Hair care is one of the most heavily assistant-mediated consumer categories in India, because the buyer is asking a question rather than shopping. People type their symptom into ChatGPT, Perplexity or an AI Overview and describe a situation in a full sentence, then take the answer as a shortlist. Those answers are unusually cautious in this category, because the models hedge on anything health adjacent, which means they lean hard on credentialed sources, consistent entity data and third party corroboration rather than on brand marketing copy. Our AEO work here is built for that behaviour: entity consistency across the surfaces the models retrieve from, symptom-shaped question and answer depth, review presence with real recency, and placement in the roundups these systems actually cite.
Assistant behaviour in hair care differs from other consumer categories in ways that change the work. First, the models hedge. Ask any major assistant what to use for hair loss and the response typically opens by recommending a dermatologist, separates cosmetic from clinical options, and names minoxidil as the drug-regulated option before it names any brand. Citations are therefore weighted toward medical and expert sources, and a brand appears alongside them only if its own content is credentialed and cautious in the same way. Second, the query is descriptive. Buyers write full situations mentioning postpartum timing, a medication, water quality, a scalp symptom or a hair type, and the answer is assembled from sources addressing that exact combination, so long and honestly hedged pages beat broad category pages. Third, corroboration matters more than assertion. Because these systems are avoiding a health-adjacent mistake, a claim appearing only in brand voice is discounted while one appearing on the site, in reviews and in third party editorial is treated as reliable. That splits the work into entity hygiene, extractable content depth, and off-site corroboration, with the last being the part most brands underfund.
