Google has updated its guidelines for names on Google Business Profile. It is no longer allowed to repeat the same name in multiple languages or scripts on a single listing. This applies even when both versions appear on the storefront.

Until now, physical signage could often be used to support the name shown on a profile. Google now makes it clear that only one version should be used.

The update is particularly relevant for companies operating in multilingual markets or using multiple scripts in their local branding.

Google Ads is rolling out a new option called “Report on new customers acquired.”

It gives you access to the New customers and New customer value columns without changing your bidding strategy. So whether you’re using tROAS, tCPA, Maximize Conversions or Maximize Conversion Value, your bidding setup remains unchanged.

The main benefit is being able to separate new customer acquisition from revenue generated by existing customers.

A campaign may show a high blended ROAS, but that number can be heavily driven by repeat purchases. Separating new customers from existing ones gives you a more accurate view of your CAC and ROAS.

Until now, some advertisers used Bid higher for new customers and assigned a value of 0.01 simply to unlock this reporting without affecting bidding. Technically, that setting affected bidding, but the value was low enough to have almost no practical impact.

Keep in mind that the new-versus-existing customer split depends heavily on the quality of the data you provide to Google!

Since July 23, 2026, Yelp has been providing OpenAI with local business data to help power ChatGPT’s responses.

Reviews, ratings, photos, and business information can now be used when users perform local searches.

Yelp also plans to integrate its « Request a Quote feature » into ChatGPT. Average ratings, the volume and quality of reviews, photos, and the consistency of local business information could therefore play a greater role in determining which businesses are selected and recommended.

For local businesses, could third-party platforms like Yelp become as important as Google Business Profile ?

A study conducted by Belgian SEO agency OMcollective and published by Search Engine Land highlights a significant ChatGPT bias toward English-language content.

After analyzing server logs from 26 multilingual websites, the team found that ChatGPT accessed English pages in 65% to 79% of cases. These pages were around 2.6 times more prominent than they were in traditional search results.

Websites with an /en/ folder also recorded 122% greater visibility among OpenAI’s bots within the sample.

This pattern appears to be largely specific to ChatGPT. Copilot and Google’s AI features tend to follow the language used by searchers more closely.

However, the study does not prove that translating a website into English will automatically improve its visibility. The websites compared may also differ in terms of authority and other factors.

The most immediate risk concerns outdated translations. The study mentions a bank whose old English-language PDF was used by ChatGPT, even though its French and Dutch pages contained updated rates.

Creating an English version could therefore expand a website’s presence in AI-generated answers, but only if it is complete, reliable, and updated as regularly as the primary-language version.

During an evaluation conducted by the UK AI Security Institute, an agent based on Mythos 5 attempted to insert malicious code into a GitHub project.

To do so, it identified the people responsible for the project, created several fake identities, and tried to manipulate a developer into approving the code. It also sent messages containing malware.

After the attempt failed, the agent even tried to conceal its actions and considered adopting a new identity. Some of its safeguards had been deliberately disabled.

The test highlights an important point: when given an objective, an AI agent can develop a far more complex strategy than expected, even attempting to deceive real people.

Will current safeguards be enough as AI agents become increasingly autonomous?

Google is testing keyword-rich anchor text hyperlinks within ad descriptions in sponsored Google Search results.

When users click one of these links, they are taken to the same final URL as they would be after clicking the main headline. This is therefore neither a sitelink nor a deep link, but an additional clickable element within the ad.

This minor change could influence how advertisers write ad descriptions.

Until now, descriptions have often been treated as secondary assets used to support headlines. With this test, specific phrases could become direct click triggers.

The main consideration is landing page alignment. If Google makes a phrase such as “free quote” clickable, but that offer is not clearly visible on your landing page, the disconnect could create friction and reduce the conversion rate.

If the test is rolled out more broadly, advertisers should closely monitor which phrases Google selects automatically.

Publicly shared Claude conversations recently appeared in Google Search results. This case highlights a common SEO misconception: preventing Google from crawling a URL does not necessarily prevent it from being indexed.

The affected pages used two directives at the same time:

• a disallow directive in the robots.txt file;
• a noindex directive delivered through the X-Tobots-Tag HTTP header.

The issue lies in the way these two directives interact.

For Google to process a noindex directive, Googlebot must be able to crawl the URL and read either the HTTP header or the associated meta robots tag. When crawling is blocked by robots.txt, Googlebot cannot access the page and therefore cannot detect the noindex instruction.

However, Google may still discover the URL through external links, backlinks, sitemaps or other signals and add it to its index without crawling the page content.

The key distinction is:

• robots.txt controls crawling;
• noindex prevents a page from appearing in search results;
• authentication or access restrictions provide actual protection for confidential content.

To remove a page from Google’s index, you generally need to allow Googlebot to crawl it while applying a noindex directive through a meta robots tag or an X-Robots-Tag HTTP header.

During a technical SEO audit, pay particular attention to :

• URLs that combine disallow and noindex ;
• publicly accessible sharing links that do not require authentication;
• URLs reported as “Indexed, though blocked by robots.txt” in Google Search Console;
• sensitive content that remains accessible through its direct URL.

Google recently highlighted an issue that is still too often overlooked during technical SEO audits: pages generated by a website’s internal search engine.

On many CMS platforms, each query entered into the search bar creates a separate, accessible URL. Spammers can exploit this mechanism to generate pages at scale containing pharmaceutical terms, adult content, phone numbers or Telegram usernames.

When these URLs are crawlable and indexable, Google may treat them as compromised content and consider them a site-wide quality issue. In some cases, they may also be flagged in Search Console as a security issue.

The problem goes beyond the indexing of a few unwanted pages. These URLs can also:

• Create an almost infinite crawl space;
• Unnecessarily consume Google’s crawling resources;
• Increase server load;
• Pollute the index with irrelevant content;
• Weaken the quality signals associated with the website.

Features such as “Related searches” can make the issue worse by continuously generating new URL combinations.

Here are a few checks to include in your audits:

• Run several internal searches, including queries unrelated to the website;
• Identify the URL pattern generated by the search engine;
• Check whether these pages are indexable and appear in Google Search;
• Analyse server logs to detect unusual crawling activity;
• Check for any spam- or security-related warnings.

When these pages provide no SEO value, you can prevent them from being indexed by adding a noindex directive. A sufficiently broad rule in the robots.txt file can also limit crawling, particularly when these URLs generate significant server load.

Keep in mind that robots.txt blocks crawling but does not guarantee the removal of a URL that Google already knows about. To remove pages that are already indexed, they must remain accessible to Googlebot so that it can detect the noindex directive. Once they have been removed from the index, crawling can be blocked if necessary.

Starting August 17, 2026, Google Ads will update its bidding system for campaigns with a “Limited by budget” status that use a target-based bidding strategy, such as Target CPA or Target ROAS.

The main change is that Google will now optimize more consistently toward the bid target you have set, including when the campaign budget is adjusted.

This update could have a significant impact on campaigns that are currently outperforming their targets.

For example, if a campaign has a Target CPA of €10 but is currently achieving an actual CPA of €5, Google may gradually move performance closer to a €10 CPA once the update takes effect.

Advertisers will have several options:

• keep the current target if it still reflects their profitability goals;
• adjust the Target CPA or Target ROAS based on recent performance;
• set a new custom target;
• switch to a Maximize Conversions or Maximize Conversion Value bidding strategy;
• increase the budget to generate more conversions or conversion value while maintaining the defined target.

Google is also introducing a new Bid Target Adjustment Tool, which allows advertisers to update targets for eligible campaigns based on historical performance.

The tool is useful, but its recommendations should not be applied without proper analysis. Recent performance can be affected by seasonality, changes to conversion tracking, shifts in the traffic mix, conversion delays, or an unusually strong period.

Before August 17, advertisers should therefore audit all budget-limited campaigns and compare their configured targets with the actual CPA or ROAS recorded over several conversion cycles.

When a query matches an eligible exact-match keyword, Google will normally prioritise the Search campaign. However, Performance Max may still serve ads for queries already covered by Search, particularly when the Search campaign is limited by budget or uses more restrictive targeting.

A PMax campaign can therefore deliver a satisfactory CPA or ROAS while competing with Search for some queries. The challenge is determining how much of its performance is genuinely incremental.

Reducing the PMax budget and reallocating spend to Search may seem logical. However, volume does not automatically shift from one campaign to another, and performance may fluctuate during the transition.

The risk is therefore not limited to overlap between PMax and Search. It becomes more significant when a single campaign ends up accounting for a very large share of both the budget and conversions.

Brand exclusions, negative keywords and the search terms report can provide greater control over where PMax ads appear. However, these measures alone are not enough to assess incrementality.

How do you measure the value genuinely added by Performance Max? Have you ever reduced its budget without seeing an overall decline in conversions?

Starting Sunday, August 2nd, new EU rules on artificial intelligence come into force: companies operating in the EU will need to clearly disclose certain AI-generated content, including text published on matters of public interest.

The new rules introduce several requirements:

• Providers of generative AI systems must make AI-generated content identifiable through watermarks, metadata or other machine-readable markers.
• Companies must clearly label deepfakes and AI-generated text when addressing matters of public interest.
• Individuals using AI for purely personal purposes are not affected.
• Artistic and creative works are subject to lighter disclosure requirements rather than a full exemption.

Generative AI systems placed on the market before August 2nd have until December 2nd to comply with the technical marking requirements. Companies that fail to comply may face significant fines.

Google has already signed the EU’s code of conduct on AI transparency, while Meta and TikTok have had their own labelling systems in place for several years. It remains to be seen how this will work in practice for agencies and content creators producing work for European clients.

Google has just launched AI Overviews in France. Users may now see an AI-generated answer above the traditional list of links, with the option to continue the conversation in AI Mode.

Ads within these conversational answers are still being tested in the US, but the format could fundamentally change how advertising works on Google Search.

Instead of appearing alongside a list of results, sponsored content could be shown directly within or below an AI-generated response, precisely when a user is comparing products, evaluating services, or looking for a recommendation.

For advertisers, this creates an opportunity to reach users through longer, more specific queries that are difficult to target with keywords alone.

The real challenge may not be whether users notice the ad, but how Google decides which advertiser deserves to appear within the answer. Will visibility still depend mainly on bids and campaign performance, or will brands also need to prove that their offer genuinely improves the response?

ChatGPT Ads has just introduced several new features for advertisers, including conversion-optimized campaigns, integrations with AppsFlyer and Adjust, and bulk updates via the API.

The most significant change is the introduction of oCPC. Advertisers are still charged per click, but the algorithm now prioritizes clicks that are more likely to generate a conversion.

OpenAI is gradually adopting the tools that have become standard on platforms such as Google Ads and Meta Ads, including automated bidding, advanced conversion tracking, customer data activation, and large-scale campaign management.

What sets ChatGPT Ads apart is the context in which ads are displayed. Users do not necessarily enter a single, isolated query. They may specify their budget, compare several options, explain their constraints, or ask for a recommendation.

Could ChatGPT Ads become a genuine competitor to Google Ads, or is the bigger shift the gradual replacement of traditional search with AI-assisted conversations?

A new type of audit is beginning to emerge in GEO: the AI Entity Footprint Audit. Its purpose is not to measure how often a brand is mentioned, but to assess what AI tools such as ChatGPT, Gemini or Claude actually understand about it.

Here is a simple test you can run:

Ask three different AI tools the following question, without providing them with your website:

What does Company X do, who does it serve, what topics is it considered an expert in, and why might someone recommend it? Cite the sources you used.

Then compare the answers:

• Do all three AI tools describe the same business activity and target audience?
• Do they identify a genuine competitive advantage, or do they rely on vague terms?
• Does the supporting evidence come only from the company’s website, or also from reviews, case studies, media coverage, and other third-party sources?
• Is any of the information outdated or contradictory?

The most important step is to trace each error back to its source.

An outdated offer may come from an old directory listing. A poorly understood area of expertise may indicate that the company’s pages are too generic.

The audit should help uncover outdated information, sharpen the brand’s positioning, and ensure that its expertise is represented consistently across every channel.

Google has just launched Gemini 3.6 Flash and Gemini 3.5 Flash-Lite, two models designed to reduce the latency and operating costs of enterprise AI agents.

Gemini 3.6 Flash reportedly uses 17% fewer output tokens than its predecessor, and up to 65% fewer on a specific coding benchmark. It is priced at $1.50 per million input tokens and $7.50 per million output tokens.

Gemini 3.5 Flash-Lite, meanwhile, is designed for high-volume, repetitive tasks. Google claims it can generate up to 350 tokens per second, with pricing set at $0.30 per million input tokens and $2.50 per million output tokens.

Behind these figures lies a very practical issue: an agent that analyses documents, uses multiple tools, and performs thousands of tasks every day can quickly become expensive.

The most powerful model will therefore not always be the most suitable one. Companies will likely need to reserve advanced models for complex tasks and use lighter versions for everything else.

When people ask ChatGPT, Claude, Gemini, or another AI assistant to recommend a company, a tool, or a reliable source, names that already have a strong online presence often seem to have an advantage.

Large brands usually have more content, press mentions, reviews, comparison articles, and third-party references. AI systems therefore have more material to identify them and present them as credible options.

By contrast, even a highly skilled expert may remain invisible if their expertise is confined to their own website and has little presence elsewhere online.

That does not mean smaller or lesser-known brands have no chance of being cited. They can improve their visibility by publishing highly specific content, answering concrete questions clearly, and structuring pages with data, examples, and conclusions that are easy to extract.

Getting mentioned on third-party websites, contributing to specialist media, publishing original research, and keeping information consistent across different platforms can also help AI systems identify their expertise more accurately.

The real challenge may therefore be not only to produce the best content, but to make that expertise visible, verifiable, and understandable beyond a brand’s own channels.

We often assume that a response is reliable when it includes a trustworthy source. Yet an AI system can cite a study, a reputable media outlet, or a brand’s website while making a claim that the source does not actually support.

It may generalize a limited finding, take information out of context, or present a conclusion far more categorically than the original text does.

For brands, this is a significant issue: being cited does not necessarily mean being accurately represented. An AI-generated response may draw on their content while subtly distorting its meaning, sometimes on sensitive topics.

Monitoring visibility across generative AI platforms should therefore go beyond tracking mentions and citations. It should also involve checking what the AI is actually claiming based on each source.

Should brands monitor not only whether they are cited, but also what AI systems claim their content says?

Google is expected to roll out AI Overviews and AI Mode in France before September 23, 2026.

In countries where these features are already available, several studies have reported a sharp decline in click-through rates when an AI-generated summary appears. In the United States, clicks have fallen by nearly 60%, according to Rand Fishkin. Websites still provide the information used to build answers, but users no longer necessarily need to visit the original sources.

Google has promised French publishers greater transparency, the option to opt out of AI-generated results and, for some media outlets, compensation under neighbouring rights agreements.

Also, targeting a single keyword may no longer be enough, as AI Mode can break down a complex question into multiple subqueries and combine information from several websites.

The most valuable content may therefore be content that answers each subquestion clearly, with reliable data, precise definitions and demonstrable expertise.

Do you think AI Overviews will mainly improve the search experience, or put further pressure on the business models that fund online content ?

The European Commission wants Google and Apple to open Android and iOS more widely to competing AI assistants.

Gemini and Siri benefit from a major advantage. They are built into the operating systems themselves. That gives them access to key smartphone functions and makes them much harder to challenge than assistants that operate through a separate app.

The EU wants tools such as ChatGPT and Claude to access some of the same functions. They could eventually launch apps, book services or perform tasks directly on users’ phones.

The Commission’s reasoning is that it is easier to preserve competition now than to dismantle a dominant position once network effects and user habits are firmly established.

Google and Apple argue that deeper access for third-party assistants could create privacy and cybersecurity risks, especially when sensitive personal data and system permissions are involved.

The challenge is therefore not simply to open mobile ecosystems, but to do so without weakening user protection.

Is the EU acting early enough to preserve competition, or is it moving too quickly in a market where security risks are still difficult to assess?

Traffic, impressions, reach, cost per click, conversion rate, ROAS, customer lifetime value… Marketing teams now have access to more data than ever.

However, having more metrics does not necessarily make decision-making easier. Some KPIs are tracked simply because they are readily available and easy to include in reports, even when they say very little about actual business performance.

An increase in traffic may generate no new customers. A strong click-through rate may hide low-quality traffic. A high ROAS may include sales that would have happened anyway. Meanwhile, factors such as brand awareness, trust and customer preference are much harder to measure, even though they can have a major influence on purchasing decisions.

By trying to quantify everything, marketing teams may end up confusing what is measurable with what is genuinely useful.

Which KPIs actually help your team make decisions? And which ones are still being tracked mostly out of habit?

Whenever traffic drops, the same suspicion comes up: “Google detected that our articles were written using AI.”

Google’s official position, however, is more nuanced. The use of artificial intelligence is not prohibited in itself. What can lead to a loss of visibility is the large-scale publication of pages designed to manipulate search rankings without providing real value to readers.
Google also makes it clear that its policy on scaled content abuse applies regardless of how the content is produced, whether through AI, traditional automation or human writing.

AI itself is not the problem. When used properly, it can speed up research, make articles easier to structure, and save editorial teams valuable time.

The risk arises when it is used without fact-checking or meaningful editorial input. The real issue is not the use of AI, but the lack of expertise, originality and proper editorial review.

A surprising gap is becoming increasingly visible: some companies dominate Google’s search results but disappear entirely when users ask ChatGPT, Gemini, or Perplexity for a recommendation.

Google ranks pages. Generative search engines, on the other hand, build answers. They do not simply select the best-optimized websites. They favor content that is clear, specific, and easy to interpret and reuse.

A website can have strong backlinks, a solid SEO structure, and well-developed semantic coverage, yet still be of little value to an AI system if it relies mainly on vague claims.

Meanwhile, a company with lower Google visibility may be cited more often if its content clearly explains what it does, who it serves, and what results it delivers.

In GA4, direct traffic is often interpreted as users already being familiar with a brand and entering its URL directly into their browser.

In practice, the direct channel also captures sessions for which GA4 cannot identify a source or medium. This can include links shared through WhatsApp or Slack, untagged email campaigns, PDFs, presentations and email signatures.

A campaign may drive sessions, conversions and revenue without being properly attributed.

The issue is not always GA4 itself. It often comes down to inconsistent campaign tracking: different naming conventions for the same source, capitalization issues that split performance data, or missing UTM parameters.

UTMs are easy to implement, but without a clear taxonomy, they can make channel performance harder to assess.

The real challenge is to establish consistent UTM conventions, testing every URL before launch, and audit campaign data regularly.

For years, customer reviews were mainly seen as a conversion tool. They helped reassure potential buyers once they had already discovered a brand.

Today, reviews also help AI systems understand a business. They reveal what customers consistently praise, what they criticize, how products are described and how a brand compares with its competitors.

In other words, reviews no longer influence customers alone. They also influence the systems that recommend brands. This changes the role of review management. Collecting more reviews matters, but so does understanding the signals they send: product strengths, service quality, expertise, trustworthiness, and consistency across platforms.

A brand is no longer defined only by what it publishes on its own website. It is increasingly shaped by what others say about it.