Prescriptive Analytics

Definition

What it is
Prescriptive analytics is a set of methods and tools that recommend specific actions to achieve a desired outcome, given predictions, constraints, and business rules. Where predictive analytics estimates what is likely to happen, prescriptive analytics determines what should be done next to optimize against defined objectives such as profit, revenue, or utilization.

How it relates to marketing
In marketing, prescriptive analytics operationalizes data-driven decisions. It uses forecasts, customer models, and constraints (budgets, inventory, contact limits, compliance rules) to recommend optimal actions: which segments to target, what offers to send, where to allocate budget, and how to sequence interactions across channels. It moves from “who is likely to respond” to “which offer, through which channel, at what time, under current constraints, is best for this customer or segment?”

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How to calculate prescriptive analytics

There is no single formula, but most prescriptive approaches use some form of optimization:

1. Define the objective function
   Examples:
   • Maximize profit:
     [
     \max \sum_{i} \sum_{j} x_{ij} \cdot (r_{ij} – c_{ij})
     ]
     where
     ( x_{ij} ) = decision variable (e.g., 1 if customer (i) receives offer (j), else 0)
     ( r_{ij} ) = predicted revenue from customer (i) if given offer (j)
     ( c_{ij} ) = cost of offer (j) to customer (i)
   • Maximize expected response:
     [
     \max \sum_{i} \sum_{j} x_{ij} \cdot p_{ij}
     ]
     where
     ( p_{ij} ) = predicted probability of desired behavior (click, purchase, renewal, etc.)
2. Define constraints
   Examples:
   • Budget:
     [
     \sum_{i} \sum_{j} x_{ij} \cdot c_{ij} \leq B
     ]
   • Contact frequency:
     [
     \sum_{j} x_{ij} \leq K \quad \forall i
     ]
   • Channel capacity:
     [
     \sum_{i} x_{ij} \leq C_j \quad \forall j
     ]
   • Compliance rules (do-not-contact, segment eligibility, regions) modeled as equality or inequality constraints or logic constraints.
3. Solve the optimization problem
   Common methods:
   • Linear programming (LP)
   • Mixed-integer programming (MIP)
   • Heuristics and metaheuristics (greedy algorithms, genetic algorithms, simulated annealing)
   • Reinforcement learning and bandit algorithms for sequential decisions

The output is a set of recommended actions and allocations that satisfy constraints while optimizing the objective. Marketers do not usually build these solvers, but they specify objectives, constraints, and acceptable trade-offs.

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How to utilize prescriptive analytics

Audience and offer selection

• Use predicted probabilities and values as inputs and let the prescriptive engine decide:
  • Which customers to contact
  • Which offer or message to assign
  • Which customers to suppress to preserve margin or reduce fatigue
• Incorporate contact rules (max contacts per period, channel preferences, regulatory constraints) directly into the optimization.

Budget and media allocation

• Allocate budget across channels, campaigns, or markets to maximize expected outcomes, subject to:
  • Overall budget constraints
  • Minimum and maximum spend per channel
  • Diminishing returns curves or response functions
• Run allocation scenarios for different budget levels or objectives (e.g., maximize profit vs. maximize reach within a target CPA).

Pricing and discount strategy

• Recommend price points or discount levels that:
  • Respect guardrails (minimum margin, brand guidelines, legal conditions)
  • Consider predicted demand and profitability
• Use prescriptive models to determine which customers receive discount offers, and at what level, to avoid unnecessary margin erosion.

Journey and interaction orchestration

• Given a set of possible next actions (email, call, ad exposure, no contact), select:
  • The best action for each customer at a given point in time
  • The timing and sequence of actions across channels
• Use prescriptive rules to balance:
  • Short-term conversion vs. long-term relationship metrics
  • Individual optimization vs. operational limits (agent capacity, inventory, channel caps)

Operations and service

• Optimize call center or chat routing using predicted value, churn risk, and issue type.
• Recommend escalation, retention offers, or service levels that match customer value and risk, under staffing and policy constraints.

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Comparison to similar approaches

Aspect	Descriptive Analytics	Diagnostic Analytics	Predictive Analytics	Prescriptive Analytics	Rules-Based Targeting / Business Rules
Main question answered	What happened?	Why did it happen?	What is likely to happen?	What should we do?	What do we do in predefined situations?
Time orientation	Past	Past	Future	Future	Present/future, based on static logic
Primary output	Reports, dashboards	Insights on drivers	Scores, probabilities, forecasts	Recommended actions, allocations, policies	If–then logic, segment assignments
Methods	Aggregation, visualization	Root-cause analysis, segmentation	Statistical models, machine learning	Optimization, simulation, decision engines	Manual rules, decision tables
Use of constraints	Minimal	Minimal	Often implicit (in data)	Explicit: budget, capacity, policy, compliance	Encoded directly into rules
Automation level	Low	Low	Medium (feeds into processes)	High (can automate decisions under governance)	Medium (as far as rules are updated)
Typical marketing examples	Campaign reports, funnel metrics	Channel or segment performance analysis	Churn prediction, response modeling	Next-best-action, budget allocation, offer selection	Eligibility rules, suppression lists, triggers

Prescriptive analytics frequently uses outputs from predictive analytics and sometimes coexists with rules-based systems, but it adds an explicit optimization layer to select actions under constraints.

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Best practices

• Start from a clear objective and scope
  • Define a precise optimization goal (e.g., “maximize 90-day profit,” “maximize qualified opportunities,” “minimize churn at fixed budget”).
  • Decide whether you are optimizing per customer, per segment, per campaign, or at portfolio level.
• Work jointly with finance, operations, and legal
  • Capture real constraints and policies accurately: budgets, capacity, contract terms, regional regulations, contact policies.
  • Validate that recommended strategies are practical to execute.
• Use reliable predictive inputs
  • Feed prescriptive engines with well-calibrated predictive models (propensity, LTV, churn risk), not raw heuristics.
  • Validate that predictive models behave sensibly across segments and do not embed obvious bias that would be amplified by optimization.
• Reflect real-world trade-offs in the objective function
  • Include both revenue and cost, and where needed, penalties for over-contact, customer dissatisfaction, or inventory risk.
  • Consider multi-objective optimization or weighted objectives if multiple outcomes matter (e.g., revenue, margin, and fairness across segments).
• Pilot and test before broad rollout
  • Run controlled experiments comparing prescriptive policies to business-as-usual or expert-designed strategies.
  • Start with a limited domain (e.g., one segment or product line) to validate performance and operational impact.
• Maintain transparency and control
  • Provide clear documentation of objective functions, constraints, and key assumptions.
  • Allow for overrides and guardrails where business owners need final control (e.g., exclusions for sensitive segments).
• Monitor performance and adjust
  • Track both technical metrics (solver convergence, stability) and business metrics (profit, response rate, retention, customer complaints).
  • Update constraints and objective functions as conditions, regulations, or strategy change.
• Integrate into operational tooling
  • Surface recommendations directly in CRM, marketing automation, ad platforms, and call center systems.
  • Define playbooks and workflows that specify how teams act on recommendations, including exceptions and escalation paths.

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Future trends

• More real-time decisioning
  • Prescriptive engines will operate in near real time, adjusting actions as new events occur (web activity, app behavior, service interactions), especially in dynamic bidding and next-best-action contexts.
• Closer integration with generative and predictive AI
  • Predictive models will estimate likely outcomes; prescriptive layers will choose actions; generative systems will tailor creative and messaging that fits those decisions.
• Wider use of reinforcement learning and bandit methods
  • Systems will increasingly learn optimal policies over time from interaction data, balancing exploration (trying new actions) and exploitation (using known good actions).
• Multi-objective and constrained optimization
  • Marketers will optimize against multiple objectives at once (profit, equity across segments, sustainability targets), under regulatory and operational constraints.
• Stronger governance and responsible decisioning
  • Formal frameworks will be used to assess fairness, explainability, and compliance of prescriptive systems, especially where pricing, eligibility, and service levels are affected.
• No-code and low-code prescriptive tools
  • Marketers and analysts will configure objectives, constraints, and policies directly in interfaces, with optimization engines running behind the scenes.

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Related Terms

• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Optimization
• Next-Best-Action (NBA)
• Marketing Mix Optimization
• Uplift Modeling
• Decision Engine
• Reinforcement Learning
• Constraint Programming