From Keywords to Jobs: Rethinking SEO with JTBD + LLMs

“People don’t search for features. They search to solve problems.” – Every B2B buyer ever

The Play:
Traditional SEO is dying a slow death.
Optimizing for keywords isn’t enough — because keywords don’t equal intent.

The future?
Map content to Jobs-To-Be-Done (JTBD).
Then use LLMs to bridge the gap between messy queries and structured buying journeys.

The Framework:
JTBD x LLMs = Intent-Driven Content Engine

1. Map Jobs, Not Just Terms:
   Start with real customer interviews. Identify the “jobs” they hire your product to do.

2. Train AI on Buyer Language:
   Feed those jobs into GPT or Claude and ask for variants in buyer phrasing across awareness, consideration, and decision stages.

3. Create Content for Jobs, Not Just Traffic:
   Build clusters around use cases, not volume — your pipeline will thank you.

Use Case:
A B2B HR tech company ditched keyword stuffing and focused on “jobs” like:
→ “Streamline remote onboarding”
→ “Reduce time-to-hire for technical roles”

They used AI to expand content around these and saw:
+42% in demo conversions
+3x growth in decision-stage traffic

Tools + Prompts:

• Tools: Hotjar, GPT-4, Notion

• Prompt:

“Here’s a job a buyer is trying to solve: [job]. Write 3 TOFU, 3 MOFU, and 3 BOFU blog titles tailored to this journey.”

Try This:
Reply with JOBS and I’ll send you my JTBD SEO content map template.

PS – Keywords show what people type. Jobs show what they need. Guess which one closes deals?