How a Junior SEO Specialist Found 347 Low-Competition Keywords in Three Days

I spent last week shadowing Maya, a junior SEO specialist at a mid-sized online course platform. She had one assignment that seemed straightforward but turned into something much more interesting than either of us expected.

Her manager asked her to find keyword opportunities for their new data analytics course. The brief was simple but vague, which is where most keyword research projects fall apart before they even start.

Day one was rough. Maya started with the obvious seed keyword "data analytics course" and pulled it into Ahrefs. The search volume looked great at 8,900 monthly searches, but the keyword difficulty score of 78 made it clear this wasn't going anywhere. She told me she felt stuck because every variation she tried had the same problem—high competition, established sites dominating the first page.

Here's where things got interesting. Instead of forcing those high-volume terms, she switched her approach completely. She opened a spreadsheet and started listing every specific topic covered in the actual course modules. Not course-related keywords, but the granular skills students would learn.

The course had a module on data cleaning in Python. Rather than targeting "Python data cleaning," she broke it down further. She typed "handling missing values Python" into the keyword tool. Volume was only 320 searches per month, but the difficulty dropped to 22. She found another one right after that specific pandas functions for duplicate removal, just 180 searches but almost no competition.

By the end of day two, she had this method down to a system. She'd take each course module, extract three to five specific skills or problems it solved, then run those through the keyword tool. When she found something promising, she'd check the "Questions" tab to see what people were actually asking.

One pattern emerged that surprised both of us. Questions starting with "how to fix" or "why does" consistently showed lower competition than "how to" questions. Searchers having specific problems, not just learning generally.

The spreadsheet grew fast. Maya created columns for search volume, difficulty, current ranking page types, and whether the course content actually addressed that specific query. This last column mattered more than she initially thought because she had to reject about 40% of keywords where the course didn't quite match the search intent.

Day three was refinement. She grouped keywords by search intent, identified clusters where one piece of content could target multiple related terms, and flagged quick wins where competition was nearly nonexistent. The final count was 347 keywords, with 89 marked as immediate opportunities.

What made this work wasn't fancy tools or secret techniques. Maya succeeded because she stopped thinking like someone trying to rank and started thinking like someone with a specific problem looking for an answer.