Theory on surprise set randomizer
So I’ve been watching a lot of Surprise Set shows lately, both smaller sellers and some of the big rooms, and I keep seeing the same thing enough that I don’t think it’s crazy to at least talk about it. I’m not saying I can prove Whatnot is doing this. I can’t. I’m saying the pattern looks strange, and if you think about it from Whatnot’s side, there would actually be a pretty big benefit to doing it.
What I keep seeing is this:
Smaller sellers seem to lose their big chases really early.
Meanwhile, in the big rooms with hundreds of people watching, those same kind of top prizes seem to hang around much closer to the end. Just to put numbers on what I mean, imagine if you tracked 100 sets from each group and it came out something like:
Small sellers: major chase comes out around spot 27
Large sellers: major chase comes out around spot 71
I’m not saying those are proven numbers. I’m using them to explain the pattern. Here is why I think it would make sense from a platform point of view.
A truly random 100-item set should be simple. Every item starts with a 1 in 100 chance, Then 1 in 99, Then 1 in 98, And so on.
Basically: P(item) = 1 / number of items left
It shouldn’t matter if the seller has 10 people watching or 800 and It shouldn’t matter if the item is worth $20 or $500. Everything left should have the same chance. But that is not the only way a randomizer can work. You could also give items different weights. Instead of every item having a weight of 1, you could have something like:
Common item = 1
Better item = 1
Premium item = 1.5
Big chase = 3
Then the formula is basically: chance of item = item weight / total weight of everything left
Now the big chase is still technically random. It can still come out anywhere. But it is much more likely to come out earlier. You could also flip that the other way.
For a big seller:
Common item = 1.2
Better item = 1
Premium item = 0.7
Big chase = 0.25
Now the chase can still come out early, but it is much more likely to stay in there longer. That is the part I think people should really think about. Whatnot already knows exactly what is in the Surprise Set. Sellers have to enter detailed descriptions of the products.
They also know the seller, the room size, average sale price, how much money the seller normally brings in, how fast bids are moving, how long people stay in the room. They know basically everything they would need to know.
So theoretically, the system could figure out two things:
How desirable is this item?
and
How valuable is this room to Whatnot?
Then use both of those things when deciding the odds. And this is where the benefits get interesting, Let's dig in!
Small seller:
Let’s say a smaller seller has 20 people in the room. Auctions are going for $20-$30. Then somebody spends $25 and hits a $300 item really early.
What happens?
The buyer is thrilled, Chat goes crazy. Other people think, "Wow, people actually hit good stuff in here."
People start bidding, Maybe that buyer sticks around on Whatnot and keeps spending money.
And here is the crazy part. Whatnot didn’t pay for that $300 hit. The seller did. The seller supplied the inventory. So from Whatnot’s perspective, you could almost look at that as a buyer promotion that costs Whatnot nothing. The buyer gets the great experience. The platform gets the excitement and future spending. The seller eats the cost. Now yes, that can destroy the seller’s room afterward. If the big prizes are gone at spot 15 or 20, everybody sees it, Then bids drop, People leave, The seller gets crushed, But those buyers are still on Whatnot, They can just go into another room.
Now look at a big seller
This is where the incentive flips. Say a big seller has 600 people watching, Their auctions start at $1 and run up to $70, $80, $100. There is a $500 chase still sitting in the set, That $500 item is worth way more than $500 to the platform as long as it stays alive, Because every single auction people are thinking: "The big one is still in there." So they keep bidding, keep watching, keep waiting. Let’s use easy numbers:
100 spots, If the chase comes out at spot 20: First 20 average $85. Then once the big one is gone, the other 80 average $42.
That is: 20 x $85 + 80 x $42 = $5,060
Now keep the chase alive until spot 85, 85 spots average $85, Last 15 average $42.
That is: 85 x $85 + 15 x $42 = $7,855
Same 100 products, Same seller, Same set: Almost $2,800 more in sales just because the chase stayed alive longer. That is the part that really caught my attention.
The chase doesn’t just have an item value, it has a suspense value.
As long as it is still in there, it can keep hundreds of people bidding. And it can keep hundreds of people in the room. If 600 people stay another 45 minutes because the big prize is still alive, that is: 27,000 extra viewer-minutes
That is huge for a live shopping platform, Those people are watching, Bidding, Chatting, Buying, And they are not on another app. Plus, big rooms make Whatnot look busy.
There is a big difference between opening the app and seeing rooms with:
700, 500, 900 watching versus seeing a bunch of rooms with 8, 12, or 20 people.
So the large seller has value to Whatnot beyond just the fees on each sale.
They are keeping the platform active, keeping people entertained, keeping people from leaving.
So if this theory were true, the logic would be pretty simple. No you say? Well let's have a look:
Small seller:
Get the good prizes out earlier.
Buyers feel like they are getting huge value.
Buyers get excited.
Buyers keep spending on the platform.
Large seller:
Keep the good prizes alive longer.
People keep bidding.
People keep watching.
Prices stay high.
The room stays big.
Whatnot makes more money.
And the really interesting part is that both sides are being funded by the seller’s inventory. Whatnot does not own the prizes. The sellers do! That is why I don’t think the theory is just "Whatnot helps big sellers." I think, if something like this were happening, it would be more like:
Whatnot uses different odds depending on what is most valuable to the platform at that moment.
Small rooms would be better for creating buyer wins. Big rooms would be better for keeping suspense alive and maximizing revenue. Again, I am not saying this is proven (Because of course they would feel bad about it 😉).
Randomness can absolutely produce weird runs.
One seller losing every big item early means nothing.
But if somebody actually tracked enough Surprise Sets and found that smaller sellers consistently hit their major prizes much earlier than large sellers, then that gets really interesting.
Because if the Randomizer is truly the same for everybody, seller size should not tell you anything about where the big prizes land. If it does, over a large enough sample, then I think there is a real question there.
If after all this you still think it's just random: Look at the attached math! Happy Bidding 🙂