  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.
