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What is Prescriptive Analytics

Prescriptive analytics is the key concept behind many machine-controlled systems and allows advanced knowledge to be amended into easy selections.

It is now easier to control collected information to empower real business value owing to the precise amount of data now available to the companies. But it can be challenging to recognize the best approach to analyze specific data. One of the best options is to use prescriptive analytics to help your business determine data-controlled strategic decisions. Also, prescriptive analytics help you get rid of the limitations of standard data analytics practices, including:

  • Running through valuable resources on housing data that doesn’t advise business decisions
  • Spending time scrutinizing unused data sets
  • Losing unique revenue streams and insights

Prescriptive Analytics Definition:

As per the prescriptive analytics definition, it is a process that analyzes data and offers immediate recommendations on ways to optimize business practices satisfying multiple predicted outcomes. It takes data as input and broadly understands it to suggest predictions on what could happen. Also, it suggests the best steps to be taken depending on the instructed simulations.

Prescriptive analytics is the final tier in contemporary computerized data processing. It uses identical modeling structures to predict outcomes and combines business rules, machine learning, artificial intelligence, and algorithms to simulate different approaches to the numerous predicted outcomes. Lastly, it advises the optimal actions to optimize the business practices. So, it finally explains “what should happen.”

Prescriptive analytics eliminates the speculation of data analytics. For marketers and data scientists, it proves to be time-saving. This is because it understands the meaning of its data and determines what dots should be connected to provide audiences with a beneficial and highly personalized user experience. Though prescriptive analytics appears small-scale at the moment, it is steadily evolving over the years as Artificial Intelligence (AI), and machine learning becomes more accessible.

Examples of Prescriptive Analytics

Prescriptive analytics benefits the healthcare industry, banking, travel, manufacturing, marketing, online learning, and many more. Here are a few examples of Prescriptive Analytics in several widespread sectors:

 1. Use of Prescriptive Analytics in Hospitals and Clinics:

One of the best prescriptive analytics examples is its use in the healthcare sector. Hospitals and clinics use prescriptive analytics to enhance the outcomes for patients. It uses healthcare data to assess the profitability of different processes and treatments. Moreover, it can assess the official clinical methods.

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Prescriptive Analytics can investigate which hospital patients have the maximum risk of re-admission. Based on this analysis, it instructs healthcare providers to keep the re-admission to the hospital or emergency room at bay.

2. Use of Prescriptive Analytics for Airlines:

Prescriptive Analytics helps airlines’ CEOs maximize their company’s profits. It automatically modifies ticket prices and accessibility depending on weather, customer demand, and gasoline prices.

For example, the Prescriptive Analytics algorithm can analyze whether the current year’s Christmas ticket sales from New York to Los Angeles are lagging or leading compared to last year. Based on this analysis, it automatically lowers prices while also considering the higher fuel prices.

3. Use of Prescriptive Analytics in Banking, Financial Services, and Insurance (BFSI):

You can find various prescriptive analytics examples when it comes to financial institutions. These institutions can propose Prescriptive Analytics algorithms for managing risk and cost-effectiveness by scrutinizing historical trading data. Certain insurance companies also use risk assessment models to offer better premium information regarding insurance policies for clients.

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4. Use of Prescriptive Analytics in manufacturing:

Big production machines can have a broad range of minor changes. Price prediction is inevitable to tackle these changes. Prescriptive Analytics can accurately predict current production, material handiness, power consumption, and more. It can also help optimize productive capacity, conforming to the delivery schedule and consolidating final assembly lines.

Manufacturers can use Prescriptive Analytics to model prices on different factors like storage, production, and discoveries. It helps determine the optimum settings to boost yield without compromising efficiency.

5. Use of Prescriptive Analytics for sales and marketing:

Prescriptive modeling is a mathematical process that benefits brands aiming to reinforce their marketing techniques. It can help run promotional campaigns and predict the customer interests and segments’ consumption.

6. Use of Prescriptive Analytics in Supply Chain and Logistics:

Prescriptive Analytics is essential for route optimization in the Supply Chain industry. Generally, logistics companies use it to avoid logistical issues like improper shipping locations. They use Predictive Analytics for improved route planning while saving time, money, and resources.

7. Use of Prescriptive Analytics to improve business efficiency:

Prescriptive analytics ensure businesses can save time and use data to develop a process that will make them stand out from their competitors. Business efficiency significantly increases with the use of Cloud-based prescriptive analytics tools.

8. Use of Prescriptive Analytics in creating Data governance strategy:

Prescriptive analytics also permits a degree of caution from the viewpoint of ethics. For example, generating automated recommendations or decisions depending on a computer’s student data analysis can raise questions about privacy and impartiality, such as– Do learners provide consent? Who can access the data and results?

The learner’s predictions can be inaccurate if the collected data is not entirely precise. This can lead to wrong decisions or recommendations about the learner. A data governance strategy can be implemented, and the prescriptive analytics models can be used to emphasize validation.

9. Examples of prescriptive analytics in online learning:

Prescriptive Analytics is extensively used in specific learning management systems (LMS) and learning technologies. The following points clarify how it enhances online learning:

Certain online learning tools use prescriptive analytics to recognize the content learned. These tools present content yet to be mastered. So, it is one of the best prescriptive analytics examples of exploring adaptive learning.

Certain LMSs allow administrators to define the specific rules for actions or automated feedback to happen. For example, if an employee is about to finish a training course, the system may recommend them to go through various resources to acquire the skills required for the previous course.

Certain LMS promise reduction of the training time for employees by acknowledging previous knowledge and proficiency baselines. They aim to recommend resources or training courses that best suit the learners.

Other common examples demonstrating Prescriptive Analytics:

  • Prescriptive analytics can evaluate whether a local fire department should need residents to empty a particular area when a wildfire is burning around.
  • It can predict whether an article on a specific topic will be popular among readers depending on data about people’s search results and social sharing of relevant topics.
  • It can adapt a worker training program in real-time depending on how the worker responds to each lesson.

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How does prescriptive analytics work?

Generating recommendations or automated decisions need unique algorithmic models. It also needs help from the analytical technique to get a clear direction. A recommendation or decision can be generated only after knowing the problem and its solution. Consequently, prescriptive analytics begins working with a problem and generates recommendations or automated decisions for accurate prediction.

Example explaining the working of Predictive Analytics:

An organization’s training manager can use predictive analysis to discover that most learners without a specific skill can’t complete a particular course. In this case, prescriptive analytics can suggest actionable strategies. The corresponding algorithm can identify the learners who need that course but lack specific skills. Subsequently, it provides an automated recommendation that they must take up an additional training resource to learn that missing skill.

The quality of data and the algorithmic models developed are directly proportional to the accuracy of a generated decision or recommendation. The strategy that works for a company’s training requirements may not be useful to another one. So, it is recommended to tailor Predictive Analytics models for every requirement uniquely.

Latest Prescriptive Analytics Software and Tools

  • Improvado
  • Alteryx
  • Tableau
  • RapidMiner
  • Sisense
  • Birst
  • Knime
  • AIMMS
  • Talend
  • Looker

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What are the key benefits of Prescriptive Analytics?

(i) Prescriptive Analytics can make data-controlled decisions that recommend specific actions depending on various factors. (ii) It reduces the odds of human bias or error. It streamlines complex decisions by simulating a wide range of scenarios and offers the probability of various outcomes. (iii) The best prescriptive analytics tools collapse data silos to evaluate an integrated data set and then offer immediate, detailed recommendations on your best action.

What are the differences between Predictive Analytics and Prescriptive Analytics?

(i) Predictive Analytics forecast possible outcomes without providing guidance. Prescriptive Analytics provides explicit recommendations for a specific business decision. (ii) Predictive Analytics usually focuses on limited aspects of your business, whereas Prescriptive Analytics focuses on interdependencies and models on your whole business. (iii) Predictive Analytics needs human decisions, while Prescriptive Analytics provides data-controlled recommendations that don’t need a human decision.

What are the challenges associated with Prescriptive Analytics?

(i) Certain situations need human decisions. (ii) Invalid inputs lead to invalid outputs. (iii) Training and evaluation of your model are required to ensure the accuracy of Prescription Analytics. (iv)Prescription Analytic needs time to improve. (v) All organizations, situations, and campaigns may not need Prescription Analytics, so the effort of setting it up is worthless.

What’s the future of Prescriptive Analytics in the Cloud?

Prescriptive analytics need in-depth data analysis, so a flexible and reliable location for data storage is a must. Cloud storage will meet this need. Cloud data warehouses will make it possible to understand Prescriptive Analytics easily. Moreover, these warehouses will store information and support various proprietary tools and external integrations.

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