Agentic Personalisation of Cross-Channel Marketing Experiences
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
Consumer applications provide ample opportunities to surface and communicate various forms of content to users. From promotional campaigns for new features or subscriptions, to evergreen nudges for engagement, or personalised recommendations; across e-mails, push notifications, and in-app surfaces. The conventional approach to orchestration for communication relies heavily on labour-intensive manual marketer work, and inhibits effective personalisation of content, timing, frequency, and copy-writing. We formulate this task under a sequential decision-making framework, where we aim to optimise a modular decision-making policy that maximises incremental engagement for any funnel event. Our approach leverages a Difference-in-Differences design for Individual Treatment Effect estimation, and Thompson sampling to balance the explore-exploit trade-off. We present results from a multi-service application, where our methodology has resulted in significant increases to a variety of goal events across several product features, and is currently deployed across 150 million users.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 6 of its 22 references that are in the map. First posted on arXiv 19 Jun 2025.
Builds on
6 of 22 references.
References · 6
- 2017A Tutorial on Thompson Samplingcited by 433
- 2015Deep Reinforcement Learning in Large Discrete Action Spacescited by 161
- 2021Granger Causality: A Review and Recent Advancescited by 63
- 2021Causal Effect Inference for Structured Treatmentscited by 39
- 2024Sliding-Window Thompson Sampling for Non-Stationary Settingscited by 17
- 2024Contrastive representations of high-dimensional, structured treatmentscited by 7
Built on by
Citing papers per year: 2025: 1; 2026: 2.
3 papers.
Similar, not cited
Close in wording, no citation either way
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