  Online media provides opportunities for marketers through which they can
deliver effective brand messages to a wide range of audiences. Advertising
technology platforms enable advertisers to reach their target audience by
delivering ad impressions to online users in real time. In order to identify
the best marketing message for a user and to purchase impressions at the right
price, we rely heavily on bid prediction and optimization models. Even though
the bid prediction models are well studied in the literature, the equally
important subject of model evaluation is usually overlooked. Effective and
reliable evaluation of an online bidding model is crucial for making faster
model improvements as well as for utilizing the marketing budgets more
efficiently. In this paper, we present an experimentation framework for bid
prediction models where our focus is on the practical aspects of model
evaluation. Specifically, we outline the unique challenges we encounter in our
platform due to a variety of factors such as heterogeneous goal definitions,
varying budget requirements across different campaigns, high seasonality and
the auction-based environment for inventory purchasing. Then, we introduce
return on investment (ROI) as a unified model performance (i.e., success)
metric and explain its merits over more traditional metrics such as
click-through rate (CTR) or conversion rate (CVR). Most importantly, we discuss
commonly used evaluation and metric summarization approaches in detail and
propose a more accurate method for online evaluation of new experimental models
against the baseline. Our meta-analysis-based approach addresses various
shortcomings of other methods and yields statistically robust conclusions that
allow us to conclude experiments more quickly in a reliable manner. We
demonstrate the effectiveness of our evaluation strategy on real campaign data
through some experiments.
