Sustained Impact of Agentic Personalisation in Marketing: A Longitudinal Case Study
This addresses the need for scalable CRM strategies in consumer applications, though it is incremental as it builds on existing adaptive learning systems.
This paper tackled the problem of sustaining performance in marketing personalisation by comparing human-managed and autonomous agent phases over 11 months, finding that human management achieved the highest engagement lift while autonomous agents maintained a positive lift.
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.