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arXiv · 2302.05762

Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape

Fynn Oldenburg·Qiwei Han·Maximilian Kaiser
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Abstract

As advertisers increasingly shift their budgets toward digital advertising, accurately forecasting advertising costs becomes essential for optimizing marketing campaign returns. This paper presents a comprehensive study that employs various time-series forecasting methods to predict daily average CPC in the online advertising market. We evaluate the performance of statistical models, machine learning techniques, and deep learning approaches, including the Temporal Fusion Transformer (TFT). Our findings reveal that incorporating multivariate models, enriched with covariates derived from competitors' CPC patterns through time-series clustering, significantly improves forecasting accuracy. We interpret the results by analyzing feature importance and temporal attention, demonstrating how the models leverage both the advertiser's data and insights from the competitive landscape. Additionally, our method proves robust during major market shifts, such as the COVID-19 pandemic, consistently outperforming models that rely solely on individual advertisers' data. This study introduces a scalable technique for selecting relevant covariates from a broad pool of advertisers, offering more accurate long-term forecasts and strategic insights into budget allocation and competitive dynamics in digital advertising.

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Fynn Oldenburg, Qiwei Han, Maximilian Kaiser. 2023-02-11. Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape. https://arxiv.org/abs/2302.05762

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