CIA-Towards a Unified Marketing Optimization Framework for e-Commerce Sponsored Search

TL;DR

This paper introduces CIA, a unified bidding framework for e-commerce search advertising that optimizes advertiser demands, balances platform revenue, and enhances user experience through impression-level bidding and simulation-based performance prediction.

Contribution

The paper presents CIA, a novel unified bidding framework that integrates impression-level bidding and simulation to improve e-commerce sponsored search advertising.

Findings

CIA improves advertising performance in offline simulations.

CIA achieves significant online performance gains on Taobao.

CIA is deployed as a major bidding tool in Taobao Search Advertising.

Abstract

As the largest e-commerce platform, Taobao helps advertisers reach billions of search queries each day via sponsored search, which has also contributed considerable revenue to the platform. An efficient bidding strategy to cater to diverse advertiser demands while balancing platform revenue and consumer experience is significant to a healthy and sustainable marketing ecosystem. In this paper we propose \emph{Customer Intelligent Agent (CIA)}, a bidding optimization framework which implements an impression-level bidding to reflect advertisers' conversion willingness and budget control. In this way, CIA is capable of fulfilling various e-commerce advertiser demands on different levels, such as Gross Merchandise Volume optimization, style comparison etc. Additionally, a replay based simulation system is designed to predict the performance of different take-rate. CIA unifies the benefits of three parties in the marketing ecosystem without changing the Generalized Second Price mechanism. Our extensive offline simulations and large-scale online experiments on \emph{Taobao Search Advertising (TSA)} platform verify the high effectiveness of the CIA framework. Moreover, CIA has been deployed online as a major bidding tool in TSA.