Keyword Grouper User Guide

Keyword Grouper User Guide

Version: 1.0 | Category: Keyword Tools | Launch Keyword Grouper

Managing large keyword lists can quickly become overwhelming. Whether you’ve exported thousands of terms from a keyword research tool or scraped a competitor’s rankings, the raw data is often chaotic and difficult to act upon. Keyword Grouper solves this problem by automatically clustering your keywords into logical, organised groups.

What Does the Keyword Grouper Do?

The Keyword Grouper takes a list of keywords and sorts them into clusters based on shared characteristics. You can group by word count, common terms, or custom patterns that you define. As a result, you’ll transform a messy keyword dump into structured ad groups, content silos, or themed clusters ready for action.

This tool is particularly valuable for PPC specialists building ad groups, content strategists planning topic clusters, and SEO professionals organising keyword research. Instead of manually sorting hundreds or thousands of keywords, you can achieve the same result in seconds.

Grouping Methods Explained

Keyword Grouper offers seven different ways to cluster your keywords. Each method suits different use cases, so understanding when to use each one will help you get the best results.

Group by Word Count

This method separates keywords based on how many words they contain. Head terms (single words) go into one group, while long-tail phrases (five or more words) go into another. The tool creates the following categories:

  • 1 word (head terms) — Broad, competitive keywords like “shoes” or “insurance”
  • 2 words — Slightly more specific terms like “running shoes” or “car insurance”
  • 3 words — Medium-tail phrases like “best running shoes” or “cheap car insurance”
  • 4 words — Longer phrases with clearer intent
  • 5+ words (long-tail) — Highly specific queries like “best running shoes for flat feet women”

Use this method when you want to segment keywords by search intent depth. Generally speaking, longer keywords indicate more specific intent and often convert better, while shorter keywords drive more volume.

Group by First Word

This method clusters keywords that start with the same word. For example, all keywords beginning with “buy” end up in one group, while those starting with “best” form another.

This approach works well for identifying intent patterns. Keywords starting with “how” typically indicate informational intent, whereas those beginning with “buy” or “order” signal transactional intent. Consequently, you can quickly separate your list into different funnel stages.

Group by Last Word

Similar to the first word method, this clusters keywords by their final word. All keywords ending in “price” go together, as do those ending in “review” or “near me”.

This method proves especially useful for identifying product categories or modifiers. For instance, keywords ending in “shoes”, “boots”, and “trainers” can help you build separate product-focused ad groups or content hubs.

Group by Common Root Words

This is the most sophisticated automatic grouping method. The tool analyses your entire keyword list, identifies the most frequently occurring terms, and clusters keywords around those shared roots.

For example, if you have 500 keywords and 47 of them contain the word “running”, those 47 keywords form a group. The tool processes your list from most common to least common terms, so each keyword joins the most relevant group first.

Keywords that don’t share common terms with enough others end up in an “ungrouped” category. You can adjust the minimum group size to control how small a cluster can be before keywords get relegated to the ungrouped pile.

Contains (Custom)

Sometimes automatic methods don’t match your specific needs. The “Contains” method lets you define exactly what terms to look for. Simply enter your target terms (one per line or comma-separated), and the tool groups all keywords containing each term.

For example, if you enter “london, manchester, birmingham”, the tool creates three groups: one for all keywords containing “london”, another for “manchester”, and a third for “birmingham”. Any keywords that don’t contain any of your specified terms end up in a “no match” group.

This method is ideal when you already know how you want to segment your keywords, such as by location, brand name, or product line.

Starts With (Custom)

This custom method groups keywords that begin with your specified text. Unlike the automatic “first word” method, you can match partial words or phrases.

For instance, entering “how to” would group all keywords starting with that exact phrase. This proves useful for isolating question-based queries or specific phrase patterns that span multiple words.

Ends With (Custom)

The counterpart to “Starts With”, this method groups keywords by their ending text. Enter terms like “near me, in london, online” to cluster keywords by these common suffixes.

Local SEO specialists often find this method valuable for separating location-modified keywords from general terms.

How to Use the Keyword Grouper

Getting started with Keyword Grouper takes just a few steps. Here’s how to transform your keyword chaos into organised clusters:

  1. Add your keywords — Paste your keyword list into the input box (one keyword per line) or click “Load” to import a text file
  2. Choose a grouping method — Select one of the seven methods described above
  3. Configure options — Adjust settings like deduplication, case sensitivity, and minimum group size
  4. Select output format — Choose nested list, CSV, or TSV depending on your workflow
  5. Click “Group Keywords” — The tool processes your list and displays the grouped results
  6. Export your groups — Copy to clipboard or save as a file

Options and Settings

Several options help you fine-tune the grouping process:

Remove Duplicate Keywords First

Enable this option to eliminate duplicate keywords before grouping. This is particularly useful when combining exports from multiple tools, as duplicates are common. The deduplication happens before any grouping logic runs.

Show Keyword Counts Per Group

When enabled (the default), each group header displays the number of keywords it contains. This helps you quickly assess the size of each cluster. Turn it off if you want cleaner output for further processing.

Case-Sensitive Matching

By default, matching is case-insensitive, so “London” and “london” are treated the same. Enable this option if you need to distinguish between different capitalisations. This only affects the custom methods (Contains, Starts With, Ends With).

Minimum Group Size

This setting applies only to the “Common Root Words” method. It determines how many keywords must share a term before they form a group. The default is 2, meaning any term appearing in at least two keywords creates a cluster.

Increase this value when working with very large keyword lists. Setting it to 5 or 10 reduces the number of small groups and keeps your output more manageable. Keywords that don’t meet the threshold end up in the “ungrouped” category.

Output Formats

Keyword Grouper supports three output formats to fit different workflows:

Nested List

The default format displays groups as headers (marked with ##) with indented keywords below each one. This is easy to read and works well for quick review or pasting into documents. Example:

## running (12)
  best running shoes
  running trainers uk
  cheap running gear

## walking (8)
  comfortable walking shoes
  best walking boots

CSV (Comma-Separated Values)

Outputs a two-column spreadsheet format with “keyword” and “group” columns. This imports directly into Excel, Google Sheets, or any spreadsheet application. Each keyword appears on its own row with its group assignment.

TSV (Tab-Separated Values)

Similar to CSV but uses tabs instead of commas as delimiters. Some applications handle TSV more reliably, especially when keywords contain commas. Choose this format if you encounter import issues with CSV.

Practical Use Cases

Here are some real-world scenarios where Keyword Grouper saves significant time:

Building PPC Ad Groups

Export your keyword research, paste it into Keyword Grouper, and use the “Common Root Words” method. The resulting clusters often map directly to ad groups with tightly themed keywords. This improves Quality Score and makes ad copy writing much simpler.

Planning Content Silos

Group keywords by first word or common terms to identify natural topic clusters. Each group can become a content hub or pillar page, with individual keywords becoming supporting articles that link back to the hub.

Separating Intent Types

Use “Starts With” with terms like “how, what, why, best, buy, cheap” to segment keywords by search intent. Informational keywords (how, what, why) require different content than transactional ones (buy, cheap).

Local Keyword Organisation

For local SEO campaigns, use “Contains” with your target cities or regions. This quickly separates location-specific keywords from generic terms, helping you prioritise local landing pages.

Product Category Mapping

E-commerce sites can use “Ends With” to group keywords by product type (“shoes”, “boots”, “sandals”). This helps map keywords to existing category pages or identify gaps in your site structure.

Tips for Better Results

A few strategies will help you get more from Keyword Grouper:

  • Clean your list first — Run keywords through Keyword Scrubber before grouping to remove duplicates, empty lines, and formatting issues
  • Start with automatic methods — Try “Common Root Words” first to see natural clusters, then use custom methods for specific needs
  • Adjust minimum group size — For lists over 1,000 keywords, increase minimum group size to 5 or higher to avoid dozens of tiny clusters
  • Combine methods — Process your list once, export the “ungrouped” keywords, then run them through a different method
  • Use CSV for spreadsheets — The CSV format makes it easy to add additional columns, apply filters, or merge with other data in Excel or Google Sheets

Integration with Other Tools

Keyword Grouper works seamlessly with other Phrase Foundry tools:

  • Keyword Scrubber — Clean and deduplicate your keyword list before grouping
  • Keyword Bloom — Expand your seed keywords first, then group the expanded list
  • Keyword Combiner — Generate keyword combinations, then group them by theme

A typical workflow might involve exporting keywords from your research tool, cleaning them with Keyword Scrubber, expanding variations with Keyword Bloom, and finally organising everything with Keyword Grouper.

Need help? Get in touch with questions or feature requests.