Successful modern businesses are increasingly driven by data. For example, large banks such as JPMorgan Chase use prescriptive analytics in their fraud detection models to reduce false positives (i.e., legitimate transactions that have been flagged as fraudulent) by up to 30%. Businesses of all sizes benefit from effective data use, but they have to know what kind of data they need and what they’re actually trying to do with it—whether that’s forecasting demand, anticipating risk, or informing a future decision.

Predictive and prescriptive analytics each play a different role in helping businesses address uncertainty. But if you don’t understand how they work or when to use them, you could end up with a forecast you can’t act on or a strategy that’s missing context. Below, we’ll explain how these two forms of analytics differ, when to use each, and how they enable smarter decision-making in real-world business scenarios.

What’s in this article?

• What is predictive analytics?
  
• What is prescriptive analytics?
  
• How are prescriptive and predictive analytics different?
  
• How do businesses use predictive analytics?
  
• How do businesses use prescriptive analytics?
  
• When should you use predictive vs. prescriptive analytics?
  
• What are common use cases for predictive analytics?
  

What is predictive analytics?

Predictive analytics uses historical data to forecast outcomes. It spots patterns in past data, then applies statistical models and AI to estimate what’s likely to happen. The result is a probability rather than a certainty, but it’s often enough to make better business decisions.

Examples of predictive analytics include:

• A retailer that projects sales for the holiday season
  
• A bank that flags transactions that might be fraudulent
  
• A subscription platform that predicts which users might cancel
  

What is prescriptive analytics?

Whereas predictive analytics forecasts likely outcomes, prescriptive analytics goes further: it recommends specific actions based on those forecasts. This usually involves weighing multiple possible scenarios, running simulations, and refining for specific outcomes under constraints (e.g., budget, time, resources).

Assume your model predicts a cash shortfall next quarter. A prescriptive system might recommend cutting certain expenses, adjusting your marketing budget, or renegotiating supplier terms—actions specific to your context and data.

How are prescriptive and predictive analytics different?

Prescriptive and predictive analytics are often linked, because the former builds on the latter. But they serve distinct purposes and produce very different outputs. Understanding the difference between the two matters, especially when you’re deciding which approach to apply to a business problem. Predictive analytics tells you what’s most likely to happen, while prescriptive analytics tells you what the best action to take is.

For instance, a predictive model might tell you there’s a 70% chance that a customer will leave in the next month. A prescriptive model takes that forecast, evaluates different options (e.g., offering a discount, adjusting the onboarding experience), and it recommends the most effective course of action based on likely outcomes, constraints, and trade-offs.

In terms of function, predictive analytics is about estimating probabilities using historical data and patterns. Prescriptive analytics incorporates those probabilities into a broader decision framework. It uses methods such as simulations, optimization, and scenario analysis to suggest the most effective strategy.

Prescriptive models often involve a wider set of variables—real-time data, business rules, and external constraints—because their purpose is to give actionable next steps. Whereas predictive analytics might isolate a trend, prescriptive analytics considers context, objectives, and downstream effects.

You might use predictive analytics to identify a likely surge in product demand. But to respond well, you’d need prescriptive analytics to figure out how to adjust inventory, allocate budget, or reroute logistics to meet that demand with minimal disruption or cost.

How do businesses use predictive analytics?

Predictive analytics gives businesses time to plan and act with confidence. When you can see what’s likely to happen, you can respond earlier and smarter.

Here’s how you can use predictive analytics in business operations.

Stronger decision-making

Data-based forecasts help mitigate uncertainty. Whether you’re deciding how much inventory to order, where to open a new location, or which market to target, predictive models can offer directional clarity.

Early warnings for risk

Lenders use predictive analytics to spot borrowers who might default. Security teams use it to detect anomalies that could signal fraud or cyberattacks. By identifying problems before they escalate, you gain a valuable window for intervention.

Operational efficiency

Knowing what’s likely to happen can help you improve logistics and resource allocation.

Here are some examples of changes that businesses might make based on predictive analytics:

• Delivery fleets could schedule proactive maintenance before breakdowns.
  
• Retailers might adjust staffing or inventory based on demand forecasts.
  
• Manufacturers could tweak production to match expected order volume.
  

The results of these kinds of changes tend to be less waste, fewer disruptions, and higher margins.

More targeted customer engagement

Predictive analytics can flag which customers are most likely to leave and which ones are ready to upgrade. With that insight, teams can customize outreach more precisely by:

• Sending retention offers before customers check out
  
• Recommending products based on purchase signals
  
• Prioritizing leads based on the likelihood of conversion
  

With predictive analytics, you can get a better return on your effort, and customers get a more relevant experience.

How do businesses use prescriptive analytics?

Prescriptive analytics helps teams act on their data. Instead of revealing patterns or forecasting outcomes, it delivers concrete, data-backed recommendations specific to your goals and constraints.

Here’s what that can do for your business.

Faster, more confident decisions

Prescriptive analytics points to specific next steps and explains why they make sense. This can decrease decision paralysis, especially when the stakes are high or time is short.

Smarter resource allocation

Prescriptive models consider multiple factors and competing priorities to suggest how to get the most out of your resources.

For example, they might advise you to:

• Improve maintenance timing to reduce downtime without overservicing
  
• Distribute inventory across warehouses to minimize shipping costs and delays
  
• Sequence staffing schedules to match projected demand by region or shift
  

These models help you make smarter, data-driven decisions about how to use your resources more strategically.

Proactive risk management

Predictive analytics flags potential risks, and prescriptive analytics tells you what to do about them. For example, the latter might advise you to assign verification steps for high-risk transactions or offer alternate loan structures for applicants who wouldn’t qualify under standard terms.

Deeper personalization

Prescriptive systems can determine the best action to take for each customer based on their behavior. This helps make your outreach more personalized and effective, while removing the guesswork. It’s the difference between sending a generic promotion and offering something a customer is statistically likely to care about at exactly the right moment.

When should you use predictive vs. prescriptive analytics?

Predictive and prescriptive analytics are designed for different types of questions. The right one in any situation depends on what kind of insight you want and how mature your data capabilities are.

In some cases, a reliable forecast is all a business needs. If you’re making a single-point decision (e.g., setting next month’s ad budget, estimating warehouse space), a predictive model might provide enough clarity to proceed. Businesses can use predictive analytics to:

• Understand what’s likely to happen next
  
• Spot trends, flag potential risks, or forecast future conditions
  
• Reduce uncertainty in areas such as customer behavior, sales projections, inventory demand, and financial risk
  

But as decisions get more complex—with dependencies, competing priorities, or meaningful financial consequences—you’ll need a way to both anticipate outcomes and choose among options. Prescriptive analytics helps you assess trade-offs and find the most effective strategy based on your actual constraints. Businesses can use prescriptive analytics to:

• Receive clear, data-backed recommendations on what actions to take
  
• Manage multiple variables, constraints, or possible trade-offs
  
• Enhance a process or outcome
  

In many business scenarios, predictive and prescriptive analytics work best in tandem. Predictive analysis flags a likely event or outcome (e.g., demand surge, customer churn, machine failure). Prescriptive analysis then evaluates options and suggests the most effective response (e.g., rerouting inventory, adjusting pricing, increasing service touchpoints).

What are common use cases for predictive analytics?

Predictive analytics has applications in almost every industry, whenever businesses need to anticipate behavior, demand, or risk. Here are some common use cases across different types of businesses.

Fraud detection and credit risk

Financial institutions rely heavily on predictive models to manage risk. Transaction monitoring systems flag payments that look suspicious based on patterns of past fraud. Credit scoring models predict the likelihood that a borrower will default on a loan. This helps lenders make smarter underwriting decisions and set interest rates that reflect risk exposure. These models develop in real time and continually refine themselves as new data becomes available.

Churn prediction and customer retention

In subscription-based businesses, predictive analytics helps flag which customers are likely to cancel before they actually do. Usage patterns, support interactions, and behavioral signals (e.g., fewer logins, reduced engagement) feed into churn models that score risk at the individual level. Marketing and support teams can then intervene proactively with retention offers, targeted messaging, or account outreach.

Product recommendations and personalization

Predictive systems usually inform the recommendation engines you encounter online. They analyze purchase histories, browsing behavior, and demographic data to anticipate what a customer will probably want next. Ecommerce platforms use these models to recommend products each customer is most likely to buy. Media platforms apply similar logic to recommend shows, playlists, or articles based on prior viewing and engagement patterns.

When done well, this can both boost conversion and improve the user experience.

Demand forecasting and inventory planning

Retailers and supply chains use predictive models to forecast demand by product, region, channel, and time frame. Historical sales trends, seasonality, promotional calendars, and even external factors such as weather and macroeconomic signals all feed into the prediction. This insight can drive more precise inventory purchases, warehouse planning, and distribution logistics.

The goal is to avoid running out of stock, free up working capital, and reduce over-ordering by matching supply to expected demand as closely as possible.

Predictive maintenance

In industries such as manufacturing, logistics, and aviation, predictive maintenance models are important for ensuring high uptime. Sensors collect data on vibrations, temperature, usage hours, and other indicators of wear and tear. Predictive models then estimate the likelihood of failure and recommend optimal service timing.

This lets teams service equipment just before issues arise, which avoids costly downtime and unnecessary scheduled maintenance. This is a shift from reactive to preemptive operations.