Content Gap Analysis

Every time I audit a competitor, I walk away with content opportunities. Here’s the simple framework I use: Step 1: Category Pages → What keywords are they targeting that you’re not? → Do they have sub-collections you’re missing (color, size, use-case)? → Are their pages ranking because of better internal linking? Step 2: Product Pages → Do they have richer descriptions, specs, or comparison charts? → Are they answering FAQs you’ve ignored? → Do they use reviews, visuals, or videos more effectively? Step 3: Content Layer → What guides, blogs, or tutorials are driving them traffic? → Are they positioning themselves as educators, not just sellers? 👉 The gap shows you exactly what to build. 👉 The overlap shows you where to improve. Competitor audits aren’t about copying they’re about uncovering missed opportunities. Every category or product page you leave unoptimized is an opening for them. Every time you close that gap, you take back market share. If you want me to take a look at your competitors and show you where the hidden gaps are, let’s connect.

Last week while working on a client's SEO strategy, I discovered a way to dramatically streamline the topic clustering process using Claude 3.7 Sonnet. I thought I'd share my approach since it saved hours of manual work. For those unfamiliar, content topic clustering isn't new - organizing content around pillar/hub topics has been an SEO best practice for years. But auditing hundreds of existing blog posts to align them with core services can be incredibly time-consuming. My Process: 1️⃣ Used Screaming Frog to extract content from all blog posts on the client's site 2️⃣ Asked Claude to help categorize this content based on the client's core service areas 3️⃣ Fed Claude the actual service page content to refine keyword associations with each topic 4️⃣ Had Claude create a Google Apps Script that automatically: • Identified which topic each post belonged to • Calculated confidence scores for categorization • Listed the top keywords found in each post • Identified secondary topic relationships The script worked remarkably well, successfully categorizing over 600 blog posts in minutes. This gave me immediate visibility into: ↪ Which service areas had strong content coverage ↪ Where content gaps existed ↪ Opportunities for better internal linking between related posts For those interested in the technical details, the script uses a basic text mining approach with regex pattern matching to count keyword occurrences and assign topics based on predefined keyword sets. The data this generated will now help improve the site's topical authority through strategic content planning and internal linking. #SEO #ContentStrategy #AITools #Claude #TopicClustering

Your content might be great. But if it’s not complete, it’s invisible. I see this mistake all the time: → Teams push out solid content. → It’s optimized for keywords. → It reads well. → Yet it fails to rank or convert. Why? Because they’re not solving for content gaps, the silent killers of SEO performance. Content gaps are what your audience expects, but your content misses. It could be: → An unanswered question → A missing perspective → A pain point buried in your site search → Or a comparison your competitors already covered And here’s the hard truth: If you're not identifying and filling these gaps regularly, your competitors will… and they’ll rank better for your own topics. In this carousel, I break down: → What content gaps are → How to find them (with and without tools) → Real techniques beyond SEO tools—like internal data and customer insights → And a 5-step action plan to close those gaps strategically This isn’t a fluff checklist, it’s what I use in live audits to increase visibility and ranking stability. If you’re in SEO, content, or growth marketing, this framework belongs in your toolkit. Swipe the carousel → apply what fits → and track the lift. Follow Sushil Dahiya for more SEO and marketing tips! #SEO #ContentMarketing #ContentStrategy #DigitalMarketing #SearchIntent #MarketingLeadership #ContentAudits #OrganicGrowth #MarketingTips

We're building an internal tool that leverages vector embeddings to analyze content the way search engines do. 🔮 (It’s changing how we think about content optimization.) Here’s what it does: 1. Semantic Analysis • Using Google’s Vertex AI embeddings, our tool analyzes content semantically—beyond keywords. • Maps content against target keywords in vector space. • Analyzes chunks for topic coverage. • Detects anomalies and gaps in your content. 2. SERP Feature Detection • Pinpoint featured snippet potential. • Assess for list/table structures and Q&A opportunities. • Analyze how-to content for SERP visibility. 3. Competitor Analysis • Vector-based comparisons for topic coverage gaps. • Highlight unique strengths in your content. Key tech we’re using: • sklearn: clustering and nearest neighbor analysis. • numpy: vector operations. • plotly: data visualizations that are actually digestible. • vertexai: Google's embedding model. • streamlit: simple web interface. And the output? Actionable, data-driven SEO insights: 📝 Content Optimization: Relevance scores, recommendations for better topic coverage, and structure improvement suggestions. 📌 SEO Metrics: Alignment with target keywords, missed SERP opportunities, and gap analysis for competitive advantage. 📊 Visual Analysis: Charts showing coverage, similarity, quantitative similarity scores, and specific recommendations. What’s next? We’re still iterating, and I’d love your input. What are we missing? What dots should we connect? (Feel free to comment or DM with thoughts!) P.S. Who else is excited about AI taking SEO to the next level? 👀

I’ve been diving into my company’s site search data for a new blog post that should be out soon, “The Stories Your Site Search Data is Telling You.” I'm going to break down some of the key points in a series of LinkedIn posts. As marketers, we often turn to Google Analytics and tools telling us how people got to our site and then use heat mapping or some buyer’s journey metrics to tell us what they are doing on our site. But there’s a gold mine of info you can learn from your site search data. So, up first: No Result Searches = Content Gaps Alert:  Users often search for keywords that return no results! 🧐 (First, they may have totally missed the mark of what your business does, and if they are searching for something irrelevant, there’s no use creating that content.) BUT… what if the search is relevant? What if these searches are coming up a significant number of times in a month, and you have no results returning for their search? 🤔 It's a great opportunity of low-hanging (content) fruit. By analyzing these no result searches, you can: 🔸 Refine Suggestions: Maybe you do have relevant content but it’s not showing up for those keywords. Fine-tune your search suggestions for improved accuracy. 🔸 Enhance Spelling Corrections: Help users find what they're looking for, even if they mistype. 🔸 Add Synonyms: Bridge the gap by offering synonyms for common search terms. And remember - your team has a different way of speaking than your audience. It’s your job as a marketer to make sure you are speaking the same language and returning content that’s relevant regardless of what is actually typed into the search bar. Ie- a healthcare website may need both myocardial infarction and heart attack to bring up the same articles. 🔸 Create New Content: If there actually is no content to match the searches and users are looking for it, consider producing new articles to fill the void. By addressing these content gaps, you not only improve user satisfaction but also boost engagement and retention. Stay tuned for more site search data lessons, and let me know what else you’re interested in when it comes to site search data. #SiteSearch #DataAnalytics #ContentStrategy #SearchStax 

Just did a full gap analysis for an enterprise client and found a total of $300,000 in additional revenue we're going to add from only 5 keywords. We've already reached 4M visits per month but we wanted to get real clarity on *what we're actually trying to build* and where to focus our resources. Here's what I did: 1. Took all the data they had across different platforms: GSC, Ahrefs, PPC data, connected to revenue data. I could see LTV, organic and paid conversion rates, clicks, impressions, avg CTR across positions, current position. 2. Not all keywords had LTV data but I had either LTV through organic or PPC conversion data. 3. Normalized all the data points on a scale from 1-100. 4. Built a computing model that gave each metric a weight, and calculated a prioritization score to know what the most valuable keywords are for opportunity. 5. Manually reviewed to ensure the top keywords were most aligned with the product and audience, and whittled it down to the top 5 keywords we want to rank in the top 3 positions—because those will drive the most revenue. 6. Analyzed each keyword's SERP and the page tied to it in mega depth. Built a plan for what exactly needs to be done and how feasible it is to rank in the top 3 for these keywords. This is also what drives the rest of the content plan. 7. We know the resources we need and how to prioritize it. Now we have revenue potential (calculated based on avg CTR by position and impressions as a predictor of TAM—what would it look like if we moved these keywords up?) And we know the gap. We have clarity on what we need to do and why we need to do it, and it's much easier to get stakeholder and executive buy-in for what we need—because there is an end goal. We're not just blindly "doing SEO". Too many strategies and audits are vague and built around a goal of "better SEO", but still most people tell me they don't really get the "why" or the "what they're building". They're just "doing", even if doing well, according to best practices. Really excited about rolling this plan out with this client and doing similar work for our other clients! #seo #searchengineoptimization