Valuing an Engagement Surface using a Large Scale Dynamic Causal Model
Abstract
With recent rapid growth in online shopping, AI-powered Engagement Surfaces (ES) have become ubiquitous across retail services. These engagement surfaces perform an increasing range of functions, including recommending new products for purchase, reminding customers of their orders and providing delivery notifications. Understanding the causal effect of engagement surfaces on value driven for customers and businesses remains an open scientific question. In this paper, we develop a dynamic causal model at scale to disentangle value attributable to an ES, and to assess its effectiveness. We demonstrate the application of this model to inform business decision-making by understanding returns on investment in the ES, and identifying product lines and features where the ES adds the most value.
Cite
@article{arxiv.2408.11967,
title = {Valuing an Engagement Surface using a Large Scale Dynamic Causal Model},
author = {Abhimanyu Mukerji and Sushant More and Ashwin Viswanathan Kannan and Lakshmi Ravi and Hua Chen and Naman Kohli and Chris Khawand and Dinesh Mandalapu},
journal= {arXiv preprint arXiv:2408.11967},
year = {2024}
}
Comments
10 pages, 5 figures. Accepted at Applied Data Science track of KDD 2024, Barcelona, Spain