Content Brief Builder

Jobs-to-Be-Done is a framework from Clayton Christensen (Harvard Business School): customers don't buy products, they hire them to do a job. A functional job is a practical task ("compare several vendor options quickly"). An emotional job is about how they want to feel ("feel confident in the decision"). A social job is about how they want to be perceived ("present a well-researched recommendation to my team").

AI system queries are increasingly framed as jobs rather than product searches. Picking the job type shapes the tone, the evidence emphasis, and the closing call-to-action.

The opening paragraph is the #1 citation zone — research on ChatGPT citation patterns has shown a substantial share of cited passages comes from the first 30% of a page. If the content type mismatches the reader's stage, the piece gets skipped regardless of how good the opening is. The opening itself follows the universal Answer-First requirements (methodology §6.3); the journey stage determines which kind of content surrounds that opening.

Per methodology §3.5, each journey stage calls for a distinct content type:

• Awareness — educational content; science foundations; problem explanation
• Consideration — comparison guides; expert reviews; testimonials; feature breakdowns
• Decision — authority signals; testimonials; scientific backing; thought leadership

Note on AIDA's "Interest" stage: the classic AIDA funnel splits the early funnel into Awareness and Interest. This tool uses the three-stage model from methodology §3.5 because the two funnel stages produce the same content-type guidance; splitting them would not change the writing instruction.

Category Entry Points are the real-life situations that cause someone to think about a product category in the first place. The 7W framework (from Professor Jenni Romaniuk, Ehrenberg-Bass Institute) organises CEPs into seven dimensions: Who, What, When, Where, Why, With whom, How (emotional state).

A strong CEP-anchored piece opens by describing the situation before introducing the solution. This matches how real AI queries are phrased — situation-first, product-second. The piece becomes findable through situational queries, not just product queries.

Why multi-select: a single situation usually hits several CEP dimensions at once. "End-of-quarter review preparation" is simultaneously a WHEN (time-based trigger), a WHY (motivation: proving results), and a HOW (emotional state: pressure). Tagging all three gives the writer a richer frame and helps the piece rank against queries phrased through any of those lenses.

Skip this field entirely for foundational explainer pieces not anchored to a specific situation ("How does [category] work?").

In AI-driven retrieval, each H2 section of your page is effectively a standalone citation unit — AI systems frequently pull a single section rather than paraphrasing the whole page. A page with 6 self-contained H2 sections, each directly answering a real query, gives you 6 chances to be cited rather than 1.

Write queries exactly as a real person would type them: conversational, question-shaped, with the mix they actually use (problem queries, comparison queries, decision queries). These are your sentinel queries — the ones you'll track in ChatGPT, Perplexity, and Gemini to measure citation lift.

Aim for 3–6 queries for a full-length spoke. Hub pages usually have 1–2 queries, tightly branded. FAQ clusters can have 10+.

AI systems use entity recognition to decide what a page is "about." Content that refers to its subject generically ("our product", "this tool", "the [category]") gives AI no signal to connect the content to the underlying entity — so the content effectively becomes invisible to AI-based retrieval for queries about that entity.

The entity-first pattern is simple: on first mention, use the full entity name plus its category (e.g. "[Brand]'s [Product Name] [Category]"); on later mentions, use a shortened but still specific form (e.g. "the [Product Name]"); never use ungrounded generic references.

The Princeton GEO Study (Aggarwal et al., KDD 2024) found that adding quotations from credentialled experts improves AI citation rates by 40–44% — the single highest-impact content technique measured. Adding statistics with sources improves citation rates by 30–40%. Source citations to credible outbound sources adds another 30–40%.

These gains only materialise if the evidence is real — named experts, verifiable numbers, Tier-1 sources (peer-reviewed journals, government data, established industry research). Deciding the evidence at the brief stage, rather than leaving it to the writer to invent, is what separates briefs that produce citable content from briefs that produce filler.

A single high-quality statistic and a single named expert quote, placed in the right sections, will usually outperform a piece stuffed with vague claims.