In dieser Folge besprechen wir den Artikel "Crawling-Fehler verstehen, bewerten, beheben – was die Google Search Console dir wirklich verrät" von Ralf Dodler. Er erklärt, wie man Crawling-Fehler in der Google Search Console (GSC) versteht und behebt, um die SEO einer Website zu verbessern. Es wird erläutert, dass Crawling-Fehler unvermeidlich sind, aber wichtige Signale dafür darstellen, wie Google eine Website verarbeitet. Der Text unterscheidet zwischen verschiedenen Arten von Fehlern und erklärt, wie man die Daten in der GSC interpretiert. Abschließend wird ein systematisches Vorgehen zur Identifizierung und Behebung von Fehlern vorgestellt, wobei betont wird, dass die Fehlerbehebung Teil einer umfassenden SEO-Strategie ist.

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This episode discusses the blog article “Phrases Over Keywords: Semantic Content Optimization” by Ralf Dodler on Dodler Consulting. This article emphasizes the shift from keyword-based to phrase-based search engine optimization for content. It argues that phrases, rather than individual keywords, better capture the thematic core and user intent, resulting in more relevant copy. The author explains how semantic optimization works by recognizing typical language patterns that convey meaning and are analyzed by search engines. Practical advice is given on how to find such relevant phrases and use them specifically in the writing process to make texts more structured, credible and user-friendly. Finally, a checklist is presented for checking the semantic quality of content and building topical authority. The conclusion emphasizes that phrases structure meaning and thus form the basis for effective communication.

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This episode discusses the blog article “N-Grams Explained: Pattern Recognition in Text” by Ralf Dodler on Dodler Consulting. It explains the concept of N-grams as a tool for recognizing patterns in texts. It illustrates how N-grams break down language into word sequences to reveal recurring combinations and linguistic habits. The article shows how N-gram analysis can improve the quality of content by revealing thematic relevance and semantic gaps, and how N-gram optimization leads to more precise and coherent texts, while also explaining typical application errors and the benefits of N-gram tools.

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