also surface relevant content from creators you are not yet working with, including product-tagged posts and user-generated content, along with performance data to help you make smarter partnership decisions. For brands that have found creator marketing fragmented and time-consuming to manage, this is a meaningful practical improvement.

Turning Customer Conversations Into Sales Opportunities

Perhaps the most forward-looking announcement is the expansion of Meta’s Business Agent Platform, which allows businesses to deploy AI directly within their customer messaging. Rather than using channels like WhatsApp purely for notifications, brands can now create personalised, conversational shopping experiences at scale. The potential is real: one early adopter reported over 15,000 AI-powered shopping conversations in its first week live in a new market. With over one million businesses already using the platform, this is not a distant possibility but a growing commercial reality. For brands that want to convert everyday customer interactions into revenue, this is worth exploring now rather than later.

There has been a gap at the centre of AI search marketing since it became a topic worth discussing. The question everyone wanted answered was simple: if a user sees your brand recommended in an AI-generated answer but does not click through, does it actually matter? Do they come back and search for you later? Does being visible in AI responses translate into anything commercially measurable?

Similarweb has just published the first study to attempt a direct answer. The research connects AI recommendation data with downstream web traffic behaviour using a consumer panel, rather than just counting referral clicks. The findings are significant and, for anyone thinking seriously about where visibility is actually coming from in 2026, they are worth understanding properly.

What the study found

Brands appearing in ChatGPT recommendations were 2.5 times more likely to receive a site visit within seven days than comparable brands that were not recommended. That multiplier held consistently across six different brand comparison scenarios in the dataset. It is the first time AI recommendation data has been tied to actual downstream web behaviour rather than just citation counts or referral URLs.

The mechanism matters. Of the downstream traffic generated after an AI recommendation, 55.9% arrived via branded search, meaning users who saw a brand mentioned in a ChatGPT response subsequently Googled that brand by name. They did not click a link in the AI answer. They went away, remembered the brand, and searched for it directly. The AI recommendation acted as a brand introduction that triggered traditional search behaviour, not a direct referral.

This is the finding that changes the measurement conversation. Most teams tracking AI search performance are measuring referral clicks from platforms like ChatGPT and Perplexity. Those clicks are real but they are a small fraction of the commercial effect. The larger effect, the one that drives more than half of the downstream visits, is invisible in standard analytics. It shows up as branded search, as direct traffic, or not at all.

The quality of AI-referred traffic is materially higher

The Similarweb data also quantifies what many practitioners had been observing anecdotally: that the visitors who do arrive directly from AI platforms engage more deeply than those arriving from traditional search. Users arriving from ChatGPT spent an average of 15 minutes on site, compared to 8 minutes for Google referrals. They generated 12 page views per visit against 9 from Google. They converted to transactional sites at a 7% rate, compared to 5% from Google referrals.

Volume is low. Intent is high. These are users who have already been through a filtering process inside an AI conversation before they arrive on your site. They are further along in their decision-making than the average organic search visitor, which explains both the deeper engagement and the higher conversion rates.

A separate piece of data reinforces this pattern from a different angle. On 7 May 2026, ChatGPT changed how it surfaces brand links, making brand names clickable callouts placed directly inside responses rather than citing sources in follow-up prompts. In the week after that change, total ChatGPT referral traffic increased by 157.7% across tracked websites. But homepage referrals surged by 354.7%. Before the update, roughly 26 to 32% of ChatGPT referrals landed on a brand homepage. After it, around 60% did. Users are arriving at the front of a brand, not a specific article or product page, which is consistent with AI operating as a brand discovery layer rather than a search engine.

Where AI sits in the purchase journey

The study also includes survey data on where in the purchase journey consumers find AI tools most useful. At the discovery and initial ideas stage, 35% said AI tools are most useful at that point, compared to 13.6% for traditional search engines. At the researching and comparing options stage, the split narrows: 30% for AI tools versus 20% for search. At the finding where to buy stage, the gap narrows further: 24.3% for AI tools versus 22.1% for search.

AI dominates the earliest stage of the funnel. This reinforces what the 2.5x traffic multiplier is actually measuring. AI is not replacing search. It is sitting upstream of it, shaping which brands users subsequently search for, visit, and consider. The case for investing in AI visibility is not that AI referral clicks will replace organic traffic. It is that AI shapes the pool of brands that end up in organic search at all.

The measurement problem this creates

The dominant metric in AI search reporting right now is referral click volume from platforms like ChatGPT and Perplexity. That number is being used to make decisions about whether AI visibility investment is working. Based on Similarweb’s data, that is the wrong metric. Referral volume understates the effect by ignoring the downstream branded search that the recommendation triggers. It also does not capture users who never click through at all but whose subsequent direct visits or brand searches were influenced by the AI response.

Some researchers estimate that up to 70% of AI-influenced traffic arrives in analytics as direct traffic or branded search with no attribution to the AI source. This is sometimes described as dark AI traffic. It is real, it is commercially significant, and it is currently invisible to most reporting stacks.

The practical implication for how to measure AI visibility is clear. Stop measuring it primarily by referral click volume. Start measuring:

• Brand mention share across AI platforms, meaning how frequently your brand appears in relevant AI-generated answers relative to competitors
• Branded search volume trends in Google Search Console, which may be rising as a downstream effect of AI mentions before any direct referral traffic appears
• Direct traffic trends, particularly homepage visits, which the Similarweb data suggests are increasingly a downstream signal of AI visibility rather than traditional direct navigation
• Sentiment context of AI mentions, since being mentioned in a negative context is measurably worse than not being mentioned at all

SEO is a fundamental strategy that all online businesses need to be implementing in 2026 to remain competitive in the online landscape. With the emergence of AI and similar trends such as GEO (AI Optimisation) these practices are now becoming more critical than ever. With the emergence of AI content, any site implementing human written high quality, informative content on a fast site with a strong product will be sure to stand out from the pack. 

However unlike with paid media, it is harder to calibrate a true return on investment for SEO. So much of the work goes on behind the scenes and it is a long term project. SEO can take 6 months to 12 months to see a noticeable rise in online traffic and conversion rates. While most users will often interact with multiple different marketing strategies (paid media, social media, organic) before choosing your product. This means it’s not always easy to exactly attribute which channel they initially came from.

In this article, we will discuss how best to determine how successful your SEO campaigns are with ROI models.

What is ROI?

ROI stands for return on investment and simply means how profitable your investment was by calculating the financial revenue of an investment versus the costs of it. For example if you spent £30,000 on an SEO campaign and generated £120,000 in revenue from organic search throughout the year then your ROI would be 300%. This means for every £1 you spent you generated £3.

How to Measure ROI for SEO

Branded vs Non Branded Traffic

One of the crucial aspects people overlook when measuring return on investment for organic search is branded vs non branded traffic. People often report on overall search performance, which makes sense in theory, but if people are searching directly for your brand in Google before clicking on it, this isn’t really your SEO campaigns having an impact even though it may be attributed this way.

On Google Search Console you can see which search terms people are using to find and click through onto your site. Make sure you read this report thoroughly going through branded traffic figures and making sure you factor them into your organic traffic figures when modelling them for ROI figures.

Track Organic Revenues and Conversions

To calculate ROI for your SEO campaigns your are going to need two free to use Google tools

• Google Analytics 4

• Google Search Console

In Google Analytics 4 you can track any type of conversion on your site such as an e-mail sign-up, form submit, product order and more. You can set up unique conversion types specific to your site that are then labelled as key events in GA4. 

Google Search Console gives you all your data on organic traffic such as which landing pages and queries are generating traffic through Google search. Once you connect your GSC account to your GA4 account you will be able to access two new reports in GA4.

• Google Organic Search Queries 

• Google Organic Search Traffic 

These two reports will tell you which keywords users are typing in to find your pages and which landing pages are generating traffic organically. You can then use the Google Organic Search Traffic report to identify which organic landing pages are generating the most conversions/ revenue. You can then combine this data into a Google Looker Studio dashboard using their filters to allow 

Creating a Google Looker Studio dashboard that tracks the customer journey from search queries to conversions allows the user to see exactly what people are searching for on Google and how it’s driving conversions. This means that you can calculate ROI for your SEO campaigns around specific URLs and make informed data decisions on what content to create going forward.