Difference Between Predictive and Prescriptive Analytics

Business Analytics has always been key in adopting a focused approach and highlighting grey areas. Top management companies invest a lot in research and analytics to find suitable approaches to overcome their shortcomings. Analytics is one of the topmost trends in recent decades. Within the business domain, every sector is pursuing to incorporate analytics tools in their business operation.

Usually, analytics is broadly categorized into three types: Descriptive Analytics, Predictive Analytics, and Prescriptive Analytics. In this piece, we will discuss about difference between predictive and prescriptive analytics. Before knowing the differences, let us know first about Predictive and Prescriptive analytics.

Predictive Analytics

Predictive Analytics refers to studying and understanding the data in a lot of depth to forecast future trends and suggest action based on the data results. In other words, processing large data to make second guesses of the future. In predictive analysis, data crunching, monitoring trends, and observing patterns are done to predict the future. Data are studied in depth, literally to make a second guess to follow the next course of action. Machine Learning and algorithms are used extensively in conducting Predictive Analytics. Weather Forecasting is a textbook example of predictive analysis. Data from several observatories are compiled to predict weather results.

Benefits of Predictive Analytics

Organizations are looking for predictive analytics tools to resolve their difficult problems and explore new opportunities available in the market. Some of the common uses of predictive analytics include:

• Market Campaign Optimization: Predictive analytics helps determine customer feedback or sales. It aids businesses in growing, retaining, and attracting their core customers.
• Detecting Forgery: A combination of different analytics tools would help recognize and mark criminal behavior, identify patterns, and curb fraud. With large-scale advancements in digital products and devices, cyber fraud is emerging as a major concern for law enforcement agencies. High-performance-based analytics tools are capable of identifying and studying all actions that may show abnormalities that may suggest fraud and highlight threats and vulnerabilities in real time.

Examples of Predictive Analytics

Organizations or Companies cut across all sectors have come forward to leverage predictive analytics to make their services or products efficient, examining potential threats, maintenance optimization, and security. Three examples are listed below that will give you an understanding of how the analytics tools are shaping the business strategies and models:

• DC Water Drives Down Water Loss
  The District Columbia Water and Sewer Authority (DC Water) has employed predictive analytics to curb water loss in its system. Pipe Sleuth combines a neural network model with an advanced deep learning model to conduct image analysis of tiny diameters of sewer pipes. By studying these reports, it prepares a condition assessment report.
• Rolls Royce Maintenance Optimization Schedule and Curbing Carbon Footprint
  Roll Royce, which is the world's biggest manufacturer of aircraft engines, has taken the help of predictive analytics to curb the quantity of carbon in its engine product and also to pursuing ahead with maintenance optimization to assist their customers keep their planes in their air longer.
• PepsiCo is Tackling Supply Chain Issues with the help of Predictive Analysis
  PepsiCo was restructuring its e-commerce sales, and the field sales team was assisted by predictive analytics to remain aware of when a retailer would be out of stock. The company has formed the Sales Intelligence Platform, which merges PepsiCo's supply chain and retailer data to forecast out-of-stock and alert users to record.

Predictive Analytics Tools

The use of Predictive Analytics Tools provides real-time and deep information into an almost unlimited set of business activities. These tools are quite useful as they forecast and identify several types of patterns and behaviors, for example, when to renew stock, or the right moment to launch a marketing campaign, forecasting by examining data collected over time.

Given below are the top-notch predictive analytics and solutions that are widely used for the purpose:

• Amazon Sagemaker
• IBM SPSS
• SAS Viya
• Alteryx Analytics Automation Platform
• TIBCO

Different Model Based Upon Predictive Analytics

Models serve as the foundation of predictive Analytics. These are templates that users turn to and look for historical and present data, which are then transformed into actionable inputs, collating into positive long-term results. Some of the most common predictive models include

• Predictive Maintenance Model: Predicting the likelihood of essential equipment failure.
• Customer Lifetime Value Model: Identify customers who are inclined to increase their investment in products and services.
• Quality Assurance Model: Detect and mitigate defects to prevent customer dissatisfaction and unnecessary expenses in delivering products or services.
• Customer Segmentation Model: Segment customers according to shared traits and purchasing patterns.

Prescriptive Analytics

Prescriptive is a game-changing force in data analytics, providing actionable inputs for future courses of action. It manifests the pinnacle of the analytics journey as it stretches to itself just describing or predicting future results. Prescriptive is considered to be a massive force in analytics because it does not just make future predictions or challenges, but it also provides actionable suggestions for the problem. This type of analytics uses advanced tools like machine learning, computational modeling, and artificial intelligence and examines massive amounts of data, processing multiple possible effects to suggest the best action.



Prescriptive Analytics is one step ahead. In addition to highlighting the issue, it will recommend possible actionable strategies fully endorsed by data scientists to implement it.

Prescriptive Analytics is employed in a diverse array of sectors, such as finance and healthcare. This data analytics helps make data-backed decisions, and businesses should move ahead with foresight and precision.

Benefits of Prescriptive Analytics

As businesses are facing a massive volume of data, the search for actionable inputs has driven businesses to look for advanced data analytics techniques. Prescriptive Analytics gives immense benefits to the organization that would truly transform how they work and operate. Some of the benefits of prescriptive analytics are given below:


• Precise and Data-Driven Decision-Making: Prescriptive analytics for its role in assisting decision-making, which is fully backed by data and is precise. It is different from other analytics, which are entirely dependent on intuition, past experiences, and gut feeling. Prescriptive Data ensures that decisions associated with marketing strategies or supply chain optimizations are created with a high degree of precision, accuracy, and confidence.
• Optimising Operational Efficiency: Operational bottlenecks and hurdles can slow down the growth of an organization and curb profitability. Prescriptive analytics comes with a modern and forward-looking approach and its automation technology is capable of identifying potential inefficiencies and recommends the points to streamline the business processes. This type of analytics gives the utmost efficiency, optimization of inventory levels, boosting production schedules, and improving budgetary appropriations.
• Tapping Potential Opportunities: Staying ahead of the curve is important in the competitive market. Prescriptive analytics is not just limited to recognizing challenges; it offers all possible new opportunities. Examining market trends, monitoring consumer behaviors, and observing competitiveness can illuminate future expansion areas and explore new places on social media platforms.
• Risk Mitigation and Management: In a business setup, Risk is omnipresent. However, by deploying prescriptive analytics, the organization will have an edge in identifying potential challenges and acting on them in advance. Prescriptive Analytics is capable of forecasting supply chain blockades, market fluctuations, and cybersecurity threats, allowing businesses to remain proactive strategy.
• scaling Up Personalisation: Personalisation has become a necessity, and Predictive analytics gives businesses the perfect opportunity to tailor their services or products to individual customer choices. Analysis of customer data can reveal surprising buying behavior. It can help companies provide personalized experiences, boosting customer satisfaction and loyalty.
• Competitive Edge: Differentiation is key in a saturated market. Prescriptive analytics comes into the picture here. With data analysis techniques, organizational decision-making can be more informed and can prove itself different from others.

Application of Prescriptive Analytics

Prescriptive analytics has brought radical transformation in the decision-making process and has impacted several sectors. The organization needs actionable inputs based on hard data that could bring efficiency and optimization to their work. Below is the list of sectors that have deployed prescriptive analytics tools at a massive scale to bring efficiency to their work:

• Supply Chain Management: Efficiency acts as a key in the supply chain mechanism. Prescriptive analytics processes historical data and predicts to suggest distribution strategies and optimal procurement. It helps in timely deliveries, enhancing customer satisfaction and cutting down costs.
• E-Commerce: E-commerce giants like Amazon, Flipkart, Meesho, etc., have deployed prescriptive analytics tools at a massive scale to optimize their user experience. They observe purchase patterns and personalized product suggestions and work on pricing methods.
• Financial Services: Prescriptive Analytics can be used to devise Investment Strategies and Risk Management in the Finance sector. It studies marketing trends and credit pasts to recommend lending strategies and investment opportunities. Therefore optimizing returns with less risk.
• Healthcare: It helps form the patient's treatment plan by assessing the patient's present health situation and medical history. It also takes into account lifestyle issues and genetic predispositions and ensures that the proposed treatment plan meets the individual requirements, therefore improving patient conditions.

Difference Between Predictive Analytics and Prescriptive Analytics

Tangible Differences exist between Predictive Analytics and Prescriptive Analytics based on the use of data, nature of insights, decision support, and complexity and applications of these tools. Here are the differences that have been listed below, which will eliminate doubt if there are any;

Basis	Predictive Analytics	Prescriptive Analytics
Use of Data	Predictive Analytics assesses historical data to forecast potential results. It is dependent upon past data and historical models to forecast outcomes. It observes past patterns and suggests future trends based on that. However, it does not recommend any specific suggestions.	Prescriptive analytics is different from Predictive analytics because it also recommends the appropriate future course of action and gives the best recommendation based on forecasts. This type of analytics is not limited to analyzing past data but also factors in diverse scenarios and their associated constraints.
Nature of Insights	Predictive analytics forecasts future occurrences by analyzing historical data and patterns. It gives valuable insight into potential future scenes that include sales predictions or customer interactions, helping businesses plan for this kind of fallout.	Prescriptive analytics goes beyond predictions by intelligently recommending specific actions to optimize outcomes. It guides what actions should be taken to achieve the best results, making it a step beyond mere predictions.
Navigating Complexity with Decision Support	Predictive models are relatively less complex than prescriptive models. They focus on data analysis and statistical techniques to forecast. However, they need the use of extensive optimization algorithms. Predictive models give decision support for anticipated outcomes. They assist organizations in making an informed decision; however, they need to provide specific inputs.	Prescriptive models are usually complex as they take into account several variables, objectives, and constraints. They actively endorse decision-making by giving a wide array of actionable inputs. They offer guidance on what steps need to be taken to get excellent results while also factoring in several scenarios and constraints.
Applications	It has found its suitability in a wide array of sectors such as telecommunications, retail, healthcare, finance, marketing, and healthcare etc.	It is generally deployed where the decision-making is

Conclusion

The blend of predictive analytics and prescriptive analytics marks a transformative era in the decision-making process. Predictive analytics is known for delivering foresight into future trends, giving organizations an anticipated warning to change their working pattern according to the demands and planning their things accordingly. However, with the evolution of prescriptive analytics, the decision-making domain has changed radically and has given an extra edge to decision-makers since they have specific data and recommendations that work to their advantage to the fullest. It is a revolutionary technique because it offers actionable inputs and recommendations; prescriptive analytics empowers decision-makers by not just forecasting future problems but also giving specific suggestions and recommendations to deal with those kinds of problems.

Its importance can be gauged from the fact that these types of analytics tools are used in a wide array of sectors, such as healthcare, business, e-commerce, finance, retail, etc. These sectors deploy such tools at massive levels to scale up their profits and improve their operations. These analytics create powerful synergy, fostering innovation, efficiency, and competitive advantage in an increasingly complex and data-rich environment. As the organization maximizes this, the decision-making process becomes more informed, pushing toward success in the ever-dynamic and evolving market environment.

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