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

TL;DR

This longitudinal case study shows that autonomous agents can sustain positive marketing performance over time, reducing the need for continuous human oversight after initial setup.

Contribution

It provides empirical evidence that autonomous agentic systems can maintain marketing gains, highlighting a scalable approach to CRM personalization.

Findings

Active human management yields higher engagement lift.

Autonomous agents sustain positive performance during passive phases.

A symbiotic model combines initial human oversight with autonomous scaling.

Abstract

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.