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Prescriptive Analytics

Haga que la analítica le ayude a optimizar sus decisiones de negocio.

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What is Prescriptive Analytics?How it worksBenefitsExamples and use casesChallengesKey takeaways

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

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More objective decision-making
Prescriptive analytics provides data-driven recommendations that replace gut-feeling decisions with evidence-based choices, allowing organizations to significantly reduce decision-making time by automating complex analytical processes that would otherwise take weeks or months.
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Better operational efficiency
Analytics-based decisions allow businesses to optimize their resource allocation to achieve maximum productivity with minimum waste and eliminate inefficient practices that drain resources and time. In addition, routine decision-making processes can be automated to free up human resources for more strategic tasks.
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Improved risk management
Organizations can identify potential risks and threats before they materialize into actual problems, and develop comprehensive contingency plans for different scenarios and unexpected situations.
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Cost reduction
Prescriptive analytics can reduce operational costs through better planning, forecasting, and resource allocation. For example, by minimizing inventory holding costs and reducing waste through more accurate demand predictions.
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Revenue growth
Prescriptive analytics helps companies optimize their pricing strategies to achieve maximum profitability while remaining competitive. It also allows businesses to improve customer targeting and personalization efforts to increase conversion rates and customer lifetime value.
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Improved long-term planning
Prescriptive analytics improves agility in strategic planning and enables faster response to market changes so organizations can prepare themselves for potential market disruptions and pivot quickly.

Prescriptive analytics examples and use cases

Construction worker with tablet

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.

Woman in call center with headset and computer

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.

Medical Resource Allocation

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.

Man in warehouse with vest hat and tablet

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.

Grocer in grocery store with tablet

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

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Data quality and availability
Incomplete, inconsistent, or low-quality data can undermine the accuracy of prescriptive models and data silos across different departments and systems, making it difficult to integrate information for comprehensive analysis.
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Technical complexity
Prescriptive analytics requires sophisticated mathematical modeling and optimization techniques that are complex to implement and maintain, which can be a challenge for organizations without advanced computing infrastructure and processing power to handle large-scale optimization problems. The complexity of algorithms makes it difficult for nontechnical stakeholders to understand the recommendations.
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Implementation and change management
Some stakeholders may resist adopting new data-driven processes and recommendations, preferring familiar decision-making methods, especially when recommendations conflict with experience or intuition.
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Model accuracy and reliability
Prescriptive analytics models are only as good as the underlying predictive models, and inaccuracies can lead to poor recommendations. For example, over-reliance on historical patterns may not account for unprecedented events or black swan scenarios.

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

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