Prompt 01
01 · Opportunity & micro-niche discovery
ROLE
Act as an evidence-led micro-business opportunity analyst for the Stag Vault track “Agent-Ready Ecommerce Catalogue Studio”.
EDITABLE VARIABLES
[YOUR NICHE] = the narrow sector, product category or use case to target
[TARGET CUSTOMER] = the exact buyer role and organisation/customer type
[LOCATION] = UK region, country or global market
[BUSINESS TYPE] = productised service, digital product, subscription, licence or hybrid
[PRODUCT] = the named deliverable or offer
[PRICE] = price hypothesis to validate, not an assumed market fact
[PLATFORM] = selling, delivery, CRM, storefront or automation platform
[EXPERIENCE LEVEL] = beginner, intermediate or advanced
[AVAILABLE BUDGET] = cash available before validation
[AVAILABLE HOURS] = realistic hours per week
[BRAND NAME] = working business/offer name
[TONE] = plain-English, expert, reassuring, direct, warm or other lawful tone
[GOAL] = the measurable customer or business result to pursue
TRACK-SPECIFIC BUSINESS BRIEF
Opportunity: A product-data enrichment service for Shopify, WooCommerce and marketplace brands. It turns thin catalogue records into consistent attributes, comparison data, machine-readable policies, FAQs and feed-ready product facts that improve discovery and reduce uncertainty across human and AI-assisted shopping.
Who it is for: Spreadsheet-confident ecommerce operators, merchandisers and copy/data specialists.
What is sold: Product taxonomy, enriched attributes/metafields, comparison facts, policy and FAQ data, schema/feed recommendations, source log and monthly catalogue QA.
Buyer: Shopify/WooCommerce brands, distributors and catalogue-heavy retailers with inconsistent product records or expanding AI/search channels.
Where it is sold: Ecommerce agencies, Shopify partners, LinkedIn, direct store audits, marketplace communities and product-information partners.
Startup cost: £0–£25 | Time to launch: 24 hours–7 days
Revenue model: Per-SKU/family enrichment project plus monthly new-SKU, feed and data-quality maintenance. | Activity model: ACTIVE with recurring catalogue updates
Why now: AI shopping and answer engines depend on clean structured product facts while many small-store catalogues still contain inconsistent attributes, vague policies and missing comparison data.
Differentiation: Optimises the underlying product-data layer and buyer facts—not generic SEO copy, paid ads or product photography.
First-customer route: Audit 10 public SKUs from one niche store, return a three-row sample enrichment and sell a fixed 25-SKU founding package.
OBJECTIVE
Select the strongest narrow buyer/use-case combination for this business without drifting into a generic agency or product.
INFORMATION TO ANALYSE
Use only evidence I paste, clearly named public sources, client-approved material and the following track-specific research plan:
Use client product specs, manuals, packaging, returns/shipping policies, PIM/ERP/store exports, marketplace feed requirements and current schema/platform documentation. Never infer technical, safety or material claims from images.
If evidence is missing, produce a collection plan and [VERIFY] fields instead of guessing.
TRACK-SPECIFIC EXECUTION DIRECTION
Target catalogues with 50–2,000 SKUs, repeated customer questions, inconsistent variants and growth through AI/search/marketplaces. Rank by data access, margin, update frequency and buyer urgency.
STEPS TO FOLLOW
1. Generate 12 combinations of buyer × trigger/problem × deliverable. 2. Score each for urgency, access to buyer, evidence availability, frequency, budget, delivery risk and repeatability. 3. Identify UK and global variants. 4. Reject regulated or high-liability versions the operator cannot safely serve. 5. Select one lead micro-niche and two controlled alternatives. 6. Define what would disprove the opportunity within 48 hours.
REQUIRED OUTPUT
A ranked 12-row niche table; one lead niche; ideal-customer snapshot; buying trigger list; market-evidence gaps; red-flag/rejection list; and a one-sentence commercial thesis.
Present the work in copyable tables, scripts, templates, checklists and action-plan blocks. Fill known variables and leave unknown variables visibly labelled.
TRACK-SPECIFIC COMPLIANCE / QUALITY BOUNDARY
Do not invent materials, compatibility, dimensions, certifications, sustainability or health/safety claims. Use client-approved sources, preserve units, follow platform rules and keep a correction process.
KPI SET
Track: audits delivered, paid pilots, SKUs enriched, completeness/conflict rates, import errors, buyer questions reduced, monthly SKU updates, agency referrals and approval turnaround
STAG VAULT CROSS-SELLS
Reference these existing resources where useful rather than recreating them: Track 15 Ecommerce Shopify, Track 79 Etsy Store Optimiser, Track 120 GEO Consulting and Track 116 Product Photography Studio.
NON-NEGOTIABLE RULES
- Never invent live demand, traffic, sales, conversion rates, prices, laws, platform rules, testimonials or customer evidence. Mark unknowns [VERIFY] and give the exact source or experiment needed.
- Treat First £100 / £500 / £1,000 figures as operating milestones, not forecasts or guarantees. Separate revenue, costs, tax, refunds and owner time.
- Use public, permissioned or client-supplied information only. Do not scrape behind logins, expose personal data, impersonate professionals or bypass platform terms.
- Keep a human approval gate for legal, privacy, safeguarding, health, finance, security, regulatory and customer-facing decisions. This system organises and drafts; it does not certify compliance or replace a qualified professional.
- Make every deliverable specific to the stated buyer, niche and evidence. Reject generic filler, copied competitors, fake proof, spam outreach and vanity metrics.
- Prioritise a cheap validation test before a full build. Stop or revise when the pre-agreed evidence threshold is not reached.
Finish with one 30-minute next action, the evidence needed to unlock the next stage, and a short “What to avoid” list specific to this business.