26 Aug 2022

Predictive Analytics: How Can It Improve Business Process Planning?

Predictive analytics is the analytical tool used for predicting future outcomes of an action based on the analysis of historical data. It helps business planners plan their strategies and actions according to the analysis to mitigate risks and enhance efficiency.?

Predictive Analytics: How Can It Improve Business Process Planning?

Advanced analytics and modeling techniques have made business planning very efficient and accurate. Four types of advanced data analytics are used for business forecasting and planning processes, including Descriptive, Diagnostic, Predictive, and Prescriptive analytics. Out of these, predictive analytics is used for predicting future scenarios due to any action performed in the business. This blog post will discuss in detail what predictive analytics is and how it can transform business processes. Read on.

What is Predictive Analytics?

Predictive data analytics uses big data analysis techniques and models to predict a business's future outcomes and performances. It looks at the current and historical data and patterns to determine the likelihood of the scenarios emerging again. This helps business planners and investors to align their resources to take advantage of future scenarios. Predictive data analysis can also improve risk assessment and operational efficiency.

Understanding Predictive Analytics

Once you understand predictive analysis, you can use the vast data by analyzing the past scenarios, where you have faced challenges, and how likely they can happen again. This can be very helpful for businesses in planning the processes so that the risks are mitigated with better efficiency. One predictive analysis example is its application in supply chain management. First, a mathematical model is prepared to represent the current trends to apply this tool in supply chain planning. You may have to test numerous models to find the model closest to reality. Once you find that model, you need to enter the historical data and test the model until it can predict the outcomes of the past correctly. Next, add current data to the model and use the model to predict future outcomes for further analysis.

What Are the Applications of Predictive Analytics?

A variety of businesses use this technique regularly to get information about target customers and operational results. The list of predictive analytics applications is long. Here are some of the major use cases of this technique in multiple verticals.

1. Customer Segmentation
 Customer segmentation enables businesses to classify customers by shared traits. Businesses evaluate their market differently based on the factors that provide the most value to their company and products. The use of predictive data analysis techniques helps them target the markets based on accurate analysis and critical indicators and determine the segments of customers who will be more interested in the company's products.

2. Next Best Action
  Determining the primary marketing goals and target customer is a primary use case of the predictive analysis tool. It provides a primary view of what should be the marketing approach of a business. This tool can give information about the customers in each segment, and after analyzing the data, what will be the best way to approach them?

3. Predictive Maintenance
Managing capital outlay is a difficult task for an organization that has made a significant investment in equipment and infrastructure. Businesses can analyze the insights into the maintenance cycle of equipment and machinery; companies can set proper timelines for preventive maintenance, plan upcoming expenses, and streamline downtime.

4. Quality Assurance
Quality assurance is one of the major factors contributing to customer experience and the bottom line of all business operations. Inefficient quality control can adversely affect customer satisfaction. Using predictive data analysis can help in determining the inefficient and high-risk modules and shifts your QA approach from reactive to proactive.

5. Risk Modeling
 Prevention and prediction play a significant role for businesses simultaneously. Risk in a company may come in any form and source. When predictive analytics is applied to significant data insights, it can provide potential risk areas to be addressed. Risk evaluation and mitigation become easier when combining analytics applications and a risk management approach.

What Are the Benefits of Predictive Analytics?

Predictive data analytics tools can benefit businesses in many ways, providing insights well in advance. Hence, businesses can plan operations according to the predictions obtained after analytics. Here are some primary benefits of predictive analysis for businesses:

  • Gain an advantage over competitors
  • Explore new revenue streams
  • Detect fraud early
  • Optimum Performance and process efficiency
  • Improved asset utilization
  • Enhanced production quality
  • Better collaboration between departments
  • Risk mitigation

Types of Predictive Analytical Models

Predictive analytical models are primarily the techniques used to prepare a framework to which the data is added for analysis and predict future trends and outcomes. Analytical models are prepared to support all four types so analytics. Understanding analytical models give users context to the problem or issues they are facing in their business processes and planning. Following are some of the commonly used predictive analytics models:

  • Decision Trees
  • Linear Models
  • Neural Networks
  • Support Vector Machines
  • Cluster Models

There are many other predictive modeling used for different use cases which you can learn about here.

Challenges of Predictive Analytics

Any analytical technique is a complex process; hence, it comes with complexities and challenges during implementation. The significant challenges while implementing Predictive analytics in a business are as follows.

  • Expertise: The predictive analytics solutions are designed mainly for data scientists with in-depth knowledge of python and statistical modeling. Before starting to apply this analysis technique, most teams need a dedicated data scientist.
  • Adoption: Although people are still beginning to understand the importance of advanced analytics, they are skeptical about adopting it because of its complexity.
  • A long list of Prerequisites: A predictive analytics project comprises a long list of steps that must be performed before and after the analysis. With every update in data, these steps need to be performed, which places an extra burden on the data science team.

Predictive Analytics Tools

Traditionally, the analytics tools used were spreadsheets, where data analysts worked for months with data sets to develop a predictive model. Modern-day analytics tools are also evolving with AI and machine learning. Advanced analytics tools for predictive analysis are easier to implement in the already existing business applications. Also, as the tools evolve, they are becoming easier to understand and work with for the end users.

How 3SC Can Help?

Finding the right analytical tools for your enterprise is vital for the success and efficiency of business processes. 3SC offers tailor-made AI-powered analytical solutions for descriptive, diagnostic, prescriptive, and predictive analytics for supply chain management and?Integrated business planning. Our advanced analytics solutions, such as Collaborative Demand Platform and Demand curve, not only optimize business planning but also help find recommended actions to eliminate any risks and enhance productivity.

FAQs

How can predictive analytics be incorporated into business planning?

It can be used to analyze past data to predict events in the future so that you can plan your business processes in a way that mitigates the risks, enhances your understanding of the markets, and maximizes profits.

Why is predictive analytics important?

When you know what is likely to happen in the future, it gives you a better understanding of your customer behavior. Hence you can work positively toward enhancing your customers' experience and building a better business.

What are predictive analytics examples?

Some of the most prominent examples of sectors applying this analytical technique include healthcare, pharmaceuticals, banking, manufacturing, education, logistics, and financial management.

Is predictive analytics similar to machine learning?

Machine learning is the technology that assists the predictive analytical technique in assessing data and building a predictive model.

Is predictive analytics part of AI?

Artificial intelligence, or AI, is another technology used for building statistical models to analyze past data and make predictions about future events after the analysis.

Which algorithm is used for predictive analysis?

The most used algorithms for predictive data analysis are random forest, generalized linear model; gradient boosted model, K-Means, and Prophet.

How do you measure predictive accuracy?

It can be measured by calculating the difference between the predicted value and actual values.

What is the difference between forecasting and predictive analytics?

Forecasting allows you to plan business processes based on a single variable. In contrast, predictive analytics predicts future outcomes based on the analysis of different data variables that helps in understanding the results on a micro level.

What is the future of predictive analytics?

As more businesses understand the importance of advanced analytics and how it can benefit them, adoption of these tools is increasing. Also, as AI/ML technologies evolve, analytics are becoming more accurate, giving valuable insights to business planners.


 
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