Does ChatGPT recommend your product? How AI search is reshaping UK pharmaceutical e-commerce

Category: GEO / AI / LLM Visibility | Format: Thought Leadership | Audience: Digital Strategy, CMOs, Innovation Teams

---

Introduction: Consumers no longer search, they ask

Picture the scene. A British consumer experiences persistent joint pain. Five years ago, they would have typed "best joint pain relief" into Google, browsed through ten links, compared product pages on Boots.com or Superdrug, then made their decision. Today, they open ChatGPT and simply write: "What food supplement is most effective for joint pain?"

Within seconds, the AI assistant presents them with a structured response: three or four named products, selection criteria, precautions. No ten pages of results to wade through. No adverts to filter out. A direct, reasoned answer that resembles personalised pharmaceutical advice.

This scenario is no longer science fiction. It happens millions of times daily across the world, and the UK is no exception. According to industry estimates, ChatGPT now exceeds 300 million weekly active users globally, with the UK ranking among the top ten adoption markets in Europe. Perplexity AI, Google AI Overviews, Microsoft Copilot: conversational search interfaces are multiplying and radically transforming how consumers discover, evaluate and choose health products.

For consumer healthcare (CHC) brands present in the UK market, the question is no longer whether this change will affect them. The question is: does your product feature in the answers these AIs provide?

---

The rise of AI search in the health journey

A paradigm shift in product discovery

Traditional search relies on a well-known model: users enter keywords, the search engine returns a list of links, and users navigate through the results. This model is being fundamentally challenged by Large Language Models (LLMs).

AI assistants like ChatGPT, Perplexity or Google Gemini don't return a list of web pages. They synthesise information, formulate direct answers and, increasingly, recommend specific products. In April 2025, OpenAI launched integrated shopping features in ChatGPT, enabling users to search for products, compare prices and get recommendations directly within the conversational interface—all without paid advertising initially, therefore based solely on relevance as perceived by the model.

This shift has major implications for the health sector:

• Fewer clicks, more implicit trust. When an AI recommends a product, consumers tend to accord that recommendation a level of trust comparable to expert advice. Studies conducted by digital strategy consulting firms indicate that AI-generated recommendations benefit from significantly higher trust rates than traditional advertising.
• Concentration of choices. Whilst Google displays ten organic results per page, an LLM typically mentions only two to five products in its response. Being in that restricted selection becomes a decisive competitive advantage.
• "Zero-click" extends to e-commerce. Google AI Overviews already provides direct answers for numerous health queries, reducing traffic to retailers' product pages. LLMs amplify this phenomenon.

Why the health sector is particularly affected

UK pharmaceutical e-commerce presents characteristics that make LLM visibility even more strategic than in other sectors:

1. High-involvement purchases

Health products aren't impulse buys. Consumers research, compare, gather information. According to market data, purchase journeys for food supplements or OTC medicines involve an average of 4 to 7 touchpoints before decision. AI assistants naturally insert themselves into this intensive research phase.

2. Increased need for trust

In health, trust is the primary decision criterion. British consumers, accustomed to pharmacy advice, seek perceived expertise. AI responses that cite studies, reviews and scientific arguments address this need in a way that advertising cannot match.

3. UK regulatory complexity

The UK regulatory framework—the MHRA for medicines, the CMA for consumer protection, the ASA for advertising—creates an environment where reliable information is particularly valued. AIs that rely on regulated sources and compliant content have a natural advantage, and brands whose content meets these standards are more likely to be recommended.

4. The UK market in full acceleration

Online sales of health products in the UK represent a growing share of the total pharmacy market, with online health and beauty continuing to expand at approximately 15% annually. Platforms like Boots, Superdrug, LloydsPharmacy and Amazon.co.uk drive this growth. In this expansion context, product discovery channels diversify rapidly—and LLMs are part of it.

---

How do LLMs decide to recommend your product?

Understanding LLM recommendation mechanisms is essential for any AI visibility strategy. Unlike traditional search algorithms (such as Amazon's A9/A10 or Google's PageRank), LLMs operate according to different logics.

Content signals that matter

Authority and source credibility

LLMs synthesise information from vast data corpora. They favour sources perceived as authoritative: institutional sites, scientific publications, specialist media, and product pages rich in factual information. For a CHC brand in the UK, this means that the quality of content on your product listings (Boots, Superdrug, LloydsPharmacy), on your brand site, and in media that mention you directly influences your AI visibility.

Authority signals include:

• References to clinical studies or certifications
• Mentions by healthcare professionals
• Consistency of information across sources
• Presence of structured data (schema markup) on own sites
• Mentions in recognised health sector publications

Content completeness and depth

LLMs tend to recommend products for which they have rich and detailed information. A product listing with a generic title, three vague bullet points and no A+ content has far less chance of being cited than a complete listing with:

• A descriptive title including brand, active ingredient, format and dosage
• Bullet points detailing benefits, usage instructions and specifics
• Enhanced content (A+ Content, Enhanced Brand Content)
• Information on ingredients, certifications and regulatory warnings

The consumer voice: reviews and UGC

Customer reviews constitute a powerful signal for LLMs. Volume, average rating, recency and review content influence how an AI perceives a product. A product with 2,000 reviews at 4.5 stars on Amazon.co.uk will have a much stronger digital footprint than a competing product with 50 reviews at 3.8 stars.

Review elements that carry most weight:

• Total volume and velocity (pace of new reviews)
• Average rating and stability over time
• Richness of textual review content (details on effectiveness, usage)
• Brand responses to reviews, particularly negative ones

Technical signals

Structured data and schema markup

For proprietary sites (DTC) and retailers that implement them, structured data (schema.org) helps AIs understand and categorise products. Health sector-specific schemas—MedicalProduct, Drug, HealthTopicContent—are particularly important.

Cross-platform consistency

LLMs cross-reference information from multiple sources. If your product displays contradictory information between Amazon.co.uk, your brand site and Boots listings, this reduces the trust the model places in your data. Consistency of information (price, ingredients, claims, dosage) across all digital touchpoints is a credibility factor.

Content freshness

The most recent models, notably those integrating real-time web search (like Perplexity or ChatGPT with browsing), favour recent information. Regularly updated content sends a signal of relevance and timeliness.

---

New frontiers: ChatGPT Shopping, Google AI Overviews and Perplexity

ChatGPT Shopping: recommendation without advertising

ChatGPT's shopping features represent a turning point. The tool now enables users to search for products, see images, reviews, prices and purchase links directly in conversation. The crucial point: at this stage, these recommendations aren't sponsored. They're based on what the model estimates to be the most relevant products for the user's query.

For the CHC sector in the UK, the implications are considerable:

• The best-referenced brands in the digital ecosystem are favoured. If your product has abundant reviews, complete listings and editorial mentions, it's more likely to appear.
• The absence of paid advertising means organic content quality is the only lever. Impossible to buy your place in a ChatGPT recommendation (for now).
• The conversational format favours precise answers. "What's the best magnesium for stress?" calls for a response with product names, not a list of categories.

Google AI Overviews: health zero-click

Google AI Overviews (formerly SGE) generates synthetic responses at the top of results pages for numerous queries, including health queries. In the UK, deployment is progressive but accelerating. For queries like "best probiotic for digestion" or "effective vitamin D supplement", Google can now provide a direct answer that significantly reduces clicks to retailer sites.

What this changes for brands:

• Organic traffic to product pages may decrease if Google answers directly
• Brands cited in AI Overviews gain considerable visibility
• Content cited by Google AI Overviews tends to come from sources perceived as highly reliable

Perplexity AI: the conversational search engine with sources

Perplexity AI positions itself as a Google alternative by combining web search with AI-generated responses. Each response is accompanied by cited sources, making it particularly suitable for health queries where information traceability matters. UK early adopters increasingly use it for in-depth health research.

---

Data and trends: the measurable impact on health e-commerce

AI search adoption in the UK

UK AI assistant adoption figures are significant and rapidly growing:

• Mass adoption: According to recent studies, a growing proportion of UK internet users have already used a generative AI assistant, with this figure progressing rapidly quarter on quarter. The 18-35 age group is most affected, but usage extends across all age groups.
• Health queries: Health-related questions rank among the most popular categories on AI assistants. Queries cover symptoms, treatments, food supplements and product comparisons.
• Purchase intent: A growing share of AI users report having purchased a product following an AI assistant recommendation. This behaviour is particularly marked in categories where trust and information are central—like health.

The concentration effect: who wins, who loses?

Initial LLM visibility analyses in the health category reveal concerning trends for brands that haven't anticipated this shift:

• Leaders consolidate their position. Brands with rich content, numerous reviews and frequent editorial mentions are disproportionately recommended by LLMs. The gap with challengers widens.
• Retailer own brands are under-represented. LLMs tend to recommend brands with strong identity and rich content history, which favours national brands over own brands—at least at this stage.
• Products "invisible" online are invisible to AIs. If your product doesn't have substantial digital presence (detailed product listings, reviews, mentions), it simply doesn't exist in LLM databases.
• Traditional search result position isn't directly correlated. Being first on Amazon.co.uk for a keyword doesn't guarantee ChatGPT recommendation. LLMs assess relevance differently.

Key sector data

The UK market context reinforces the urgency to act:

• The UK online health and beauty market is in sustained expansion, driven by players like Boots with its comprehensive online platform, and Amazon.co.uk's growing presence in the health category.
• The click-and-collect model, strong in the UK thanks to the dense network of pharmacies, constitutes a bridge between online research and physical purchase—a bridge where AI recommendations play an upstream prescriber role.
• The majority of UK pharmacies maintain significant online presence through major chains like Boots, LloydsPharmacy, and Well Pharmacy, creating substantial space for digitally-savvy players.

---

Why your brand cannot afford to ignore LLM visibility

The cost of inaction

Not investing in LLM visibility carries concrete and growing risks:

1. Silent market share loss

Unlike a ranking drop on Amazon.co.uk (which you can detect in real-time), LLM visibility loss is invisible unless you measure it. Your competitor may be systematically recommended by ChatGPT for "best joint pain relief" without your knowledge. By the time you realise, your target consumers' search habits have changed.

2. Brand prescription erosion

British consumers historically place great importance on pharmaceutical advice. AI assistants position themselves as a complement (or partial substitute) to this advice. If your brand is absent from these recommendations, it loses a growing prescription channel.

3. First-mover advantage

Like traditional SEO in its early days, brands investing early in LLM optimisation build cumulative advantage. Model training data, accumulated quality signals, editorial mentions: all these elements mutually reinforce over time.

The specificity of UK regulatory framework

The UK regulatory framework adds a layer of complexity but also opportunity:

• MHRA: Only MHRA-regulated sources can provide medicine-related advice online. LLMs must (and tend to) respect this reality in their recommendations, which advantages brands distributed via compliant channels.
• CMA: Health claims on food supplements and health products are strictly regulated. Content compliant with these regulations is perceived as more reliable by LLMs.
• ASA: The obligation to clearly distinguish sponsored content from editorial content reinforces organic content value—exactly the type of content LLMs favour.

Brands investing in rigorous, compliant and detailed content benefit from dual advantage: they comply with regulations AND send authority signals that LLMs value.

---

Action plan: how to optimise your LLM visibility in the UK

Step 1: Audit your current AI visibility

Before optimising, you must measure. Here are concrete actions:

• Query main LLMs (ChatGPT, Perplexity, Google Gemini, Microsoft Copilot, Claude) with queries your target consumers would use: "What's the best [product category]?", "What do you recommend for [symptom/need]?", "Comparison [your brand] vs [competitor]"
• Document responses: Note which products are recommended, which arguments are advanced, which sources are cited
• Compare with competitors: Is your brand present? In what position? With what qualifiers?
• Repeat regularly: LLM responses evolve over time as models are updated and new data integrated

Step 2: Optimise your content for LLM signals

Priority actions to improve your LLM visibility:

On e-commerce platforms (Amazon.co.uk, Boots, Superdrug) :

• Enrich your product titles with key information: brand, active ingredient, dosage, format, main benefit
• Write detailed and factual bullet points, not just marketing copy
• Invest in A+ / Enhanced Brand Content with scientific information and trust elements
• Generate and maintain substantial review volume with high ratings
• Respond to negative reviews professionally and informatively

On your brand site :

• Create in-depth content on your products, ingredients and benefits
• Implement structured data (schema markup) adapted to the health sector
• Publish quality editorial content (articles, guides, FAQs) positioning your brand as reference

In the editorial ecosystem :

• Obtain mentions in recognised UK health media
• Collaborate with healthcare professionals for expert content
• Ensure consistency of your information across all sources

Step 3: Establish continuous monitoring

LLM optimisation isn't a one-off project. It's an ongoing process requiring:

• Regular monitoring of your visibility across different LLMs
• Tracking recommendation changes after model updates
• Competitive intelligence on products recommended in your category
• Correlation between optimisation actions and visibility results

Step 4: Integrate LLM visibility into your global strategy

LLM visibility shouldn't be treated in isolation. It's part of a broader Digital Shelf strategy:

• Search + LLM: Good ranking on Amazon.co.uk indirectly reinforces your LLM visibility (LLMs analyse retailer result pages)
• Content + LLM: Quality product content serves both product page conversion and AI visibility
• Reviews + LLM: Abundant positive reviews are a quality signal for both search algorithms AND LLMs
• Compliance + LLM: Content compliant with UK regulatory requirements (MHRA, CMA, ASA) is also content LLMs consider reliable

---

How Smile Analytics positions you at the forefront

In this context of rapid transformation, CHC brands in the UK need tools capable of measuring, tracking and optimising their visibility across new product discovery channels. This is exactly what Smile Analytics offers with its LLM visibility tracking feature.

What Smile Analytics LLM tracking enables

• Automated LLM visibility monitoring: Smile Analytics systematically queries main AI assistants (ChatGPT, Perplexity, Google Gemini, and others) with queries relevant to your category and products. No more manual work required.
• Time-series tracking: Visualise your LLM visibility evolution week by week, detect trends and measure the impact of your optimisation actions.
• Competitive benchmarking: Compare your LLM presence to direct competitors. Identify queries where you're absent and competitors are recommended.
• Alerts and notifications: Receive alerts when your LLM visibility changes significantly, for example after model updates or competitor content modifications.
• Integration with Digital Shelf KPIs: LLM visibility is presented alongside your other key indicators (search ranking, content score, reviews, availability), for complete digital performance overview.

An integrated Digital Shelf vision

Smile Analytics isn't limited to LLM tracking. The platform offers complete visibility of your CHC product e-commerce performance across major UK retailers (Amazon.co.uk, Boots, Superdrug and many others), covering:

• Organic and paid search visibility
• Product content quality and completeness
• Review and reputation monitoring
• Competitive monitoring
• Retail media optimisation
• And now, visibility in AI recommendations

This integrated approach is essential because, as we've seen, the signals feeding LLM visibility are the same as those determining e-commerce platform success: quality content, positive reviews, consistent and compliant information.

---

Checklist: actions to launch immediately

For digital strategy directors, CMOs and innovation teams who want to act without delay, here's a summary checklist:

Immediate (this week):

• Conduct an LLM visibility audit: Query ChatGPT, Perplexity and Google Gemini with 10 queries corresponding to your key categories
• Document results: Which products are recommended, which competitors appear, which arguments are highlighted
• Identify the most critical gaps between your desired and actual visibility

Short term (30 days):

• Launch comprehensive product content audit on your Amazon.co.uk, Boots and Superdrug listings
• Prioritise product listing enrichment for strategic references (top 20% of turnover)
• Implement review generation programme for under-represented products
• Verify consistency of your product information across all platforms

Medium term (90 days):

• Implement editorial content strategy (blog, guides, FAQs) on your brand site to reinforce authority signals
• Deploy structured data (schema markup) on your proprietary site
• Establish regular (ideally automated) LLM visibility tracking
• Integrate AI visibility metrics into monthly Digital Shelf performance reports

Long term (6-12 months):

• Build comprehensive GEO (Generative Engine Optimisation) strategy
• Train your content and e-commerce teams in LLM optimisation specifics
• Establish partnerships with health media and KOLs to strengthen editorial footprint
• Evaluate and invest in LLM monitoring tools like Smile Analytics to industrialise tracking

---

Conclusion: The time to act is now

AI search isn't a distant trend. It's already here, progressing rapidly, and redrawing the rules for UK pharmaceutical e-commerce. Consumers asking ChatGPT "which probiotic to choose" or Perplexity "best allergy treatment without prescription" expect concrete answers with product names. If your brand isn't featured, it leaves the field open to competitors.

The UK market, with its rapid online health and beauty growth, demanding regulatory framework and dense pharmacy network in digital transition, presents both challenge and opportunity. Brands investing now in their digital content quality, customer review richness and cross-platform presence consistency will build lasting advantage—not only on traditional search engines and e-commerce platforms, but also in AI assistant recommendations becoming a prescription channel in their own right.

LLM visibility is the new Digital Shelf frontier. And like any frontier, the first arrivals gain the greatest benefit.

---

Want to measure and optimise your product visibility in AI recommendations? Smile Analytics offers unique LLM tracking functionality that continuously monitors how AI assistants reference your products. Request a demo to discover how your brand positions in ChatGPT, Perplexity and Google AI Overviews responses—and what you can do to improve your visibility.

---

Keywords: ChatGPT product recommendations, AI health search, UK brand LLM visibility, AI shopping assistant, UK pharmaceutical e-commerce, pharma GEO, generative search optimisation