This is like an online shop or airline that quietly adjusts prices for each customer the way a skilled market trader does—watching how you browse, what you’ve bought before, and how urgent you seem—then offering a price it thinks you’ll accept right now.
Traditional pricing is static and leaves money on the table—either prices are too low (lost margin) or too high (lost sales). AI-driven dynamic and personalised pricing aims to optimise price per user and per moment, balancing conversion and profit while reacting to demand, competitors, and customer behaviour in real time.
If executed well, the moat comes from proprietary behavioural and transaction data (who buys at what price, in what context), continuously improved pricing models, and deep integration into ecommerce or fare‑management workflows that make it hard for customers and competitors to replicate quickly.
Classical-ML (Scikit/XGBoost)
Structured SQL
High (Custom Models/Infra)
Model retraining cost and latency as catalog size, traffic, and features (behavioural signals, competition feeds) grow; plus data-quality and governance constraints across markets and channels.
Early Majority
Compared with generic rule-based dynamic pricing, this approach emphasises more granular, potentially per-customer or per-session price optimisation that blends real-time behavioural data, demand patterns, and possibly fairness/ethics constraints into the pricing logic.