Using AI to Build More Relevant Content for the Buyer Journey
summary
Argues that traditional meaning-based marketing fails and relevance wins by modeling audience intent from actual search behavior, using Google Trends and query mining to map informational, navigational, transactional tasks. Describes building a periodic table of keywords and content that moves from unbranded queries to brand pages, claiming a content page for "cognitive computing" grows from five queries to 1,400 queries in six months and later becomes a top-ranked knowledge-graph result. Recommends segmenting users into business people vs specialists and tailoring content buckets to their tasks.
AI-written summary · about
chapters (10)
- 0:00Traditional communication
- 1:03Relevance over meaning
- 1:55Marketing challenges
- 3:40Search-first strategy
- 5:05Analyzing search data
- 6:18Voice search and content
- 7:12Task-based intent
- 8:49Intent mapping case study
- 10:20Modeling audience personas
- 12:33Buyer journey complexity
related talks
47mNavigating the SEO Landscape: Trends and Tactics
25mWinning the AEO/GEO "Answer War": Why Structured Data is the Only AI Moat That Matters
47mMastering Google's Search Generative Experience: Knowledge and Recommendations
45mSEO for AI: Be found by AI search apps like chatGPT
44mHow to Optimise Your Content for AI Models - Get Found, Be Visible
45mMarketing in the Age of AI: Strategies for Staying Ahead of the Curve
44mAI in Digital Marketing: Benefits and Use Cases
20mThe AI-Driven DXP: New Horizons for Marketers