Does a single star rating help you pick the right product?
A star average tells you a product is good without telling you what is good about it. That gap matters more now that AI agents shop on our behalf and cannot ask a follow-up question. This tests whether segmented product reviews produce better choices than one score, for people and for the agents buying for them.
The prediction
Decision accuracy is higher with segmented reviews than with a star average, and the gap widens when the buyer's stated priority is not the thing that drove the score.
How it works
- What gets compared
The same products shown three ways. A star average alone, a star average with written reviews, and reviews segmented by aspect with no overall star.
- The stated priority
Every participant is given one before they choose, such as needing delivery by Friday or caring most about durability. Without it there is no correct answer and accuracy cannot be measured at all. This is the single most important design decision in the study.
- Who does it
People, and separately an AI agent given the identical task. It runs across more than one model, so where the models disagree gets shown rather than buried.
- What gets measured
Whether the choice matched the stated priority, how confident the chooser was, and whether confidence tracked correctness or ran ahead of it.
What would prove me wrong
If accuracy is the same across all three conditions, or if segmentation helps only when the priority already matches what drove the score, the thesis is wrong and the result says so.
The number
No number yet. When there is one it goes here, whether or not it says what I wanted it to say.