Agentic Personalisation of Cross-Channel Marketing Experiences

TL;DR

This paper introduces a novel, automated approach for personalising cross-channel marketing that significantly improves user engagement by using a sequential decision-making framework and advanced causal inference techniques.

Contribution

It proposes a modular decision-making policy framework that automates content personalisation, leveraging Difference-in-Differences and Thompson sampling for effective optimisation.

Findings

Significant increases in user engagement across multiple product features.

Successful deployment across 150 million users.

Enhanced automation reduces manual effort in marketing orchestration.

Abstract

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.