How does prescriptive analytics differ from predictive analytics?

Predictive analytics forecasts outcomes (what will happen). Prescriptive analytics recommends actions (what should you do about it). For example, predictive might say "demand will increase 30% next week." Prescriptive would say "increase inventory of these specific SKUs by these specific amounts, and schedule two additional shifts to handle the volume."

Is prescriptive analytics suitable for mid-market businesses?

Yes, but start with targeted applications. You do not need to implement prescriptive analytics across the entire business at once. Start with a high-value decision that is repeated frequently (pricing, scheduling, inventory ordering) where even small improvements generate significant returns. Modern cloud tools have made sophisticated optimisation accessible to businesses of all sizes.

Should prescriptive analytics decisions be automated?

It depends on the stakes and complexity. Low-risk, high-frequency decisions (email send times, inventory reorders below a threshold) can be fully automated. High-value or complex decisions should present recommendations for human review and approval. As confidence in the system grows, the degree of automation can increase. Always maintain human oversight for decisions with significant financial or legal implications.

Book Free Assessment Calculator

Prescriptive Analytics

Also known as:decision analyticsoptimisation analyticsdecision science

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."