1\section{Conclusion}\label{sec:concl}23Taking a price-comparison service's database as it is---posted prices, online channels, eleven weeks, no stores---we have tried to extract what it identifies and to say clearly what it does not. The architecture that emerges has four load-bearing elements. Banners differ by 10--25 log points on identical products, persistently, and mostly inside the two chains whose discount and conventional formats carry the same UPCs. Promotions are rare in catalogues but deep, recur every few weeks, account for half of all posted-price changes, double measured volatility, and return exactly to their reference price within the window. Cross-banner price co-movement is small and, where it is visible, is the calendar of a chain rather than a response to a rival. Measured ``inflation'' on catalogue data is driven by what enters and leaves the catalogue unless the index is matched-model. Underneath these sits a measurement fact that any user of scraped grocery prices should check for: a storefront that serves store-specific prices to anonymous sessions produces an oscillating series whose amplitude is cross-store dispersion, not price change.45A longer, store-identified, regular-price-logging collection---three changes to an existing pipeline---would turn several of this paper's descriptive statements into tests: whether reference prices drift after sales over a year, whether the two-point pre-sale elevation in clean feeds survives, how dispersion across stores of the same banner compares with dispersion across banners, and what a Quebec household at a given address can actually save. We have set the index, the matching procedure and the registration up so that those tests can be run on the same code.6