利用大规模动态因果模型评估参与面的价值

Abhimanyu Mukerji, Sushant More, Ashwin Viswanathan Kannan, Lakshmi Ravi, Hua Chen, Naman Kohli, Chris Khawand, Dinesh Mandalapu
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引用次数: 0

摘要

随着最近网上购物的快速增长,人工智能驱动的 "参与界面"(ES)在零售服务中变得无处不在。这些参与界面可以实现越来越多的功能,包括推荐购买新产品、提醒客户订单以及提供送货通知。了解参与面对客户和企业价值驱动的因果效应仍然是一个未决的科学问题。在本文中,我们开发了一个规模动态因果模型,以厘清 ES 的价值归属并评估其有效性。我们展示了该模型的应用,通过了解 ES 的投资回报,确定 ES 能带来最大价值的产品线和功能,为企业决策提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Valuing an Engagement Surface using a Large Scale Dynamic Causal Model
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.
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