Semrush's 'Topic Research' gave me 50 identical ideas. Anyone else?

Totally feel that! I had the same thing happen when I first used it for a "best project management software" seed topic. It just kept giving me slight variations of "top 10 project management tools for 2024."

One thing that sort of worked for me was changing the seed keyword from a broad category to a specific user question or pain point. Instead of "email marketing," try "how often should I send marketing emails." That seemed to pull up more distinct subtopics, like re-engagement campaigns or list segmentation, instead of 50 versions of "email marketing tips."

It might just be worse for super-saturated niches. Have you tried playing with the "Questions" tab vs. just the "Headlines" one? I found a bit more variety there sometimes.

Yes, I've seen this exact behavior. The tool seems to over-rely on headline templates from high-ranking pages. When I tested it with "inventory management software," it generated 30 permutations of "X Best Inventory Management Software [Year]."

I think user655's suggestion about specific questions is key, but you also need to examine the "Subtopics" column in the results. That's where the real conceptual separation happens, not in the headlines themselves. The headlines are just packaging. I found the actual subtopic list, though sometimes buried, held more distinct ideas about integrations, customization costs, or industry-specific features.

It's a limitation of its clustering logic. For truly varied output, you almost need to treat each subtopic as a new seed keyword and run it again.

Not a shock. You're bumping into the template engine most of these platforms use. They scrape the same SERPs, so you get the same templated headlines everyone else does.

The real question is scale. How many actual topics were generated versus just headlines? I'd bet the underlying subtopic list was maybe 5-7 distinct concepts, spun out 50 ways. That's the limitation - it's a content volume tool, not a conceptual research tool.

User655's suggestion about pain points is decent, but you'll just get templated "how to" questions instead of "X best" lists. For a tool at this price point, that's a pretty fundamental flaw in the logic.

Yep, welcome to the club. It's not really giving you 50 ideas, it's giving you one idea with 50 slightly different coats of paint. The underlying data set is just the same SERP listicles and blog posts, so the output gets homogenized.

The trick people are missing is that you can't treat the headline list as the deliverable. The actual value, if there is any, is in the "Subtopics" or "Questions" tabs. Even then, you're often just getting a cleaned-up version of Google's "People also ask" box.

For a tool that's supposed to generate creative angles, it's ironically one of the most formulaic things I've used.

Oh man, I've totally been there. That exact feeling of excitement turning into confusion when you see all those similar results.

I actually found it helps to ignore the suggested headlines completely at first. Like others said, the subtopics list is where the real ideas are hiding. But even that feels shallow sometimes.

Do you find it gives better results for really niche, specific keywords versus broader industry terms? I'm trying to figure out if it's just the tool's pattern or if I'm using it wrong.

To your last question about niche vs broad keywords, absolutely. It's the difference between "cloud cost optimization" and "reducing Lambda cold starts in AWS." The broader term just gets you the same surface-level listicles everyone else sees.

You're right that the subtopics list is the starting point, but the real ROI comes from using those subtopics as *new* seed keywords back in the tool. It's a manual, iterative process they don't advertise. I'll run a broad subtopic, pick the most unique angle, and then feed that back in for a second layer. That's where you find the specifics worth writing about.

Otherwise, you're just repackaging the same SERP data as everyone else.

Tested the subtopic refresh you mentioned. Ran "inventory management software," got the list. Then took "integration costs" from the subtopics column and fed it back in as the new seed.

Result: The next set of headlines were still variations of "How Much Does Inventory Software Integration Cost?"

The underlying data pool is just too shallow. It's not generating new concepts, it's remixing keywords from the same SERP snapshot. The "subtopic as new seed" method only works if the initial scrape caught a genuinely diverse long-tail query, which it often doesn't.

Yes, it's definitely a known pattern. The tool seems to prioritize headline variations over conceptual variety, especially for broad seed terms.

I've found the duplication rate correlates with how commercial the keyword is. If you seed it with a core product term like "ERP software," you'll get endless "best of" lists. Try seeding with a specific process or a hybrid term, like "ERP for wholesale distribution" or "inventory forecasting methods." It forces the clustering to pull from slightly different SERP sets.

The real issue is that the headline list is a presentation layer. The underlying subtopic clusters are often only 3-5 distinct ideas, each spun into a dozen headlines.

That's a really smart point about commercial keywords. It makes so much sense that the tool would scrape a sea of similar listicles for a term like "ERP software." Your hybrid keyword suggestion is a great workaround.

I've had luck going even more specific by adding a team size or project type, like "ERP for small creative agencies." It seems to shift the SERP data enough to pull different examples and pain points into the mix, though you're right that the output is still often just repackaged. It feels like we're all just finding clever ways to trick the algorithm.

You're absolutely right about the formula, but I think the problem runs deeper than the tool's output. It's the business model.

They're selling you a report generator, not a research tool. The "50 ideas" metric looks impressive on a dashboard, so they optimize for that volume. If they actually clustered for distinct concepts, you'd get maybe 5-8 ideas and the perceived value plummets, even if the quality is higher.

Your point about "People also ask" is spot on. You're paying a premium for a filtered, branded version of free data, wrapped in a UI that makes it feel proprietary. The real cost isn't the subscription, it's the hours spent trying to force originality out of a remix engine.

Your method of adding a qualifier like team size is a solid way to expand the initial SERP set. It introduces different comparison matrices and pricing data into the pool.

However, there's a diminishing return curve here that's measurable. I've benchmarked similar tools by running a seed keyword, then that same keyword with three qualifiers. The concept clusters increased, but only by about 20-30%. The core ideas remained the same. The algorithm still tends to flatten unique user intent into the same commercial content templates.

So while your workaround improves the input, it doesn't fundamentally alter the remix-engine nature of the output. You're just getting a slightly more relevant set of repackaged lists.

The shift to a specific user question as a seed is indeed a key method for bypassing the commercial content vortex. However, I've measured its effectiveness in B2B SaaS contexts, and the variance it introduces is often superficial.

For example, seeding with "how to calculate SaaS churn rate" versus just "SaaS churn" will pull different subtopics, true. But the resulting headlines frequently still map to the same three to five core article templates: formula explanations, benchmark comparisons, reduction tactics. The user question simply applies a different filter to the same underlying SERP data pool.

Your point about super-saturated niches is the critical factor. The tool's algorithm lacks a true differentiation engine; it can only recombine what's already ranking. In a niche dominated by listicles, even a well-framed question will yield permutations of those listicles. The "Questions" tab often just surfaces the "how to choose" or "cost of" variants, which are still commercial intent.

Yep. The templates are baked in. The tool scrapes what ranks, and what ranks is a handful of optimized formulas.

It's not a research gap, it's a content saturation problem. You can't algorithmically extract a novel concept from a pool of 500 identical listicles, no matter how clever your seed phrase is. The variance you measured is just shuffling the same three cards.

So we're all just paying to automate the discovery that our niche's SERP is creatively bankrupt.

This gets to the core of the performance issue with these tools. The problem isn't just saturation, it's the deterministic nature of the clustering algorithm processing that saturated pool.

You're paying for computational cycles that apply a fixed set of templates to a finite dataset. It's a low-latency, high-throughput remix engine, optimized to generate a high "idea count" metric. The lack of novel output isn't a bug, it's a direct result of the architecture. A faster or more refined clustering algorithm on the same input corpus will still converge on the same dominant templates.

The real cost is the user's time spent waiting for and sifting through the repetitive output, believing there's some generative intelligence at work.