How do you use user interest modeling for SEO content planning?

Yeah I use user interest modeling for content ideas but it takes a mix of tools. I start by grabbing data from Google Search Console for actual queries that hit my site, then combine that with Reddit or niche forum scraping using something like Keyworddit to see what topics people really discuss. Also I overlay SparkToro to see what my audience follows and talks about across social. That mashes up search intent with real audience chatter, so I get ideas that aren't just high-volume keywords but actually match what my visitors care about. Cluster topics in Sheets, tag by intent or theme, then map that to what you already have so new pages fill gaps.

Yeah I use user interest modeling a lot for content planning, it goes way beyond search volume. I use SparkToro to see what my target audience talks about and shares, which podcasts they listen to, social accounts they follow, stuff like that. Google Search Console is pretty useful too, looking at the weird queries that get impressions but low clicks, those can show interest gaps. Reddit and niche forums are gold for seeing what questions come up constantly. For landing pages I'll also use customer survey data or Hotjar on-page polls to spot pain points and topics people want more of. Then I map all that to search intent and build out clusters, sometimes going for topics with low or zero search volume if I see genuine demand or chatter.

Yeah we use user interest modeling pretty much every quarter, especially for ecommerce categories where just following search volume misses a ton of actual intent. Main thing for me is overlaying GA4 (or BigQuery if you've got it) behavioral data with stuff like SparkToro audience insights, Reddit and Quora scraping, and even email click data if you have a big enough list. For tools, SparkToro is solid for seeing what your audience talks about and follows, but I always map that to what site sections people are browsing and what they search internally (if you have a site search log). We do a lot of exporting product pageview clusters, then running them through NLP models (could be OpenAI, but even a simple keyword count is a start) to see topic overlap. This is how we found gaps like pre-purchase guides and troubleshooting content that doesn't have much search volume but drives a ton of assisted conversions and email signups when we hit the right pain point. So basically, combine behavioral analytics, lightweight audience research, and topic clustering to spot what actually matters to your users, not just what Google reports as high volume.