arXiv:2510. 12049v4 Announce Type: replace-cross Abstract: We quantify the short-term impact of Generative Artificial Intelligence (GenAI) on sales performance through a series of large-scale randomized field experiments involving millions of users and products at a leading cross-border online retail platform.
arXiv:2603. 29888v2 Announce Type: replace-cross Abstract: In collaboration with Alibaba, we study how a generative AI assistant affects service performance in e-commerce after-sales operations.
arXiv:2608. 08395v1 Announce Type: cross Abstract: Generative AI is shifting digital commerce from browsing toward agentic search, in which consumers delegate product discovery to AI agents.
E-Commerce Bench is an open‑source benchmark that simulates a year‑long e‑commerce operation, requiring LLM agents to manage multiple online stores, negotiate with suppliers, optimize sales, fulfill orders, handle returns, and manage cash flow. The environment uses real product and supplier data, a calendar of promotions and shocks, and deterministic customer and negotiation models to enable reproducible evaluation. The study evaluates 18 state‑of‑the‑art models across seven metrics, finding no single model dominates, with GPT‑5.6 Sol achieving the highest year‑end assets but lagging in fraud avoidance and operational efficiency.
arXiv:2604. 04468v2 Announce Type: replace Abstract: Evaluating retail strategies before deployment is difficult, as outcomes are determined across multiple stages, from seller-side persuasion through buyer-seller interaction to purchase decisions.
The paper introduces a production-ready framework that connects e‑commerce search and CRM systems via AI‑powered Product Research Agents. These agents detect users with exploratory purchase intent, perform multi‑agent research using behavioral data, external knowledge, and catalog information, and then send personalized product recommendations through WhatsApp. In a 23‑day deployment, the system sent about 15,000 notifications, achieving higher click‑through rates than standard campaigns and generating downstream purchases and GMV gains.
arXiv:2608.29843v1 Announce Type: cross Abstract: Posted prices for AI inference have fallen steadily since 2024, yet the measured speed of that fall depends almost entirely on the method of measurem...
arXiv:2606. 17931v1 Announce Type: new Abstract: In recent years, electronic (E) commerce services have rapidly increased in the daily lives of people, which helpsthem to purchase products online.
arXiv:2607. 20349v1 Announce Type: cross Abstract: Generative AI can produce book-length works of fiction at near-zero cost.
arXiv:2607. 12056v1 Announce Type: new Abstract: Online shopping is increasingly shifting toward a model in which AI agents independently search for products, compare options, evaluate constraints, and carry out parts of the purchasing process for users.
arXiv:2608. 08621v1 Announce Type: new Abstract: Running a business is a challenging form of intelligent work.
The study investigates how Large Language Models (LLMs) acting as surrogate consumers are influenced by marketing pricing cues such as just‑below pricing and promotional framing. Using a tool called "Tool‑Lab" to trace information acquisition, the researchers found that when no cost is imposed, pricing cues rarely mislead LLMs, but when acquisition costs are introduced under a vague goal prompt, LLMs tend to omit important diagnostic attributes and make suboptimal choices similar to human heuristics. The findings suggest that marketing heuristics in AI‑driven shopping are shaped more by storefront information architecture than by inherent LLM limitations.
The paper introduces DCEO, a data‑driven framework that learns item‑level proxy scores directly aligned with long‑term user objectives in e‑commerce search. It aggregates these scores into a user‑level metric, measures alignment via relative causal effect, and uses an actor‑critic model to generate context‑dependent fusion weights for multiple objectives. Offline experiments and a 41‑day online A/B test show DCEO improves GMV by 0.36% over traditional proxies.