Text analysis

Let’s take a look at the first section of the report. It presents a list of the analyzed keywords, Proximity level, and Relevance.
Proximity level — a score on a scale from 0 to 100% that displays how close a keyword is in terms of semantic similarity to the cluster topic.
Relevance — a relative score that shows how well your page is optimized for the keyword comparing with top pages from the SERP. Hover over the color strip to view a minimum, average and maximum relevance score for the keyword among your competitors and your relevance to the keyword as well.

Below are recommended words for Title, H1 and Body sections of your page.

Recommendation words — words suggested for your Title if you see a Not included mark under the Status column. We display words’ lemmas in recommended words, but you can use these words in any form tailored to your text intent.
LSI Rank — word’s importance for Title on a scale from 0 to 100%. It’s calculated as a ratio of the recommended word occurrences to occurrences of other words from the ‘’bag of words” we collected from Titles of competitor URLs.
Chance — the percentage of competitors from the analyzed group that has the word in Title.
Status — the column may have three values. Included - the word is present in Title on the landing page. Not included - the page doesn’t have the word in its Title. Overspam - the word is used too many times in Title comparing to other pages from the analyzed group; it’s worth removing some of the excessive word occurrences. If you didn’t  specify the target URL, we’ll show the usage of the word’s lemma in Title on competitors’ pages in the percentage ratio to the overall number of unique words from their Titles - this feature will be useful for content marketers and copywriters.
Recommendations for H1 and Body work a similar way.
The last part of the report presents the text from the Body area of the landing page. Upon analyzing the Body text on a group of competitor URLs, we provide insights on the minimum, average and maximum number of words in Body area among your competitors and the number of words on your page.

Spending credits

How the credits for Text Analysis are spent:
1 keyword = 5 credits.
Number of keywords * 5 = number of spent credits.

For example: Running text analysis for the cluster of 2 keywords, you will spend 10 credits:
2 * 5 = 10 credits.

Please note: Credits for Clustering, Text analysis, Domains batch analysis and Keywords batch analysis are general.