Search has undergone a seismic shift in the last few years. LLMs have changed how consumers research, discover and buy. What does this mean for brands? It’s more challenging than ever to get in front of the right audience, at the right moment.
Influencing LLMs is now the name of the game, and the brands that can understand how to do this will be the ones that come out on top.
Tradedoubler is working on the tools to drive results in this new era of search. We spoke to Corin Ward, Director of AI at Tradedoubler, to hear about the company’s new solution, Emna.ai, how it works, and why it matters in this new landscape.
Madaline Dunn: How are consumers using LLMs within their shopping journeys?
Corin Ward: Consumers are increasingly using LLMs as a starting point for product discovery and purchase research. That shift is happening both inside traditional search, with Google’s AI Overviews appearing above paid and organic results, and outside it, as consumers turn directly to ChatGPT or Perplexity as a first port of call. Instead of typing short keywords and working through multiple results, they are asking more specific, conversational questions, like “what’s the best skincare for sensitive skin?” In other words, product discovery is moving from search-led browsing to AI-led recommendation.
MD: And how are users interacting with the results in LLMs?
CW: Users are treating LLM results less like a search results page and more like a guided conversation. Instead of scanning a long list of links, they consume a curated answer, ask follow-up questions, and use the model to narrow the decision.
Trust is also building: Yext’s 2025 study found that 62% of consumers trust AI to guide their brand decisions, although many still cross-check results. Importantly, that cross-checking often happens after the LLM has already shaped the shortlist, meaning brands need to earn inclusion in the answer, not just visibility on the results page. Behaviour still varies by platform and user confidence, but the direction is clear: AI discovery is becoming more conversational, more guided and, with features like ChatGPT’s Instant Checkout, increasingly transactional.
MD: What sort of implications does this have for brands?
CW: Browsing journeys used to be fragmented across several links and sources. With LLMs, that journey is compressed into one curated answer, a handful of citations and even fewer direct recommendations. Exposure is more limited, but the opportunity is much greater when a brand is included.
This sits within a wider zero-click shift: SparkToro’s 2024 research shows that nearly six in ten Google searches in the U.S. and EU end without a click. That creates a “black box” challenge, because clicks and ad impressions are no longer guaranteed in an LLM-led journey. Brands therefore need to take proactive steps to understand their share of voice in LLMs, how they compare to competitors, which content is contributing to that visibility, how it changes over time and how they can optimise it moving forward.
MD: And so, from your research, what factors drive visibility within LLMs?
CW: A lot of the foundations of good GEO have strong crossovers with SEO. Google reinforced this in its recent guidance on optimising for generative AI features, stating: “The best practices for SEO continue to be relevant because our generative AI features on Google Search are rooted in our core Search ranking and quality systems.” That applies specifically to Google’s AI experiences rather than every LLM, but the principle is important: AI search engines still reward relevant, high-quality content that answers what users are actually asking.
What matters is having a diverse range of content across multiple touchpoints: your own content, publisher authority, off-site signals, reviews, comparisons and accurate product-level information all matter. We are also seeing that citations and recommendations are not the same thing. A brand can be cited in an answer without being recommended, which means brands need to understand what is actually influencing the final answer.
MD: What is Tradedoubler working on to provide a greater understanding of what influences AI?
CW: Tradedoubler has launched Emna.ai to help brands understand how they appear in LLMs and what is influencing that visibility. It shows a brand’s share of voice across AI-generated answers, which domains and articles are being cited, how often they appear, and how relevant those sources are — across owned content, Tradedoubler publishers and wider third-party sources. It also identifies where existing content supports a brand’s key products, features and messages, and where there are gaps. The goal is to move beyond attribution alone and give brands a clearer view of influence: what is shaping AI answers, which content is contributing, and where they can take action.
MD: How does Emna.ai work?
CW: Emna.ai connects to the major LLMs and runs brand-level market insights around the prompts people are asking at different stages of the funnel. It identifies where a brand appears, calculates share of voice, and provides a granular breakdown of what is driving that visibility — down to the specific articles, domains and publishers being cited, alongside competitor performance. From there, we can build campaigns around the prompts that matter most to a brand’s marketing goals, using content creation, publisher activation and ongoing optimisation to increase share of voice over time.
MD: What makes it different from the other tools out there?
CW: GEO, AEO and LLM ranking is a very loud space right now. Many tools are running the typical SaaS model, where brands pay a monthly fee and then work with in-house content teams to act on the insights themselves. Emna.ai is different because it works as a campaign tool, aligned to a brand’s marketing goals and the prompts their users are actually asking. It combines measurement with execution, helping brands understand what is influencing their share of voice and then activate content and publishers to improve it over time.
MD: How can brands use the tool to improve their visibility within LLMs?
CW: With Emna.ai, brands can understand what is being cited, why it is being cited and how it is being used within LLM answers. From there, we can identify visibility gaps and generate content in the brand’s tone of voice, aligned to their marketing goals, for activation across our publisher network. Brands can then track the impact of the campaign daily, looking not only at overall share of voice, but also share of voice across specific prompts, with SWOT-style analysis against key competitors. The aim is to create a continuous improvement loop: understand the influencing content, activate through relevant publishers, measure the impact and optimise over time.
Emna helps brands improve their visibility in generative AI responses, climbing the answer rankings and staying there.
MD: For those who have had early access, can you provide some insight into the impact the tool has had?
CW: Our first GEO campaign in the skincare sector delivered strong results. The client moved from outside the top five to number 4 in France, while AI visibility increased from under 5% to 30% in less than two weeks. On one of the most competitive prompts in the market, this gave the brand stronger share of voice, better visibility and a more informed strategy around what influences LLM results.
MD: And, finally, how does this release fit into your broader goals and long-term plans?
CW: We are heavily invested in AI at Tradedoubler, both from a product and efficiency point of view. Emna.ai greatly expands our product offering because the shift to LLM-led discovery has implications for all stakeholders in affiliate marketing: brands want to understand how they appear in AI answers, and publishers want to understand the value of their content in a new age of user journeys without a click. It also allows us to unlock higher-value conversations beyond the traditional affiliate ecosystem, with brand marketing teams, publishers, media houses and other partners.
At its core, our business has always been relationship-driven, built around visibility, transparency and performance. Emna.ai takes that into the AI discovery layer, helping brands and publishers understand how influence is created as decision journeys move upstream.
