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