14 research outputs found
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
Do not Waste Money on Advertising Spend: Bid Recommendation via Concavity Changes
In computational advertising, a challenging problem is how to recommend the
bid for advertisers to achieve the best return on investment (ROI) given budget
constraint. This paper presents a bid recommendation scenario that discovers
the concavity changes in click prediction curves. The recommended bid is
derived based on the turning point from significant increase (i.e. concave
downward) to slow increase (convex upward). Parametric learning based method is
applied by solving the corresponding constraint optimization problem. Empirical
studies on real-world advertising scenarios clearly demonstrate the performance
gains for business metrics (including revenue increase, click increase and
advertiser ROI increase).Comment: 10 page
Learning multi-touch conversion attribution with dual-attention mechanisms for online advertising
In online advertising, the Internet users may be exposed to a sequence of different ad campaigns, i.e., display ads, search, or referrals from multiple channels, before led up to any final sales conversion and transaction. For both campaigners and publishers, it is fundamentally critical to estimate the contribution from ad campaign touch-points during the customer journey (conversion funnel) and assign the right credit to the right ad exposure accordingly. However, the existing research on the multi-touch attribution problem lacks a principled way of utilizing the users' pre-conversion actions (i.e., clicks), and quite often fails to model the sequential patterns among the touch points from a user's behavior data. To make it worse, the current industry practice is merely employing a set of arbitrary rules as the attribution model, e.g., the popular last-touch model assigns 100% credit to the final touch-point regardless of actual attributions. In this paper, we propose a Dual-attention Recurrent Neural Network (DARNN) for the multi-touch attribution problem. It learns the attribution values through an attention mechanism directly from the conversion estimation objective. To achieve this, we utilize sequence-to-sequence prediction for user clicks, and combine both post-view and post-click attribution patterns together for the final conversion estimation. To quantitatively benchmark attribution models, we also propose a novel yet practical attribution evaluation scheme through the proxy of budget allocation (under the estimated attributions) over ad channels. The experimental results on two real datasets demonstrate the significant performance gains of our attribution model against the state of the art