Prescriptive Analytics
The most advanced form of analytics that not only predicts what is likely to happen but recommends specific actions to take, using optimisation, simulation, and decision science.
In-Depth Explanation
Prescriptive analytics represents the highest level of analytics maturity. It goes beyond prediction to recommend specific actions, quantify their expected impact, and even automate decision-making. It answers "what should we do?" and "why?"
Prescriptive analytics techniques:
- Optimisation: Finding the best solution given constraints (resource allocation, scheduling, pricing)
- Simulation: Testing different scenarios to understand outcomes (Monte Carlo simulation)
- Decision trees: Mapping decision paths and expected outcomes
- Reinforcement learning: Learning optimal strategies through trial and feedback
- Recommendation engines: Suggesting next-best-actions for customers or processes
- Constraint programming: Solving complex scheduling and allocation problems
Common prescriptive analytics applications:
- Dynamic pricing: Automatically adjusting prices based on demand, competition, and inventory
- Resource optimisation: Allocating staff, equipment, or budget for maximum efficiency
- Supply chain optimisation: Determining optimal inventory levels, reorder points, and routing
- Treatment recommendations: Suggesting the best action for each customer situation
- Portfolio optimisation: Balancing risk and return in investment decisions
- Schedule optimisation: Creating optimal staffing or production schedules
Prescriptive analytics in practice:
- A churn prediction model identifies at-risk customers (predictive)
- The prescriptive layer recommends the best retention offer for each customer based on their value, risk level, and historical response to similar offers
- The system quantifies the expected impact (e.g., "this action has a 65% probability of retaining the customer, worth $5,000 in annual revenue")
Implementation considerations:
- Requires strong predictive analytics foundation
- Needs clear decision frameworks and business rules
- Must integrate with operational systems for action execution
- Requires ongoing monitoring of recommendation effectiveness
- Human oversight remains important for high-stakes decisions
Business Context
Prescriptive analytics closes the gap between insights and action, enabling businesses to make optimal decisions systematically rather than relying on individual judgement for every decision.
How Clever Ops Uses This
Clever Ops implements prescriptive analytics for Australian businesses, building recommendation engines, optimisation models, and automated decision systems that translate data into optimal actions. We help clients move beyond dashboards to systems that actively recommend and execute the best course of action.
Example Use Case
"A staffing company implements prescriptive analytics that recommends optimal shift scheduling based on predicted demand, staff preferences, compliance requirements, and cost constraints."