How can Perplexity help with keyword research?

Perplexity can help discover and analyze candidate keywords. It can combine information from the web, your own keyword performance data, and specific pages to find new terms, analyze search intent, and identify semantically related terms and clues about local searches. An article published by tenten.co in December 2024 describes these methods, but does not establish that the suggested terms necessarily have high search volumes or will improve rankings or increase clicks. The following summarizes the article’s methods; its feature descriptions may no longer apply.

Start with existing data, then look for overlooked candidate keywords

The article divides keyword research into three stages: discovery, analysis, and selection and grouping. It suggests that AI is particularly suited to helping with the first two, more research-intensive stages. Rather than simply asking for “popular keywords,” provide data to guide the analysis:

• Provide your own keyword performance data, such as a channel’s CSV report.
• Ask Perplexity to analyze stronger- and weaker-performing terms, then combine that analysis with information from the web to suggest new candidate keywords.
• Keep the suggestions on a list for evaluation rather than immediately treating them as target keywords worth pursuing.

The article illustrates this workflow with a YouTube channel example but provides no measured results. Calling suggested terms “high potential” does not mean their performance has been verified.

Use search intent and specific pages to find questions your content could answer

Search intent analysis focuses on the question “What answers do searchers expect to find?” Using “weight loss” as an example, the article explores needs such as fat burning and dietary planning for different age groups to suggest directions for content. These are examples from the article, not evidence that all searchers share the same expectations.

Another approach is to provide a list of specific URLs and ask Perplexity to analyze the pages and extract related terms and phrases. For example, “preventing skin aging” could lead to topics involving food, skincare product choices, and daily habits. These suggestions can help identify additional questions for content to address, but a term’s relevance alone does not establish search demand or justify a standalone article.

At Procrustes, we turn the questions that matter to clients into articles, with ongoing content planning, writing, and updates.

Distinguish data from clues in local and trend research

For local keyword research, the article describes first uploading regional keyword data from Google Keyword Planner, then asking Perplexity to suggest local and long-tail terms. This workflow relies on regional data rather than asking AI to determine local demand without evidence. The article does not establish that the two tools connect automatically.

Trend research can draw on discussions across the web and social platforms. The article uses discussions about Gemini 2.0 from that period as an example, suggesting that they may offer clues traditional tools have not yet captured. However, this historical example cannot establish current interest, and social discussion does not amount to confirmed search volume.

Organize the workflow with project spaces, but do not confuse organization with validation

The article also describes how to use Spaces to keep research topics separate, upload documents, and configure models and instructions. It uses search intent reports to illustrate automation. These are feature descriptions from that time; check whether the features are still supported before using them. Perplexity’s role in research is to help gather, expand, and organize information. The article does not establish that using it removes the need to verify search volume, keyword difficulty, or actual results.