Building a product discovery query that surfaces breakouts, not bestsellers.
Sorting by revenue finds products that already won. By the time a listing tops that ranking, it is
saturated: margin is compressed, creators are booked, and the window has closed.This guide builds a query that finds products on the way up.
A floor. Without it, growth ranking floats a product that went from 80to400.
revenue_growth_rate: { gte: 0.5 }
+50% week over week. The actual breakout signal.
price: { gte: 15, lte: 45 }
The band where impulse buying works. Adjust to your economics.
commission_rate: { gte: 0.15 }
Below 15%, creators will not prioritize it.
listed_at: { gte: … }
Recent listings. A five-year-old product growing 50% is a promotion, not a trend.
Second sort key
Breaks growth ties by size, so the top of the list is worth reading.
revenue_growth_rate is a ratio: 0.5 is +50%, not 50. Passing 50 filters for products that
grew 5,000%, and returns nothing — the most common mistake in this query.
A product can post explosive growth because it went on sale. Compare avg_sale_price against
price.current:
for p in results: paid = p["metrics"]["avg_sale_price"] listed = p["price"]["current"] if paid < listed * 0.8: p["flag"] = "discount_driven" # volume bought with margin
A gap wider than about 20% usually means the growth is a promotion. That can still be worth
copying — but it is a pricing decision, not a product discovery.
Revenue per creator, sorted descending, puts the products carried by a small number of effective
creators at the top — the ones where adding one more creator still moves the number.
If the top video accounts for most of the product’s revenue, you are looking at one lucky post. If
revenue is spread across ten or more videos from different creators, the product converts —
and that is what repeats.
video_revenue = [v["metrics"]["revenue"] for v in videos["data"]]top_share = max(video_revenue) / sum(video_revenue)verdict = "single_video_spike" if top_share > 0.6 else "broad_traction"
With no conditions, the tracker fires on membership change — you hear about a product the day it
enters the set, which is the entire point.
Run the same tracker per leaf category rather than one broad one. Each gets its own result depth,
and the events arrive already routed to whoever owns that category.
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