AI Share of Voice Benchmarks for D2C Brands in 2026
We ran over 6,000 AI queries across ChatGPT, Gemini, and Perplexity for 10 D2C categories. Here's what average Share of Voice looks like — and what separates the brands at the top.
The research methodology
We ran 6,200 product queries across ChatGPT (GPT-4o), Gemini 2.5, and Perplexity across 10 D2C categories in Q1 2026. Queries covered three types: specific product requests, category exploration, and comparison queries. Each category received 200+ queries per platform.
The headline finding
Median brand AI Share of Voice across all categories: 0%.
The majority of D2C brands we tracked appeared in zero AI-generated product recommendations across 200+ queries in their category. The correlation between product quality and AI Share of Voice is weak. The correlation between catalog data completeness and AI Share of Voice is strong.
Category benchmarks
Skincare & Beauty Top quartile SOV: 12-18%. Median: 2%. Key differentiator: structured active ingredient data and certification flags. Brands with explicit, concentration-level ingredient data appeared 4x more frequently than brands with generic descriptions.
Supplements Top quartile SOV: 8-14%. Median: 1%. Key differentiator: third-party testing certifications and dosage specificity. NSF Certified and Informed Sport brands appeared in 67% of supplement recommendation responses where a specific concern was mentioned.
Apparel Top quartile SOV: 6-10%. Median: 0%. Key differentiator: fit attribute completeness and material composition. Most apparel brands — including large, well-known ones — showed weak AI visibility due to poor catalog attribute data.
Home Decor Top quartile SOV: 9-15%. Median: 1%. Key differentiator: dimension data and material/style structured attributes. Brands with precise dimension data appeared disproportionately in room-specific queries.
Jewelry Top quartile SOV: 7-12%. Median: 0%. Key differentiator: metal purity and gemstone certification data. GIA/IGI certified brands dominated AI recommendations for fine jewelry queries.
Platform differences
ChatGPT was most selective — recommending fewer brands per query, weighting structured data quality most heavily. ChatGPT SOV is the hardest to achieve and the strongest signal of catalog quality.
Gemini showed strongest correlation with Google Merchant Center data quality. Brands with enriched Merchant Center feeds had meaningfully higher Gemini SOV.
Perplexity was most accessible for smaller brands. Several smaller brands with excellent on-page schema achieved 4-6% Perplexity SOV despite near-zero ChatGPT or Gemini visibility — confirming that web-crawl-based approach benefits well-structured smaller players.
What separates top-quartile brands
Four characteristics consistently appeared across every category:
1. Complete schema.org Product markup — full attribute set for their category, not just title and price.
2. Structured category-specific attribute data — discrete, parseable fields rather than prose descriptions.
3. Third-party certifications as structured attributes — properly marked up, not mentioned in description text.
4. Off-site presence — editorial coverage, comparison articles, and review site mentions that AI agents can cite.
The compounding advantage
The most strategically important finding: AI recommendation visibility compounds. Products that get recommended generate clicks and purchases — positive signals that influence future recommendation weighting.
Brands with 0% SOV today aren't just missing current traffic. They're missing the feedback loop that determines recommendation positioning for years. The brands that move now will be significantly harder to displace in 2027.
Run your brand's AI Share of Voice audit free at vialtry.com/grader — see your exact SOV across ChatGPT, Gemini, and Perplexity.