The zero-click journey describes a scenario in which customers find answers or complete transactions without ever visiting a brand’s own website. Instead of clicking through search results, users interact via rich search features (featured snippets, knowledge panels), voice assistants, or AI chatbots that deliver answers directly. For example, search engines increasingly present instant answers and AI-generated summaries on the results page, so users often get what they need without clicking any link. Voice assistants (Alexa, Siri) and chatbots work the same way: a user can ask a question and the system answers out loud or in chat. The result is that a growing share of queries now end without a click to an external site. This shift has accelerated with the rise of generative AI and answer engines: one study found that about 60% of searches end without the user moving to another destination site. In practice, a “zero-click journey” might mean a shopper asks Google “best vacuum for pet hair” and buys the top pick via Google or an integrated ad, or asks ChatGPT “book me a train to London” and completes the purchase on the spot. In all these cases the user’s need is met without landing on a brand page – the answer and action happen in situ.

Across industries, analytics confirm this trend. For instance, Similarweb reports that zero-click search behavior is steadily rising as engines answer more queries directly. A recent Search Engine Land guide notes “over half of Google searches now end without a click” thanks to AI overviews and instant answers. Bain & Company research similarly finds 80% of consumers rely on “zero-click” results in at least 40% of searches, with 60% of queries ending on the results page. In practical terms, this means customers get instant answers (via featured snippets, knowledge graphs, chat AIs, etc.) and often transact before seeing a website. For marketers, visibility now often means “presence within AI answers” rather than traditional click-based traffic. In short, the zero-click journey is a new customer experience where discovery, comparison and even purchase can occur entirely on third-party platforms or AI interfaces, without ever loading a brand’s site.

AI’s Role in the Zero-Click Shift

Artificial intelligence is the engine driving the zero-click revolution at every funnel stage. Three key AI-powered forces are in play:

• Conversational and voice search. Chatbots (like OpenAI’s ChatGPT, Bing Chat or Google Bard) and voice assistants (Alexa, Google Assistant, Siri) transform search into a dialogue. Users pose questions in natural language, and the AI understands context and intent. These systems retrieve and synthesize information, then respond in conversational text or voice. For example, generative chatbots are increasingly used for product discovery: one survey found 70% of ChatGPT queries have broad “generative intent” rather than simple search intent. In short, AI assistants solve problems directly (e.g. “Plan me a weekend trip to Edinburgh”), often performing multiple tasks on the user’s behalf. This collapses several traditional search steps into one.
• Generative AI answers and overviews. Traditional search engines have begun integrating large language models to deliver AI-overviews and featured snippets. Google’s “AI Overviews”, Microsoft’s Copilot, Perplexity, and ChatGPT’s answers compile information from multiple sources into a summary. For many queries, these AI-generated answers replace the need to click any link. In March 2025 Google showed AI overviews on over 13% of desktop queries. ChatGPT Search and other LLM-based tools similarly provide direct answers or recommendations. The upshot is that when users get high-quality summaries on the SERP (search engine results page), click-through rates drop dramatically. A Pew study confirms that when an AI answer appears, users “are less likely to click result links”. Instead, they trust the AI’s response. This means brands must earn presence in those AI answers – getting cited or referenced by the model – rather than merely ranking in organic search.
• Predictive analytics and personalization. AI also fuels predictive targeting throughout the funnel. Modern marketing platforms use machine learning to analyse customer data and anticipate needs. For example, predictive models can score which customers are likely to churn, or which offers will spur a purchase. Targeted promotions, driven by AI, then serve personalised deals via email, push notifications or on-site banners at just the right moment. Generative AI further aids personalization by automatically tailoring marketing messages: it can rewrite ad copy, social posts or email campaigns in the tone that resonates with individual segments. In effect, AI makes the funnel proactive. Rather than waiting for a click or form fill, companies can predict and prompt the next action. This influences the journey before the user even initiates a search. In practice, retailers are already using AI to automate content creation and customer segmentation, enabling sophisticated one-to-one marketing at scale. The result is a smarter funnel: at each stage, AI anticipates customer intent and either supplies answers (zero-click) or delivers pinpointed messaging.

Metrics and Measurement Change

Because of these AI shifts, traditional marketing metrics are becoming less relevant. In a zero-click world, clicks and website visits no longer capture influence. As MarTech writer Tanya Thorson observes: “visibility still matters, but its meaning has changed. Brands are now judged by how often they appear in AI-generated answers and trusted citations – not by traffic volume”. SEO expert Curtis Weyant echoes this, noting that SEO strategy must evolve from clicks to “visibility-focused AI SEO” that emphasizes brand presence and authority. New benchmarks include:

• Answer inclusion rate: How often brand content is cited in AI answers or summaries.
• Entity presence index: Frequency of brand or product mentions across AI answers and knowledge graphs.
• Source authority score: A measure of how trustworthy and up-to-date a brand’s content is perceived in AI summaries.
• AI citation frequency: How often an AI result (Google, Bing, Perplexity, etc.) explicitly cites the brand’s content.

These metrics shift focus from site traffic to awareness and trust within AI experiences. Companies now track when their IP is used in “bite-sized moments” – snippets or voice answers – rather than on-page behaviour. Marketers must therefore invest in tools that monitor where their content appears in generative results, and report on visibility in “high-value queries” rather than just click-throughs.

Generative Engine Optimization (GEO)

As AI search grows, a new discipline has emerged: Generative Engine Optimization (GEO), also called generative SEO. GEO is the practice of adapting content and online assets so that generative AI platforms (LLMs) will retrieve, summarise and cite them in responses. In effect, GEO treats AI models as search engines. The term was coined by researchers in late 2023. Its goal is to ensure brands appear in AI answers, not just on search listings.

GEO differs from classic SEO. Instead of focusing on ranking for keywords, GEO emphasizes clear, authoritative content that AI can trust and easily digest. As HubSpot notes, generative SEO “optimizes content to be cited, referenced, and synthesized by AI engines like ChatGPT, Perplexity, Claude, and Google AI Overviews”. Key tactics include:

• Structured Q&A and tutorials. FAQs, how-tos and listicles help AI find concise answers. Agencies advise adding clear question-and-answer format on your site, since AI models often pull full sentences from content. For example, rewriting a product page to explicitly answer “What is X?” and “How do I use X?” can increase its chance of being used as an AI answer source.
• Semantic clarity and completeness. Content should use natural language, synonyms and context so the AI model fully understands it. HubSpot observes that AI queries average ~23 words, so GEO content often needs to cover broad intent and answer follow-up prompts. Brands are restructuring content around helpful narratives rather than just inserting keywords.
• Up-to-date, authoritative information. AI engines tend to cite trusted or recent sources. Maintaining fresh, well-cited content boosts your “source authority score”. It also means brands must avoid outdated or gated content, as SEO experts warn that PDFs and content behind login hamper AI access.
• Rich data and metadata. Providing schema markup, product data feeds and consistent product descriptions helps AI agents ingest brand information. As one marketer noted, tools like Syndigo are being used to ensure product catalogs are uniform and machine-readable for LLMs. Similarly, optimising images with descriptive alt text makes visual content accessible to AI.
• Narrow focus on “air cover”. Recognize that many AI answers rely on community or news content. The eMarketer report highlights that most AI answers reference sources beyond the top Google links. Brands may need to broaden their content formats (blogs, Q&A sites, even podcasts or videos) to become “air cover” for AI interfaces.

In practice, GEO means thinking of content as feeds for AIs, not just pages for clicks. As a Wix study puts it, we’re in an era of “generative intent and GEO, where users want the system to do something for them”. Brands are learning to serve AI first: answering questions directly, then gently nudging toward purchase, rather than expecting the user to click through. The goal is not necessarily to drive a click, but to influence the answer or outcome. For instance, a recipe site might reformat its instructions so that when an AI is asked for quick cooking tips, it can simply quote the steps. Even small changes – like rewriting headlines as complete sentences or adding a brief summary at the top of pages – can help AIs extract and relay the information.

GEO is still nascent, but it’s growing in importance. Industry analysts stress that GEO should be managed alongside traditional SEO as part of a unified strategy. In fact, eMarketer urges companies to treat AI as a “branding channel” and optimise for it separately from SEO. This might mean dedicated analysis of AI traffic (where possible), testing content with tools like ChatGPT to see if it surfaces, and iterating quickly. Early data already shows that AI engines trust different sources than typical search: fewer than 10% of sources cited by ChatGPT or Gemini are among Google’s top-10 search results. In other words, sites that dominate traditional rankings may be invisible to LLMs, so brands must broaden their reach.

In summary, GEO requires brands to speak the language of AI: organise content into clear facts and narratives, cite reputable sources, and keep information current. It often means shifting resources toward owned content strategies that build expertise and authority (blogs, ebooks, data studies) since these “feed the AI’s knowledge”. Done well, GEO ensures that even if a customer never clicks through to your site, your content still earns their trust and preference when AI leads the conversation.

Agentic Commerce: AI Agents as Shoppers

The most radical extension of zero-click is agentic commerce – where autonomous AI agents handle shopping tasks on behalf of users. In this emerging model, customers delegate the entire purchase journey to an AI. The agent researches options, compares products, negotiates prices or promotions, and executes the transaction, all in alignment with the user’s goals and constraints.

As McKinsey analysts explain, agentic commerce is “shopping powered by intelligent AI agents capable of anticipating, personalizing, and automating every step of the process to create frictionless, proactive experiences.”. It’s not just “click and buy” — the agent can take multistep actions. For example, imagine an AI helping you relocate: it could autonomously sell your old furniture, source a house in the new city, hire movers, and reorder kitchen supplies, all without you visiting any shopping sites. What took a human days of research and multiple websites might happen seamlessly in minutes via one interface.

This trend is already underway. According to McKinsey, 44% of users who have tried AI-powered search now prefer it as their primary search method. Retail traffic from generative AI platforms is exploding: Adobe reports that as of mid-2025, GenAI browsers and chat services drove a 4,700% year-over-year increase in visits to U.S. retail sites. BCG notes that soon more than half of consumers anticipate using AI shopping assistants by 2025, and those who do arrive with higher purchase intent (10% more engaged than typical visitors).

The implications for e-commerce and loyalty are profound:

• Seamless, inventory-driven commerce. Agents can connect directly to retailers’ back-end systems via APIs. Companies like Perplexity and ChatGPT are already embedding commerce functions. For instance, Perplexity launched “Buy with Pro”, letting users browse products and checkout in-chat via PayPal. ChatGPT similarly rolled out an “Instant Checkout” feature (initially on Etsy) so customers can complete purchases inside the chat. Google’s forthcoming AI shopping mode will do much the same with Google Pay. In effect, the marketplace is shifting away from web pages to in-chat transaction flows. Users might ask an AI “Find me a new phone under £400”, and the agent will compare retailer feeds, apply any loyalty discounts, and buy the best match – all without the user browsing any site.
• Personalised bundling and pricing. Agents can negotiate complex deals by pooling offers from multiple merchants. McKinsey envisions agents bundling packages (e.g. holiday trip with flights + hotel) or negotiating loyalty upgrades automatically. They might dynamically re-price based on inventory and customer intent, ensuring optimal matches for both buyer and seller. This could drive new subscription or service models (like “premium AI concierge”), and challenge the very nature of loyalty programmes. Instead of simply collecting points, loyalty must work within the agent ecosystem – for example, by exposing reward status via APIs so any agent can apply it. As one strategist asks: “What does brand loyalty mean when decisions are delegated?” In an agentic world, the brand must be discoverable by algorithms – and trustable by them.
• Trust, risk and brand rethinking. Agentic commerce raises big questions of trust and brand identity. Who does a consumer trust when their agent is shopping? McKinsey points out that trust becomes “abstract, filtered through layers of data and automation.” For global brands, this means clear communication about privacy, the right to override agent decisions, and ethical safeguards. BCG warns that agents tend to prioritise price and efficiency over brand loyalty, which could weaken traditional loyalty programs. Brands will need to reinforce their value proposition in this new context – for example, by building “agent-ready” experiences that highlight unique benefits (better service, sustainability, quality) even when the human isn’t choosing. Loyalty may pivot from simple points to trust signals an agent can recognize: certifications, guarantees, or “branded experiences” that an AI can factor into its decision.
• Platform power and disintermediation. Finally, agentic commerce changes the competitive landscape. Companies may find themselves navigating whether to launch their own agents or integrate with big AI platforms. McKinsey notes that many businesses will face the existential choice of how to welcome “agentic traffic”. Retailers that fail to adapt risk being bypassed as AI becomes the new gatekeeper. Indeed, BCG warns that the shift will erode retailers’ direct traffic and first-party data. If an agent shops on your behalf, you might not even know which brands the user considered. This intensifies the need for brands to be present at the moment of intent, not just at checkout – for example by syndicating products across AI channels or partnering on data. It also implies exploring new revenue models (e.g. fees for agent referrals, “sponsored suggestions” in AIs) as traditional ad-based channels give way to integrated, conversational marketplaces.

In summary, agentic commerce is a seismic shift. As McKinsey puts it, companies must move from “optimizing clicks to earning trust from algorithms”. For the customer journey, this means the final purchase stage may itself be conducted by an AI assistant. Brands will need to ensure they can still influence outcomes – by being visible to agents and by maintaining the trust that agents pass from user to brand.

Strategic Implications for Marketers

Taken together, these trends mean marketing strategy must evolve on two fronts: agile AI innovation and firm grounding in fundamentals. Key implications include:

• Scale AI experiments now, but keep core channels strong. Leaders emphasize that AI is an opportunity, not a substitute for fundamentals. Bain counsels that “search engine optimization will not be enough” alone, but also that SEO (and by extension, any owned channel) “remains an important and effective means of reaching consumers”. In practice, brands should invest in pilot projects – e.g. building customer chatbots, integrating generative search features, or experimenting with personalized AI promotions – to learn fast. McKinsey similarly urges bold experimentation: companies that “embrace this moment” with flexible architectures and trial-and-error can “shape the new reality”. At the same time, marketers should keep investing in their own media (websites, apps, email lists, social channels) and traditional media (paid search, social ads, OOH advertising) because these remain crucial for brand awareness and first-party data. Owned content still fuels AI trust: having rich, authoritative material on your site not only drives potential clicks, it also provides the fodder that AI models cite (improving your “AI citation frequency”).
• Blend SEO and GEO. Gone are the days of chasing keyword ranks alone. Marketers must integrate classic SEO tactics (quality backlinks, keyword targeting) with GEO tactics (structured answers, content for LLMs). As Wikipedia notes, SEO, answer-engine optimization and GEO should be “complementary aspects of a unified content strategy”. For example, continue optimizing site content for organic visibility, but also ensure it’s AI-friendly – using schema markup, FAQs, clear summaries and citations. Measure success in broader terms: beyond click-through rates, track brand mentions and “AI visibility” in SERPs, voice results and chatbots. Update analytics frameworks: integrate search-console data with tools that track answer appearances and monitor brand context in AI summaries. The goal is “visibility at the point of decision,” even if that point never brings a user to your site.
• Focus on credibility and trust. In zero-click and agentic interactions, the AI’s trust in your brand is paramount. Digiday reports that agencies are re-focusing on “reputation and credibility” across channels. Marketers should ensure consistent messaging: everything from press releases to social posts should reinforce the same brand narrative, as AI models learn from all publicly available content. Quality, accurate content becomes the new currency: as one expert notes, “the reputation and credibility that you’re perceived as having is more and more effective than having a direct answer”. In practice, this might mean investing in thought leadership articles or data-driven reports that AI bots treat as reliable sources. It also means monitoring what the AI is saying about you – e.g. checking ChatGPT or Perplexity responses that mention your brand – and quickly correcting any misinformation, since negative AI summaries could damage brand perception before a user even clicks.
• Reinvent loyalty and CX. With AI agents as customers, loyalty programs and customer experience will need rethinking. Marketers should design loyalty so that agents can recognise and redeem rewards autonomously (e.g. via APIs). Personalisation must go deeper: AI can tailor offers in real time, so loyalty benefits might become more dynamic (points multipliers, agent-exclusive deals). At the same time, ensure customers feel in control: maintain human-in-the-loop options and transparency about how agents operate, to preserve trust. Forward-thinking firms will also explore “agent engagement” strategies – for instance, having branded skills on popular platforms, or co-branding with device makers (as suggested in Google’s AI Mode where purchases go through Google Pay).
• Measure new outcomes. The shift demands new KPIs. Marketers should track metrics like AI answer share, new-customer discovery via voice or chat, and long-term indicators of trust (brand sentiment in AI feeds). Traditional funnel metrics like bounce rate lose meaning if users never arrive. Instead, measure how often brand content appears in search snippets or voice answers, and use surveys or conversion lifts to infer impact. Setting up “visibility dashboards” for AI channels – even if they are black boxes – will be important. Tools and agencies are already emerging to do this; some firms are even reverse-engineering LLM APIs to see which part of a page the model values. These insights can guide content updates and SEO/GEO prioritization.

In sum, marketers must balance innovation and investment. AI presents massive opportunities but also upheaval. Those who integrate AI-driven tactics – conversational search, predictive targeting, generative content – into their strategies while maintaining strong fundamentals (brand building, SEO, owned media) will be best positioned. As Bain observes, brands should refine SEO and owned channels, and simultaneously invest in new AI-readiness measures. Likewise, McKinsey suggests that companies which “scale up their AI initiatives” quickly will gain an edge. The takeaway is clear: experiment with AI, but do not forsake the assets you already have. The future funnel will use all channels, old and new – and the most successful brands will be those that orchestrate them together.

Case Studies: Pioneering in a Zero-Click World

Tech Giant: AI-Powered Q&A. A major tech company (imagine “TechCo”) wanted to ensure its products were visible in generative search answers. The marketing team created a suite of concise Q&A pages and updated product descriptions to be more conversational. They structured each page with clear question headings and short, authoritative answers so that AI systems like Google’s snippets and ChatGPT would pick them up. As a result, TechCo’s content began appearing in AI Overviews: ChatGPT’s sources started citing their white papers and Google’s AI features began pulling data from TechCo’s blogs. Even though site traffic from organic search fell, the AI citations spiked, and brand searches grew as people heard the brand name from AI recommendations. This hypothetical illustrates the SEO→GEO transition: by adapting content specifically for AI summarisation, TechCo increased its zero-click visibility and maintained mindshare even when clicks were down.

Retail Brand: Voice Shopping and Personalisation. Consider a home goods retailer, “ComfortHomes,” that introduced an AI voice shopping assistant. Customers could say “Alexa, reorder my shower cleaner” or “Hey Google, what’s a good bedside table for my room style?” ComfortHomes had prepared by tagging its product catalogue with rich attributes (size, style keywords) and by developing a voice app that could complete purchases. Through this, many routine orders happened entirely via voice: the customer merely approved the agent’s suggestion and said “buy it.” Behind the scenes, AI was running predictive analytics on each user’s purchase history, so the assistant offered the most relevant items. This led to a new form of loyalty: rather than clicking on weekly deals, customers came back by voice commands. Internally, ComfortHomes tracked success by measuring how many sales were initiated via its voice skill versus traditional web, and found that those customers had higher retention. This fictitious example shows how conversational AI and predictive models can create seamless, loyalty-driving experiences without a click – in line with industry forecasts that AI assistants will drive substantial retail spend.

Financial Services: AI Chatbot for Customer Support. In banking, a digital challenger (similar to UK’s Starling Bank) implemented a generative AI chatbot for customer service. The chatbot, integrated with the bank’s knowledge base, could answer queries about account balances, transactions, or loan options. It was trained on real support dialogues and brand guidelines, ensuring it spoke with the bank’s tone. Within months, the chatbot resolved over 90% of standard customer questions without human intervention. Customers saw faster responses (often immediately after asking) and agents were freed to tackle complex issues. Crucially, the chatbot was also optimized for SEO/GEO: common Q&A prompts from users on the website became source material, and Google’s search started pulling answers directly from the bank’s FAQ when people asked basic questions like “how to freeze my debit card.” This dual approach – AI assistant for support and structured content for search – kept the bank visible at zero-click points and improved customer satisfaction. (Starling’s real chatbot reached a 91% resolution rate, an inspiration for this scenario.)

Travel and Lifestyle: Agentic Itinerary Planner. Imagine an online travel agency (“GlobeTravel”) experimenting with agentic commerce. They partnered with a startup to create “GlobeBot,” an AI that plans vacations end-to-end. Users could simply say, “Plan me a week in Rome in June for a family of four.” GlobeBot would gather preferences (budget, hotel style, airline loyalty), scan dozens of sites for deals, negotiate multi-service bundles, and present a complete itinerary via an app. In tests, this agent improved conversion: 85% of test users booked at least one component through the bot. It also negotiated a family discount on a tour package, something a typical user might not have done manually. GlobeTravel monitors performance not by web traffic, but by AI sessions completed and revenue per agent session. This hypothetical case shows how early adopters in travel could leverage agents to create frictionless booking – foreshadowing McKinsey’s vision of agents pre-assembling shopping plans from contextual cues.

These examples highlight how forward-looking companies might respond to the zero-click era: by optimizing content for AI visibility, deploying conversational AI, and even embracing agentic commerce pilots. In each case, success hinges on understanding the AI-driven context and integrating brand strategy into it. As one industry observer noted: “brands are realizing that years of hard-earned search equity are being reshaped overnight as AI moves from search engines to answer engines”. Those who experiment and adapt will capture the “new market position” even without conventional clicks.

Future Outlook: The Journey Ahead

Looking ahead 3–5 years, the zero-click phenomenon is likely to deepen and spread:

• Ubiquitous AI interfaces. AI assistants will become integrated into every channel: smart homes, AR/VR devices, connected cars and more. Asking questions or shopping via voice or vision will be second nature. For example, one could envisage smart glasses that recognise products in store and instantly order them via an AI agent. The boundary between “online” and “offline” commerce will blur, as AI merges digital and physical data.
• Shopper agents everywhere. Agentic commerce could become mainstream. By 2030, McKinsey projects up to $3–5 trillion globally in agent-mediated sales. Many routine purchases (groceries, essentials, renewals) may shift to subscription-like models managed by AI (e.g. your “Home AI” auto-reorders detergent when you’re low). AI agents may even negotiate on the fly: imagine an airline paying a small fee when agents automatically secure your seat upgrade on your behalf. The logistics and payments infrastructure will evolve – protocols like Agent-to-Agent payments and AI broker intermediaries may arise to handle these flows.
• New competition and collaborations. Tech platforms will dominate as the new front doors. Just as Google is now planning in-SERPs payments, we can expect more consolidation: big AI “superapps” that combine search, chat, shopping and fintech. Traditional brands may need to partner with these ecosystems or risk obsolescence. At the same time, new startup opportunities abound: firms that prepare content for AI, mediate between brands and agents, or specialize in AI-specific marketing could thrive.
• Evolving trust and regulation. Consumer acceptance of AI decision-making will grow, but so will scrutiny. Issues like data privacy (the “AI dark funnel” where interactions are invisible to brands) and algorithmic bias will become front-of-mind. Regulations such as the EU’s AI Act may impose standards for transparency and accountability on AI-generated advice. Brands will need to be proactive in demonstrating the fairness and safety of AI features (e.g. allowing users to verify agent decisions). Successful companies will emphasize explainable AI – making their bots and overviews clear about data sources and reasons for recommendations.
• Refined customer journeys. The very notion of a “funnel” may need rethinking. Instead of linear stages, marketers will orchestrate dynamic, branched journeys across AI, voice, social and human touchpoints. For instance, a user might start with an AI chat (zero-click), move to a virtual try-on session (immersive AR), and finish with a voice checkout – all on different platforms but linked by data continuity. Marketers will invest heavily in “journey orchestration” tools that unite signals from LLMs, CRM systems, and even IoT sensors, creating a seamless experience.

In this future landscape, the core challenge remains the same but the tactics evolve: ensuring your brand is present and trusted wherever the customer is, even if that’s inside an AI’s “mind.” As one analyst puts it, “search is changing quickly” and discovery is fragmenting across chat, social, voice, and more. The companies that thrive will be those that not only appear in AI answers but also build genuine relationships through every interface – human or machine. In a sense, human ingenuity is amplified, with AI as partner. By staying curious, experimental and customer-centric, marketers can turn the zero-click world to their advantage, rather than be sidelined by it.

(ankit.kankar@mmactiv.com)