Online advertising revenue forecasting: an interpretable deep learning approach

• Autor(a) principal: Würfel, Max
• Data de Publicação: 2021
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10362/122676
• topic: Deep learning; Time series forecasting; Interpretability; Online marketing; Domínio/Área Científica::Ciências Sociais::Economia e Gestão

This paper investigates whether publishers’ Google AdSense online advertising revenues can be predicted from peekd’s proprietary database using deep learning methodologies. Peekd is a Berlin (Germany) based data science company, which primarily provides e Retailers with sales and shopper intelligence. I find that using a single deep learning model, AdSense revenues can be predicted across publishers. Additionally, using unsupervised clustering, publishers were grouped and related time series were fed as covariates when making predictions. No performance improvement was found in relation with this technique. Finally, I find that in the short-term, publishers’ AdSense revenues embed similar temporal patterns as web traffic.