Customer Data Stories

About the Project

This project will investigate how the availability of collective and personalized data summaries impacts end-users and customers, and what behavioural changes are enacted as a result. Improving customer decision making is an under-studied area. In collaboration with global insurance company, Allianz Partners Australia, this research will explore the use of data stories as an approach to improve customer engagement and decision making. A crowd-sourced approach will be utilised, and further in-the-wild studies will be conducted to validate and improve the method with the help of Allianz customer networks that span international health and life, travel, and automotive insurance.


About the Candidate

The ARC Training Centre for Information Resilience (CIRES) invites highly motivated and committed candidates to apply for a fully funded PhD position focused on how to improve data curation through a crowd-sourced approach. In line with CIRES’s industry engagement objectives, the position is defined and co-funded in close collaboration with Allianz Partners Australia, the world’s largest diversified insurance company. This scholarship is one of two CIRES projects with Allianz related to organisational and transformational aspects of data, algorithms, and AI.

The successful candidate is expected to have a good background in data science, data analytics, or machine learning and will develop novel data-driven marketing methods making research contributions over the entire Artificial Intelligence pipeline: from data collection and curation to model training and deployment with end users including evaluation.

This project seeks to develop a strong and capable future leader who can undertake data analysis in a data-sparse environment focussing on personalised marketing, with the proposed model and research tasks able to be adapted and applied to other human-in-the-loop tasks.

For this position, CIRES is seeking a candidate with an understanding of concepts from applied statistics/probability, machine learning, algorithms and complexity, human-computer interaction, and marketing. This project also requires proficiency in the Python programming language, and machine learning software packages such as Pytorch or Tensorflow.



project researchers
A/Prof Gianluca Demartini (Principal Advisor)
Dr Wen Hua
Prof Shazia Sadiq
partner investigator