Prescriptive analytics is the next step in business analytics. Unlike predictive analytics, which only looks at past data to make predictions, prescriptive analytics considers both past data and current conditions to prescribe the best course of action. Keep reading to learn more about prescriptive analytics and how it can help your business.
What is prescriptive analytics?
So, what is prescriptive analytics? Prescriptive analytics is a subset of business analytics that uses mathematical models to prescribe actions to improve performance. It goes beyond descriptive and predictive analytics by not only describing what has happened and what might happen in the future but also providing guidance on what actions should be taken to achieve specific goals. Prescriptive analytics relies on simulation, optimization, and machine learning techniques to analyze data and identify the best course of action for a given situation. Businesses can benefit from prescriptive analytics in several ways. If a company has large amounts of data, complex business processes with many interactions, a need for better decisions and faster action, etc., then prescriptive analytics can help.
What are the benefits of prescriptive analytics?
Organizations can use prescriptive analytics in several ways, including optimizing operations, making better decisions, and enhancing customer engagement. Organizations can use prescriptive analytics to identify areas where they can improve efficiency or effectiveness. For example, a retailer might use prescriptive analytics to determine which products are most likely to be purchased together and recommend that those products be placed near one another in the store.
Organizations can also use prescriptive analytics to help them make better decisions about everything from what products to offer customers to how much inventory to stock. For example, a bank might use prescriptive analytics to decide whether it should approve a loan for a particular customer or not. By understanding how customers interact with their products and services, organizations can use prescriptive analytics to create more engaging customer experiences. For example, an airline might use prescriptive analytics to determine what type of music will be most appealing to passengers on a particular flight route and then personalize the in-flight entertainment accordingly.
What industries use prescriptive analytics?
Prescriptive analytics has already found a home in several industries, including health care, finance, and manufacturing. One industry that is particularly well suited for prescriptive analytics is energy. In the energy industry, there are many factors that need to be considered when making decisions about how to allocate resources. Prescriptive analytics can help energy companies optimize their operations by considering all of the relevant variables and recommending the most efficient course of action.
Telecommunications companies can also benefit from using prescriptive analytics. Telecommunication companies use prescriptive analytics to optimize network usage and bandwidth allocation. Telecommunication companies will also be able to determine which offers are most likely to attract new customers, identify service issues that are causing customers to churn, and recommend ways to improve network performance. For higher education, colleges and universities have been increasingly using data mining and predictive modeling techniques to improve their operations. Prescriptive analytics can be used by educational institutions to help them make decisions about things such as student retention, course selection, and financial aid allocation.
Lastly, the health care industry is well suited for prescriptive analytics. Healthcare providers are constantly faced with difficult decisions about how to best allocate their resources in order to provide the best possible care for their patients. Prescriptive analytics can be used by healthcare providers to help them make decisions about things such as which treatments to offer, which drugs to prescribe, and where to open new clinics or hospitals. Ultimately, any business that needs to make decisions based on many variables can benefit from using prescriptive analytics. By using mathematical models to analyze the relevant data, companies can streamline their decision-making processes and achieve better results.
How do you set up prescriptive analytics?
The first step to setting up prescriptive analytics is to identify what goals you want to achieve. What business outcomes do you want to see improved? Once you have a plan in mind, you need to gather data to help you achieve that goal. The type of data suitable for prescriptive analytics varies because there are many different data sources. The most crucial factor is that the data is accurate and up to date. You’ll need to have information on past events, as well as current and future trends, to make informed decisions.
Some of the most common data sources for prescriptive analytics include customer data, location data, time data, data on business processes, data on financial performance, data on human resources, and data on machinery. Let’s expand customer data, location data, and time data. Customer data can include past purchases, demographic information, and contact information. Location data can include GPS coordinates, traffic patterns, and other geographical information. Time data can consist of data on past and future time intervals, as well as information on seasonal trends.
Once you have the data, you need to determine which mathematical models can help you achieve your goal and identify the inputs and outputs for the models. Mathematical models are used to prescribe actions or policy changes to improve an organization’s performance. The models use historical data to identify patterns and trends and then recommend actions to take advantage of or respond to those patterns and trends. The models are typically built using two main approaches: simulation or optimization. Simulation models use a mathematical representation of how a system works to test different scenarios and explore the effects of other policies or actions. Optimization models use algorithms to find the best possible solution to a problem, such as finding the most efficient way to distribute resources or products.
After the models have been identified, you need to set up the infrastructure to run the models. This includes setting up the software and servers required to run the models, as well as determining how the models will be accessed by users. The final step is to test the models and ensure they produce the desired results. After the models have been validated, you can use them to improve business outcomes.