# Sustained Impact of Agentic Personalisation in Marketing: A Longitudinal Case Study

arXiv: https://arxiv.org/abs/2604.08621 · HTML: https://mlsanity.com/paper/2604.08621/

## Key facts

- No citing papers in the map yet (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 8 of its 25 references that are in the map.
- First posted on arXiv 9 Apr 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 8 of 8)

- [Machine Learning for Variance Reduction in Online Experiments](https://mlsanity.com/paper/2106.07263/) (2021)
- [Procedural Memory Is Not All You Need: Bridging Cognitive Gaps in LLM-Based Agents](https://mlsanity.com/paper/2505.03434/) (2025)
- [Offline Reinforcement Learning for Mobile Notifications](https://mlsanity.com/paper/2202.03867/) (2022)
- [Should I send this notification? Optimizing push notifications decision making by modeling the future](https://mlsanity.com/paper/2202.08812/) (2022)
- [Multi-objective Optimization of Notifications Using Offline Reinforcement Learning](https://mlsanity.com/paper/2207.03029/) (2022)
- [Agentic Personalisation of Cross-Channel Marketing Experiences](https://mlsanity.com/paper/2506.16429/) (2025)
- [PushGen: Push Notifications Generation with LLM](https://mlsanity.com/paper/2512.14490/) (2025)
- [Fair Notification Optimization: An Auction Approach](https://mlsanity.com/paper/2302.04835/) (2023)

## Abstract (arXiv)

In consumer applications, Customer Relationship Management (CRM) has traditionally relied on the manual optimisation of static, rule-based messaging strategies. While adaptive and autonomous learning systems offer the promise of scalable personalisation, it remains unclear to what extent ``human-in-the-loop'' oversight is required to sustain performance uplift over time. This paper presents a longitudinal case study analysing a real-world consumer application that leverages agentic infrastructure to personalise marketing messaging for a large-scale user base over an 11-month period. We compare two distinct periods: an active phase where marketers directly curated content, audiences, and strategies -- followed immediately by a passive phase where agents operated autonomously from a fixed library of components. Our results demonstrate that whilst active human management generates the highest relative lift in engagement metrics, the autonomous agents successfully sustained a positive lift during the passive period. These findings suggest a symbiotic model where human intervention drives strategic initialisation and discovery, yet autonomous agents can ensure the scalable retention and preservation of performance gains.