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Forecasting online advertising costs: interpretable deep learning of the competitive bidding landscape
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Resumo(s)
As advertisers increasingly shift their budgets toward digital advertising, forecasting ad vertising costs is essential for making budget plans to optimize marketing campaign re turns. In this paper, we perform a comprehensive study using a variety of time-series
forecasting methods to predict daily average cost-per-click in the online advertising mar ket. We show that forecasting advertising costs would benefit from multivariate models
using covariates from competitors’ cost-per-click development identified through time series clustering. We further interpret the results by analyzing feature importance and
temporal attention. Finally, we show that our approach holds several advantages over,
first, models that individual advertisers might build based on their own data, and second,
existing tools from Google.
Descrição
Palavras-chave
Digital marketing Online advertisement Time series forecasting Time series clustering Deep learning Temporal fusion transformer