The advertising industry has recently witnessed proliferation in native ads, which are inserted into a web stream (e.g., a list of news articles or social media posts) and look like the surrounding nonsponsored contents. This study is among the first to examine native ads and unveil how their effectiveness changes across serial positions by analyzing a large-scale data set with 120 ads. For each ad, the authors use separate “natural experiment” studies to compare the ad’s performance as its serial position varies. Subsequently, they conduct a meta-analysis to generalize the results across all studies. The results reveal vastly asymmetric effects of native ad serial position on publishers’ metrics (click-based) versus advertisers’ metrics (conversion-based). As serial position lowers (i.e., from rank 1 to a lower rank), there are only modest changes in publishers’ metrics, but drastic reductions in advertisers’. This pattern is unique to native ads and has not been indicated by prior research on ad serial position. Moreover, the authors show the moderating effects of audience gender and age. The findings provide new and timely implications for researchers and marketers.

As the online advertising industry has evolved into an age of diverse ad formats and delivery channels, users are exposed to complex ad treatments involving various ad characteristics. The diversity and generality of ad treatments call for accurate and causal measurement of ad effectiveness, i.e., how the ad treatment causes the changes in outcomes without the confounding effect by user characteristics. Various causal inference approaches have been proposed to measure the causal effect of ad treatments. However, most existing causal inference methods focus on univariate and binary treatment and are not well suited for complex ad treatments. Moreover, to be practical in the data-rich online environment, the measurement needs to be highly general and efficient, which is not addressed in conventional causal inference approaches.In this paper we propose a novel causal inference framework for assessing the impact of general advertising treatments. Our new framework enables analysis on uni-or multi-dimensional ad treatments, where each dimension (ad treatment factor) could be discrete or continuous. We prove that our approach is able to provide an unbiased estimation of the ad effectiveness by controlling the confounding effect of user characteristics. The framework is computationally efficient by employing a tree structure that specifies the relationship between user characteristics and the corresponding ad treatment. This tree-based framework is robust to model misspecification and highly flexible with minimal manual tuning. To demonstrate the efficacy of our approach, we apply it to two advertising campaigns. In the first campaign we evaluate the impact of different ad frequencies, and in the second one we consider the synthetic ad effectiveness across TV and online platforms. Our framework successfully provides the causal impact of ads with different frequencies in both campaigns. Moreover, it shows that the ad frequency usually has a treatment effect cap, which is usually over-estimated by naive estimation.

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In online advertising market it is crucial to provide advertisers with a reliable measurement of advertising effectiveness to make better marketing campaign planning. The basic idea for ad effectiveness measurement is to compare the performance (e.g., success rate) among users who were and who were not exposed to a certain treatment of ads. When a randomized experiment is not available, a naive comparison can be biased because exposed and unexposed populations typically have different features. One solid methodology for a fair comparison is to apply inverse propensity weighting with doubly robust estimation to the observational data. However the existing methods were not designed for the online advertising campaign, which usually suffers from huge volume of users, high dimensionality, high sparsity and imbalance. We propose an efficient framework to address these challenges in a real campaign circumstance. We utilize gradient boosting stumps for feature selection and gradient boosting trees for model fitting, and propose a subsampling-and-backscaling procedure that enables analysis on extremely sparse conversion data. The choice of features, models and feature selection scheme are validated with irrelevant conversion test. We further propose a parallel computing strategy, combined with the subsampling-and-backscaling procedure to reach computational efficiency. Our framework is applied to an online campaign involving millions of unique users, which shows substantially better model fitting and efficiency. Our framework can be further generalized to comparison of multiple treatments and more general treatment regimes, as sketched in the paper. Our framework is not limited to online advertising, but also applicable to other circumstances (e.g., social science) where a 'fair' comparison is needed with observational data.

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