Publicação
Drivers and prediction of organic search engine ctr: the impact of differently phrased titles
| Resumo: | 33% of web traffic in the $5.7 trillion e-commerce industry originates from organic search engines. Thus, website providers benefit from understanding the drivers of click-through rates (CTR) on organic searches. However, existing literature focuses on position as the primary CTR influence, disregarding other result page characteristics. To solve this problem, we conduct an elaborate data analysis and determine suitable CTR prediction modeling techniques. We discover novel patterns impacting CTR and find tree-based models to outperform state-of-the-art deep-learning models. Furthermore, we conduct an NLP-based analysis of result titles and show that particular formulation patterns can significantly influence a result’s CTR. |
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| Autores principais: | Fubel, Erik |
| Assunto: | Organic click-through rate Ctr prediction E-commerce Serp features Shap Nlp |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | 33% of web traffic in the $5.7 trillion e-commerce industry originates from organic search engines. Thus, website providers benefit from understanding the drivers of click-through rates (CTR) on organic searches. However, existing literature focuses on position as the primary CTR influence, disregarding other result page characteristics. To solve this problem, we conduct an elaborate data analysis and determine suitable CTR prediction modeling techniques. We discover novel patterns impacting CTR and find tree-based models to outperform state-of-the-art deep-learning models. Furthermore, we conduct an NLP-based analysis of result titles and show that particular formulation patterns can significantly influence a result’s CTR. |
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