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Prescriptive terms
Optimization, simulation and recommendations: what to do about it.
Decision intelligence A discipline combining data, analytics, and decision modeling to design, support, and improve how organizations make decisions. Dynamic pricing Automatically adjusting prices in near real time based on demand, inventory, timing, and competitor behavior. Linear programming (LP) An optimization method that finds the best outcome for a problem whose objective and constraints are all linear relationships. Markdown optimization Using data and models to decide when, how much, and on which products to cut price so excess inventory clears at the best return. Monte Carlo simulation A technique that models uncertainty by running a scenario many times with randomly sampled inputs to see the range of outcomes. Multi-armed bandit (MAB) An adaptive experiment that shifts traffic toward better-performing variants in real time, instead of splitting it evenly until a fixed end date. Next-best-action (NBA) A system or process that recommends the single most valuable action to take for a specific customer at a specific moment. Optimization modeling Using mathematical models to find the best possible decision under a defined set of constraints and an objective to optimize. Prescriptive analytics Analytics that recommends specific actions by combining predictions with optimization or decision rules. Propensity model A predictive model that scores how likely an individual is to take a specific action, such as buying, churning, or clicking. Recommendation system A system that predicts and ranks the items a specific user is most likely to want, such as products, content, or actions. Reinforcement learning A learning approach where an agent learns a strategy by taking actions in an environment and receiving rewards or penalties over time. Scenario planning A planning method that builds and stress-tests a small number of distinct, plausible futures rather than a single forecast. What-if analysis Testing how a model's output changes when one or more input assumptions are changed, to explore specific alternative outcomes.