e-commerce marketing teams

Abstract: Generative AI interfaces can insert sponsored content at multiple points in an answer, where click-through rates (CTRs) depend on the advertiser-position pair. Under the cascade model, users read ads sequentially and stop after their first click, so both ad assignment and display order affect welfare. For arbitrary pair-specific CTRs, Balseiro et al. (WWW 2026) gave a $4/(1-\varepsilon)$-approximation whose monotonicity has not been established, and a monotone $O(\log m)$-approximation for $m$ candidate positions. Earlier truthful constant-factor mechanisms require factorized CTR models and do not apply. Whether monotonicity is compatible with a constant-factor approximation in this general setting was left open by that work. We give a polynomial-time mechanism for arbitrary pair-specific CTRs and any limit on displayed ads that is truthful and individually rational in expectation and achieves a 2-approximation to optimal cascade welfare. Our mechanism selects an advertiser-position matching and a designated last ad, then randomly thins earlier ads so that each selected ad's expected cascade CTR is the same fraction of its standalone CTR. This yields an exact closed-form expression for expected welfare, which we maximize over a bid-independent lottery range using linear programming. Exact optimization ensures monotonicity, while a random-prefix decomposition proves the factor-two guarantee, which is tight for this range. We also show that exactly optimizing the previous welfare surrogate can violate monotonicity, and evaluate welfare and running time on synthetic instances.

Abstract: Personalized marketing can increase customer engagement, satisfaction, and conversion. While existing personalization approaches have become effective at matching the right product to the right customer, the visual representation of advertisements remains generic and only weakly tailored to the individual. Prior research shows that generative artificial intelligence can improve the creation of personalized advertisements, particularly for text, and that image generation models can support scalable advertisement production. However, little research has examined how detailed customer information can be systematically translated into fully AI-generated, personalized advertising imagery at scale on a technical level. To address this gap, we propose AdMan, a multi-agent pipeline that transforms customer data into personas, generates personalized advertisement images conditioned on product reference images, and applies an LLM-based judge agent for automated quality control. We implement the pipeline with two different model configurations and evaluate it across four products, using six celebrity personas for qualitative inspection, and 100 real customer profiles, producing 1745 advertisements. The evaluation combines a qualitative expert focus group and a quantitative artifact-rate assessment. The results show that the pipeline can generate photorealistic and personalized advertisements. At the same time, performance varies substantially by product complexity and model configuration. Our findings extend the literature on AI-based personalized advertising by demonstrating the feasibility and current limitations of fully automated image generation for advertising.

Abstract: AI shopping assistants increasingly redirect consumer discovery, creating an urgent need for tools that support seller-side competitive decision-making. We present a multi-agent AI system that automates competitive visibility measurement and root cause diagnosis in LLM-mediated ecommerce. The system introduces Agentic Share-of-Search (ASoS) as the decision target, deploys query agents across leading AI platforms, and uses a ReAct-based diagnostic agent to recommend prioritized merchandising interventions. A 100-trial ablation study, presented as a feasibility evaluation of this prototype, shows the agent recovers the ablated signal in 39% of trials (95% CI: 30.0% - 48.8%, 5.5x over chance), rising to 63.9% among high-correlation ablations.