Pricing

A pricing team gives an LLM several hundred property listings and asks a sensible question: Which characteristics help predict the selling price? The model returns an equally sensible list. Swimming pools. Granite countertops. Scenic views. Green lawns. Kitchen islands. Everything sounds plausible. That is the problem. The list describes what generally makes a house attractive. It does not necessarily describe what separated expensive from inexpensive houses in this particular collection, sold in particular locations, during a particular year. The LLM has supplied real-estate conventional wisdom when the business needed dataset-specific evidence. ...

A store manager does not usually make assortment and pricing decisions inside a clean optimization textbook. More often, the decision lives in a less glamorous place: a sales spreadsheet, a distributor agreement, an approval memo, last month’s exception report, a half-remembered rule about which customer can handle which category, and one person in the room saying, “This SKU always works in that region.” Retail intelligence, in other words, often begins as a pile of semi-structured clues wearing a business-casual disguise. ...

TL;DR for operators Marketplace operators usually worry that pricing algorithms learn the oldest trick in commerce: stop undercutting each other and raise prices. That worry is real. But this paper makes a more interesting point: when sellers use algorithms to optimise both product prices and sponsored-ad bids, collusion can move through the cost side before it moves through the price side.1 ...