Online Evaluation of Audiences for Targeted Advertising via Bandit Experiments
This addresses the challenge for firms running digital advertising campaigns by improving audience targeting efficiency, though it is incremental as it builds on existing bandit methods.
The paper tackles the problem of matching advertising creatives to overlapping target audiences by introducing an adaptive contextual bandit algorithm that partitions audiences into disjoint sub-populations, resulting in more efficient performance compared to naive split-testing or non-adaptive methods, as demonstrated in experiments.
Firms implementing digital advertising campaigns face a complex problem in determining the right match between their advertising creatives and target audiences. Typical solutions to the problem have leveraged non-experimental methods, or used "split-testing" strategies that have not explicitly addressed the complexities induced by targeted audiences that can potentially overlap with one another. This paper presents an adaptive algorithm that addresses the problem via online experimentation. The algorithm is set up as a contextual bandit and addresses the overlap issue by partitioning the target audiences into disjoint, non-overlapping sub-populations. It learns an optimal creative display policy in the disjoint space, while assessing in parallel which creative has the best match in the space of possibly overlapping target audiences. Experiments show that the proposed method is more efficient compared to naive "split-testing" or non-adaptive "A/B/n" testing based methods. We also describe a testing product we built that uses the algorithm. The product is currently deployed on the advertising platform of JD.com, an eCommerce company and a publisher of digital ads in China.