Predictive and prescriptive analytics are two different data analytics techniques that complement each other. While both of them are involved in analyzing vast datasets, they serve different purposes and deliver unique insights.
These two approaches should not be confused and must be used in various ways. In this blog, we will look at their fundamental differences, applications across various sectors, and real-world impact on decision-making.
But before we start, let’s briefly define these machine learning techniques.
Predictive analytics falls under the umbrella of machine learning and employs historical data, statistical algorithms, and machine learning techniques to determine the probability of future outcomes. By analyzing patterns in past data, this approach can provide insights into what will occur in the future and offer forecasts of potential scenarios.
For instance, a retailer can utilize predictive analytics to anticipate future product demands by studying past sales data. This enables organizations to make well-informed decisions by preparing for likely outcomes.
Nevertheless, predictive analytics’ shortcomings include dealing with the quality and completeness of data and ensuring that the models stay up-to-date with changing trends, which requires ongoing adjustments and validation.
Prescriptive analytics is also a subset of machine learning that takes predictive analytics a step further. Instead of just predicting what could happen, it also suggests actions you can take in response to those predictions. This is achieved through the use of complex algorithms, computational modeling, and machine learning.
For example, in the logistics industry, prescriptive analytics can help determine the most optimal delivery routes in real time. Similarly, in the healthcare sector, it can assist insurance providers in designing personalized health plans that optimize both cost and patient satisfaction.
However, there are several obstacles associated with prescriptive analytics. The primary challenges include the complexity of integrating various data sources, the computational cost of running sophisticated models, and the need for continuous updates to adapt to new data or changing conditions, which can be resource-intensive.
We’ll discuss more specific examples later in the blog, but let’s examine the main differences between predictive and prescriptive analytics.
Here are some areas where predictive and prescriptive analytics differ from each other:
| Aspect | Predictive Analytics | Prescriptive Analytics |
| Scope | Models certain aspects of the business. | Models the entire business. |
| Functionality | Forecasts what’s likely to happen and predicts when it will happen. | Forecasts what is likely to happen and recommends specific business decisions. |
| Outputs | Non-actionable; only identifies the need to make a decision. | Actionable; provides tangible and measurable suggestions |
| Optimization | Optimizes one function at the expense of the others. | Considers interdependencies. |
| Basis for Analysis | Data-driven but usually based on predetermined scenarios with finite options. | Data-driven and not bound by static rules. |
| Decision Guidance | No direct guidance on decisions. | Directly establishes the best decisions for the business. |
Predictive and prescriptive analytics are increasingly used in companies’ strategic decision-making. Here are some examples of how different industries use them:
As we’ve seen, many industries use predictive vs. prescriptive analytics to shape the future to their advantage. In this section, we look at some industry best practices from some prominent companies.
The global logistics company DHL has invested over $350 million in digital transformation, which includes integrating predictive analytics to foresee potential disruptions and manage inventory more efficiently. As DHL is creating a “central nervous system” for its operations, this approach has led to more seamless operations and reduced bottlenecks.
FedEx – another major logistics company – has been using prescriptive analytics for route optimization to determine the most efficient paths for delivery vehicles. The company’s commitment to sustainability is demonstrated through the implementation of practices that save time and fuel, and reduce emissions.
American Airlines is leading the way in utilizing prescriptive analytics for flight scheduling and crew assignments within the travel industry. By analyzing crew availability, flight patterns, and passenger demand data, the airline is able to optimize schedules and staff allocation. These data insights help minimize delays, improve employee satisfaction by managing work hours better, and enhance overall operational efficiency.
The use of predictive and prescriptive analytics in finance has revolutionized how institutions handle risk, optimize operations, and enhance customer relations.
To illustrate, Deloitte leverages predictive analytics to improve financial forecasting. They employ statistical models that incorporate a range of economic and operational data points, allowing firms to be better prepared for different economic conditions.
On the prescriptive analytics front, according to research, a large European bank has implemented AI-powered collateral management optimization. As a result, it has streamlined the decision-making process, reduced customer costs, and increased business efficiency, achieving savings significantly above standard industry practices.
Philips Healthcare utilizes predictive analytics to detect early signs of patient deterioration in ICUs and general wards. Their algorithms analyze various signs to forecast potential critical interventions, such as the chances that a pneumonia patient will be readmitted to ICU within 48 hours of discharge. This approach has proven highly efficient, especially during high-stakes environments like the COVID-19 pandemic.
On the other hand, hospitals with geographically distributed facilities, like Dijon University Hospital Center in France, use prescriptive analytics to manage intra-hospital patient transport. By applying real-time data to optimization models, the hospital ensures timely patient transfers for emergency surgeries or other interventions, significantly reducing wait times and enhancing patient care.
In renewable energy, GE Renewable Energy integrates predictive and prescriptive analytics across wind, solar, and hydropower assets. Predictive models forecast energy production and potential system failures. In contrast, prescriptive models suggest exact operational adjustments to optimize overall efficiency, such as adjusting the angle of solar panels to maximize energy capture.
Similarly, Vestas – a leader in wind energy – utilizes predictive analytics to forecast wind power generation, which helps optimize turbine performance and maintenance schedules. The predictive models analyze weather data and historical turbine performance to predict energy output and potential turbine issues.
In addition, prescriptive analytics models recommend ways for the company to adjust turbine operations, such as temporarily shutting down specific turbines to balance grid supply with demand during periods of low energy usage.
The retail giant Amazon uses predictive analytics to forecast customer demand and preferences. This information informs stock levels and the placement of items within its vast distribution system. With the help of predictive analytics, Amazon makes sure popular items are readily available, thus minimizing shipping times and costs.
The retail giant also goes hand-in-hand with prescriptive analytics, helping it dynamically adjust pricing and promotions based on real-time market data and customer purchasing behaviors. This helps maximize its sales by offering the right products at the correct prices at optimal times.
The make-up giant Sephora employs predictive analytics to understand customer preferences and predict future buyer behaviors. This information is used to tailor marketing efforts to the right customers, such as a particular blush to a specific demographic that usually buys it. Using these predictive insights, Sephora prescribes personalized recommendations and promotions to individual customers, improving and increasing customer loyalty.
Finally, this last section will briefly compare and contrast descriptive, predictive, prescriptive, and diagnostic analytics. Although they are all examples of an AI model, each serves a unique purpose and provides different insights based on available data.
| Descriptive Analytics | Predictive Analytics | Prescriptive Analytics | Diagnostic Analytics | |
| Primary Purpose | To summarize historical data and identify what happened. | To use statistical models and forecasts to determine what might happen in the future. | To suggest specific courses of action and the likely outcomes of each decision. | To investigate data and find the cause of events that have already happened. |
| Data Used | Past data from internal sources. | Historical data, along with statistical modeling, machine learning, and algorithms. | Insights from predictive analytics, rules, algorithms, and occasionally machine learning. | A mix of past and real-time data. |
| Analytics Approach | Descriptive statisticsReportingData visualization | Statistical analysisForecasting modelsMachine learning | SimulationComplex event processingRecommendation systemsOptimization models | Drill-downData discoveryCorrelationsPattern recognition |
| Outcome | Insights into past trends and patterns. | Future outcomes or behaviors based on existing data. | Advice on possible actions to take for desired outcomes. | Explanation of why something happened is often done through data examination and hypothesis testing. |
Here’s a short FAQ section to clarify unanswered questions:
Descriptive analytics uses methods like data aggregations and mining to summarize past data to understand what happened. On the other hand, predictive analytics uses historical data to model and forecast potential future outcomes. The relationship between them is sequential – descriptive analytics provides the foundational insights necessary to inform the predictive models that predict future scenarios.
Predictive analytics forecasts future events. It uses statistical techniques and machine learning models to analyze historical data and predict what is likely to happen based on identified trends and patterns.
The difference between prediction and forecast is very subtle. Prediction is a more general term used for estimating the possibility of certain outcomes, often in a broad context. On the contrary, forecasting is more about predicting future trends or events based on historical data and analysis, typically within a defined period.
