How does pricing optimization work?

Pricing optimization works by combining internal data — historical sales, costs, inventory levels, promotional history — with external signals such as competitor prices, market demand, and customer behavior, then applying machine-learning algorithms to model how customers respond to different price points.

The engine estimates price elasticity at the SKU, segment, or channel level, so recommendations reflect how much volume is likely to move if a price goes up or down. From there, it proposes the optimal price for each scenario — a new launch, a routine repricing, a promotional window — balancing margin, volume, and service-level constraints.

Flowlity's pricing optimization continuously learns from actual sales outcomes, refining its elasticity models over time. Pricing teams validate recommendations through guardrails, push them automatically to connected channels, and monitor performance through dashboards — turning pricing into a measurable, data-driven discipline rather than a spreadsheet exercise.