What is Prescriptive Analytics?
La analítica prescriptiva se considera la forma más sofisticada de analítica y a menudo se alcanza con inteligencia artificial. Prescriptive analytics goes beyond describing what happened in the past (descriptive analytics), what caused something to happen (diagnostic analytics), and predicting what is likely to occur in the future (predictive analytics). It prescribes specific actions that can be taken to cause a desired future outcome.
A physician may use data, your symptoms, and their experience, to identify a patient's ailment, but that alone will not provide a cure. Determinar el padecimiento del paciente y cómo tratarlo es el problema más importante de última milla. The same is true with prescriptive analytics. Collecting data and identifying appropriate analytic techniques only creates insights. Dónde y cómo los negocios utilizan esa información para generar óptimas participaciones del cliente o decisiones de negocios en tiempo real es el verdadero valor de la analítica. After all, insights for insights' sake will not improve a business, grow revenue, or create a delightful customer relationship.
“Data without a decision is a distraction.” — Gareth Herschel, Vice President, Gartner Group
How Prescriptive Analytics work
Prescriptive analytics represents the most advanced form of data analysis, going beyond descriptive and predictive analytics to recommend specific actions for optimal outcomes.
Prescriptive analytics combines historical data, real-time information, and advanced algorithms including machine learning, artificial intelligence, and optimization techniques to determine the best course of action for any given scenario.
A general approach to prescriptive analytics usually includes these 9 steps:
Step 1: Data collection and integration
- Gather relevant data from multiple sources such as real-time data streams, customer data, market data, transactions, etc.
- Review the data to ensure quality and consistency across all sources
- Store the data in a centralized repository for analysis
Step 2: Data preparation
- Clean, process, and prepare the data for analysis: Remove missing values, resolve inconsistencies, and update the format to be suitable for modeling
Step 3: Descriptive analysis
- Analyze what happened in the past
- Create baseline understanding of historical patterns and trends
- Establish key performance indicators (KPIs) and metrics
Step 4: Predictive modeling
- Use applied intelligence, machine learning algorithms, the historical patterns and trends identified in step 3, and statistical methods to predict future outcomes
- Generate probability scenarios for different outcomes
Step 5: Optimization
- Consider business constraints (budget, resources, regulations)
- Apply mathematical optimization techniques
- Evaluate multiple possible actions and their trade-offs
Step 6: Scenario simulation
- Run “what-if” analyses for different decision options
- Model the potential impact of various choices
- Account for uncertainty and risk factors
Step 7: Provide actionable recommendations
- Compares each scenario’s predicted outcomes to uncover which will yield the best results based on the KPIs identified in step 1
- Rank alternatives by expected outcomes
- Provide clear, specific, and actionable recommendations highlighting the best actions
Step 8: Implementation and monitoring
- Execute the recommended actions
- Track performance against predictions and KPIs
- Continuously refine models based on new results
Step 9: Track and refine
- Learn from outcomes to improve future recommendations
- Update models with new data and insights, taking changing business conditions into consideration
The benefits of Prescriptive Analytics
Prescriptive analytics examples and use cases

Transportation & logistics
Vehicle routing
Determine the most efficient set of routes for a fleet of vehicles to service with a known set of customers or locations, while minimizing costs such as total distance, or travel time, and respecting constraints like vehicle capacity, delivery time windows, and driver regulations.

Telecomunicaciones
Call center staffing
Forecast call volumes and optimize workforce staffing to ensure customer demand is met at the lowest possible cost while maintaining service-level agreements such as average wait time or call abandonment rates.

Health & life science
Medical resource allocation
Distribute limited resources, such as hospital beds, ventilators, staff, medical supplies, and so on across patients, departments, or regions in a way that maximizes patient outcomes and system efficiency while minimizing inequities.

Energy & utilities
Production scheduling
Determine the precise output levels of production facilities, coordinating resources to minimize costs, meet demand forecasts, and comply with operational and regulatory requirements.

Communication & retail
Inventory management
Determine the right levels of inventory to hold, replenish, and distribute across stores, warehouses, and channels in order to meet customer demand while minimizing holding costs, stockouts, and waste. Robust optimization practices are commonly used to control for uncertainty in forecasts and predictions used in the models.
Challenges
Key takeaways
Our FICO Xpress Optimization solution uses prescriptive analytics to help business users solve complex problems in minutes, for faster deployment, increased revenue, and better customer satisfaction.
- Your organization may have good data and a full cadre of analytic scientists, but can you use the insights to make better decisions? Si la ciencia de datos no puede equipararse con sus problemas de negocios y, fundamentalmente, con optimizar el compromiso de los clientes en tiempo real, ¿entonces para qué sirve?
- Prescriptive analytics, unlike predictive analytics or AI by itself, is about mapping data and analytics to YOUR business problem. En lugar de simplemente ofrecerle información, la analítica prescriptiva fundamenta las acciones que debería tomar.
- There are proven methodologies to mapping all analytics to business engagement rules — what you can do, what inventory is available, what your optimal margin requirements or risk thresholds are — to enable everyone in your business to optimally engage customers and create the best relationships — relationships that optimize both business and customer satisfaction.
- Prescriptive analytics is the step beyond analytics or artificial intelligence. Luego de haber recopilado datos y obtenido información, a través de procesos humanos o robóticos, utilizar esos conocimientos en interacciones humanas en tiempo real solo es posible si invierte en metodologías prescriptivas.
- FICO has invested decades in developing and improving prescriptive analytic techniques and created a unique and proven methodology that combines data, analytics, AI, decision rules, and optimization in a unique and powerful manner to deliver prescriptive analytics to businesses in every industry imaginable, including financial services, energy, transportation and logistics, retail, government, and many others