What is Prescriptive Analytics?
May 25, 2021 ● 5 min read
Big Data is now available from a wide range of places, which sometimes can be found hard to comprehend and manage. There are many ways to categorize and analyze new data, so today we will introduce you to one of them - Prescriptive Analytics.
Table of Contents
- What Is Prescriptive Analytics?
- Benefits of Utilizing Prescriptive Analytics
- What Is The Difference Between Descriptive and Prescriptive Analytics?
- What Is The Difference Between Predictive and Prescriptive Analytics?
- Examples of Prescriptive Analytics
What Is Prescriptive Analytics?
It’s a type of business analytics that helps companies and organizations make informed decisions by analyzing raw data. In other words, it analyzes data and gives instant suggestions for the best action to take in a given scenario. The goal is to find the best solution or steps to take.
Prescriptive analytics uses data from descriptive and predictive analytics to recommend the best solution for a given situation. It takes and understands the “what we know” data (descriptive analytics) and predicts what might happen based on it (predictive analytics) to simulate different approaches to all these outcomes so that it can finally suggest the best course of action.
It’s the final or third step of business analytics and the natural progression from predictive and descriptive analytics procedures.
Prescriptive analytics uses a combination of techniques and tools such as algorithms, business rules, machine learning, and artificial intelligence to help marketers and data scientists understand the meaning of their data and connect the right dots to deliver a highly customized user experience to their audience.
It provides companies with suggestions on the best actions to take to achieve business objectives such as profits, customer satisfaction, and cost savings. These recommendations use optimization technology to solve complex problems with numerous decision variables, trade-offs, and constraints.
Benefits of Utilizing Prescriptive Analytics
Here is the list of advantages of prescriptive analytics:
- Make real-time informed decisions. It makes the decision-making process easier by using a data-driven approach. This, in turn, allows you to use new opportunities and stay one step ahead of competitors.
- Create a highly personalized user experience. This can help you deliver timely discounts, recommend products, and provide site recommendations in real-time.
- Optimize business actions. Not only that you can see what may happen in your business, but you can also see how to make it happen even better by making data-driven decisions.
- Improve productivity. It makes the decision-making process better, easier, and cost-effective throughout the entire organization, which means improved productivity of every department.
- Solve complex problems. It can help to identify the issues that prevent your business from growing.
The bottom line is that prescriptive analytics can help increase efficiency, limit risk, meet business goals, prevent fraud, and create more loyal customers. When used effectively, it can help companies make smart decisions instead of making instinctive decisions based on inadequately informed conclusions.
This type of data analytics can simulate the likelihood of different outcomes and present the probability of each one, which helps companies better understand the level of uncertainty and the risk they face and plan accordingly.
What Is The Difference Between Descriptive and Prescriptive Analytics?
Descriptive analytics provides valuable insights into the past, whether that was a month ago or a minute ago. It describes raw data in a way that you can understand. It allows you to learn from past behaviors and summarizes various aspects of your business. Think of it as “what you know”.
Prescriptive analytics, on the other hand, provides advice. It allows you to “prescribe” numerous possible actions and guides you towards the best solution. It not only understands the raw data and predicts what will happen, but also explains the reasons and gives suggestions for the right actions to accomplish future goals. Think of it as “what should happen”.
Both are types of data analysis, but the first one focuses on describing raw data so that you can understand it, and the second focuses on giving you advice on what to do next.
What Is The Difference Between Predictive and Prescriptive Analytics?
Even though both types of business analytics are forward-looking, there is a significant difference between them. Predictive analytics predicts what might happen, while prescriptive analytics tells you how to achieve it.
Predictive analytics uses historical data and statistical and modeling techniques to determine the probability of a specific event. Prescriptive analytics identifies the probability of an event and shows specific options and determines the best one for a given situation.
Examples of Prescriptive Analytics
Analysts across organizations are using Prescriptive Analytics to improve their processes.
Here are a few examples:
Marketing and Sales Agencies
These agencies use massive amounts of customer data to arrive at the best marketing strategies, such as how to price products or how to combine them. Prescriptive analytics allow marketers to target the right audience more precisely and deliver the right marketing messages rather than relying solely on instinct and experience.
In this industry, cost-effective delivery is vital. By solving logistical issues like incorrect shipping locations or improving route planning, companies can minimize energy consumption and save time and money. Instead of hiring a number of dispatchers and analysts to decide on the best methods of action, businesses in this industry can use prescriptive analytics to get the best recommendations and advice.
Hospitals and Clinics
Healthcare administration and patient care providers are taking advantage of prescriptive analytics to improve clinical care and scheduling and deliver high-quality services for the best patient satisfaction. Thanks to data from prescriptive analytics, hospitals and clinics can find the best times for follow-up appointments and scheduling treatments.
Companies in this industry use this type of data analytics to find the best patient cohorts and tests for clinical trials. As a result, the cost of testing is reduced.
The chance to model prices based on many factors allows manufacturers to make better decisions regarding new discoveries, storage, and production.
Prescriptive analytics can maximize airline companies’ profits by automatically adjusting availability and ticket prices based on weather, customer demand, gasoline prices, and other factors.
Prescriptive analytics is valuable to businesses as it helps them to grow sales, optimize operations, and manage risk. It allows them to spend less time going through documents and spreadsheets and more time doing more productive activities.