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

• URL: http://arxiv.org/abs/2302.05762v1
• Date: Sat, 11 Feb 2023 19:26:17 GMT
• Title: Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape
• Authors: Fynn Oldenburg, Qiwei Han, Maximilian Kaiser
• Abstract summary: We perform a comprehensive study using a variety of time-series forecasting methods to predict daily average cost-per-click (CPC) in the online advertising market.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As advertisers increasingly shift their budgets toward digital advertising, forecasting advertising costs is essential for making budget plans to optimize marketing campaign returns. In this paper, we perform a comprehensive study using a variety of time-series forecasting methods to predict daily average cost-per-click (CPC) in the online advertising market. We show that forecasting advertising costs would benefit from multivariate models using covariates from competitors' CPC development identified through time-series clustering. We further interpret the results by analyzing feature importance and temporal attention. Finally, we show that our approach has several advantages over models that individual advertisers might build based solely on their collected data.

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