Workflow: Turn product demand into page blueprints
Map the full search demand landscape around the product: volumes, commercial intent, and keyword clusters. This tells you which keywords to target, what page type the SERP expects, and where the real organic opportunity lies.
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Prompt
Using Semrush keyword data for {country}:
Map the search demand landscape for the product in category {niche}.
Step 1 — Identify 3–5 core intent clusters:
* product name / branded searches
* category searches (generic)
* use-case searches ("best {niche} for [job]")
* comparison searches ("{niche} vs", "{niche} alternatives")
* transactional modifiers ("buy {niche}", "{niche} price")
Step 2 — For each cluster, run keyword research and extract the top 2–3 representative keywords via a bulk keyword lookup (several keywords in one request).
Step 3 — For each keyword return: search volume, keyword difficulty, intent (commercial / transactional / informational), and dominant SERP page type. Look at the organic search results (including SERP features) to identify SERP page types and active SERP features if the bulk lookup doesn't return SERP-feature data.
Return ONE table:
Columns:
* intent_cluster
* example_keyword
* monthly_volume
* kd
* intent
* dominant_serp_page_type
* opportunity (high/med/low)
Opportunity rule:
* high = volume ≥ 500 AND kd ≤ 65 AND intent = commercial or transactional
* med = volume 100–499 OR kd 66–80
* low = volume < 100 OR kd > 80
Limit: 12 rows.
Complete all keyword calls before returning the table.
If volume data is unavailable for a keyword, mark as low_data.
Also return: ONE sentence — primary keyword recommendation for the product page title tag.
Example Output (illustrative)
- A table of up to 12 rows grouping keywords into 3–5 intent clusters (generic category, evaluation, branded, comparison, transactional) — each with monthly volume, KD, intent label, dominant SERP page type, and opportunity rating (high / med / low)
- A one-sentence primary keyword recommendation for the page title tag