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Forecasting cost-per-click of keywords in Google’s competitive paid search advertising market: a time-series clustering approach
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Resumo(s)
Accurately forecasting Cost-per-Click of paid search advertising is essential for performance
marketers to allocate budgets that optimize marketing campaign returns. In this study, we perform
a comprehensive analysis using various time-series forecasting methods to predict daily average
CPC of keywords in the car rental sector. Our results show the power of statistical models on noisy
keyword-level CPC time-series on short to medium horizons, only being outperformed by more
complex neural networks on longer horizons. Advanced forecasting approaches leveraging
competition did not yield significant accuracy improvements. Additional experiments with fine tuned foundational models for time-series showed promising results, optimizing practicality and
accuracy.
Descrição
Palavras-chave
Time-series forecasting Search advertising Deep learning Time-series clustering Graph neural networks Foundational models Digital advertising Cost-per-Click