I used to think blog outlines were about gut feel and experience. You’d brainstorm some sections, maybe check a couple competitors, and start writing. But after years of analyzing what actually ranks and what doesn’t, I’ve learned that the best outlines aren’t guessed. They’re engineered from real SERP data.

The difference between a content piece that ranks on page three and one that sits in the top three positions often comes down to structure. Not just what you cover, but how you organize it, which questions you answer, and how closely your outline mirrors what Google has already decided is the authoritative way to address a topic.

This is the exact workflow I use to build blog outlines using ChatGPT, SERP tools, Google Sheets, and two essential Chrome plugins. It’s methodical, it’s data-driven, and it produces outlines that align with what Google already rewards while still giving you room to add unique value.

The Tools That Power This System

Before diving into the process, let me explain what each tool does and why it matters.

SERP Location Changer: Google’s results vary dramatically by location. A search in London returns different pages than the same search in New York or Nairobi. This tool lets me view results exactly as they appear in my target market, which means I’m analyzing the actual competition and SERP features my audience sees.

SERP Snippet Extractor (Chrome Plugin): This plugin pulls all the top ranking competitor URLs, page titles, and meta descriptions in one click. Instead of manually copying links one by one, I get bulk data instantly.

Detailed SEO Plugin (Chrome Plugin): This extracts People Also Ask questions and can go several levels deep. PAA questions are gold because they represent real user questions that Google considers contextually relevant to your keyword.

Google Sheets: This is my data processing hub. I use it to consolidate everything, clean it up, extract heading structures from competitor pages, and transform raw data into something usable.

ChatGPT: This handles the semantic heavy lifting. It filters irrelevant content, groups topics logically, and generates the initial outline structure based on all the data I’ve collected.

Starting With Your Target Keyword

Every outline begins with a single target keyword. This isn’t just any keyword. It’s the primary query that defines the search intent for the entire piece. The keyword determines which SERP features I’ll analyze, which competitors I’ll study, and ultimately what structure the content needs to follow.

For example, if I’m targeting “how to choose running shoes,” that’s fundamentally different from “best running shoes 2025.” The first is educational and decision-focused. The second is comparison and recommendation-focused. The outline structure for each would be completely different.

Analyzing the SERP From the Right Location

Once I have my target keyword, I search it using a SERP location changer set to the exact country or city I’m optimizing for. This step is critical because localization affects everything. A UK audience might see different brands, different buying patterns, and different content angles than a US audience for the exact same keyword.

When the SERP loads, I ignore the ads completely and focus on organic results and SERP features. I pay special attention to whether there’s an AI Overview or Featured Snippet present because these condense Google’s understanding of the topic into a few key points. If they’re there, they’re telling me what Google thinks must be covered.

Extracting Competitor Data in Bulk

This is where the SERP Snippet Extractor plugin saves me hours. I click it once and it copies all the URLs, page titles, and meta descriptions from the top ranking competitors. I paste this into a new Google Sheet.

Each competitor URL becomes a data source. These aren’t just links. They’re examples of content that Google has already validated as authoritative for this query. By analyzing their structure, I’m reverse engineering what Google considers complete coverage of the topic.

Going Deep With People Also Ask Questions

Next, I activate the Detailed SEO Plugin and extract People Also Ask questions. But here’s the key: I don’t just grab the surface level questions. I set it to Level 3, which pulls deeper, more contextually connected questions.

PAA questions are incredibly valuable because they represent the natural question progression users have around a topic. If someone searches “how to start a podcast,” they might also wonder “what equipment do I need for podcasting,” “how much does podcast hosting cost,” and “do I need a script for my podcast.” These questions map directly to sections in your outline.

I paste all these questions into the Google Sheet in a dedicated section.

Capturing Google’s Condensed Understanding

If the SERP shows an AI Overview or Featured Snippet, I copy the entire visible text and paste it into the sheet. This content is essentially Google’s executive summary of the topic. It highlights the must-cover subtopics and often reveals the angle Google thinks is most important.

For instance, if the AI Overview for “email marketing strategy” emphasizes segmentation and automation right away, that tells me those concepts need prominent placement in my outline.

Adding Your Keyword Cluster

If I already have a keyword cluster for this topic (using the clustering workflow I detailed in my other article), I paste those cluster keywords into the sheet as well. At this point, the sheet contains everything I need: competitor URLs, PAA questions, AI Overview content, and cluster keywords. This is the raw material.

Filtering Out the Noise With ChatGPT

Not every PAA question is relevant. Some are tangential, some are too basic, and some address a different intent entirely. This is where ChatGPT becomes invaluable.

I copy all the extracted questions and paste them into ChatGPT with a prompt asking it to categorize them as relevant or irrelevant based on my target keyword. After ChatGPT returns the categorization, I manually review the results because AI can miss context that I know matters for the specific audience or market I’m targeting.

Once I’ve confirmed the filtering is accurate, I paste only the relevant questions back into the Google Sheet in a fresh cell.

Extracting Heading Structures From Competitors

Now comes the technical part. I need to see how competitors have structured their content, which means extracting all their H1, H2, and H3 headings.

First, I take the competitor URLs I pasted earlier and transpose them so each URL sits in its own cell. Then I use this Google Sheets formula:

=QUERY(FLATTEN(IMPORTXML(G1,"//h1|//h2|//h3")))

This formula pulls every H1, H2, and H3 heading from the competitor page. I repeat this for each competitor URL. The result is a list of all the headings each competitor used to structure their content.

Normalizing the Heading Data

The extracted headings come back messy. They’re all in one column but formatted inconsistently. To clean this up, I select all the extracted headings and paste them into a single cell. Yes, everything goes into one cell.

Then I apply this formula:

=TRANSPOSE(SPLIT(REGEXREPLACE(K21, "\\s{2,}", "|"), "|"))

This splits everything out so each heading occupies its own cell, making the data much easier to work with in the next step.

Filtering Competitor Headings for Relevance

Just like with the PAA questions, not every competitor heading is useful. Some are branded (“About Our Company”), some are navigational (“Table of Contents”), and some are just filler.

I copy the normalized headings and paste them into ChatGPT with a prompt to identify relevant headings, remove duplicates and branded sections, and retain only headings aligned with the target keyword intent.

The filtered headings go back into the Google Sheet alongside the cluster keywords, relevant questions, and AI Overview content. Now I have a consolidated dataset of everything that matters.

Creating Semantic and Logical Groupings

This is where the outline starts to take shape. I select all the consolidated content (relevant questions, cluster keywords, AI Overview text, and filtered competitor headings) and paste everything into ChatGPT.

I ask it to group the content semantically and logically. Instead of a flat list of topics, I want organized groups that represent coherent sections. For example, if I’m outlining an article about email marketing, I might end up with groups like “Building Your Email List,” “Crafting Effective Campaigns,” “Segmentation Strategies,” and “Measuring Performance.”

These groups become the skeleton of the outline.

Generating the Initial Outline

I take the grouped content and paste it into a fresh ChatGPT conversation. Then I prompt ChatGPT to generate a blog outline using these groups as input.

The result is a rough outline that reflects real search intent, actual SERP structure, genuine user questions, and competitive coverage. It’s not perfect yet, but it’s grounded in data rather than guesswork.

Fine Tuning for Quality and Flow

The generated outline is a starting point, not the finished product. This is where experience and editorial judgment come in.

Heading Count Target: I aim for 15 to 20 total headings, combining both H2 and H3 tags. Fewer than that and the content lacks depth. More than that and it starts to feel fragmented or padded.

Merge Overlapping Sections: If two H2s are covering nearly the same ground, I combine them or reposition one as an H3 under the other.

Ensure Logical Progression: The outline should flow from foundational concepts to advanced tactics, or from problem to solution, depending on the intent. I rearrange sections if the progression feels awkward.

Prioritize High Impact Content: Questions with high search volume or keywords with strong commercial intent get prominent placement. Lower value sections get demoted or cut entirely.

Remove Filler: If a section doesn’t add unique value or answer a real user question, it gets removed. Every heading should justify its existence.

Use H3s Strategically: H3s are for depth, examples, case studies, and clarifications. They support the H2 but shouldn’t introduce entirely new topics.

Check Intent Alignment: I confirm the outline satisfies the dominant search intent without mixing conflicting intents. An article targeting “how to start a blog” shouldn’t suddenly shift into “buy blog hosting plans” unless the intent genuinely overlaps.

Why This Workflow Works So Well

Most content creators start with a blank page and their own assumptions about what should be covered. This workflow flips that. It starts with what Google has already proven works, what users are actually asking, and what competitors are already ranking for.

By combining SERP data, competitor structure, and user questions, you’re building an outline that aligns with how Google and real users already understand the topic. You’re not guessing. You’re engineering content structure based on validated ranking signals.

At Proworks SEO, this is the exact outlining methodology we use to build content that ranks consistently and scales efficiently. The outlines we create aren’t just well organized. They’re strategically aligned with search behavior and competitive reality.

If you’re tired of publishing content that doesn’t rank, the problem might not be your writing. It might be your outline. Start with structure, build it from real data, and the content that follows will be infinitely easier to rank.