22 research outputs found
A dynamic pricing model for unifying programmatic guarantee and real-time bidding in display advertising
There are two major ways of selling impressions in display advertising. They
are either sold in spot through auction mechanisms or in advance via guaranteed
contracts. The former has achieved a significant automation via real-time
bidding (RTB); however, the latter is still mainly done over the counter
through direct sales. This paper proposes a mathematical model that allocates
and prices the future impressions between real-time auctions and guaranteed
contracts. Under conventional economic assumptions, our model shows that the
two ways can be seamless combined programmatically and the publisher's revenue
can be maximized via price discrimination and optimal allocation. We consider
advertisers are risk-averse, and they would be willing to purchase guaranteed
impressions if the total costs are less than their private values. We also
consider that an advertiser's purchase behavior can be affected by both the
guaranteed price and the time interval between the purchase time and the
impression delivery date. Our solution suggests an optimal percentage of future
impressions to sell in advance and provides an explicit formula to calculate at
what prices to sell. We find that the optimal guaranteed prices are dynamic and
are non-decreasing over time. We evaluate our method with RTB datasets and find
that the model adopts different strategies in allocation and pricing according
to the level of competition. From the experiments we find that, in a less
competitive market, lower prices of the guaranteed contracts will encourage the
purchase in advance and the revenue gain is mainly contributed by the increased
competition in future RTB. In a highly competitive market, advertisers are more
willing to purchase the guaranteed contracts and thus higher prices are
expected. The revenue gain is largely contributed by the guaranteed selling.Comment: Chen, Bowei and Yuan, Shuai and Wang, Jun (2014) A dynamic pricing
model for unifying programmatic guarantee and real-time bidding in display
advertising. In: The Eighth International Workshop on Data Mining for Online
Advertising, 24 - 27 August 2014, New York Cit
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
Optimal delivery in display advertising
In display advertising, a publisher targets a specific audience by displaying ads on content web pages. Because the publisher has little control over the supply of display opportunities, the actual supply of ads that it can sell is stochastic. We consider the problem of optimal ad delivery, where an advertiser requests a certain number of impressions to be displayed by the publisher over a certain time horizon. Time is divided into periods, and in the beginning of each period the publisher chooses a fraction of the still unrealized supply to allocate towards fulfilling the advertiser's demand. The goal is to be able to fulfill the demand at the end of the horizon with minimal costs incurred from penalties associated with shortage or over-delivery of ads. We describe optimal policies that are both simple in structure and easy to implement for several variations of this problem
Offline Evaluation of Response Prediction in Online Advertising Auctions
Click-through rates and conversion rates are two core ma-chine learning problems in online advertising. The evalua-tion of such systems is often based on traditional supervised learning metrics that ignore how the predictions are used. These predictions are in fact part of bidding systems in on-line advertising auctions. We present here an empirical eval-uation of a metric that is specifically tailored for auctions in online advertising and show that it correlates better than standard metrics with A/B test results