Prescriptive Analytics Explained: Recommending Optimal Actions

Prescriptive analytics is the most advanced form of business analytics, moving beyond describing what happened and predicting what will happen to recommending what actions to take. It uses optimization algorithms, simulation models, and decision intelligence to identify the best course of action given constraints, objectives, and uncertainties.

While descriptive analytics answers "what happened," diagnostic analytics answers "why," and predictive analytics answers "what will happen," prescriptive analytics answers the crucial question: "what should we do?"

How Prescriptive Analytics Works

Define the Decision Problem

Every prescriptive analytics application starts with clearly defining:

Objectives: What are you trying to maximize or minimize?

• Maximize revenue, profit, customer satisfaction
• Minimize costs, waste, delivery time, risk

Decision variables: What can you control?

• Prices, inventory levels, staffing schedules
• Marketing spend allocation, route selection

Constraints: What limitations must you respect?

• Budget limits, capacity constraints
• Regulatory requirements, service level agreements
• Physical limitations, time windows

Build the Analytical Model

Prescriptive models translate business problems into mathematical formulations:

Optimization models find the best solution given constraints:

• Linear programming for proportional relationships
• Integer programming for discrete decisions
• Nonlinear programming for complex relationships
• Mixed-integer programming for combined problems

Simulation models explore possible scenarios:

• Monte Carlo simulation for uncertainty
• Discrete event simulation for processes
• Agent-based simulation for complex systems

Decision models structure complex choices:

• Decision trees for sequential decisions
• Multi-criteria decision analysis for trade-offs
• Reinforcement learning for adaptive decisions

Generate and Evaluate Recommendations

The model produces recommendations based on inputs:

1. Current state data feeds into the model
2. Algorithm identifies optimal or near-optimal solutions
3. Results are translated into actionable recommendations
4. Sensitivity analysis shows how recommendations change with different assumptions
5. Human decision-makers review and approve actions

Key Techniques in Prescriptive Analytics

Mathematical Optimization

Optimization finds the best solution within constraints:

Example - Pricing Optimization:

Objective: Maximize profit Variables: Price for each product Constraints:

• Prices must be positive
• Price relationships between products
• Demand elasticity effects

The optimization algorithm tests combinations to find prices that maximize total profit while respecting all constraints.

Simulation and Scenario Analysis

When analytical solutions are impractical, simulation explores possibilities:

Example - Inventory Planning:

Run thousands of scenarios with different demand patterns, supply disruptions, and lead time variations. Identify inventory levels that perform well across most scenarios, not just the expected case.

Simulation handles uncertainty that pure optimization cannot easily address.

Machine Learning for Decisions

ML models can learn optimal decisions from historical data:

Example - Dynamic Pricing:

A reinforcement learning model learns which prices maximize revenue by observing how customers respond to different prices over time. The model continuously adapts as market conditions change.

Heuristics and Metaheuristics

For complex problems where optimal solutions are computationally infeasible:

Genetic algorithms evolve solutions through selection and mutation Simulated annealing explores solutions while gradually reducing randomness Tabu search systematically explores neighborhoods while avoiding revisits

These methods find good (though not guaranteed optimal) solutions to otherwise intractable problems.

Business Applications of Prescriptive Analytics

Supply Chain Optimization

Determining what, when, where, and how much to produce and ship:

• Production scheduling across facilities
• Inventory positioning in distribution networks
• Transportation routing and load optimization
• Supplier selection and order allocation

Business impact: 5-15% reduction in supply chain costs while improving service levels.

Workforce Scheduling

Creating optimal employee schedules:

• Matching staffing levels to forecasted demand
• Respecting employee preferences and regulations
• Minimizing overtime while maintaining coverage
• Balancing workload across team members

Business impact: 3-8% labor cost reduction with improved employee satisfaction.

Marketing Mix Optimization

Allocating marketing budgets across channels:

• Determining spend levels by channel
• Timing campaigns for maximum impact
• Balancing acquisition and retention investment
• Optimizing creative and message allocation

Business impact: 10-30% improvement in marketing ROI through better allocation.

Revenue Management

Maximizing revenue through dynamic pricing and inventory control:

• Setting prices based on demand, competition, and inventory
• Allocating limited capacity across customer segments
• Managing overbooking and cancellation policies
• Optimizing promotional pricing and discounts

Business impact: 2-7% revenue improvement without demand increase.

Treatment Optimization in Healthcare

Recommending personalized treatment plans:

• Selecting optimal drug combinations
• Scheduling treatments based on patient response
• Allocating scarce resources like organ transplants
• Balancing efficacy with side effect risk

Business impact: Improved patient outcomes with reduced treatment costs.

Implementing Prescriptive Analytics

Start With High-Value Decisions

Not every decision warrants prescriptive analytics:

Good candidates:

• Repeated frequently (daily, weekly)
• Have significant financial impact
• Involve multiple variables and constraints
• Currently rely on gut feel or simple rules

Poor candidates:

• One-time strategic decisions
• Low-stakes choices
• Simple problems with obvious answers
• Decisions dominated by non-quantifiable factors

Balance Automation With Judgment

Prescriptive analytics supports decisions - it should not always make them automatically:

Full automation: Low-stakes, high-frequency decisions (ad bid optimization) Recommendation + approval: Medium-stakes decisions (pricing changes) Decision support: High-stakes decisions (strategic investments)

Human judgment remains essential for context that models cannot capture.

Plan for Continuous Improvement

Prescriptive systems require ongoing refinement:

• Monitor recommendation quality and adoption
• Gather feedback from decision-makers
• Update models as business conditions change
• Expand scope as capabilities mature

Ensure Transparency

Decision-makers need to understand recommendations:

• Explain why the recommendation is optimal
• Show trade-offs and alternatives
• Provide sensitivity analysis
• Enable what-if exploration

Black-box recommendations face adoption resistance and miss opportunities for human insight.

Prescriptive Analytics Maturity

Organizations typically progress through analytics maturity stages:

1. Descriptive: Reporting what happened
2. Diagnostic: Understanding why
3. Predictive: Forecasting what will happen
4. Prescriptive: Recommending optimal actions

Each stage builds on the previous. Organizations attempting prescriptive analytics without solid descriptive and predictive foundations often struggle.

Prescriptive analytics represents the frontier of business intelligence - transforming data from historical record into active decision guidance.