What is prescriptive analytics?
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Published: 2 May 2024
Contributor: Cole Stryker

What is prescriptive analytics?

Prescriptive analytics is the practice of analyzing data to identify patterns, which can be used to make predictions and determine optimal courses of action.

Prescriptive analytics is a subdiscipline within data analytics, itself a practice situated within the disciplines of business analytics and business intelligence, which are broadly defined as the conversion of data into actionable insights.

There are four key types of data analytics, with prescriptive analytics being the most advanced:

  • Descriptive analytics: “What happened?”

  • Diagnostic analytics: “Why did it happen?”

  • Predictive analytics: “What might happen next?”

  • Prescriptive analytics: “What should we do next?”

While all four types of analytics are useful to tell the story within data, prescriptive analytics differs from the other types in its focus on not only predicting future outcomes but also recommending actions or decisions to achieve wanted outcomes or prevent undesirable ones. It’s not just, “What might happen in the future?” but “What should we do to prepare for the future?”

Organizations use prescriptive analytics for tasks as varied as customer segmentation, churn prediction, fraud detection, risk assessment, demand forecasting, prescriptive maintenance and personalized recommendations. While the practice precedes the advent of big data, the prevalence of large volumes of historical data within organizations has accelerated the practice.

Today, prescriptive analytics tools use many statistical techniques from predictive modeling but also take advantage of artificial intelligence and machine learning algorithms and models. Analytics software uses machine learning models trained on large amounts of data, enabling analysts to more accurately identify risks and opportunities, which guides and improves business leaders’ decision-making.

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Prescriptive versus predictive analytics

Prescriptive analytics adds a recommendation layer on top of predictive analytics, and differs from it in terms of focus, scope and approach.


Prescriptive analytics focuses on recommending actions or decisions to optimize outcomes based on predicted future scenarios. It answers questions like "What should we do to achieve the best possible outcome?" and "How can we mitigate risks or capitalize on opportunities?"


Predictive analytics typically focuses on limited aspects of the business, whereas prescriptive analytics takes into account interdependencies between business functions.


In predictive analytics, analytics techniques like optimization algorithms, decision theory and business rules are incorporated to generate actionable insights. Domain expertise and understanding of broader business contexts factor into the process as well.

Use cases and benefits of prescriptive analytics

Prescriptive analytics offers a wide range of benefits across industries and applications. Here are some of the top benefits along with examples of prescriptive analytics:

Better decision-making

Prescriptive analytics empowers organizations to make data-driven decisions by providing insights into future trends and outcomes. For example, consider a retail chain that wants to forecast demand for a new product. With predictive insights based on historical consumer behavior data, the retail chain can make more informed decisions about whether, when and how to release, price and promote the new product.

By continuously refining prescriptive models, experimenting with new data sources, and exploring innovative approaches, businesses can differentiate themselves in the market and maintain a competitive edge. In healthcare, where making judgments on future outcomes can be a life-and-death matter, prescriptive analytics can be used to decide on optimal treatments or drugs based on many factors.

Enhanced operational efficiency

Prescriptive analytics helps organizations optimize their operations by improving resource allocation and streamlining business processes. By predicting maintenance needs, managing inventory levels and optimizing production schedules, businesses can minimize costs and reduce waste.

Imagine a manufacturing company with an assembly line consisting of various interconnected processes, including component procurement, assembly, quality control and packaging. Prescriptive maintenance can be used to analyze data from sensors, such as temperature, vibration and pressure readings, and predict failure rates so facility managers can service their equipment proactively.

Risk mitigation and fraud detection

Prescriptive analytics helps organizations identify and mitigate risks by detecting anomalies and trends indicative of potential threats. In sectors such as financial services, insurance and cybersecurity, models can assess credit risk and detect fraudulent activities, thereby protecting assets and preserving trust.

Prescriptive analytics assigns risk scores to individual transactions or entities based on their likelihood of being fraudulent. By considering various risk factors such as transaction amounts, frequency, location and customer behavior, advanced analytics algorithms can prioritize alerts and focus investigative efforts on high-risk transactions or entities. This helps fraud detection teams allocate their resources more effectively and respond promptly to potential threats.

Improved customer experience

By anticipating customer needs and preferences, businesses can deliver personalized experiences and tailored solutions. Prescriptive analytics enables organizations to segment their customer base for improved targeting, and offer specific recommendations and other anticipatory engagements based on what the model thinks customers want.

Today’s firms can reduce churn by predicting how customers want to engage with brands and products, using data-driven decision-making where before they could only make guesses. These tools are not just for sales and marketing—they’re for the whole organization. Everything about a product’s development and evolution over time can now be informed by prescriptive analysis that recommends the best actions that will drive customer satisfaction.

How prescriptive analytics works

Prescriptive analytics typically follows these steps:

  1. Defining the problem: First practitioners must define what the model needs to predict to determine the right approach. There are many types of models that are suited to specific use cases. Using the right model and the right data are key for getting the best results faster, and more cost-effectively.

  2. Data collection and preprocessing: The process begins with gathering relevant data from various internal sources and external data from third-party providers. The quality and quantity of data collected are crucial for the accuracy and effectiveness of models. When the data is collected, it undergoes preprocessing to clean, transform and prepare it for analysis. This may involve handling missing values, removing duplicates, standardizing formats and encoding categorical variables. Data preprocessing helps to ensure that the data is consistent and suitable for modeling.

  3. Feature selection and engineering: Next, relevant features are selected or engineered from the data set to use as inputs for models. This step involves identifying the most informative features that have predictive power and may require domain expertise to determine which variables are most relevant to the prediction task.

  4. Descriptive and predictive analytics: Before applying prescriptive analytics, organizations typically perform descriptive analytics to understand past performance and predictive analytics to forecast future outcomes. Descriptive analytics involves summarizing and visualizing data to gain insights into historical trends and patterns, while predictive analytics uses statistical and machine learning models to forecast future events or behaviors.

  5. Prescriptive modeling: Prescriptive analytics solutions involve building mathematical models and optimization algorithms to recommend business decisions that will lead to the best possible business outcomes. These models take into account various factors such as constraints, objectives, uncertainties and tradeoffs. This builds on the output of descriptive and predictive analysis, giving recommendations on how an organization ought to respond to various potentialities.

  6. Deployment: After evaluation, the models are deployed into operational systems or applications where they can make real-time predictions and suggestions on the best course of action. This may involve integrating the models into existing software systems, APIs or dashboards to automate decision-making processes or provide prescriptive insights to users. Automations can help to make insight gathering and use more seamless.

  7. Monitoring and refinement: Models require ongoing monitoring and maintenance to ensure their effectiveness and relevance over time. This involves monitoring model performance, updating models with new data, retraining models periodically and refining models to adapt to changing circumstances or evolving patterns in the data.

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