Google has announced that citations within AI Overviews and AI Mode will now have new formats and displays which will hopefully boost CTR, making AEO even more crucial for business and their digital presence moving forward. In this article we’ll go through the 5 different ways Google has announced they will display links in 2026.

Further Exploration

Google have introduced a “further exploration” section to AI Overviews that will appear at the bottom of AI responses that encourages further reading on websites that are topical authorities on a specific subject. This is one of the first indications of AI Overviews being used to truly being used to platform links rather than use them subtly like in previous iterations of AI Overviews. Getting into these further exploration links could be crucial for business’ as they are the most clearly displayed links to date yet in AI Overviews and if they appear often, business will be pushing to ramp up their AEO

Subscriptions Links

Subscription linking allows readers who pay to read your content link their subscriptions to their Google account. These subscriptions will then appear in AI Overviews, as seen below, so people can see if information has come from one of their trusted sources, and improving the chances of CTR. This is mainly going to be a good feature for news publications that use subscription models to allow people access to their content.

Community Advice

A lot of people, when searching online, especially when asking question based searches want to seek out advice from others who have shared similar experiences. Attaching “reddit” to the end of searches, is a tactic from users to often seek out advice from others.

Now AI Overviews will include previews from of public perspectives and online discussion from communities for certain, question based searches. These new forms of links will help community based cites and forums get more links in AI overviews.

Expanded Links within Content

AI Overviews already includes links throughout content so you can dig further into research as your reading through AI content. The latest update still allows for seeing links directly where you need them but now these links will just be more prevalent in AI searches.

Website Hover

When you hover over an inline link the website that the information is being pulled from will now appear with their web icon, page and website name attached. Once again this feature is just building out on existing linking features to make websites more obvious in AI Overviews and improve CTR.

A lot of these new features being added into AI Overviews and AI Mode will improve click-through-rate to websites, which means being seen and recognised as a topical authority by Google and appearing in AI Overviews will be more important than ever going forward.

Agentic Engine Optimisation, or AEO, has become one of the more discussed topics in digital marketing in 2026. The coverage has generated a fair amount of heat but not always much light, partly because the tooling landscape is still developing and partly because a lot of what is being written treats AEO as something entirely new when, in practice, it shares most of its foundations with work that good SEO practitioners have been doing for years.

This article covers the tools that are actually useful right now, split into those with a clear proven use case and those worth testing as the category matures. But before getting into the tools, it is worth addressing the terminology question directly, because the shift in how AI systems retrieve and surface content has caused some confusion about what has actually changed and what has not.

AEO and SEO: different names, same foundation

Despite what some of the more excitable coverage suggests, AEO is not a replacement for SEO, and it does not require a completely different strategic approach. The two disciplines share the same core premise: make your content easy to find, easy to understand, and clearly relevant to what someone is trying to know or do.

The difference is in the audience being optimised for. Traditional SEO focuses on helping human searchers find and engage with your content. AEO extends that to cover AI systems that fetch, parse and use your content to formulate answers, often without a human ever clicking through to your site. The signals that matter are largely the same: well-structured pages, clear headings, specific and accurate information, strong authority and credible backlinks. What changes is the layer of intent you apply when thinking about how that content is read and used.

Research consistently shows that AI Overviews and similar systems draw heavily from pages that already rank well organically. Google has been clear for some time that content quality and audience relevance are the primary factors that determine whether a site performs. AEO does not change that message. It adds a layer of consideration: once your content is authoritative and well-structured, is it also legible and extractable for AI systems working within processing constraints?

In practical terms, we treat AEO and SEO as the same discipline. The same improvements that help AI systems cite your pages accurately also make those pages clearer and more useful for human readers. They are not in tension. The tools below reflect that: some are well-established SEO tools that remain highly relevant in an AEO context, and some are newer platforms built specifically to measure AI search visibility.

The tools with a clear, proven use case

1. LLM assistants used with a defined methodology

ChatGPT, Claude and Gemini are themselves useful AEO research tools when used intentionally rather than ad hoc. The most practical applications are competitive landscape research, content gap analysis, prompt testing to understand how AI platforms respond to queries in your category, and entity and topical coverage audits.

Asking an LLM what it knows about your brand, your competitors and your sector, and then interrogating where the gaps and inaccuracies are, is a fast and accessible way to understand your current AI search position. Most businesses have not done this basic audit, and a meaningful first pass takes less than an hour. It is also one of the few AEO-relevant activities that costs nothing beyond the time invested.

2. Google Search Console

Search Console remains one of the most important tools in any AEO workflow, primarily because it provides direct performance data from Google: the platform that produces the AI Overviews appearing above organic results for a significant proportion of searches. Understanding which of your pages are currently being surfaced in AI Overviews, and which are not, gives you a baseline from which to measure the impact of content changes.

It also helps identify the queries where AI Overviews are appearing for keywords you rank for, which is increasingly important as AI-generated answers above the fold reduce click-through rates on the organic listings below them. Knowing where you are losing clicks to AI answers on your own target keywords is the starting point for deciding where to focus content improvement efforts.

3. Google Trends

Google Trends serves a different purpose than Search Console, but it is equally valuable for AEO strategy. Where Search Console tells you how you are performing, Google Trends tells you where demand is heading. It does not give absolute search volume, but it gives relative momentum across topics and queries, which is often more strategically useful when trying to get ahead of emerging patterns rather than simply responding to existing ones.

For AEO specifically, rising query trends can signal emerging answer opportunities you can address before your competitors do. AI systems tend to favour content that is well-established and authoritative on a topic, which means the window for getting in early is narrow. Identifying rising demand trends through Google Trends and creating strong content quickly is one of the more practical ways to build AI citation presence in a new area before it becomes competitive.

4. SE Ranking and SE Visible

SE Ranking is the platform we use day-to-day for client SEO work, and its relevance to AEO has grown considerably over the past 12 months. The AI Overviews Tracker monitors how your keywords are performing within Google’s AI-generated results, including citation frequency, source analysis, and estimated traffic impact from AI Overviews. It also identifies which competitor domains are being cited in AI answers for keywords you are targeting, which is actionable competitive intelligence.

The AI Search Toolkit extends this further by tracking brand mentions and linked citations across AI Overviews, AI Mode, ChatGPT, Gemini and Perplexity. You can monitor how often your domain is cited, whether citations are linked or unlinked, and how this compares to named competitors over time.

SE Visible is a companion product that sits alongside SE Ranking and focuses specifically on brand AI visibility at a strategic level: how your brand is presented, ranked and perceived across AI systems. It provides a Brand Visibility Index that measures performance over time and competitive benchmarking across AI platforms. For agencies managing multiple client accounts, the combination of SE Ranking for tactical execution and SE Visible for strategic oversight is a coherent and cost-effective approach.

5. Semrush

Semrush has expanded its feature set to include AI Overviews tracking and visibility data, making it one of the more complete tools for monitoring how content performs across both traditional search and AI-generated results within a single platform. For teams already using Semrush for keyword research, position tracking and site auditing, the AI visibility layer adds meaningful value without requiring a separate tool or workflow.

The topic clustering and content gap analysis features are particularly relevant for AEO, helping identify where topical coverage is thin relative to what AI systems are pulling from competitors. Thin or fragmented coverage in a topic area is one of the more common reasons a site gets passed over in AI-generated answers in favour of a competitor with more comprehensive, well-organised content on the same subject.

6. Profound

Profound is purpose-built for AI search monitoring. It tracks how platforms, including ChatGPT, Perplexity, Google AI Overviews, and Claude discover, surface and cite your brand and content. It monitors brand mention frequency and sentiment, competitor share of voice, and the specific prompts that trigger your content to appear in AI-generated answers.

The most useful shift Profound enables is in the metric itself. Rather than asking where you rank in a search result, you can ask: when AI answers a question in your category, are you in the answer? The cross-platform view, covering multiple AI engines simultaneously rather than one in isolation, is its most distinctive feature and makes competitive benchmarking significantly more meaningful than single-platform tracking.

It is not a cheap tool and is better suited to businesses with an existing content and SEO foundation. For agencies managing multiple clients in competitive sectors, the monitoring and benchmarking functionality is particularly valuable.

7. Screaming Frog

Screaming Frog has been a technical SEO staple for years, and its relevance extends directly into AEO. Many of the technical issues that prevent AI agents from correctly parsing and using your content are exactly the issues Screaming Frog identifies: missing or misconfigured structured data, poorly structured heading hierarchies, thin or duplicated page content, and slow server response times.

Running a Screaming Frog audit with a focus on schema markup completeness, heading structure, and page-level content depth is one of the most practical first steps in any AEO improvement programme. The tool now integrates with Google Search Console and PageSpeed Insights, making it straightforward to cross-reference technical findings with actual performance data.

8. Google Rich Results Test and Schema Markup Validator

Structured data is one of the cleaner signals available for AI retrieval. Schema markup for FAQs, services, reviews, products and local business information gives AI systems a reliable, machine-readable layer of data to draw from, independent of how the surrounding content is written or formatted. Getting this right is a relatively contained piece of work that can have a disproportionate impact on how accurately your content is cited.

Both tools are free. The Rich Results Test checks whether your structured data is correctly implemented and eligible for enhanced display in search results. The Schema Markup Validator checks for errors and warnings at a more granular level. For businesses in sectors where FAQ, review or service schema are applicable, a structured data audit is one of the most immediately actionable AEO improvements available.

How to approach this practically

The AEO tools market has grown faster than the evidence base for what actually works. Many platforms are repackaging existing SEO or content analytics functionality under AEO branding without meaningfully changing what they measure. The most reliable signal for whether a tool is genuinely useful is whether it changes a specific decision you make about your content or your site.

A practical starting sequence looks like this. Use an LLM to audit your current brand position across AI platforms in your category. Use Google Search Console to understand which of your pages are appearing in AI Overviews and where the gaps are. Use Google Trends to identify rising demand patterns worth targeting early. Use Screaming Frog and the schema validation tools to fix any technical issues preventing your content from being correctly parsed. Then use SE Ranking, Semrush or Profound, depending on the depth of monitoring your situation requires, to track how your visibility is changing over time.

Starting with the fundamentals, well-structured content, strong authority signals, accurate structured data, and a clear technical foundation will deliver more impact sooner than any monitoring platform can on its own. The monitoring tools tell you whether the work is making a difference. They are not a substitute for doing the work.

What is slop?

Slop, or AI slop, is a term coined for low-quality and value, often repetitive or very generic content produced at scale with the assistance of AI.

It’s becoming increasingly common for short-form video content like reels and shorts to feature marketing slop.

Is Slop hurting marketing?

Slop definitely has an impact on the quality and output of some marketers. AI should be utilised to enhance and improve upon what already exists, rather than diluting or replacing quality marketing content. Some of the main harms slop has for marketing are:

• Reducing engagement rates, audiences won’t engage in low-quality disengaging content
• Oversaturating the content market, because its low quality makes it easier to produce, thus the churn is greater and can dominate algorithms
• Runs the risk of diluting branding, slop tends to fall in the generic, overdone category, risking the brand’s unique tone of voice being watered down.
• Audiences can become fatigued, and slop in particular can be churned out, risking boring the audience and creating a disconnect

But slop isn’t just risking negative audience response and platform performance; it can result in marketers overly relying on automation, which won’t help them stand out.  This lack of human touch takes away the depth and meaning often created in content; AI is not able to capture the same emotion or soul that a human can.

Actionable Fixes for using AI to enhance quality

The key to fixing slop is to use AI with responsibility and consideration. AI is an invaluable tool, especially when looking to polish and perfect something you’ve already created.

Editorial standards

One of the key ways this can be corrected is by setting an editorial standard. Acting as a framework for creators to work within. Think of it as a quality check before it’s released, ask yourself:

1. Is this on brand?
2. Does this make sense?
3. Is this content relevant?
4. Is the AI component noticeable? Is that the intention?

Enforcing your brand voice

Branding is a crucial aspect of content marketing, so portraying your brand authentically is especially important. Referring to brand guidelines for tone, style and messaging ensures that AI isn’t filling in the blanks with generic, non-tailored types of content.

Use AI in assistance, not as the final version

AI can be mistakenly used to recreate what was a perfectly great human-designed piece of content, when really the AI should be part of the ideation and development process. AI should not replace the thinking or creativity of a person or brand; doing so is disingenuous.

A simple workflow you could follow is to use AI to brainstorm, research and help influence structure. Your role is to then use your opinion, voice, and editorial capabilities to flesh out and produce content. AI should help to accelerate thinking, not replace it.

OpenAI’s latest move could have much bigger implications for digital marketing than it might first appear. ChatGPT is reportedly beginning to test cost-per-click, or CPC, advertising, shifting part of its model away from simple ad visibility and towards measurable performance.

That matters because it pushes ChatGPT closer to the territory traditionally dominated by Google. Instead of being seen only as an AI assistant or an experimental branding space, it starts to look more like a genuine performance marketing channel.

For marketers, this is another sign that the platforms shaping search, discovery and digital advertising are continuing to blur together.

What is changing?

Up to now, much of the conversation around advertising in ChatGPT has centred on visibility and impressions. The introduction of CPC ads changes that dynamic. It means advertisers may be able to pay when someone actually clicks, rather than simply when an ad is shown.

That is a major shift in mindset. Impression-based advertising is often useful for awareness, but CPC pricing brings the conversation much closer to ROI, lead generation and commercial performance. In simple terms, it makes ChatGPT feel more like a channel marketers can compare directly with established search and paid media platforms.

Why this matters more than it might seem

The important part here is not just the pricing model itself. It is what the pricing model says about where OpenAI sees ChatGPT going next.

CPC advertising is strongly associated with performance marketing. It is built around the idea that advertisers want to pay for action, not just presence. Google has dominated that world for years because search behaviour often carries very clear intent. People type in what they want, click on a result, and frequently go on to buy, enquire or convert.

By moving towards CPC, ChatGPT is stepping into that same conversation. It suggests OpenAI wants advertisers to view the platform as a place where commercial outcomes can happen, not just brand exposure.

That gives this change a wider significance. It is not merely a product tweak. It is a strategic signal.

What marketers should be thinking about

For most marketers, the big question will not be whether CPC ads exist in ChatGPT. It will be whether the clicks are actually valuable.

That is where the comparison with Google becomes important. Search advertising works well because users often reveal strong intent. Someone searching for a service, a product or a solution is already partway down the decision-making path. ChatGPT will need to prove that conversational interactions can create similarly meaningful opportunities.

That means marketers should be thinking about things like:

• how intent shows up in AI conversations
• whether ChatGPT users are in research mode, decision mode or simply exploring
• how click quality compares with traditional paid search traffic
• whether conversions justify the cost

This is where early testing could become very valuable. Brands that get in early may be able to learn quickly, before competition increases and costs rise.

Could this become a real rival to Google?

Google’s strength has always been its ability to monetise intent at scale. That is not easy to replicate. ChatGPT may have rich conversational context, but advertisers will want evidence that this context can produce commercially useful clicks, not just curiosity.

Still, the direction of travel is clear. If ChatGPT develops stronger ad infrastructure, better measurement tools and broader self-serve ad capabilities, it becomes much easier to see how marketing budgets could begin shifting in its direction.

In other words, this is not yet a replacement for Google Search advertising, but it could become a more serious challenger for parts of the same budget.

What should brands do next?

Most brands do not need to rush blindly into this. But they should pay attention.

A sensible response would be to:

• watch how OpenAI develops its ad platform
• assess whether ChatGPT fits your audience and customer journey
• compare any future test results carefully against Google and other paid channels
• treat this as an emerging performance opportunity, not just a novelty

For marketing leaders, the real value right now is awareness. The advertising landscape is shifting again, and this looks like one of those moments that may seem small at first but become more important very quickly.

Final thoughts

OpenAI’s move towards CPC ads in ChatGPT is about much more than pricing. It is a sign that ChatGPT is evolving into a more commercially ambitious advertising platform, one that may increasingly compete for the same performance budgets that have long flowed into search.

For marketers, the takeaway is simple. ChatGPT is no longer just a tool people use to ask questions. It is beginning to look like a channel where intent, clicks and ROI may start to matter in a much bigger way.

That does not mean Google is suddenly in trouble. But it does mean the competitive shape of digital advertising is continuing to change, and marketers would be wise to keep watching.

A new publisher report suggests Google’s AI Overviews are having a major impact on organic search traffic, with clicks from traditional search down 42% across Define Media Group’s portfolio since AI Overviews began expanding in Google Search. In simple terms, that means more people are getting answers directly on the search results page, and fewer are clicking through to websites in the way they used to.

For anyone working in digital marketing, this matters because it is another sign that visibility and traffic are no longer the same thing. A brand can still appear prominently in Google, but if the answer is summarised for the user before they ever reach the site, click-through rates can fall. That is especially important for businesses and publishers that rely on evergreen content, meaning helpful pages built to rank steadily over time for informational searches.

What AI Overviews actually are

Google AI Overviews are the AI-generated summaries that now appear at the top of some search results. Rather than showing only links, Google may generate a written answer that pulls together information from multiple sources. For users, this can feel fast and convenient. For marketers and site owners, it can mean fewer visits from people who get what they need without clicking.

The biggest losses appear to be in evergreen and informational content

The Define Media findings suggest the pressure is falling hardest on informational content rather than breaking news. Before AI Overviews launched, the sites in its dataset averaged 1.7 billion organic clicks per quarter from Q1 2023 to Q1 2024. After launch, traffic dropped 16% immediately and, according to the report, never fully recovered. As Google expanded AI Overviews further in May 2025, the decline accelerated, reaching 42% below the pre-AI baseline by Q4 2025.

That does not mean content marketing is dead. It does mean old assumptions are becoming less reliable. Publishing a useful guide and expecting traffic to arrive simply because it ranks is no longer a safe strategy on its own.

Why news publishers are seeing a different pattern

One of the more interesting findings is that breaking news has held up better. Define Media says breaking news traffic grew 103% from November 2024 through early 2026 across Google Search, Google News and Discover. The reason appears to be that Google is still more cautious about using AI summaries for fast-moving stories, where facts change quickly and the risk of inaccuracy is higher.

The report also notes that AI Overviews appear much less often for news queries than for some other categories. In practice, many major news searches still trigger Top Stories instead. Top Stories is the news box Google shows near the top of results, linking users directly to publisher articles.

Google Discover is becoming more important

Another key shift is the growing role of Google Discover. Discover is Google’s personalised content feed, shown in places like the Google app and mobile home screens, where articles are recommended based on a user’s interests rather than a typed search. According to Define Media, Discover traffic across its portfolio grew 30%, and Discover and web search are now driving roughly equal traffic for the first time in its dataset.

That matters because it points to a different model of visibility. Instead of relying only on ranking when someone searches, brands and publishers may need to create content that earns passive distribution through feeds, recommendations and current interest.

What this means for marketers and clients

The main takeaway is not that search has stopped mattering. It is that search is fragmenting. Traditional blue-link traffic is under more pressure, while visibility is spreading across AI summaries, news boxes, Discover feeds and other Google surfaces.

For clients, this means performance conversations need to become more nuanced. A drop in organic clicks does not always mean a drop in relevance or visibility. It may mean Google is answering more of the query itself. That is still a commercial problem if fewer visitors reach the site, but it changes how we diagnose the issue and how we respond.

The practical response

The most sensible response is not panic, but adaptation.

• Brands should put more focus on content that offers something harder for Google to summarise away. That includes original insight, strong opinion, proprietary data, fresh commentary, first-hand experience and tools or assets users genuinely need to visit the site to use.
• They should also pay closer attention to content formats that can perform beyond classic search, including timely articles, expert commentary, brand-led thought pieces and content built with Discover visibility in mind.
• This is another reminder that digital marketing is moving away from a world where success was mainly about ranking for a keyword and collecting the click. The new environment is more complex.
• Brands still need search visibility, but they also need stronger content differentiation, broader distribution and a clearer understanding of where traffic is actually coming from.

In other words, being visible in Google is no longer enough. The real question is whether visibility still turns into visits, attention and commercial value. Right now, the answer depends more than ever on the type of content you produce and where Google chooses to surface it.

What clients should take away from this

Clients do not need to learn every technical detail behind AI Overviews. They do need to understand that the search landscape has changed. Some content types are becoming less effective at driving clicks, while others, especially timely and feed-friendly content, may be gaining importance. The winners will be the brands that stop thinking only about rankings and start thinking more broadly about attention, discoverability and why someone would choose to click at all.

Artificial intelligence is quickly becoming a new product discovery channel. More consumers are asking tools like ChatGPT for buying advice instead of browsing comparison sites or search results themselves.

A recent study analysing more than 43,000 products shown in ChatGPT recommendation carousels revealed a clear pattern. Around 83 per cent of the products recommended by ChatGPT also appear in Google Shopping results, while very few come exclusively from Bing.

For e-commerce brands, the takeaway is straightforward. Visibility in Google Shopping is now influencing whether products appear inside AI recommendations.

This does not mean AI has replaced search marketing. In reality, it means the fundamentals of e-commerce visibility, such as product feeds and shopping optimisation, are becoming even more important.

ChatGPT appears to source products from Google Shopping

The study suggests that ChatGPT retrieves product recommendations through a separate shopping retrieval process. Instead of analysing articles or blog posts to choose products, the system appears to pull candidate products from shopping indexes.

Researchers found that most products appearing in ChatGPT carousels were also present within the top 40 organic Google Shopping results for the same query.

Even more telling was the influence of ranking position. Products appearing higher in Google Shopping results were far more likely to appear in ChatGPT’s carousel. Around 60 percent of matched products were found in the top 10 Google Shopping results, and nearly 84 percent came from the top 20.

For e-commerce brands, this means Google Shopping visibility may now affect not only search traffic but also AI-generated product recommendations.

Optimise your Google Shopping feed as a core marketing asset

If AI systems are drawing heavily from Google Shopping, then the product feed itself becomes a critical ranking signal.

Many e-commerce brands treat product feeds as a technical task handled once during setup. In reality, they should be actively optimised in the same way as search content.

There are several practical tactics that can improve feed performance.

Write highly descriptive product titles

Product titles play a major role in how Google categorises and surfaces products. Instead of using short or vague titles, include key information that shoppers would search for.

Effective titles often include the brand name, product type, model, key feature and variant where relevant. For example, a generic title such as “Running Shoes” is far less useful than “Nike Air Zoom Pegasus 40 Men’s Running Shoes”.

Ensure every product attribute is completed

Google Shopping relies heavily on structured attributes to understand products. Missing attributes can reduce visibility or lead to incorrect categorisation.

Important attributes to complete include brand, product type, material, colour, size, gender, condition and GTIN or manufacturer identifiers. The more complete the feed is, the easier it is for Google to match products with relevant queries.

Use Google product categories accurately

Google allows retailers to assign categories from a predefined taxonomy. Selecting the most precise category helps Google understand where the product belongs in shopping results.

Many brands either leave this field blank or choose broad categories. Using highly specific categories improves relevance signals and can help products appear for more targeted queries.

Improve product imagery

Images are a key factor in product engagement and performance. Google prefers clear, high-resolution images with simple backgrounds that show the product clearly.

Avoid cluttered images, overlays, watermarks or heavy text. Strong product photography increases click-through rates and can improve ranking performance in shopping results.

Keep pricing and availability accurate

Google favours products with consistent and reliable data. If the product feed frequently shows incorrect pricing or items marked as available when they are not, this can affect performance.

Regularly updating feeds ensures that stock levels, promotions and price changes are reflected accurately.

Add detailed product descriptions

While titles and attributes are critical, descriptions also help Google understand the product context. Clear descriptions that mention features, benefits and specifications can improve how products match to search queries.

Avoid duplicate manufacturer descriptions where possible. Unique descriptions help products stand out.

Improve Google Shopping visibility, not just SEO

Many marketing teams still separate SEO and product feed optimisation into different silos. However, this research suggests that shopping rankings may influence visibility across both search engines and AI assistants.

That means ecommerce brands should treat Google Shopping optimisation as a core growth channel rather than a secondary task.

Improving feed quality, ensuring accurate product data and strengthening product listings can increase the chances of ranking higher in Google Shopping results.

And increasingly, those same rankings may determine whether your products appear inside AI tools like ChatGPT.

AI product discovery still depends on search infrastructure

One of the most interesting insights from the research is that AI tools are not operating in isolation. Instead, they appear to be building on existing search ecosystems.

Rather than replacing search engines, AI platforms are currently layering intelligence on top of traditional product indexes.

For e-commerce marketers, that means the foundations of product visibility remain familiar. The brands that manage their product feeds well, optimise their shopping listings and maintain strong product data will be the ones most likely to benefit as AI-powered product discovery continues to grow.

Digital marketing teams have never had access to more data. Advertising platforms, analytics tools, CRM systems and dashboards all promise insights into customer behaviour and marketing performance. Yet many organisations still struggle to answer a fundamental question: which marketing activity actually generates real customers and revenue?

One of the main reasons for this is that much of the customer journey still happens offline. Many high-value purchases, particularly in sectors such as healthcare, professional services and education, involve a phone call before a customer commits.

If those calls are not captured and analysed properly, marketing teams are left with an incomplete picture. Website traffic and form submissions may be tracked, but the conversations that actually drive decisions remain invisible.

By combining call tracking technology, artificial intelligence and CRM systems, businesses can build a far more complete view of the customer journey. Platforms such as Nimbata, HubSpot and Monday.com allow marketing teams to track enquiries, analyse conversations and connect those insights directly to revenue performance.

When these systems are connected properly, marketing reporting moves beyond traffic and clicks. It becomes a true commercial intelligence system.

Why phone conversations are critical marketing data

For many businesses, a phone enquiry represents one of the strongest indicators of buying intent. A person who calls a business is often much closer to deciding whether someone is browsing a website. Yet phone calls are historically one of the most poorly tracked parts of marketing.

Without call tracking technology, it is impossible to know which marketing channels generated those enquiries. A customer might have discovered the business through organic search, paid advertising or social media, but the marketing team cannot attribute the call accurately.

This is where call tracking platforms such as Nimbata play a critical role. By assigning unique phone numbers to marketing channels and campaigns, every call can be linked back to its source.

This immediately connects phone enquiries to marketing performance.

However, tracking calls is only the first step. The real insight emerges when those conversations are analysed and integrated into a broader CRM system.

A process-driven approach to connecting AI, phone tracking and CRM data

To make this system work effectively, it helps to think of the process as a structured series of stages. Each stage captures and enriches the customer data so that it becomes more valuable for both marketing and sales teams.

Step 1: Track where the phone call originated

The first stage focuses on identifying where the caller originally discovered the business. Call tracking systems such as Nimbata use dynamic number insertion on the website. This technology automatically replaces the phone number shown on the website depending on how the visitor arrived.

For example, visitors arriving from:

• Organic search
• Paid search advertising
• Social media campaigns
• Email marketing
• Direct website visits

will each see a different tracking number.

When a call is made, the system records the source of that visitor and links the enquiry back to the original marketing channel. This step alone dramatically improves marketing attribution. Instead of guessing which campaigns generate calls, the marketing team can see exactly which channels are responsible.

Step 2: Transcribe the conversation using AI

Once the call has been captured, the next stage is transcription. Modern call tracking platforms automatically convert phone conversations into text. This makes it possible for artificial intelligence to analyse the content of calls at scale.

Rather than manually listening to hundreds of recordings, AI can process transcripts and identify patterns in the conversations. This step transforms phone calls from isolated conversations into structured data that can be analysed.

Step 3: Segment callers into meaningful categories

After transcription, AI is used to categorise each call. The first classification identifies the type of caller. Calls are segmented into three main groups:

• New customers
• Existing customers
• Non-relevant calls such as sales outreach or internal staff calls

This distinction ensures that marketing teams are analysing genuine lead activity rather than operational noise.

Once this classification is made, the system moves to the next layer of segmentation.

Step 4: Evaluate the strength of the lead

Not all enquiries represent the same level of opportunity. Artificial intelligence can analyse the tone and content of a conversation to estimate the strength of the lead. For example, callers can be categorised along a spectrum from warm to cold.

A caller asking detailed questions about booking or availability may represent a high intent enquiry, while someone gathering general information may fall into a lower intent category.

This classification allows businesses to prioritise their follow-up activity more effectively. High intent enquiries can be routed to the sales team immediately, while lower intent leads can enter nurturing workflows.

Step 5: Identify the product or service being discussed

Another important layer of analysis focuses on the topic of the enquiry. AI systems can identify which product or service the caller is interested in. This provides valuable insight into demand patterns across different offerings.

For example, if a large proportion of calls relate to a specific treatment or service, marketing teams can adjust campaigns and landing pages to reflect that demand. This also helps sales teams prepare for conversations because they understand the context of the enquiry before engaging with the caller.

Step 6: Understand where the caller sits in the marketing funnel

Phone conversations often reveal exactly where a customer is in their decision-making journey.

By analysing the transcript, AI systems can determine whether a caller is:

• Gathering general information
• Comparing prices
• Checking availability
• Ready to book

Understanding these stages helps marketing teams refine their messaging. If many callers are asking basic educational questions, the website may need clearer explanations or additional content.

If price discussions dominate calls, messaging around payment plans or financing options may need to appear earlier in the customer journey.

Step 7: Capture structured customer data

During most phone calls, certain pieces of information are exchanged between the caller and the business. This may include the caller’s name, phone number, location or other relevant details.

AI transcription systems can extract this information automatically and pass it into the CRM system. In many cases, this data feeds directly into platforms such as HubSpot.

The result is a fully populated contact record without requiring manual data entry. This step ensures that every enquiry becomes a structured lead that can be tracked throughout the customer lifecycle.

Step 8: Preserve the original marketing source in the CRM

Once the call data reaches the CRM, one of the most important tasks is preserving the original marketing source.

If a customer first discovered the business through organic search, that source should remain attached to their record even if they later interact with email campaigns or direct website visits. Maintaining this source allows businesses to calculate accurate return on investment for each marketing channel.

Without this connection, attribution becomes unreliable and marketing decisions become harder to justify.

Step 9: Evaluate call handling performance

Another powerful use of AI is evaluating the quality of calls handled by sales teams, reception staff or customer service agents. Predefined training models and evaluation algorithms can analyse conversations and identify whether important steps were followed.

For example, the system may detect situations where:

• A caller raised concerns about price, but financing options were not mentioned
• A customer could not find an available appointment but was not offered a waiting list
• Key questions were not answered clearly

The system can then provide suggestions for improving call handling. This allows businesses to improve both sales performance and customer experience without manually reviewing every conversation.

Step 10: Record the outcome and next step for the lead

Another crucial piece of information is what happens after the call. AI analysis and CRM workflows can record whether the lead progressed, converted or stalled.

If a caller decides not to proceed after discussing the price, that reason can be captured. If a caller converts immediately after learning about financing options, that insight can also be recorded. Over time, these patterns reveal which factors influence customer decisions. Marketing teams can then adapt campaigns to address those concerns earlier in the customer journey.

Step 11: Automate follow-up and lead nurturing

Once this information is stored in the CRM, automated workflows can take over. CRM platforms such as HubSpot allow businesses to trigger actions based on lead behaviour.

For example:

• Sales teams can receive automated reminders to follow up with high-intent leads
• Priority leads can be routed to specific team members
• Cold leads can enter longer-term nurturing campaigns

Instead of aggressive sales messaging, these nurturing campaigns might include educational content such as guides, FAQs or blog articles. This softer approach maintains contact with potential customers without overwhelming them.

Step 12: Use CRM data to improve advertising campaigns

CRM data can also enhance advertising performance. Customer email addresses and phone numbers can be used in customer matching tools across advertising platforms. This allows businesses to retarget leads more effectively or exclude existing customers from campaigns.

In addition, lookalike audiences can be created based on existing customers. Advertising platforms can then identify new users who share similar characteristics.

This improves campaign efficiency and ensures that budgets are focused on the most relevant audiences.

Step 13: Connect the data to reporting dashboards

The final step in the process is bringing all this information together in reporting dashboards. These dashboards combine marketing data with commercial performance metrics so that businesses can measure true return on investment.

When systems do not integrate directly, connectors such as Zapier can bridge the gap between platforms.

In some cases, business intelligence tools such as Microsoft Power BI can act as a central data source that aggregates information from multiple systems.

The result is a reporting environment that shows not just marketing performance but real business outcomes.

The practical and commercial benefits of this approach

When this process is implemented correctly, the impact goes far beyond better marketing reports. It fundamentally changes how businesses understand their customers and manage their sales processes.

Some of the most significant benefits include:

• Accurate marketing attribution so businesses can clearly see which channels are generating genuine leads and revenue.
• Better use of marketing budgets by identifying the campaigns and keywords that produce the highest quality enquiries.
• Improved sales performance through AI-driven feedback that highlights where call handlers can improve conversations.
• More efficient lead management by prioritising high intent enquiries and automating follow-up for colder leads.
• Stronger customer insights by analysing real conversations and identifying common questions, objections and motivations.
• Smarter marketing messaging because campaigns can address the concerns customers actually raise during calls.
• Better customer experience as businesses refine how enquiries are handled and improve their booking or purchasing processes.
• Full lifecycle reporting showing how long leads take to convert, how many interactions were required and which marketing channels initiated the journey.
• Clear ROI measurement by connecting marketing data with real commercial outcomes rather than just website metrics.

Ultimately, this approach allows marketing teams to move beyond vanity metrics and focus on what truly matters: generating customers and revenue.

Bringing your marketing and sales data together

The combination of call tracking, AI analysis and CRM integration represents a major step forward in marketing intelligence. Instead of analysing isolated metrics such as clicks or impressions, businesses can now track real conversations, understand customer intent and measure the commercial impact of their marketing activity.

Platforms such as Nimbata, HubSpot and Monday.com allow organisations to build a connected ecosystem where every enquiry becomes part of a structured data process.

The result is clearer reporting, better sales performance and more effective marketing decisions.

Want to implement a similar system for your business?

Many organisations already have some of the tools needed to build this type of process. The challenge is often connecting those tools in a way that captures the right data and turns it into meaningful insight.

If you would like help assessing how this could work within your organisation, we would be happy to review your current setup.

We can evaluate your existing marketing platforms, CRM systems and call handling processes to identify how a similar framework could be implemented using your current infrastructure. If the right systems are not already in place, we can also design and deploy a new solution tailored to your business.

If you would like to explore how this approach could help you better understand your customers, improve marketing attribution and increase conversion performance, get in touch with us, and we will be happy to talk through the possibilities.

Since their introduction to search in May 2024, AI Overviews have continued to grow in size and dominate Google search. In the past year, AI Overviews have grown by over 50% in terms of their coverage on the SERP. AI Overviews now appear in 48% of searches, meaning almost half of all searches use AI. This is a far cry from the past, where often your organic SEO efforts would see you appearing at the top of the SERP. In this article, we’ll look at what this means for marketers in the space.

Decline in Click-Through-Rate

With AI now dominating search, one of the most notable changes for digital marketers has been a decline in click-through rate. Click-through rate is a metric that tells marketers what percentage of people click on the link after seeing it. Historically, sites that appeared at the top of the SERP would see higher click-through-rates as links higher up the search engine are typically seen as more reliable and trustworthy by consumers. Now that searches are being dominated by AI Overviews, people are often finding the information they need pulled straight onto the SERP without having to click throught onto websites, causing a decline in click-through-rates.

This is especially apparent in certain sectors that answers peoples more informational queries on the SERP as opposed to e-commerce searches, for example. As Roger Monti states in the Search England Journal,

“The education sector experienced the strongest expansion in the number of queries triggering AI search results, from 18% of queries in May 2025 to 83% of queries triggering AI search results by December 2025.”

“Meanwhile, healthcare queries were already triggering AIO results by a large margin since 2024, at a rate of 72% of the time. By December 2025, however, the rate at which healthcare queries triggered AIO edged up to 88%.”

We can see that sectors that strongly favour informational queries are seeing a huge uptick in AI Overviews, which means marketers in these sectors need to try to use AI Overviews to their advantage, by appearing in them, to improve performance going forward.

Greater Competition for Citation

One of the ways to address declining click-through-rates for sites, as mentioned previously, is to appear in the AI Overviews themselves. AI Overviews often collate answers from several web pages and incorporate links showing users where they have collected the information from. However, because AI Overviews use fewer links than traditional Google searches, it is a more competitive space.

One of the ways marketers are finding to appear in AI Overviews is by focusing on answering-based content. Answering users’ questions explicitly and in a clear and concise manner gives marketers a much better chance of appearing in AI Overviews. 

What this means for Marketers

Ranking in AI Overviews is fairly similar to ranking in traditional search in that search engines still value authority and credibility of sources, users have to slightly shift how they produce content to more clear answer-focused content that can pull through directly into AI Overviews if they are going to battle a declining click-through-rate.

OpenAI has shared new details on how advertising will work inside ChatGPT, and while the rollout is deliberately cautious, it signals an important shift in how people may discover brands through AI.

On a recent episode of the OpenAI Podcast, OpenAI executive Assad Awan outlined the principles guiding ads in ChatGPT, who will see them, and why trust sits at the centre of the strategy.

For non-technical marketers, here is what actually matters.

Who will see ads and who will not

Ads will only appear for users on ChatGPT’s Free and Go tiers. Anyone on Plus, Pro, or Enterprise plans will remain ad free, and Enterprise workspaces will stay completely untouched by advertising.

This instantly positions ChatGPT ads as a mass market product rather than a premium one, aimed at scale and accessibility rather than high-paying power users.

How OpenAI is protecting trust

OpenAI has been very clear that ads cannot interfere with answers. Several guardrails are already in place:

• Ads are visually and technically separate from ChatGPT responses
• Conversations are not shared with advertisers
• Sensitive topics like health and politics will not trigger ads
• Users can adjust or turn off ad personalisation, or upgrade to remove ads entirely

Crucially, the AI itself does not know when ads are present and cannot reference them unless a user explicitly asks about one. This is designed to prevent any influence on how answers are written.

Why is this different from search and social ads

OpenAI says it is prioritising user trust over advertiser value and revenue. That is a big statement, but it explains why the rollout will be slow and conservative.

For marketers, the opportunity is not about volume at launch. It is about intent.

ChatGPT is used during active thinking moments. People are researching, comparing, planning, or trying to solve a problem. Ads appearing in that context could be far closer to a recommendation moment than a scroll-based interruption.

If executed well, this could become a powerful discovery channel rather than a traditional performance one.

A big opportunity for small businesses

One of the most interesting points from the podcast was OpenAI’s long-term vision. Awan described a future where AI acts as an advertising agent.

Instead of dashboards, keywords, and complex targeting, businesses could simply describe their goals in plain language and let AI handle the setup and optimisation.

If that becomes reality, it could significantly lower the barrier to entry for small and medium-sized businesses that currently struggle with paid media complexity.

The bigger picture

Advertising is how OpenAI plans to scale access to ChatGPT without compromising its core experience. The company is betting that relevance, usefulness, and trust will ultimately outperform aggressive monetisation.

For marketers, the key takeaway is simple. AI is no longer just a tool behind the scenes. It is becoming a front door to brands, services, and decisions.

And that makes this worth paying attention to now, not later.

Google has just revealed a range of upcoming AI-powered advertising features that are designed to help brands reach potential customers in smarter, more intuitive ways, especially as people increasingly use generative AI tools to find information online.

AI-Driven Ads Inside Search Answers

One of the biggest changes coming is a new ad format that will appear within AI search responses. Instead of only showing ads in traditional search results, Google will start placing relevant product suggestions alongside AI-generated answers to user questions. These will be clearly labelled as “Sponsored,” so users understand they’re paid placements. This gives advertisers a new way to capture attention when people are relying on AI for discovery, not just traditional search.

Personalised Offers Based on AI Conversations

Google is also testing a feature called Direct Offers. This lets businesses link specific deals or promotions directly to an AI answer. For example, if someone asks a question about a product type, the AI might show a tailored special offer from a brand that sells that product. It’s an evolution of personalised advertising that meets people right where they are in an AI-assisted journey.

Easier Purchases Through AI Agents

Another priority for Google is the expansion of AI agent tools, automated helpers that can guide users through tasks, including buying products without leaving an AI chat window. Google’s new UCP system aims to make it easier and more secure for people to complete purchases inside AI environments like Gemini and AI Mode in Search.

Connecting Brands with Creators

Finally, Google is improving how brands find and work with creators by using AI to match businesses with the best user‑generated content partners. This builds on early testing of its “Open Call” feature, which lets brands request creative videos from relevant creators.

Overall, these updates show how Google is evolving advertising to blend more seamlessly with AI‑driven discovery – giving brands new opportunities to be seen in the moments people are already asking questions.