Prescriptive Analytics 101: Benefits, Limitations, and Applications Simplified

on Data Analytics, Data Driven • June 17th, 2021 • Write for Hevo

Understanding Prescriptive Analytics

Data Analytics is an essential arsenal for organizations looking to profit from granular customer insights as it helps them achieve the coveted status of being data-driven. Analytics insights fine-tune business processes by determining future outcomes and help understand historical data from newer perspectives. Subsets of Data Analytics like Descriptive Analytics, Predictive Analytics, Prescriptive Analytics, etc., are now a part of the mainstream use-cases and aid in streamlining process efficiencies. While Descriptive and Predictive Analytics focus on historical data, Prescriptive Analytics focuses on the agile and proactive side. Further, it can help optimize organizational functions to a higher degree. 

This article offers a holistic overview of the basics of Prescriptive Analytics. It also briefly explains how certain industries leverage this technology for driving better results.

Table of Contents

Introduction to Prescriptive Analytics

Prescriptive Analytics: Features
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In the Data Analytics hierarchy, Prescriptive Analytics emphasizes on uncovering the best course of action through optimization. It employs Artificial Intelligence algorithms on existing trends and events to predict future results. Although Prescriptive Analytics harnesses the benefits of Descriptive and Predictive Analytics, its key focus remains on actionable insights. It allows businesses to contextually be aware of what is likely to happen, when, and why, helping them determine which business solutions are best in terms of predetermined criteria. Additionally, it provides a higher level of understanding of specific data trends and findings.

Prescriptive Analytics bridges the gap between the data possessed by an organization and the associated implications of exploiting it for various purposes. It enables end-users of existing information systems to connect within an organizational ecosystem. Prescriptive Analytics can also help decision-makers in any organization get an understanding of how they can take advantage of a future opportunity or alleviate future pain points. Overall, it reduces the risk of making decisions.

Typically, Prescriptive Analytics is composed of Graph Analysis, Complex Event Processing, Simulation, Recommendation Engines, Heuristics, etc. These are all driven by Artificial Intelligence and Machine Learning algorithms. It is crucial to note that Prescriptive Analytics does not aim at replacing Predictive Analytics. Instead, it quantifies the effect of the outcome of Predictive Analytics to deduce what variables must be tweaked to achieve the desired results and how it can be done.

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Understanding the Benefits of Prescriptive Analytics

Today, businesses are using data as a driving force. Therefore, the importance of Prescriptive Analytics is well-known. It will not only give you valuable insights from your data but will also optimize your data-driven decisions. The key benefits of Prescriptive Analytics are as follows:

  • More Proactive: The fundamental idea behind transitioning from a predictive model to a prescriptive model is to empower organizations to become more proactive and less reactive. Prescriptive Analytics can address issues found during the Predictive Analysis and make weighted decisions for better business outcomes. 
  • Capturing Multiple Data Touchpoints and Formats: Prescriptive Data Analysis models are built on input data, business rules, and mathematical models, sourced within the organization (internal data) or collected from external touchpoints. The gathered data can either be Structured or Unstructured (like sensory or audio-visual data). Businesses can use Prescriptive Analytics to connect the dots between different data sets to garner insights into future events. This allows Prescriptive Analytics to be used in industries like healthcare and automobiles for business growth. 
  • Real-Time Insights: Enterprise leaders can examine real-time insights to boost sustained growth and stay ahead of their competitors. Simultaneously, near-real-time visibility can assist companies in anticipating and mitigating crises with quick responses. As new data is generated, Prescriptive Analytics models can automatically adjust to extract value from it faster, leaving less room for error.
  • Finding the Right Trade-off: It is possible that there are multiple solutions available to a given problem. Balancing the trade-off between attributes can be a tricky and overwhelming process. Prescriptive Analytics aims to nullify this by running complex models, analyzing, and evaluating scenarios by factoring in business rules and constraints to present the best among those solutions.
  • Maximum Use of Resources: Prescriptive Analytics helps organizations make cost-effective decisions by effectively managing resources, thereby minimizing the need to outsource. This is achieved by enabling employees with minimal knowledge to learn about data and the impact of data-driven decisions. It also allows them to participate or collaborate more on data-specific processes.
  • Gross Margin Management: Since Prescriptive Analytics is more actionable than its predictive counterpart, it empowers developers to build solutions based on the current situation while anticipating market conditions and customer behavior patterns. As a result, business teams can spend more time designing the perfect solutions rather than identifying problems. 
  • Enhanced Market Competition Analysis: Brands can run different test strategies with Prescriptive Analytics to discover and offer a superior personalized experience than their competitors. Using these analysis-powered personalized recommendations, marketers can view plans that help them significantly impact the market while resonating with customer demands.
  • Removing Bottlenecks: By identifying pain points, Prescriptive Analytics can also help solve issues in the organizational pipeline that were preventing corporate growth. Along with that, these analytics models work in conjunction with a decision support system that simulates “what if” scenarios and makes real-time adjustments.

Real-World Applications of Prescriptive Analytics

Perspective Analytics is a process that can provide valuable contributions to various business ecosystems. Due to this property, it can be applied to a number of different business markets. The real-world applications of Prescriptive Analytics are as follows:

1) Banking, Financial Services and Insurance (BFSI)

Prescriptive Analytics: BFSI
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Financial institutions can design Prescriptive Analytics algorithms for managing risk and profitability by sifting through historical trading data. Some insurance companies also employ risk assessment models to provide better premium information about insurance policies for clients. 

2) Healthcare

Prescriptive Analytics: Healthcare
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Prescriptive Analytics allows doctors to make data-backed decisions and treatment recommendations based on the medical history of patients. Apart from assessing risk magnitude, these analyses also enable them to determine the best action plan and even measure the efficacy of interventions. And for hospital admins, this analysis can assist in improving clinical care, scheduling treatments, and follow-up appointments.

3) Online Learning

Prescriptive Analytics: Online Learning
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Numerous Learning Management Systems (LMS) and websites leverage Prescriptive Analytics to promote adaptive learning. First, the LMS identifies the familiarity and proficiency of the users via assessment tests. Then based on the findings from those tests, LMS presents them with a personalized course plan.

4) Transportation and Travel

Prescriptive Analytics: Transportation and Travel
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In airline companies, Prescriptive Analytics algorithms adjust the availability of tickets and their prices on the basis of factors like traveler demand, fuel prices, etc. Even hotel booking websites use the same algorithms to determine pricing and sales pitches as per customer preferences.

5) Supply Chain and Logistics

Prescriptive Analytics: Supply Chain and Logistics
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Prescriptive Analytics is crucial for route optimization in the Supply Chain industry. Logistics companies leverage it to prevent logistical issues like incorrect shipping locations. They also rely on these analyses for better route planning at lesser energy consumption while saving time and money.

6) Manufacturing

Prescriptive Analytics: Manufacturing
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Factories can analyze real-time data with Prescriptive Analytics for enhanced inventory and production management. This includes predicting market demand, supply, and material requirements to sustain in a volatile market. Prescriptive Analytics can also assist in optimizing productive capacity, complying with the delivery schedule, and organizing final assembly lines. Such applications highlight how resourceful these analyses can be in Industry 4.0 for business growth. Lastly, manufacturers can model prices on various factors like production, storage, and discoveries. It even helps identify optimum settings to increase yield while being efficient. 

7) Marketing and Sales

Prescriptive Analytics: Marketing and Sales
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Since Prescriptive Analytics is more about modeling than experimentation, it is a perfect asset for brands looking to strengthen their Marketing techniques. It also helps run promotional campaigns and forecast demands with respect to segments’ consumption and customer interests.

To understand more about the real-world applications of Prescriptive Analytics, visit here.

Limitations of Prescriptive Analytics

Prescriptive Analytics is not a silver bullet. The insights gained from it depend on the quality of input variables and data. For instance, while missing or incorrect information can lead to false predictions, overfitting in prescriptive models can result in inaccurate predictions that are impervious to changes in data over time. The key is to identify what kind of solutions you are looking for in a given problem. Besides, every Prescriptive Analytical Model has a unique, well-defined fitness function that helps get the ideal set of solutions. 

Also, there is always a bias present when humans train Machine Learning models or design the tools. There are also ethical concerns about using these analyses to manipulate human (user) behavior. Finally, despite being powered by distributed processing, event processing technology, and pervasive computing, the abilities of Prescriptive Analytics are limited. These limitations arise due to business constraints like budget and others.

Conclusion

Prescriptive Analytics is beginning to be valued in several industrial niches. While it is not a one tool fits all assets, it guides organizations in optimizing the workflows to achieve desired outcomes. This analysis is a critical advancement in Data Analytics that promises enormous scope and depth in a future fueled by deeper and actionable data insights.

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