Valuing an Engagement Surface using a Large Scale Dynamic Causal Model

TL;DR

This paper introduces a large-scale dynamic causal model to quantify the value and effectiveness of AI-powered Engagement Surfaces in online retail, aiding business decision-making and investment assessment.

Contribution

It develops a novel causal modeling approach to measure the impact of engagement surfaces on customer and business value at scale.

Findings

Quantifies the value added by engagement surfaces.

Identifies product lines with highest ES impact.

Provides insights for optimizing ES investments.

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.