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Module 02 — Product Research — Reading the Tape Before the Crowd
The 1000-Product Shortlist Problem
11 min · interactive · Intermediate
The 1,000-product shortlist is a real problem. Operators import every Sell The Trend result, every Dropispy discovery, every category match and freeze. They have options and no decision-making framework. Today: a rubric that filters 1,000 to 5 testable candidates in 90 minutes — and the Majorka dashboard that automates three-quarters of the work.
The shortlist paralysis trap
This is a real scenario: an operator runs Niche Scraper and gets 847 silicone pet products with > 100 orders last 7d. They export the list. They stare at it. Three weeks later, they haven't launched a single test. The product space is too vast. They are waiting for the "perfect" candidate that doesn't exist.
Shortlist paralysis kills more stores than bad product picks. A good product picked and tested in week 2 beats the perfect product never tested. Good enough tested beats perfect theorized, every single time.
The fix is not to be more decisive — it's to have a filter rubric that reduces the problem space systematically. This lesson is that rubric.
The five-filter framework
When you have 1,000 candidates, apply five filters in sequence. Each filter cuts the problem space hard. After all five, you have a handful of truly viable candidates. Every filter below is something you can actually check — a real shipping option, a real ad count, a real dated receipt — not a score someone modeled for you.
Filter 1: Shippability
Cut every product that is not shippable to your target market in under 12 days.
What to check:
- Is AliExpress local warehouse available? OR ePacket available? OR AliExpress Direct?
- If ChinaPost only, reject it. (Pattern 4, 1.2, will kill your store.)
How: open the AliExpress listing → Shipping tab. This is a manual, two-minute check per candidate, and it kills the largest share of any raw list.
Cut rate: typically 40-50% of raw lists fail this filter. You go from 1,000 to 500-600.
Filter 2: Advertiser saturation
Cut every product that has so many dropshippers already running ads that CPM inflation has destroyed unit economics.
What to check:
- How many dropshippers are visible in Meta Ad Library running ads on this product? (More than 20 = saturation risk)
- How many active AliExpress sellers carry this product? (More than 8 = saturation risk)
How: Meta Ad Library (facebook.com/ads/library), search the product name, filter to your market, count active ads. If 25+, reject. Free and observable — Lesson 2.5 is the full walkthrough.
Cut rate: typically 30-40% of the filtered set. You go from 500-600 to 300-400.
Filter 3: Live receipts on the tape
This is where Majorka does the work. Paste each surviving candidate and read its tape:
- Receipts present — real, dated order adds we observed on the listing. The product is selling now, not "sold 80,000 lifetime".
- Abstention — the system says "not enough evidence yet." That is not a no. It's an honest refusal to guess. Park the candidate and re-check in a few days.
- Cooling — the listing used to put up adds and has gone quiet. Cut it.
Keep listings with live receipts. Park abstentions. Cut cooling. Do not substitute a competitor tool's volume estimate for this step — estimates are how saturated, dying products sneak onto shortlists.
Cut rate: varies with how fresh your raw list is — expect the survivor pool to drop by half or more.
Filter 4: Recency over lifetime
Among the receipt-backed survivors, read when the evidence happened. A listing with a huge lifetime order count and no recent adds is history, not a signal — all of that volume may have happened to someone else last year. Prefer candidates whose adds are recent and arriving, even if the lifetime total is modest.
How: on the tape reading, compare the 7-day adds against the lifetime count. Recent and arriving beats big and stale.
Cut rate: typically removes the "famous but finished" products — the most dangerous category on any shortlist.
Filter 5: Supplier rating
A supplier with weak positive feedback is a structural reliability problem — fulfillment and quality issues that turn into disputes and chargebacks after you've spent the ad money. Operators commonly use ~92% positive feedback as a practical floor.
How: click into each product's top suppliers on AliExpress. Check seller profile → "Positive Feedback Rate". Reject if the top supplier is below your floor. Module 3.1 covers the full 7-metric supplier read.
Cut rate: typically 10-20% of what's left.
From the survivor pool to the 5-product test list
You now have a pool of candidates that pass all five gates. The final step is a subjective sort to pick 5-10 products for actual testing.
The subjective sort criteria:
| Rank | Criteria | Your weight |
|---|
| 1 | Problem-solver vs wow-factor vs evergreen fit (2.1 — pick one type) | 40% |
| 2 | Category novelty vs saturation in your store / niche (do you already have a pet store with 6 pet products?) | 30% |
| 3 | Supplier stability (same top supplier across all 5 shortlist picks reduces supply risk) | 20% |
| 4 | Personal affinity (you believe in the product) | 10% |
Pick 5-10 products using this weighting. Don't overthink it — these are tests, not bets. You'll kill 3-4 of them in the first 30 days anyway.
The five-product test list in spreadsheet form
When you move to testing, create one master sheet. Every column is something you observed, not something a tool scored:
| Product | Receipts (7-day adds) | Cooling? | Ad Library count | Top supplier rating | Category type | Notes |
|---|
| Product A | Live, recent | No | Low | 96% | Problem-solver | Pet grooming, steady supply |
| Product B | Live, steady | No | Moderate | 94% | Evergreen | Kitchen utility, long curve |
| Product C | Abstained — re-check in 5 days | — | Low | 93%+ | Impulse | Novelty, parked not rejected |
| Product D | Live, recent | No | Low | 97% | Problem-solver | Pet/home, wide margin |
| Product E | Live but slowing | Watch | High | 92% | Wow-factor | High returns risk |
This sheet becomes your test tracking document. You launch Product A in Week 1, Product B in Week 3 (if A is still running), and so on.
Why this matters
1,000-product shortlists exist because operators haven't built a decision framework. The five-filter approach (Shippability → Saturation → Receipts → Recency → Supplier) removes subjectivity and paralysis. Apply the filters mechanically. You go from "I don't know where to start" to "here are 5 products with real evidence" in 90 minutes. Testing one of those five in the first 30 days beats spending 30 days looking for the perfect product that doesn't exist.
Module 3 (Suppliers) and the 30-day plan (1.6) show you how to validate these five and launch your first test.
A worked, hypothetical pass (no real operator, no real dollars)
The numbers below are illustrative filter counts only — there is no real operator, store, or revenue behind them. They show the shape of the funnel, which is the point of the rubric.
Start: a raw export of "pet products, 100+ orders last 7d." Raw list: 847 products.
Filter 1 (Shippability): 847 → 421. ChinaPost-only and 20+ day listings removed. Manual shipping-tab checks, batched.
Filter 2 (Advertiser saturation): 421 → 261. Removed products with 25+ visible Meta ads in the target market or 10+ AliExpress sellers.
Filter 3 (Live receipts): 261 → 9 live + 41 abstained + 211 cut. Each candidate pasted into Majorka; only listings with dated, recent order adds kept. The 41 abstentions go on a re-check list — "not enough evidence yet" is a parking instruction, not a rejection.
Filter 4 (Recency over lifetime): 9 → 7. Two listings had big lifetime counts but no recent adds — famous, finished, cut.
Filter 5 (Supplier rating): 7 → 6. One survivor's only supplier sat under the feedback floor.
Subjective sort (type fit + novelty + affinity): 6 → 5 final candidates.
Note what the funnel did at Filter 3: it didn't rank 261 products — it refused to rank 41 of them. The tools this module compared in Lesson 2.8 would have scored all 261 with equal confidence. The honest funnel is narrower and slower, and that's exactly why its survivors are testable.
Action items
- Start with your raw shortlist (import from Niche Scraper, Dropispy, or a raw AliExpress category). Count how many candidates you have.
- Run Filter 1 (shipping tab) and Filter 2 (Meta Ad Library count). Write down the remaining count after each.
- Paste each survivor into Majorka. Record three buckets: live receipts / abstained / cooling. Park the abstentions with a re-check date.
- For the receipt-backed survivors, compare 7-day adds against lifetime totals. Cut the famous-but-finished ones, then check top-supplier feedback on AliExpress.
- Select your final 5. Create the tracking sheet with the columns from the lesson (Receipts, Cooling, Ad count, Supplier, Type, Notes) and launch Product 1 in Week 1 per the 30-day plan (1.6).
Module 3, Lesson 1: the seven metrics of a reliable AliExpress supplier — how to read the signals that decide whether a supplier will scale with you or collapse at $5k/month.
Sources
- Shopify Compass — Product velocity and saturation benchmarks, 2026
- Common Thread Collective — Product shortlist methodology and TAM estimation, 2026
- Majorka tape — observed, dated order receipts; abstention when evidence is insufficient, 2026
Module 02 — Product Research — Reading the Tape Before the Crowd
The hardest skill in this business. Data-driven frameworks for spotting products at the beginning of their curve, not the end.
Lessons in this module
- The 4 Types of Winning Products (and which you should pick) · 11 min
Problem-solvers, wow-factor, impulse, evergreen — the trade-offs of each. - Trend Velocity — Catching a Winner at Day 10, Not Day 60 · 13 min
How to read a velocity curve and when to pounce. - AliExpress Signals That Actually Matter · 9 min
Ignore reviews. Watch orders, store age, and "recently ordered" pulse. - TikTok Search for Product Discovery (the right way) · 10 min
The search strings that surface rising products, not viral replays. - Meta Ad Library — Reverse-Engineering Competitor Winners · 12 min
How to tell a test from a scale, and steal the ad angle without the copy. - Why We Killed the Winning Score (and How to Read the Tape Instead) · 8 min
The post-mortem of our own headline metric — and the receipts, cooling signals and abstentions that replaced it. - Building a 20-Product Shortlist in Under an Hour · 15 min
Live walkthrough: from dashboard to validated shortlist, fast. - Beyond AliExpress — Apps That Surface Different Winners · 9 min
Sell The Trend, Dropispy, AdSpy — when each adds signal Majorka does not. - The 1000-Product Shortlist Problem (this lesson) · 11 min
How to filter from 1000 candidates to 5 testable products in under 90 minutes.