What is Prescriptive Analytics? Full Guide & Examples

What is Prescriptive Analytics?

Discover how prescriptive analytics helps businesses level up their decision-making by using past data and future trends to make well-informed choices that work.

Table of Contents

                What is prescriptive analytics?

                There are three types of business analytics companies can use to get a full picture: Prescriptive, predictive, and descriptive analytics.

                Though the focus of this article is prescriptive analytics, we'll explore the differences among these types of analytics in the next section. First, let's get a better understanding of prescriptive analytics.

                Prescriptive analytics goes beyond predictive and descriptive analytics by helping businesses what to do next

                In addition to predictive and descriptive analytics, which identify what happened in the past and what's likely to occur, prescriptive analytics gives you insights into making the best possible decisions to reach desired outcomes.

                The primary goal of prescriptive analytics is to support decision-makers in determining the next course of action. It helps businesses understand the best steps to achieve a desired goal, navigate the likelihood of worst-case outcomes, and determine how to navigate uncertainty.

                Let's say a brand wants to enhance its customer satisfaction and loyalty. Prescriptive analytics analyzes historical data—such as browsing behavior, past purchases, and business interactions—to identify unique patterns and preferences.

                With this information, the business can offer personalized product recommendations, identify effective marketing strategies, and forecast potential business outcomes. These actions enable leaders to make well-informed decisions.

                By simulating scenarios to evaluate the potential impact of various choices, decision-makers can explore the “what-if”scenarios, exploring the impact of different conditions to help determine a recommended path of action. This makes prescriptive analytics integral to an organization's strategic and real-time planning efforts.

                Prescriptive vs predictive analytics

                We've already touched on how prescriptive analytics is a step further than predictive analytics, so let's explore precisely how the two differ.

                Predictive analytics:

                • Uses historical data and statistical modeling techniques to make predictions about future events or outcomes
                • Identifies patterns and relationships within data to forecast what is likely to happen
                • Focuses on answering “What is likely to happen next?” or “What's the probability of this event occurring?”

                Prescriptive analytics:

                • It goes beyond predicting future outcomes
                • Provides actionable recommendations and decision-making guidance
                • Uses a combination of historical data, predictive modeling, optimization techniques, and business rules to determine the best course of action to achieve specific objectives
                • Focuses on answering “What should we do?” or “What is the best decision to make given the circumstances?”

                Businesses might use predictive analytics to help them determine customer churn, estimate the future demand for products and services, or assess credit risk.

                They'll likely turn to prescriptive analytics to understand how to make their supply chain more efficient, optimize prices, or offer personalized marketing.

                Both approaches are valuable, with predictive analytics helping teams understand future possibilities and prescriptive analytics guiding teams on the best actions to take.

                Businesses can take advantage of either predictive or prescriptive analytics at different levels of insight, applying the most appropriate one to serve a particular purpose.

                How prescriptive analytics work

                Prescriptive analytics uses advanced techniques, including data analysis, predictive modeling, optimization algorithms, and decision-making frameworks.

                The steps taken depend on what a company aims to achieve, the type of information available, and the nature of the business.

                However, a general approach to prescriptive analytics usually works something like this:

                1. Data collection and integration: The first step is gathering relevant data from various sources. This can include historical data, real-time data streams, customer data, market data, and any other information pertinent to the decision. The data is integrated and stored in a centralized repository for analysis.
                2. Data exploration and preparation: The data should be cleaned, processed, and prepared before performing prescriptive analysis. This involves handling missing values, resolving inconsistencies, and transforming the data into a suitable format for modeling.
                3. Predictive modeling: Prescriptive analytics builds on predictive analytics. In this step, statistical and machine learning models are applied to the prepared data to predict future outcomes or behaviors. These models use historical patterns and trends to forecast what's likely to happen.
                4. Optimization techniques: Predictive models alone aren't enough for prescriptive analytics, so optimization techniques help determine the best course of action. These algorithms consider various constraints, objectives, and potential trade-offs.
                5. Decision analysis: The optimization results are combined with business rules and constraints to evaluate scenarios and potential decisions. Prescriptive analytics compares each option's predicted outcomes to uncover which will yield the best results.
                6. Scenario simulation: Prescriptive analytics also involves simulating various scenarios to explore the consequences of different decisions under multiple conditions, addressing decision-maker's 'what-if' queries.
                7. Actionable insights and recommendations: The ultimate goal of prescriptive analytics is to provide actionable insights and recommendations to decision-makers. These insights are presented clearly and understandably, highlighting the best actions.
                8. Implementation and monitoring: Once leaders make decisions based on the prescriptive analytics recommendations, the business implements their choices in real-world operations. The decision's impact is continuously monitored and evaluated to see how well it's doing and if the business should make any changes.

                Why is prescriptive analytics important for business?

                Prescriptive analytics helps businesses move away from instinct- or gut-based decision making to making decisions based on facts and statistical probability. Prescriptive analytics empowers businesses to move beyond reactive decision-making and take proactive measures.

                It uses the power of data and advanced analytics to guide organizations toward more successful and sustainable results.

                Using data-backed analytics gives businesses a competitive advantage—they can react quicker and more effectively to changing market conditions or use the outcomes to improve internal operations and manage risk.

                Though it is effective for businesses where the cost of human error can be detrimental, such as in health care or finserv, there isn't an industry, business, or even part of a business where prescriptive analytics isn't essential for performance and growth.

                Benefits of prescriptive analytics

                Driving business growth and success is the main advantage of prescriptive analytics, but how is this achieved?

                To answer, let's explore the various benefits of prescriptive analytics.

                • Informed decision-making: Considers different scenarios and outcomes, helping minimize uncertainties and risks associated with decision-making.
                • Optimized resource allocation: Identifies the most efficient and effective distribution of resources, improving business operations and reducing costs.
                • Enhanced productivity: Highlights bottlenecks or areas for improvement so that organizations can eliminate wasteful practices.
                • Real-time responsiveness: Provides real-time insights and recommendations so decision-making responds swiftly to market opportunities and changes.
                • Personalized customer experiences: Enables businesses to understand individual preferences and behaviors to offer relevant product recommendations, targeted marketing messages, and customized pricing.
                • Improved customer satisfaction and loyalty: Improved customer experiences through prescriptive analytics means satisfied customers are more likely to stay loyal to the brand and drive positive word of mouth.
                • Competitive advantage: Helps businesses outperform competitors by making data-driven decisions, being more agile, and identifying growth opportunities.
                • Risk management and mitigation: Simulates various scenarios so businesses can develop contingency plans and better prepare for uncertainties.
                • Innovation and long-term development: Highlights customer needs and market demands so businesses can focus innovation efforts on areas with high success potential.
                • Long-term strategic planning: Gives insights into long-term objectives and the most effective paths to achieving them, ensuring decisions align with a business' vision and goals.
                • Compliance regulation: Identifies areas where non-compliance may happen, so businesses can take corrective action and avoid penalties.
                • Continuous improvement: Encourages a culture of continuous improvement by consistently evaluating decisions and strategies based on real-time data. It helps businesses stay adaptable and responsive to changing market dynamics.

                Challenges of prescriptive analytics

                Like other analytical approaches, prescriptive analytics has its challenges.

                Though fantastic for providing valuable insights and benefits, it's also a complex process that is best applied in tandem with careful planning, commitment, and collaboration.

                Recognizing and understanding how to overcome potential barriers is crucial—if you know what to be aware of, you can tailor your prescriptive analytics process to realize its full potential.

                Businesses that make sure their data is of good quality and readily available are more likely to succeed. Inaccurate or incomplete information can lead to unreliable predictions and recommendations—not particularly useful for driving data-backed decisions.

                It can also be important to understand where your data comes from, too. Integrating information from different sources can be complex due to differing formats and structures. This is why cleaning and standardizing data is vital before applying prescriptive analytics.

                One significant hurdle businesses might need to overcome is the expertise and skills gap. Even if you're clued up in data science, other teams may not be—prescriptive analytics is tricky to grasp and challenging to interpret for first-time users. If people don't understand how these complex models arrive at their recommendations, they might not want to apply them.

                A lack of knowledge and trust could slow the successful implementation of prescriptive analytics and prevent the business from using computational thinking. If this might be the case at your company, you could apply more interpretable models initially.

                You could also use visualizations or develop internal methods to explain the model's decision-making process. Addressing this challenge might necessitate additional upfront effort, but will result in a more data-driven culture within the organization—a place where prescriptive analytics can properly do its thing.

                Prescriptive analytics examples

                Prescriptive analytics has the potential to revolutionize industries and transform the way they make decisions.

                It's no wonder that many sectors are already applying prescriptive analytics to drive their everyday operations. Data-backed choices put them one step ahead of the competition and maximize the effectiveness of their efforts.

                From tailored product recommendations and dynamic pricing strategies to fraud detection and content personalization, let's look at the remarkable ways prescriptive analytics can help businesses.

                Customer analytics

                There are ample ways prescriptive analytics impacts customer analytics.

                Take an ecommerce platform—it can use prescriptive analytics to analyze their customers' actions, such as browsing and buying habits, to give the most spot-on product recommendations.

                Subscription-based? Prescriptive analytics can also help tackle reducing churn rates. By looking at usage patterns and how customers interact with them, businesses can identify those thinking of leaving them and create personalized retention plans to retain them.

                Financial services

                Protecting cash and making sound investment choices are no small tasks, so using prescriptive analytics in financial services is crucial.

                Firms can use it to analyze the numbers on risk tolerance, market trends, and individual finances to hand out the best strategies.

                Using prescriptive analytics to dive into transaction patterns and customer behavior, they can also spot fraud and leverage insights to implement more effective protection measures.

                Media and Entertainment

                By using prescriptive analytics to analyze viewers’ watch habits and recommend the perfect content, streaming platforms create a personal show picker that leaves users hooked and engaged.

                Media companies also apply prescriptive analytics to get savvy with their ad game. They use it to target the right people with their ads and put their budget where it matters most. This gives them the best deal for their ad spend and maximizes ROI.


                Online retailers use prescriptive analytics to strengthen their inventory. They analyze past sales, demand forecasts, and delivery times to keep levels right. They can focus on smooth operations and efficiency without stockouts or excess items cluttering their warehouses.

                Elsewhere, ecommerce platforms are getting smart about cart abandonments. By diving into the data and using prescriptive analytics to determine why people ditch their carts, they can employ personalized strategies that nudge customers to seal the deal.


                Using prescriptive analytics in B2B is like having another sales expert on your team.

                Businesses can analyze their information and use the insights to score and prioritize leads, allocate resources, and land deal-closing strategies. Naturally, this all leads to those all-important higher sales conversion rates.


                Prescriptive analytics is a game-changer for the future of healthcare.

                Providers can use the technology for personalized treatment plans by looking at the patient's medical history, genetic data, and how they've responded to different therapies. The result? Far better patient outcomes—something we can all get behind.

                Amplitude and prescriptive analytics

                An employee is only as good as their tools, and nowhere is this more accurate than in the world of data science.

                Applying effective prescriptive analytics software enables you to tap into your information, garner insights, and determine the most appropriate course of action for various business decisions.

                Put simply, Amplitude is the only digital analytics platform that answers what happened, why, and which actions to take next—key features of prescriptive analytics.

                Teams use our software to ask questions they'd never thought to address, exploring behavioral data on a deeper level.

                Better insights naturally lead to more clarity—businesses can use Amplitude Analytics features to spot issues, understand trends, and surface product knowledge, making changes without anxiety-inducing guesswork.

                All this means decision-makers can work confidently and quickly to guide their teams to better outcomes.

                Discoveries come faster, ideation is more efficient, and choices are infinitely more effective—essential attributes for a data-driven business.

                Ready to guide your team to better decision making?

                Prescriptive analytics is all about using the information at your fingertips to help guide your business to more effective decisions—something we at Amplitude know a thing or two about.

                Amplitude blends various tools, models, and features so you and your team can quickly get to work doing what you do best—understanding your customers and driving growth.

                The data is already there, ready, and waiting to be harnessed and applied in the best possible way.

                Let us do the heavy lifting and provide a seamless, intuitive, and collaborative solution.

                Discover how Amplitude uses prescriptive analytics to help you reach your business goals. See for yourself—get started for free today.

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