3,238 research outputs found
Statistical Arbitrage Mining for Display Advertising
We study and formulate arbitrage in display advertising. Real-Time Bidding
(RTB) mimics stock spot exchanges and utilises computers to algorithmically buy
display ads per impression via a real-time auction. Despite the new automation,
the ad markets are still informationally inefficient due to the heavily
fragmented marketplaces. Two display impressions with similar or identical
effectiveness (e.g., measured by conversion or click-through rates for a
targeted audience) may sell for quite different prices at different market
segments or pricing schemes. In this paper, we propose a novel data mining
paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and
exploiting price discrepancies between two pricing schemes. In essence, our
SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per
action)-based campaigns and CPM (cost per mille impressions)-based ad
inventories; it statistically assesses the potential profit and cost for an
incoming CPM bid request against a portfolio of CPA campaigns based on the
estimated conversion rate, bid landscape and other statistics learned from
historical data. In SAM, (i) functional optimisation is utilised to seek for
optimal bidding to maximise the expected arbitrage net profit, and (ii) a
portfolio-based risk management solution is leveraged to reallocate bid volume
and budget across the set of campaigns to make a risk and return trade-off. We
propose to jointly optimise both components in an EM fashion with high
efficiency to help the meta-bidder successfully catch the transient statistical
arbitrage opportunities in RTB. Both the offline experiments on a real-world
large-scale dataset and online A/B tests on a commercial platform demonstrate
the effectiveness of our proposed solution in exploiting arbitrage in various
model settings and market environments.Comment: In the proceedings of the 21st ACM SIGKDD international conference on
Knowledge discovery and data mining (KDD 2015
Lift-Based Bidding in Ad Selection
Real-time bidding (RTB) has become one of the largest online advertising
markets in the world. Today the bid price per ad impression is typically
decided by the expected value of how it can lead to a desired action event
(e.g., registering an account or placing a purchase order) to the advertiser.
However, this industry standard approach to decide the bid price does not
consider the actual effect of the ad shown to the user, which should be
measured based on the performance lift among users who have been or have not
been exposed to a certain treatment of ads. In this paper, we propose a new
bidding strategy and prove that if the bid price is decided based on the
performance lift rather than absolute performance value, advertisers can
actually gain more action events. We describe the modeling methodology to
predict the performance lift and demonstrate the actual performance gain
through blind A/B test with real ad campaigns in an industry-leading
Demand-Side Platform (DSP). We also discuss the relationship between
attribution models and bidding strategies. We prove that, to move the DSPs to
bid based on performance lift, they should be rewarded according to the
relative performance lift they contribute.Comment: AAAI 201
Inefficiencies in Digital Advertising Markets
Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research
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