How a Content Creator Validated Her Tutorial Ideas Through Search Data

Rachel creates software tutorials for students learning design tools. When I looked at her keyword research process from February, I found something that most course creators skip entirely—she validated demand before creating anything.

Her initial plan was to create a Figma tutorial series covering everything beginners needed. That sounds reasonable until you try to define "everything beginners need." Her first outline had 40 topics based on her own learning experience and what seemed logically necessary.

Before recording anything, she decided to check whether students were actually searching for these topics. She started with the most basic keyword tool available—Google Search itself. She typed "Figma tutorial" and looked at what autocompleted. The suggestions were more specific than she expected.

People weren't searching for "Figma tutorial for beginners." They were searching for "Figma auto layout tutorial," "how to make components in Figma," "Figma prototyping interactions tutorial." These were feature-specific, problem-specific queries. Students already knew what they needed help with.

This changed her research direction completely. Instead of validating her outline, she built a new one from search data. She opened a spreadsheet with columns for keyword, search volume, competition level, current top results, and whether those results actually answered the query well.

She used Keyword Surfer, a free Chrome extension that shows search volume directly in Google results. For each Figma-related search, she could see monthly volume and related keywords without leaving the search page. "Figma auto layout" showed 9,800 searches per month. "Figma constraints tutorial" showed 1,400. Both were specific features she'd planned to cover in passing but clearly deserved dedicated tutorials.

The competition analysis was manual but revealing. For each keyword, Rachel opened the top five results and evaluated them. Were they text tutorials or videos? How long were they? Did they assume prior knowledge? Did they solve the specific problem the search query implied? She noted gaps where existing content was outdated, too advanced, or missed the actual pain point.

One example stood out. "How to make a button in Figma" had 2,100 monthly searches. The top results were either outdated or overcomplicated. They showed how to create elaborate button systems with variants and properties, but someone searching this query probably just wanted to make a simple clickable button. She marked this as a high-priority tutorial with a clear content angle—solve the immediate need first, then mention advanced options.

Rachel discovered long-tail keywords that revealed specific student struggles. "Why is my Figma frame not showing" got 590 searches per month. "Figma text won't change color" got 320. These weren't topics she'd ever thought to teach, but they represented real problems students encountered and searched for help with.

She grouped keywords into clusters representing related learning goals. All the auto layout queries went together. All the component-related searches formed another cluster. This clustering revealed that students were searching for help in workflow order—they searched for basic shape creation, then layout, then components, then prototyping. Her tutorial series should follow that natural progression.

The validation step saved her from creating content nobody needed. Her original outline included a 20-minute tutorial on Figma's vector editing tools. Search data showed almost no one was looking for comprehensive vector tutorials—they searched for specific tasks like "how to curve text in Figma" or "how to cut out shapes in Figma." She replaced the general tutorial with five focused ones targeting actual searches.

After two weeks of research, she had a validated list of 28 tutorial topics, each mapped to specific keywords with known search volume. Every tutorial would answer a question that real students were actively searching for, rather than topics she assumed they needed.