This is like an online store’s version of airline ticket pricing: the price of a product can automatically go up or down during the day based on demand, competition, and stock levels instead of staying fixed.
Static prices leave money on the table and miss demand swings. Dynamic pricing aims to automatically set ‘the right price at the right time’ to maximize margin and/or sales volume while staying competitive, instead of relying on infrequent manual repricing.
Moat typically comes from proprietary demand data (historical sales, clickstream, competitive price monitoring), domain-specific pricing rules, and tight integration into ecommerce, inventory, and promotion workflows rather than from unique algorithms alone.
Classical-ML (Scikit/XGBoost)
Time-Series DB
Medium (Integration logic)
Real-time price computation latency and data freshness (ingesting competitive prices, inventory, and demand signals fast enough to update prices without harming user experience).
Early Majority
The concept itself is now standard; differentiation in practice comes from how granularly prices are adjusted (per user, per segment, per region), how many signals are ingested (competitor prices, browsing behavior, marketing channels), and how well business constraints (minimum margins, brand positioning, legal limits) are encoded into the optimization logic.