How AI Search Is Recommending Hair Loss Treatments

Consumer discovery of hair loss treatments is shifting from dermatologist referrals and direct-to-consumer advertising to AI-generated shortlists. When a prospective patient asks an AI platform for the best hair loss treatment, the system retrieves, compares, and recommends brands based on available public evidence. This new discovery layer is compressing the buyer shortlist around a small set of brands that AI systems trust enough to recommend, while established names with significant market presence are being retrieved but not advanced.

The LLM Authority Index benchmark for June 2026 reveals that recommendation power in the hair loss treatment category is concentrating around Nutrafol, Rogaine, and Keeps, while established clinical brands like Bosley and HairClub appear frequently in AI responses but rarely receive ranked recommendations. CiteWorks Studio interprets this benchmark to show which brands are winning the recommendation-stage visibility that matters most for commercial outcomes, and where the gaps are widest.

Methodology

1. Market studied: Hair Loss Treatments, including topical treatments, oral medications, nutraceutical supplements, and clinical hair restoration services.
2. Brands/entities included: Nutrafol, Rogaine, Keeps, Hims, Happy Head, Bosley, HairClub, Ro (Roman), Folexin, and Propecia (Merck). This universe covers major direct-to-consumer telehealth brands, over-the-counter treatments, prescription medications, and clinical restoration providers. This list may not represent a complete market census.
3. Data collection date/window: June 2026.
4. AI platforms tested: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, and Perplexity.
5. Number of prompts tested: Prompt count was not provided. The analysis is based on 1,102 observations across three public high-intent prompt clusters.
6. Prompt categories: Best Telehealth and Online Prescription Services (consideration stage), Telehealth Platform Comparisons (evaluation stage), and Telehealth and Online Prescription Pricing (decision stage). These represent three of ten tracked clusters; the remaining seven clusters are not included in the public report.
7. Definition of a mention: A mention means the company appeared in an AI-generated response, regardless of sentiment, rank, or recommendation status.
8. Definition of a valid recommendation: A valid recommendation is a positive, shortlist-quality recommendation or ranked recommendation that earns recommendation credit. Appearance in an AI response without positive framing or ranking does not constitute a valid recommendation. This distinction is the foundation of the analysis.
9. Ranking/scoring metrics used: Valid recommendation coverage, recommended top-three rate, recommended rank-one rate, average recommended rank, net sentiment/framing score, monthly AI Authority Value (a modeled metric combining recommendation value and visibility assist value), and captured share of AI opportunity.
10. Limitations: This is a point-in-time benchmark for June 2026. AI outputs can change as models update and source material shifts. Modeled values are estimates and not revenue. This report is not a full audit or full market census. Only 3 of 10 tracked prompt clusters are represented in the public dataset. Prompt count was not provided. Company-level Ahrefs data was not supplied for this report.

Key Findings

Recommendation power is concentrated in three brands. Nutrafol, Rogaine, and Keeps capture the majority of AI recommendation value across all three high-intent prompt clusters in the benchmark. Nutrafol leads overall with a monthly AI Authority Value of $3.9M and valid recommendation coverage of 24.9%. Rogaine holds the strongest rank-one position with a 14.2% rank-one rate and an average recommended rank of 1.58. Keeps ranks third at $3.2M in monthly AI Authority Value, though a meaningful share of its value derives from visibility assist rather than direct recommendations.

Brand presence does not equal recommendation power. Bosley appears in 7.7% of all observations across the 1,102-observation dataset but receives valid recommendations in only 0.8% of cases. HairClub shows a comparable pattern, with 3.0% observation presence and virtually no recommendation coverage. The analysis found that these brands are being retrieved by AI systems but are not being advanced as options at the buying moment. This is among the most commercially exposed positions a brand can occupy in AI-led discovery: visible enough to be compared, but not trusted enough to be recommended.

The pricing cluster is the highest-intent battleground. The Telehealth and Online Prescription Pricing cluster carries a $17.0M modeled monthly opportunity and the highest buyer-stage multiplier among the three public clusters. Nutrafol leads in this cluster with a 26.9% top-ten rate and $1.6M in captured value. Happy Head shows notable strength here, recording a 12.8% top-ten rate and an average recommended rank of 1.48 within the cluster. Brands that are absent from pricing-related AI responses are effectively excluded from the final purchase decision stage.

Platform performance varies significantly across AI systems. Rogaine leads on ChatGPT with a 25.1% rank-one rate and captures 30.9% of top-three recommendations on Perplexity. Nutrafol leads on Copilot with 30.9% recommendation coverage, on Google AI Mode with 30.4%, and on Google AI Overviews with 28.0%. Keeps performs best on Google AI Overviews, capturing 13.6% of platform-level recommendation opportunity. These platform-specific patterns suggest that citation architecture and source footprint vary meaningfully across AI systems and that no single platform produces uniform results.

Net sentiment scores reveal significant framing quality differences. Happy Head carries the highest net sentiment score in the dataset at 0.80, indicating strongly positive AI framing when the brand is mentioned. Rogaine scores 0.69, Nutrafol scores 0.57, and Keeps scores 0.60. Bosley records a net sentiment score of 0.11, meaning its mentions are overwhelmingly neutral rather than positive. This metric reflects the directional framing of how AI systems present each brand, not customer satisfaction or review sentiment.

What Changed in the Market

Buyers of hair loss treatments are no longer moving only from Google search results to brand websites before making a decision. They are also asking AI systems to compare providers, explain treatment options, summarize pricing tiers, surface alternatives, and generate shortlists. This creates a new discovery layer that operates before the brand website is visited and sometimes instead of it.

For a category where consumer trust and clinical credibility are primary purchase drivers, AI systems are functioning as de facto shortlist builders. The brands that appear in the top-three recommendations capture a disproportionate share of the commercial value attached to those queries. Brands that are mentioned without ranking are present in the conversation but absent from the moment the shortlist is formed.

The benchmark shows that AI platforms are not ignoring established brands like Bosley and HairClub. They are retrieving them and then declining to advance them as top recommendations. This is a structurally different problem from being invisible in AI search. It suggests that these brands have sufficient recognition in the public evidence layer to be retrieved, but they lack the structured clinical, comparison, and trust signals that AI systems rely on when justifying a ranked recommendation to a consumer.

The direct-to-consumer telehealth brands, Keeps and Hims in particular, benefit from category positioning that maps well to how AI systems categorize and compare treatment options. Brands that present clear product differentiation, pricing transparency, and accessible clinical credentials appear better positioned to receive AI recommendation credit, even when they lack the decades of market history that clinical brands like Bosley carry.

The shift also affects how competitors intercept demand. A consumer asking an AI system for the best prescription hair loss treatment is a high-intent buyer. If a competitor captures that recommendation and the querying brand does not appear in the top three, that brand has lost the buying moment before the consumer ever reached a search result or a brand website.

What the Benchmark Found

Recommendation leaders. Nutrafol is the category recommendation leader across this benchmark, with the highest valid recommendation coverage at 24.9%, the highest top-ten rate at 22.7%, and the highest monthly AI Authority Value at $3.9M. Its recommendation strength is broad and consistent across all six tested platforms. Rogaine is the rank-one leader, with a 14.2% rank-one rate and an average recommended rank of 1.58, the strongest average rank in the dataset. On ChatGPT, Rogaine captures 19.8% of platform-level opportunity with a 25.1% rank-one rate in that environment.

Visible but under-recommended. Bosley is the clearest example of a brand with significant observation presence that does not translate to recommendation power. Its 7.7% observation rate across 1,102 observations produces a top-ten rate of only 0.6% and a monthly AI Authority Value of $99.8K. HairClub follows a similar pattern. Both brands are present in AI responses and are not being chosen. The analysis found no platform where either brand achieves meaningful recommendation coverage.

Value-weighted winners. Nutrafol captures 7.3% of the total $53.2M monthly AI opportunity modeled across the three public clusters. Keeps captures 6.0%, though a notable share of its captured value is classified as visibility assist rather than direct recommendation value. Rogaine captures 4.7%. Hims captures 1.9% and Happy Head captures 0.9%. All remaining brands collectively capture less than 0.5% of the modeled opportunity.

Platform-specific leaders. Nutrafol leads on Copilot, Gemini, Google AI Mode, and Google AI Overviews. Rogaine leads on ChatGPT and Perplexity. Keeps shows its strongest relative performance on Google AI Overviews. No single brand leads uniformly across all six platforms, which means that platform-specific source and citation gaps exist even for the top performers.

Prompt-cluster leaders. In the consideration cluster (Best Telehealth and Online Prescription Services), Keeps leads in total captured value at $1.4M, driven by visibility assist performance. In the evaluation cluster (Telehealth Platform Comparisons), Rogaine leads with a 24.8% top-ten rate and a 24.5% top-three rate. In the decision cluster (Telehealth and Online Prescription Pricing), Nutrafol leads with a 26.9% top-ten rate and $1.6M in captured value. Happy Head's relative strength in the pricing cluster, at an average recommended rank of 1.48, is the most notable under-recognized performance in the dataset.

Brands with weak framing. Bosley's net sentiment score of 0.11 and HairClub's near-zero recommendation coverage indicate that both brands are being listed rather than endorsed. Neutral listing in an AI response does not carry recommendation credit and does not advance the buyer toward a purchase.

Why Visibility Is Not Enough

A brand can appear in AI answers and still fail to win the buyer shortlist. Bosley appears in 85 observations across the 1,102-observation dataset and receives only 9 valid recommendations. AI systems recognize the brand. They are choosing not to recommend it. That distinction is the commercial core of this benchmark.

Raw mention presence measures whether an AI system has retrieved information about a brand. Valid recommendation coverage measures whether the AI system treats that brand as a trustworthy shortlist option. These are separate signals, and they do not move together. A brand that is frequently mentioned but rarely recommended is accumulating observation presence without earning recommendation credit.

Top-three placement matters more to commercial outcomes than simple mention presence, and rank-one placement is the strongest signal of AI trust. Rogaine's average recommended rank of 1.58 means that when it is recommended, it is almost always the first or second option presented. That position captures the buyer's attention before alternatives are considered.

Neutral mentions do not drive commercial outcomes. A brand listed without endorsement in an AI response is present in the conversation but absent from the recommendation. Bosley's net sentiment score of 0.11 reflects a pattern in which AI systems surface the brand by name without committing to it as a choice. The buyer sees the name but receives no positive signal to act on.

Citation frequency is not endorsement. A brand that appears in multiple AI responses but is never ranked, never framed positively, and never placed in a top position is generating observation counts without commercial value. The benchmark separates these signals precisely because collapsing them into a single visibility metric obscures where the actual gaps are.

Modeled benchmark value is not revenue. The AI Authority Value is a directional metric that estimates the relative commercial weight of AI recommendation presence based on positioning, framing, and cluster-level multipliers. It is not a measure of actual sales, pipeline, or booked demand. It is a benchmark signal that identifies where recommendation value is concentrating and where it is being lost.

The Citation Layer

AI systems synthesize their responses from publicly available source material. The brands that perform best in this benchmark appear to have stronger and wider citation architecture across multiple retrievable source types.

Nutrafol's broad recommendation coverage across all six tested platforms suggests that its source footprint is wide, consistent, and retrievable by multiple AI systems. Clinical study references, dermatologist review content, editorial product evaluations, and comparison pages may be shaping how AI systems frame and rank Nutrafol across different platform contexts. This is not confirmed by direct citation data in the supplied dataset, but the pattern of platform-consistent recommendation coverage is consistent with a brand that has built a multi-source public evidence layer.

Rogaine benefits from decades of documented clinical use, a well-established generic name (minoxidil), and extensive editorial and medical reference coverage. The brand's rank-one strength on ChatGPT and Perplexity may reflect that AI systems in those environments are retrieving clinical credibility signals and third-party medical validation. The presence of minoxidil as a generic scientific term in medical literature may further support retrievability for Rogaine in clinical and comparison contexts.

Keeps and Hims appear to benefit from strong direct-to-consumer brand content, telehealth comparison pages, and pricing transparency content that maps well to the evaluation and pricing prompt clusters. Their source footprint is likely oriented toward DTC discovery content rather than clinical depth, which may explain stronger performance in consideration and pricing clusters relative to clinical recommendation contexts.

Bosley and HairClub, despite their market tenure, appear to have weaker public citation architecture for AI retrieval in the recommendation contexts tested. The analysis suggests they are retrievable but not recommendation-worthy in how AI systems synthesize the available evidence. The sources that mention them may be doing so in directory or listing contexts rather than in editorial, clinical, or comparative recommendation contexts. This is a source-type and framing issue as much as a content volume issue.

Source types that appear to shape AI recommendations in this category include official brand sites, editorial product reviews, dermatologist and clinician review content, clinical study references, comparison pages, pricing pages, consumer forum discussions, and telehealth category directories. Brands with retrievable content across a wider range of these source types are more likely to earn recommendation credit. Brands that are present only in brand-owned channels or low-authority directory listings may find their public evidence layer too thin to support ranked recommendations.

What Brands Need to Fix

Weak valid recommendation coverage. Brands with high observation rates but low valid recommendation rates, including Bosley and HairClub, need to understand why AI systems retrieve them without recommending them. The problem is most likely in the structure and type of sources that mention these brands rather than in brand awareness alone.

Low top-three and rank-one presence. Even brands with moderate recommendation coverage, including Hims and Happy Head, rarely appear in top-three or rank-one positions at scale. Brands that are consistently ranked fourth or fifth in a five-option shortlist are not capturing the commercial moment that top placement produces.

Inconsistent prompt-cluster coverage. Several brands perform well in one cluster and are absent or weak in others. Recommendation coverage needs to be consistent across consideration, evaluation, and pricing clusters because buyers move through all three stages. A brand that wins consideration but disappears at the pricing stage is losing the buyer at the highest-intent moment.

Neutral or cautionary framing. A net sentiment score close to zero, as Bosley records at 0.11, indicates that the available source material is not producing positive AI framing. Brands in this position need to examine what types of sources are being cited in relation to them and whether those sources carry endorsement quality or merely listing quality.

Thin source footprint. Brands with weak recommendation coverage may have insufficient retrievable clinical, editorial, and comparative source material. AI systems need to synthesize multiple positive and credible signals before committing to a ranked recommendation. Brand-owned content alone is rarely sufficient to drive recommendation credit.

Weak pricing and comparison content. The pricing cluster carries a $17.0M modeled opportunity, the highest of the three public clusters. Brands that lack transparent, accurate, and well-structured pricing content in search-visible and AI-retrievable formats are forfeiting the highest-intent buying context in the category.

Underdeveloped third-party validation. Clinical credibility, dermatologist endorsement, and third-party review content appear to support the strongest AI recommendations in this category. Brands that rely on brand awareness rather than third-party validation signals may be structurally disadvantaged in AI recommendation contexts.

How CiteWorks Studio Helps

1. Map AI recommendation visibility. Track prompts, platforms, company presence, valid recommendations, top-three and rank-one performance, framing, and citation sources across the hair loss treatment category and within specific prompt clusters relevant to each brand's competitive position.
2. Identify the sources shaping AI answers. Find the editorial, review, forum, clinical, directory, owned, comparison, and search-visible sources that influence brand framing and recommendation decisions across the six tested AI platforms.
3. Build the citation architecture plan. Strengthen the public evidence layer so AI systems have more accurate, consistent, and persuasive source material to synthesize when generating hair loss treatment recommendations.

Commercial Takeaway

The LLM Authority Index benchmark for June 2026 shows that AI-led discovery is reshaping how hair loss treatment buyers form their shortlists. Three brands, Nutrafol, Rogaine, and Keeps, capture the majority of recommendation value across all three high-intent prompt clusters in the public dataset. Brands outside the top three are largely excluded from the AI-generated shortlist, regardless of market tenure or clinical reputation.

The gap between brand presence and recommendation power is the most important commercial signal this benchmark surfaces. Bosley and HairClub are being retrieved by AI systems and are not being recommended. That is a structurally different problem from being invisible. These brands are present at the comparison stage and absent at the recommendation stage. The buyer sees them listed alongside competitors and then receives no AI endorsement to choose them.

The opportunity for brands in this category is to improve recommendation-stage visibility rather than to accumulate observation counts. Brands that invest in the entity, content, source, and citation architecture that AI systems use to evaluate and rank treatment options will capture more of the buying moment. Brands that rely on brand recognition alone will find themselves increasingly visible in AI responses and increasingly absent from the shortlists those responses produce.

See Where Competitors Are Being Recommended Instead

The public benchmark shows the market shape across three high-intent prompt clusters and six AI platforms. A company-specific analysis shows which prompts a brand wins or loses, which AI platforms are under-recognizing it, which source layers are shaping competitor recommendations, and what changes may improve shortlist eligibility. Contact CiteWorks Studio to request an AI Visibility Audit or AI Company Discovery Report that maps your brand's recommendation-stage visibility and identifies the specific gaps competitors are currently exploiting.

Benchmark Source

This analysis is based on the 2026 AI Market Discovery Index for Hair Loss Treatments, published by LLM Authority Index. The benchmark dataset and public industry report for this category were supplied for this analysis. The full benchmark report and supporting methodology are available through LLM Authority Index.