Every SEO tool exports keywords differently. Ahrefs sends them with surrounding whitespace. Google Search Console mixes upper and lowercase. SEMrush sometimes doubles up on entries when you combine multiple exports. If you have ever stared at a 2,000-row keyword list that you need to clean up before putting it in a content brief, you know the feeling: it should take five minutes but somehow takes forty.
Here is the exact four-step workflow I use every time a client sends me a messy export. The whole thing runs in a browser — no Excel, no Python, no installation required.
Step 1: Remove extra spaces
Before doing anything else, paste the raw keyword list into the Remove Extra Spaces tool. Leading and trailing spaces are invisible but they will break de-duplication — seo tools and seo tools are different strings to a computer, even though they look identical to a human. Extra spaces also appear between words in keywords copied from spreadsheet cells — keyword research with a double space is not the same as keyword research. One click cleans all of them.
This step takes about ten seconds and it is the one most people skip. Do not skip it. It is the difference between de-duplication that works and de-duplication that leaves phantom duplicates you will not notice until the list is already in a brief.
Step 2: Normalise case
Paste the cleaned list into the Convert Case tool and select lowercase. This step is critical before de-duplication. Without it, SEO Tools, seo tools, and Seo Tools all survive as three separate entries even though they represent the same keyword. After lowercasing, they correctly collapse into one.
This step also makes the final list much easier to work with. A keyword list in mixed case is visually noisy — your eye has to work harder to scan it. A lowercase list is uniform and fast to read. Most keyword tools and content brief templates expect lowercase input anyway, so you are future-proofing the list at the same time.
Step 3: Sort alphabetically
Take the lowercase list into the Sort Text tool and run an A→Z sort with natural ordering on. Sorting at this stage serves two purposes: it makes the next step more effective because identical items will be adjacent, and it makes the final list easier to review — you can spot variants like keyword research and keyword research tool and keyword research tools free sitting next to each other in a group.
Natural sort matters when your keywords include numbers. Standard alphabetical sort puts top 10 seo tools before top 2 seo tools because it compares character by character and 1 comes before 2. Natural sort treats the digits as a number, so top 2 correctly comes before top 10. For keyword lists with ranked or numbered modifiers, always use natural sort.
Sorting also helps you make editorial decisions faster. Once the list is sorted, the structure of the topic becomes visible. All the how to variants cluster together. All the best modifiers group up. All the branded terms appear in the same section. This is useful when you are deciding how to structure pillar content or which keywords belong on the same page.
Step 4: Remove duplicates
Paste the sorted list into Remove Duplicate Lines with case-insensitive matching on. This is the final cleanup pass. After the lowercase normalisation in step 2, this will catch any remaining duplicates. The case-insensitive setting is a safety net — even if something slipped through the lowercase step, the de-duplication will still catch it.
Use the "keep original order" option if you want the de-duplicated result to stay in the alphabetical order from step 3. Use "sort after removing" if you want a second sort pass to clean up any ordering artifacts.
Real numbers from a recent project
Last month I combined keyword exports from three tools for a client in the project management space. The raw merged list had 6,840 rows. After this four-step workflow: 4,210 unique, clean, lowercase keywords. That is 2,630 duplicates removed — 38% of the original file was redundant. The whole process took under a minute of active work, plus about thirty seconds of waiting for copy-paste operations.
The sorting step also helped the client immediately spot that they had both task management software and task management tool as separate targets sitting next to each other in the sorted list, which informed a content decision about whether to build one page targeting both or two separate pages. That kind of editorial insight is a side-effect of having a clean, sorted list — it is hard to see when the data is messy.
When your list has extra columns
If your export includes volume, difficulty, or CPC columns alongside the keyword, you will need to isolate the keyword column before running this workflow. Copy just the keyword column into a blank text file first, run the four steps, then bring the cleaned keyword list back into your spreadsheet as a VLOOKUP reference to pull the metrics back in. This is slightly more work but it is still faster than trying to clean the full CSV in Excel.
Bonus: length filtering for head vs long-tail
Once the list is clean, you can use the Sort Text tool's "shortest first" option to quickly separate head terms from long-tail phrases. Short keywords (one or two words) are typically high-volume, high-competition head terms. Long keywords (five or more words) are typically lower-volume, lower-competition long-tail terms. Sorting by length gives you a fast visual split between the two groups without having to add a word-count column in Excel.
This is useful when you are splitting a large list between teams: PPC handling short-tail for paid campaigns and content handling long-tail for organic articles. A length-sorted list makes that handoff obvious.
Try it now: Paste your keyword list into Remove Extra Spaces and work through the four steps. The whole sequence takes under a minute regardless of how long the list is.