Prompt 01
01 · Opportunity & micro-niche discovery
ROLE
Act as an evidence-led micro-business opportunity analyst for the Stag Vault track “Ecommerce Returns Reduction 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 productised merchandising and customer-information service that analyses store-supplied return reasons, audits expectation gaps and creates clearer sizing, specifications, care, comparison, FAQ and exchange content. It sells measurable experiments—not guaranteed return-rate reductions.
Who it is for: Ecommerce, CRO and customer-support operators comfortable analysing spreadsheets and rewriting product information.
What is sold: Return-reason taxonomy, high-risk SKU audit, expectation-gap fixes, size/spec/care content, photo/video brief, FAQ and exchange-flow experiment plan.
Buyer: Fashion, footwear, homeware, electronics and other online brands with measurable return volume.
Where it is sold: Shopify agencies, 3PL/returns-platform partners, ecommerce groups, LinkedIn and targeted SKU/store audits.
Startup cost: £0–£25 | Time to launch: 2–7 days
Revenue model: Audit/project by SKU family plus monthly return-reason monitoring and new-product optimisation. | Activity model: ACTIVE with recurring monitoring
Why now: Returns remain a direct margin cost and brands increasingly treat product information, fit confidence and exchange design as controllable pre-purchase levers.
Differentiation: Starts with store-supplied reason data and product-page evidence, then runs measurable content tests; it is not generic CRO or return-policy consulting.
First-customer route: Audit five public product pages and offer a no-data 'expectation gap' sample, then sell a paid analysis using the store's top return reasons.
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 return exports, reason codes, support tickets, reviews, product specs, analytics and current consumer/platform rules. Benchmark only against named comparable sources and never claim causation from a public page alone.
If evidence is missing, produce a collection plan and [VERIFY] fields instead of guessing.
TRACK-SPECIFIC EXECUTION DIRECTION
Prioritise stores with enough return data, high-margin SKUs, repeated expectation/fit complaints and control over product pages. Reject tiny samples or returns dominated by defects/logistics outside the offer.
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 hide material information, make false fit/safety claims or design unlawful return restrictions. Use store-approved product facts and current consumer-law review for policy changes.
KPI SET
Track: paid pilots, SKUs audited, return reasons mapped, content changes shipped, return/exchange/contact metrics, margin recovered, monitoring renewals and unsupported claims prevented
STAG VAULT CROSS-SELLS
Reference these existing resources where useful rather than recreating them: Track 15 Ecommerce Shopify, Track 29 Landing Page, Track 116 Product Photography and Track 25 Email Marketing.
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.