But during my time there, I observed a core conflict: shoppers often look to influencers and affiliate content to help them decide what to buy, but the people making those recommendations are usually only compensated when a purchase happens. Usually this means that the incentive is to encourage the sale, even when waiting or not buying might be better for the shopper.
That insight is what inspired me to build TickClip. It uses a simple three-outcome decision model for online shopping: Tick to buy, Clip to wait or reconsider, Skip to avoid.
The point is to build a fiduciary decision layer that views purchases from the buyer’s viewpoint, not optimizing for conversion.
I'd love feedback on the decision model, how clear the verdicts are, and what evidence you'd need to see to trust a system like this.
karambahh•1h ago
Some prices are shown as "0$" which is obviously a bug.
Seems to me that your decision model is basically "look at camelcamelcamel's slope for the item", which is a decent idea (I do this myself).