Dynamic pricing replaces the periodic price review with a feedback loop: inputs that move frequently, a rules- or model-based engine that picks a new price, and a write-path back to the catalog — the Shopify price field, a marketplace listing API, or the Google Shopping feed. Cadence is whatever the inputs move at.
In DTC the practice shows up in three operationally distinct flavors operators conflate. Marketplace repricing on Amazon or eBay is driven by competitor scrapes and Buy Box contention. Demand-based pricing on the brand’s own storefront is tied to inventory depletion, traffic spikes, or conversion-rate signals. Personalized pricing shows different shoppers different prices by cohort, source, or behavior.
The trust contract differs by surface. An Amazon shopper expects a third-party SKU’s price to move between refreshes; that is the format. The same shopper seeing a saved item shift in price between visits to the brand’s own storefront reads it as opportunism. Same mechanic, different trust contract.
Pricing engines optimize against two parameters: price elasticity (how demand responds to price) and promo depth (how far a discount can go before training buyers to wait). The contribution margin floor is the hard constraint — below it, an additional order ships contribution-negative.
Dynamic pricing helps where shoppers already expect prices to move, and damages trust where they do not.