The seeker mainly wants to know which whiskey she or Georgia Phone Number  he can try as a novice whiskey drinker. What explicit and underlying latent questions and needs does the searcher have? How do I know if I like whiskey? What is good whiskey? I don’t understand whiskey jargon, so I want to understand what I’m reading I don’t want to spend too much Georgia Phone Number money Where do I start? Which content formats are best suited to answer the questions? Text, images and products Below are the top 18 current article rankings without sitelink rankings, 4 years later. Google SERP search result for the query “whiskey for beginners”. Keywords that rank in the top 4 for the “whisky for beginners” page.

What stands out about this list

And the content of the article? Initially the main keyword. ‘Whisky for Georgia Phone Number  beginners’ is called 0x. Is this bad? New. At the time, I sometimes forgot to put keywords in the text. Instead, I dealt with the above questions. There Georgia Phone Number  are also several themes or topics in the article: Whiskey for beginners / best whiskey for beginners Learn to drink whiskey Whiskey flavours soft whiskey sweet whiskey nice whiskey Combination of soft / tasty / sweet whiskey What makes the article rank in the top 3 on all these terms? Largely because the user ‘s semantic relationship and search intent are about the same across all terms. In addition, the overarching theme is whiskey for beginners.

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佐治亚州电话号码列表

A beginner will look

For specific things that an experienced whiskey drinker Georgia Phone Number simply won’t look for. A novice whiskey drinker looks for sweet or soft whisky, because those are the entry-level whiskeys. The predominantly easy-to-drink whiskeys. Learning to drink whiskey is something you only do as a beginner. And the underlying information needs are that Georgia Phone Number  you want to know how to get started and which whiskeys. Whiskey flavours” and “nice whiskey” are typical searches for someone who knows little or nothing about whiskey. Google Search was already able to determine the semantic relationship between these searches via the self-learning algorithm in 2017 with the help of RankBrain, among others.

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