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.

A Melbourne operator called it out bluntly: "I spent 4 weeks hunting the perfect product with every tool I could afford. Competitors launched on product 247 in my shortlist and hit A$50k revenue before I'd finished analyzing product 50. I learned: 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 by 50-80%. After all five, you have 5-20 truly viable candidates.

Filter 1: AU Relevance ≥ 70

Cut every product that is not shippable to AU in under 12 days or has zero AU search demand.

What to check:

Is AliExpress AU warehouse available? OR ePacket available? OR AliExpress Direct?
If ChinaPost only, reject it. (Pattern 4, 1.2, will kill your store.)
Does the product show AU-specific search volume signals?

In Majorka: the AU Relevance score is computed and ranked. Filter by ≥ 70. Done.

If not in Majorka: manually spot-check the AliExpress listing. Check shipping methods. If only ChinaPost and 22+ days, reject.

Cut rate: typically 40-50% of raw lists fail this filter. You go from 1,000 to 500-600.

Filter 2: Saturation Score < 60

Cut every product that has so many dropshippers already running ads that CPM inflation has destroyed unit economics.

What to check:

How many active AliExpress sellers carry this product? (More than 8 = saturation risk)
How many dropshippers are visible in Meta Ad Library running ads on this product? (More than 20 = saturation risk)
What is the estimated daily revenue AUD? (If < A$80/day estimated, profit per unit is marginal)

In Majorka: the Saturation index combines advertiser density and supply density. Filter by < 60. Done.

If not in Majorka: manually check Meta Ad Library (facebook.com/ads/library) for the product name. Count active ads. If 25+, reject.

Cut rate: typically 30-40% of filtered set. You go from 500-600 to 300-400.

Filter 3: Winning Score ≥ 75

Winning Score synthesises velocity, AU relevance, saturation, supplier reliability and estimated revenue. If it's below 75, the product lacks signal on at least one critical dimension.

What does 75+ mean:

Velocity is in acceleration (not flat, not decelerating)
AU supply is proven and reliable (multiple suppliers, good ratings)
Saturation is moderate (CPMs are not yet peak)
Estimated daily revenue AUD is > A$80

In Majorka: Winning Score is ranked at row level. Filter by ≥ 75. Done.

If not in Majorka: no single source computes this. You have to manually synthesise velocity + AU relevance + saturation and trust your gut. This is inefficient. Use Majorka.

Cut rate: typically 40-50%. You go from 300-400 to 150-200.

Filter 4: Estimated Daily Revenue AUD ≥ A$80

This is the "will real customers buy this" gate.

Estimated daily revenue is computed as: (avg AliExpress orders last 7d) × (estimated margin per unit) / (number of sellers carrying product)

A product with A$50-70 daily revenue estimate means the total TAM is small. If 5 dropshippers are competing for that TAM, each operator gets A 0-14/day in realistic revenue potential. That's not enough to build a business.

In Majorka: estimated_daily_revenue_aud is shown per product. Filter by ≥ A$80. Done.

If not in Majorka: estimate it yourself. Take last-30-day AliExpress orders ÷ 30, multiply by average product price minus COGS. Divide by number of sellers. If result is under A$80, pass.

Cut rate: typically 20-30%. You go from 150-200 to 100-150.

Filter 5: Supplier Rating ≥ 92%

A supplier below 92% positive feedback is a structural reliability problem.

Why 92%? The Baymard Institute's chargeback analysis shows that suppliers below 92% positive have 2-3x higher dispute rates. Not because a few bad reviews are normal — but because suppliers below this threshold have structural fulfillment or quality issues.

In Majorka: supplier rating is computed per AliExpress seller on the product. Filter by ≥ 92% on the top 3 suppliers. Done.

If not in Majorka: click into each product's top suppliers on AliExpress. Check seller profile > "Positive Feedback Rate". Reject if the top supplier is below 92%.

Cut rate: typically 10-20%. You go from 100-150 to 80-120 viable candidates.

From 80-120 viable to the 5-product test list

You now have 80-120 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:

Product	Winning Score	AU Relevance	Saturation	Est Daily Rev	Top Supplier	Category Type	Notes
Product A	89	82	42	A 80	Chen's Store (96%)	Problem-solver	Pet grooming, steady supply
Product B	81	78	58	A 20	Ali Store (94%)	Evergreen	Kitchen utility, 12mo+ curve
Product C	76	75	61	A$95	Multiple (8 suppliers, all 93%+)	Impulse	Novelty, test bundling
Product D	88	84	51	A