Winner Spy
Find winning dropshipping products in seconds.
Happy Friday everyone!
Just wanted to share what WinnerSpy found today in the US market (Facebook Ads). Our algorithm just flagged some high-potential products that you might want to look at:
Top Pick: Self-cleaning cat litter box (Score: 28/30 - Quality: Excellent). This product has been running for 631 days! That’s a huge indicator of a stable, winning product.
Runner up: gc1wro (Score: 28/30) with 367 days of active ads.
Quick stats from today's report:
7 suggested products.
2 strong winner candidates.
Highest score: 28.
It’s amazing to see how data can cut down hours of manual research. I'm constantly tweaking the "Winner Score" formula to make it even more accurate.
Would love to hear your thoughts on these long-running winners!
Check out the tool: https://winnerspy.app
Hi everyone,
I'm excited to share WinnerSpy, a project I’ve been working on to help e-commerce sellers and dropshippers.
Finding "winning products" is often the hardest part of the business. WinnerSpy was built to solve this by providing real-time data and insights, so you can stop guessing and start selling.
Key features:
Discover trending products before they go viral.
Analyze competitor ads and performance.
Save hours on manual product research.
I’d love to get some feedback from this community! What features should I add next?
Check it out here: https://winnerspy.app
Thanks for your support!
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One thing I'd be careful with:
A product running ads for 600+ days and a product being worth copying are not necessarily the same signal.
The interesting decision is what that longevity actually means.
That's where I'd be careful.
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Great point — totally agree. Long runtime alone isn't a "copy this" signal.
That's why we score multiple signals (ad count, creative variety, recency, etc.) and label results as signals, not guarantees. We're also working on clearer presets (e.g. "new traction" vs "long-running") so users don't misread longevity.
Would a filter like "ads started in last 90 days + 3+ creatives" be useful for your workflow?
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Possibly.
The reason I stopped short earlier is that I don't think the interesting part is the filter itself.
I think there's a bigger decision sitting underneath what those signals are actually being used to conclude.
That's one of those things that can quietly shape how users interpret the product and what they end up optimizing for.
I wouldn't try to unpack that properly in a thread.
If you're curious, drop your email and I'll put together the tighter version.
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Great point on the deeper logic — would love to read your tighter version if you're open to sharing.
Email: truongthanhsbay@gmail.com
Any contribution would mean a lot while we're still in beta. Thanks again for taking the time to help!
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Sent you a note by email.
I think the decision underneath the signals matters more than the signals themselves.
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