How to find content opportunities beyond keyword research

Most SEOs rely on tools like Ahrefs for content gap analysis, but keyword research misses crucial context. In this talk, Frank walks through use cases using gap analysis to reveal missed organic opportunities. Frank will show how to leverage audience intent data and entity relationships to uncover content gaps traditional tools overlook, leaving the audience with a practical, step-by-step framework to take home.

Transcript

1. How to find content opportunities beyond keyword research Frank van Dijk DEPT® @frankvndijk
2. 2 Traditional keyword research Determining main topics Creating keyword longlist Clustering and mapping Research phase Gathering right topics Creating content plan Connect with me on Frank van Dijk
3. 3 Something like this Keywords from tool Connect with me on Frank van Dijk Clustering them Mapping them to pages
4. 4 The problem is, weʼre all looking at the same data Connect with me on Frank van Dijk
5. For many years this was fine Connect with me on Frank van Dijk 5 Source: https://www.advancedwebranking.com/free-seo-tools/google-organic-ctr
6. 6 Source: https://ahrefs.com/blog/featured-snippets-study/ & https://ahrefs.com/blog/ai-overviews-reduce-clicks-update/ But now that clicks are dropping -24.6% 2017 04/2025 -34.5% 12/2025 -58% Featured snippets First AI overviews Real AIO impact Answer blocks above organic results in Google search results. First large-scale baseline study of generative AI summaries. Updated research following the global rollout of AI Overviews. CTR for #1 dropped from 26.0% to 19.6% Immediate drop in clicks on top positions CTR at #1 has dropped to an average of 1.6% Connect with me on Frank van Dijk
7. 7 Google is the same ocean, with fewer fish Connect with me on Frank van Dijk
8. 8 These tools are just the tip of the iceberg Connect with me on Frank van Dijk
9. 9 Iʼm gonna show you how to find those alternative content opportunities to beat your competition Connect with me on Frank van Dijk
10. 10 You need a good story to move away from search volume Stakeholder: “No volume? How do we know this will work?” You: "Here's why people are asking, and here's how we'll know it works" Connect with me on Frank van Dijk
11. 11 K̶e̶y̶w̶o̶r̶d̶s̶ ̶f̶r̶o̶m̶ ̶t̶o̶o̶l̶ Finding a new data source Clustering the topics Mapping to the right pages Research phase Gathering right topics Creating content plan Communities Reddit, Quora Social media Instagram, TikTok, X Internal data Calls, emails, forms Find a data source your competitors are not using yet Connect with me on Frank van Dijk Machines Query fan-out, SERP
12. 12 Use the spaghetti method and throw it against the wall 01 Not everything you try will work Try Sticks Try Try Try Sticks Try Sticks Try Sticks Try Try 02 But what sticks, sticks for a reason 03 Nobody will be talking about this yet Thatʼs your advantage Connect with me on Frank van Dijk
13. 13 Start simple with AlsoAsked 01 Live from Google Pulls People Also Ask questions per country and language 02 Shows the follow-ups Every question branches into the next questions people ask 03 Ready to hand over Export them to a CSV and use it as input for your pipeline Connect with me on Frank van Dijk
14. 14 We should love Reddit Reddit is a pure form of user-generated content 01 Reddit is very accessible for people 02 People discuss anything and everything 03 And theyʼre brutally honest about it Connect with me on Frank van Dijk
15. 15 Meanwhile on Reddit: “is it normal that my €600 espresso machine makes me anxious every single morning?” r/espresso - 847 upvotes - 212 comments Connect with me on Frank van Dijk
16. 16 People type keywords into Google, but theyʼll tell the truth on Reddit “Espresso machine problem” “why does mine taste sour and am I an idiot for buying it” Clean, composed Doubt, emotion, the actual need Connect with me on Frank van Dijk
17. 17 You choose where to listen, not what you wanna hear It starts like any keyword research WHAT YOU SELL Espresso machines Grinders Beans & accessories Map what you sell and where you want to listen on Reddit WHERE THEY TALK r/espresso r/coffee r/BuyItForLife Connect with me on Frank van Dijk
18. 18 But reading it by hand? Forget it In this case it means 50,000+ comments in very active subreddits… Reading, tagging and grouping by hand is an insane amount of work Connect with me on Frank van Dijk
19. 19 Scrape it yourself or use a tool Scrape yourself Free, full control over what you pull, Reddit's API, or your own crawler. You handle rate limits, blocks, proxies, upkeep Needs an engineering background Connect with me on Frank van Dijk Use a tool A ready-made scraper you just configure. Handles the blocks and proxies for you Costs money, paid actor or pay-per-result
20. 20 Using Apify: a marketplace for ready-made scrapers How it works: 1. Pick a scraper (actor) 2. Set what to pull (URLs, keywords, limits) 3. Trigger API with custom script Recommendation: Reddit Scraper by Trudax Reddit Scraper Lite $3.40 / 1k Connect with me on Frank van Dijk
21. 21 Trudax Reddit scrapers are awesome Reddit Scraper Lite $3.40 / 1,000 results No monthly rent, pay per result Runs inside your free $5 monthly credits $5 free = ~1,250 results every month, free* With a margin for bad requests Connect with me on Frank van Dijk
22. 22 One API call does the work 1 2 3 Send request Scraping Reddit Structure output Send subreddits and settings as JSON Apify runs the scraper and handles proxies Get posts + comments back as JSON or CSV POST api.apify.com/v2/acts/reddit-scraper/runs { "subreddits": ["espresso"], "maxItems": 500, "includeComments": true } Connect with me on Frank van Dijk
23. 23 This is what we get back { "id": "k9f2a1", "subreddit": "espresso", "body": "why does mine taste so sour, am I an idiot for buying it?", "score": 47, "created_utc": 1718042113, "permalink": "/r/espresso/comments/..." }, ... x 50.000 Too much data to run through manually... Connect with me on Frank van Dijk
24. 24 Step 1 Clean the noise 01 Drop deleted / removed + bot accounts 02 Strip markdown, URLs, quoted replies 03 Keep body + score + timestamp Connect with me on Frank van Dijk
25. 25 Step 2 Cluster the data Embeddings Connect with me on Clustering Sentence-transformers (all-MiniLM-L6-v2) = free & local Simple: cosine similarity + a threshold "> 0.8 = same theme" or one of the bigger models (Google, OpenAI, etc) = paid & API Better: HDBSCAN finds the cluster count itself, flags noise Frank van Dijk
26. 26 Turn words into meaning “Tastes bitter and sharp” Connect with me on Frank van Dijk [0.2923, 0.9320, …]
27. 27 Clusters form in a multi-dimensional space close together = semantically similar Connect with me on Frank van Dijk
28. 28 Step 3 Name the clusters Cluster #7 200 comments: sour/bitter/acidic LLM temperature = 0 low temp = consistent output Connect with me on Frank van Dijk "Taste troubleshooting: sourness"
29. 29 From 50.000 comments to 30 or more topics with lots of content ideas Sourness troubleshooting Descaling anxiety Pods or beans? Grinder upgrade regret Connect with me on Frank van Dijk
30. 30 User-generated content shows trends before theyʼre here Trends appear here first before people start searching Connect with me on Frank van Dijk
31. 31 By the time a keyword has volume, youʼre too late most people spot the trend here Search volume Connect with me on Frank van Dijk
32. 32 But in most cases people start talking about it before itʼs a trend Search volume Connect with me on Frank van Dijk Community mentions
33. 33 We focus on velocity Velocity = mentions now vs mentions before 01 Count mentions of a term for a week 02 Compare to its own baseline 03 Spikes might mean something is brewing Connect with me on Frank van Dijk
34. 34 The pipeline is almost the same Scraping Connect with me on Velocity The Apify pull from my last example works to get the input Building a script to find opportunities based on velocity is a new step Apify has scrapers for other platforms like YouTube, TikTok, X and more Group by week, delta vs baseline, sort by fastest rising Frank van Dijk
35. 35 You can publish content before search volume even exists Search volume Connect with me on Frank van Dijk Community mentions
36. 36 Donʼt just rely on scraping, most companies have a goldmine Connect with me on Frank van Dijk
37. 37 Every customer touchpoint has valuable information Calls Emails Contact forms Anywhere a customer tells you, in their own words, what they don't understand Connect with me on Frank van Dijk Chat logs
38. 38 Hundreds of calls can be transcribed to text AI Audio that nobody plays back Connect with me on Frank van Dijk Months of customer language, as text
39. 39 Same pipeline as handling the Reddit data Transcript Clean Embed The only new step is the transcription at the beginning Connect with me on Frank van Dijk Cluster Label
40. 40 The beauty is that both the question and the answer are in the transcript ? The question “Can this coffee machine do …” ! The answer “Yes, it has …, … and a … function” Thatʼs an FAQ, pre-written Connect with me on Frank van Dijk
41. 41 One source helps the business and adds value to SEO ? Product improvements Connect with me on Missing product information Frank van Dijk Blogs Customer service pages FAQs
42. 42 Weʼve asked humans so far, letʼs now ask the machines Connect with me on Frank van Dijk
43. 43 Query/ prompt Subquery 1 Response 1 Subquery 2 Response 2 Subquery 3 Response 3 Subquery 4 Response 4 Subquery 5 Response 5 Query fan-out Connect with me on Frank van Dijk Response
44. 44 Source: Seer Interactive (2026) How many fan-outs do they use? Connect with me on 3-8 8-12 ChatGPT Google AI Mode Frank van Dijk +/- 10.7 Gemini
45. 45 Recommendation: give Kateryna a follow! You can pull this fan-out with DataForSEO Model Coverage Avg. queries $/call 95% 3.6 $0.086 90% 4.1 $0.047 65% 3.5 $0.018 60% 2.3 $0.063 45% 3.1 $0.009 25% 1.0 $0.011 10% 4.0 $0.019 gpt-5.6-sol ± gpt-5.4 ± gpt-5.4-mini ± gpt-5.5 ± gpt-5-mini ± o4-mini ± gpt-5 ± Connect with me on Frank van Dijk
46. 46 Go from a single question to hidden sub-questions “best running shoes for flat feet” stability vs motion control? This is the AIʼs own checklist to complete its answer how much arch support? best for overpronation? road vs trail for flat feet? when to replace them? Connect with me on Frank van Dijk
47. 47 The content opportunity lies in these gaps ✅ stability vs motion control? ✅ how much arch support? ❌ best for overpronation? ❌ road vs trail for flat feet? ✅ when to replace them? Connect with me on Frank van Dijk The gap/opportunity
48. 48 Then check if you show up Keep track of the rankings on those query fan-outs 01 Track your position on those fan-out queries 02 Check your presence in AIO 03 Watch visibility rise as you close these gaps Connect with me on Frank van Dijk
49. 49 Covering the question isnʼt enough If your content isnʼt checking off the following boxes it wonʼt work Inspiring Only answers the question ✓ Gives your point of view Unique Repeat Repeats what the top 10 say ✓ New data, original testing, fresh angle Authentic Written from a keyword list ✓ Written in customer language Connect with me on Frank van Dijk
50. 50 Because most SEO content is just a remix of the top 10 rankings Everybody is creating the same type of pages Connect with me on Frank van Dijk
51. 51 Google doesnʼt like this, it wants pages that give new input Information Gain Score Google holds a patent that checks the information gain Connect with me on Frank van Dijk
52. 52 Information gain means people gain more from your page than others Low gain High gain Repeats what top 10 already says New data, original testing, a fresh angle -> No value to user -> Earns a place in top 10 Connect with me on Frank van Dijk
53. 53 RAG systems seem to like this even more They're not looking for 10 sources saying the same thing Connect with me on �� �� �� �� �� �� �� �� �� �� Frank van Dijk
54. 54 We count whatʼs unique by checking the entities Connect with me on Frank van Dijk
55. 55 Because machines donʼt read words, theyʼre looking at the entities "Nike" Connect with me on Frank van Dijk entity: Nike type: company related: running shoes - Oregon - Adidas
56. 56 Turn the page into entities 1 2 3 Scrape top 10 Determine entities Find salience Scrape results and content with Python + DataForSEO Use Google Cloud NLP API to determine entities Find the gaps and where pages are standing out Connect with me on Frank van Dijk
57. 57 Google Cloud NLP determines the entities 0.91 running shoe 0.76 overpronation arch support 0.58 0.49 cushioning 0.32 heel drop midsole Connect with me on Frank van Dijk 0.12
58. 58 Free alternative Google Cloud NLP Entities + salience score, but paid per call Knowledge graph entity linking Connect with me on Frank van Dijk spaCy NER or KeyBert Entities in Python, local and free No salience, no linking, thatʼs something to add yourself
59. 59 Based on these entities itʼs just a math calculation Entity You Comp A Comp B Comp C running shoe ✓ ✓ ✓ ✓ cushioning ✓ ✓ ✓ ✓ arch support ✓ - ✓ ✓ carbon plate wear-in ✓ - - - resole costs vs new - - - - only you = your gain Connect with me on nobody yet = open territory Frank van Dijk
60. Letʼs wrap it up! Connect with me on Frank van Dijk
61. What to start with on Monday 1 2 3 Pick data source Create a pipeline Create content One that tells you most about your target audience Go from data to insights about content opportunities Create great content that adds value to Googleʼs index Connect with me on Frank van Dijk