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AI-Mediation in Retail Markets: Revenue Effects and Assortment Optimization

Omar El Housni, Marouane Ibn Brahim · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.12917 · cited by 0 works (OpenAlex) · open-access PDF

Indonesian

Asisten perbelanjaan AI semakin mempengaruhi produk yang dianggap oleh konsumen. Kami mempelajari perencanaan ritel di bawah mediasi AI, di mana pengecer memilih katalog, perantara AI membentuk shortlist yang berorientasi pelanggan, dan pelanggan memilih dari shortlist tersebut sesuai dengan model logit multinomial. Kami mempertimbangkan baik mediasi deterministik, di mana AI mengikuti peringkat spesifik segmen tetap, dan mediasi stochastic berdasarkan model Plackett-Luce. Empiris, kami membangun kumpulan data peringkat produk dari model AI terkemuka, memperkirakan model peringkat Plackett-Luce, dan mengevaluasi kinerja prediktif mereka pada rangkaian yang dipegang. Menggabungkan perkiraan ini dengan mempelajari preferensi pelanggan sintetis, kami menemukan bahwa akuntansi untuk mediasi AI dapat secara substansial mengubah rangkaian optimal dan pendapatan pelanggan. Melalui konfigur

Machine translation (Meta M2M100); the English original follows.

Abstract

AI shopping assistants increasingly influence which products consumers consider. We study retail assortment planning under AI mediation, where a retailer chooses a catalog, an AI intermediary forms a customer-facing shortlist, and the customer chooses from that shortlist according to a multinomial logit model. We consider both deterministic mediation, in which the AI follows a fixed segment-specific ranking, and stochastic mediation based on the Plackett-Luce model. Empirically, we construct a dataset of product rankings from leading AI models, estimate Plackett-Luce ranking models, and evaluate their predictive performance on held-out assortments. Combining these estimates with synthetic customer preferences, we find that accounting for AI mediation can materially change optimal assortments and revenue. Across the tested capacity configurations, ignoring mediation yields mean revenue losses between 2.52% and 11.37% relative to the PL optimum. A much simpler deterministic plug-in, based only on the ordering of fitted AI utilities, performs substantially better, with mean losses of at most 0.53%. Motivated by this performance, we study assortment optimization under deterministic mediation. We characterize its computational complexity and show that, for any fixed number of customer segments, the problem admits a fully polynomial-time approximation scheme.

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Topics: Ranking (information retrieval), Revenue, Multinomial logistic regression, Product (mathematics), Mediation, Econometrics

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