Glossary · E-commerce ML
Dynamic Pricing
Definition
Dynamic pricing is the practice of adjusting prices in real time based on demand, inventory, user context, competition, or time. Machine-learned pricing uses contextual bandits and demand models, but introduces fairness, perception, and regulatory considerations that static pricing avoids.
Ride-sharing surge pricing, airline yield management, and e-commerce markdowns are canonical examples. Algorithmic pricing must balance short-term revenue, long-term customer LTV, perceived fairness, and competitive response. Contextual-bandit-driven pricing with fairness constraints (demographic parity, equal opportunity) is an active research area.
Essays on this concept
- E-commerce ML
Dynamic Pricing Under Demand Uncertainty: A Contextual Bandit Approach with Fairness Constraints
Airlines have done dynamic pricing for decades. E-commerce is catching up — but without the fairness constraints that prevent algorithms from charging different people different prices for the same product based on inferred willingness to pay.
- Behavioral Economics
The Decoy Effect Reimagined: Dynamic Price Anchoring with Real-Time Behavioral Segmentation
A dominated third option can shift 22% more users to your premium plan. But the static decoy is dead — here's how real-time behavioral data makes asymmetric dominance adaptive.
- Business Analytics
From Dashboards to Decision Systems: Embedding Prescriptive Analytics Into Operational Workflows
Your company has 47 dashboards. How many of them changed a decision last week? Dashboards describe what happened. Decision systems prescribe what to do next — and the gap between these two is where most analytics ROI evaporates.
- E-commerce ML
Personalized Promotion Optimization: Uplift Modeling to Identify Who Needs a Discount vs. Who Would Buy Anyway
70% of promotional spend goes to customers who would have purchased at full price. Uplift modeling identifies the 30% whose behavior actually changes with a discount — and ignores the rest. The math isn't complicated. The organizational willingness to stop blanket discounting is.
- Marketing Engineering
Building a Real-Time Personalization Engine: From Contextual Bandits to Deep Reinforcement Learning
A/B tests answer 'which variant is best on average.' Contextual bandits answer 'which variant is best for this user right now.' The difference in cumulative regret — and revenue — compounds daily.
- E-commerce ML
Search Ranking as a Revenue Optimization Problem: Learning-to-Rank with Business Objective Regularization
E-commerce search is not Google search. When a user types 'running shoes,' the goal isn't to find the most relevant document — it's to surface the product most likely to be purchased at the highest margin. This reframes ranking as a constrained revenue optimization problem.
Related concepts
Authoritative references