Pricing Optimization

Pricing Optimization for margin-safe growth.

Metric Hive turns Commerce Intelligence profit data into a pricing and promotion review system. It monitors product prices, realized selling price, discounts, returns, inventory, cost rules, and contribution margin so ecommerce teams can find where to protect margin before they change prices, launch promotions, or increase campaign spend.

Check pricing readiness

The system verifies product catalog, variant or SKU coverage, order lines, discount evidence, COGS or cost rules, refunds, returns, inventory, freshness, and currency policy before showing recommendations.

Model the economics

Commerce Intelligence evaluates realized selling price, gross revenue, net revenue, discount rate, COGS, gross margin, contribution margin, return impact, and ad spend context where available.

Stage review decisions

Recommendation queues can suggest price review, promo review, discount suppression, margin guardrails, campaign exclusions, or clearance review, but decisions remain manual and do not publish price changes.

What teams can review

Pricing Optimization is a Commerce Intelligence add-on. It helps operators, finance, merchandising, and performance marketing teams inspect the economics behind product prices and promotions without rebuilding margin spreadsheets.

Product price and margin monitoringReview current price, realized selling price, discount amount, gross margin, contribution margin, return rate, inventory status, and recommendation status by product or SKU.

Promotion performance analysisCompare owned-data discount and promotion periods by orders, units, revenue, discount amount, contribution margin, return impact, and result label.

Margin leak queuesSurface low-margin products, margin-negative products, high-discount products, high-return products, and other leak signals that need review before the next promotion.

Evidence-backed exportsSend modeled pricing price-margin, promotion performance, margin leak, and recommendation datasets into BigQuery and planning workflows with grain, currency, and provenance preserved.

From commerce data to action

Each output keeps the recommendation-only policy explicit, so teams can review, approve, reject, and export without triggering external price writes.

Inputs

Owned commerce data Catalog, orders, discounts, costs, returns

Readiness

Data checks Coverage, freshness, currency, profit integrity

Model

Commerce Intelligence Gross margin, contribution margin, leaks

Queue

Manual recommendations Review price, promo, discount, campaign

Exports

Modeled datasets BI, finance, planning, BigQuery

Pricing Optimization does not do automatic repricing, personalized pricing, protected-class pricing, legal pricing advice, or commerce-platform price writeback. Competitor and MAP context are only used when policy-approved source evidence exists.