Study: perfume, United Arab Emirates: Which perfume brands do AI assistants recommend in the UAE?

The questions came from how people in the UAE actually ask. We collected 8,102 shopper posts, comments and search suggestions, and wrote 100 questions from the patterns in them: 50 in English and 50 in Gulf Arabic. None of the questions reproduces a collected post. 76 of them name no brand, and the results below come from those 76.

Each question was sent to each assistant five times, every time in a fresh conversation, with live web search on and no custom instructions. All runs were collected on 13 August 2026.

The measure is Shelf Share: the share of questions on which a brand was named in at least three of the five runs, weighted by how early in the answer it appeared. It is a share of questions, and not a count of appearances. It shows how present a brand is in the answers, and does not say whether the assistant endorsed it.

Across the 76 brand-free questions, 209 brands, stores and shopping places were ranked. No brand held a fifth of the shelf.

The picture changes once the question carries a budget.

Lattafa ranked first on the 12 low-budget questions with 67.2%, and eleventh, with 6.3%, on the 54 price-unconstrained questions. One overall score would have hidden both.

The assistants also differed. On the same 76 questions, each one named a different brand most.

• One product category and one market: perfume and fragrance in the United Arab Emirates.
• One collection day, 13 August 2026. Assistants change as their models and the web they search change, and a later run of the same questions is not expected to reproduce these figures.
• The results come from the assistants' APIs, and not from transcripts of the consumer apps. The two were compared only in a small check of 12 questions per assistant, 36 answers in all.
• The 76 brand-free questions are weighted equally, because nothing observed says how often each kind of question is asked.
• 23.2% of the citations returned resolved only to a Google redirect address and could not be attributed to a publisher.
• Five defects in how names were extracted and matched were found in this run. Four of them split one brand across several rows, which lowers its score, so a share shown for an affected brand is a floor.
• A fourth assistant, Claude, was piloted on 134 answers and left out of the scoring: on cost, on having no signed-out app to check against, and because nine of its ten most-named brands were already among the others' most-named.
• The study records which brands were named. It does not show why an assistant named them.