  Advertisers usually enjoy the flexibility to choose criteria like target
audience, geographic area and bid price when planning an campaign for online
display advertising, while they lack forecast information on campaign
performance to optimize delivery strategies in advance, resulting in a waste of
labour and budget for feedback adjustments. In this paper, we aim to forecast
key performance indicators for new campaigns given any certain criteria.
Interpretable and accurate results could enable advertisers to manage and
optimize their campaign criteria. There are several challenges for this very
task. First, platforms usually offer advertisers various criteria when they
plan an advertising campaign, it is difficult to estimate campaign performance
unifiedly because of the great difference among bidding types. Furthermore,
complex strategies applied in bidding system bring great fluctuation on
campaign performance, making estimation accuracy an extremely tough problem. To
address above challenges, we propose a novel Campaign Performance Forecasting
framework, which firstly reproduces campaign performance on historical logs
under various bidding types with a unified replay algorithm, in which essential
auction processes like match and rank are replayed, ensuring the
interpretability on forecast results. Then, we innovatively introduce a
multi-task learning method to calibrate the deviation of estimation brought by
hard-to-reproduce bidding strategies in replay. The method captures mixture
calibration patterns among related forecast indicators to map the estimated
results to the true ones, improving both accuracy and efficiency significantly.
Experiment results on a dataset from Taobao.com demonstrate that the proposed
framework significantly outperforms other baselines by a large margin, and an
online A/B test verifies its effectiveness in the real world.
