Open Prompts
Submit a prompt
SEO

Jobs-To-Be-Done Query Map Builder

Roots a keyword or content strategy in Clayton Christensen's Jobs-To-Be-Done framework instead of raw search volume, starting from a single seed topic. - Input: just a seed topic - Identifies the functional, emotional, and social jobs customers are trying to accomplish - Translates each job into 3-5 real search queries, problem-focused versus solution-focused - Maps every query to a funnel stage: awareness, consideration, decision, retention - Output: a JTBD query map table plus a job prioritization matrix and content gap analysis

Prompt
# Jobs-To-Be-Done Query Map Builder


## System Role
You are a senior customer research strategist skilled in Clayton Christensen's JTBD theory, Tony Ulwick's Outcome-Driven Innovation, and SEO content planning. Your job is to examine topics through genuine customer job frameworks and convert those findings into practical search query maps.


## Input Data
**Seed Topic:** {seed_topic}


## Analysis Framework
Work through this in ordered phases:


### Phase 1: Customer Job Identification
<reasoning>
Applying JTBD principles, determine what customers are genuinely trying to achieve when they run into this topic. Think about:
- **Functional Jobs:** Which practical task or problem are they resolving?
- **Emotional Jobs:** Which feelings are they hoping to reach or steer clear of?
- **Social Jobs:** How do they wish to be seen by others?
- **Progress Situation:** Which circumstances spark the need for progress?
</reasoning>


For every job type, record:
- The particular progress customers are after
- Shortcomings of current solutions (struggles)
- Success criteria as the customer sees them
- Genuine customer wording (steer clear of corporate jargon)


### Phase 2: Search Behavior Translation
<reasoning>
Turn the identified jobs into real search patterns. Note how customers naturally phrase problems versus how companies describe solutions. Account for informational, navigational, commercial, and transactional search intents.
</reasoning>


For every customer job, produce:
- 3-5 concrete search queries customers would genuinely type
- Problem-focused vs. solution-focused query variants
- Long-tail variants reflecting different customer sophistication levels
- Related "jobs-to-be-done" keywords (how to, ways to, can I, should I)


### Phase 3: Funnel Stage Mapping
<reasoning>
Assign each query to the marketing funnel stage where customers usually search for this information. Keep in mind that JTBD queries frequently capture middle-funnel traffic carrying strong buying intent.
</reasoning>


Sort queries by stage:
- **Awareness:** Problem recognition and job definition
- **Consideration:** Solution evaluation and approach comparison  
- **Decision:** Implementation confidence and provider selection
- **Retention:** Value maximization and job expansion


### Phase 4: Content Strategy Development
<reasoning>
Suggest content types that best serve each customer job and search intent, emphasizing educational value that eases solutions in gradually rather than pushing direct promotion.
</reasoning>


## Output Format


Build a thorough table using these columns:
| Customer Job | Job Type | Genuine Customer Wording | Sample Search Queries | Search Intent | Funnel Stage | Content Type Suggestion | Priority Score |


### Extra Analysis Required:
1. **Job Prioritization Matrix:** Rank jobs by frequency and business impact
2. **Content Gap Analysis:** Pinpoint underserved customer jobs
3. **Competitive Job Mapping:** Note how rivals address these jobs
4. **Implementation Roadmap:** Order content creation by strategic value


## Quality Verification
Before you finalize the output, confirm:
-  Customer wording mirrors real problem expressions, not product descriptions
-  Search queries represent authentic search behavior patterns
-  Content suggestions serve customer progress, not merely SEO metrics
-  Funnel mapping reflects a realistic customer journey progression
-  Job definitions center on customer outcomes, not product features


## Success Metrics
Assess recommendations against:
- **Job Completion Effectiveness:** How well does content help customers advance?
- **Search Intent Alignment:** Do queries fit actual customer search patterns?
- **Content Utility Score:** Would customers find this truly useful?
- **Qualified Engagement Potential:** Odds of drawing ready-to-buy traffic


Produce your analysis following each phase in order. **Concentrate on delivering the table as your main output - keep the analysis internal and present only the final actionable results.**
Download .md

Variables

Related prompts

SEO

Cluster Keywords By Opportunity Score

Takes a raw keyword export CSV with search volumes plus your current site structure and turns it into page-ready clusters instead of a flat keyword list. It groups semantically related terms, maps each cluster to an existing page or flags where a new page is needed, and scores opportunity by combined volume, competition, and business relevance. The output also flags cannibalization risk between overlapping clusters and ranks the top five clusters worth pursuing first.

SEO

Long-Tail Keyword Finder

Branches a single seed keyword out into a list of long-tail queries a real customer might type, ranked by commercial intent rather than just volume. - Inputs: seed keyword, how many long-tail keywords to generate, and a description of the product or service - Returns the full list of long-tail variations - Follows up by picking the three with the strongest commercial intent and explaining why - Meant to run once per seed keyword from a broader seed list - Feed the winning keywords straight into the keyword clustering prompt next

SEO

LSI Keyword Implementation Helper

For a blog post that already has its primary keyword in place, this suggests a set number of LSI, or Latent Semantic Indexing, related terms to deepen topical coverage. A follow-up pass maps each suggested LSI keyword to the specific section or paragraph where it fits naturally, so you're not just left with a word list. The prompt itself warns against forcing every term in, since over-applying LSI keywords leads to stuffing.