  Online retailers often use third-party demand-side-platforms (DSPs) to
conduct offsite advertising and reach shoppers across the Internet on behalf of
their advertisers. The process involves the retailer participating in instant
auctions with real-time bidding for each ad slot of their interest. In this
paper, we introduce a bid optimization system that leverages the dimensional
bidding function provided by most well-known DSPs for Walmart offsite display
ad campaigns. The system starts by automatically searching for the optimal
segmentation of the ad requests space based on their characteristics such as
geo location, time, ad format, serving website, device type, etc. Then, it
assesses the quality of impressions observed from each dimension based on
revenue signals driven by the campaign effect. During the campaign, the system
iteratively approximates the bid landscape based on the data observed and
calculates the bid adjustments for each dimension. Finally, a higher bid
adjustment factor is applied to dimensions with potentially higher revenue over
ad spend (ROAS), and vice versa. The initial A/B test results of the proposed
optimization system has shown its effectiveness of increasing the ROAS and
conversion rate while reducing the effective cost per mille for ad serving.
