Predictive Analytics : Definition and How it Works

Is it your dream to be able to see the future? Is it your dream to control your future? Well, guess what? Your dream is finally happening. The new way to see the future emerged in the form of predictive analytics. With the advancement of current technology, you can easily predict what might happen to your business in the future.

Many variables support the operation of your business; you have to move one step ahead to avoid any potential threats that might harm your business operation. Every possible risk can be easily avoided by utilizing the potential of predictive analytics.

So, what is predictive analytics? How does it work? And how can it improve the efficiency of your business in the future? Please read this article below as we continue to explore the definition of predictive analytics and how it works.

What is Predictive Analytics?

According to IBM, Predictive analytics is a branch of advanced analytics that predicts future outcomes using historical data combined with statistical modeling, data analytics techniques, and machine learning.

With the help of predictive analytics, you can easily make an unpredictable future become easily predictable. So, what is a predictive analytics example? Suppose a mining company wants to reduce its operation costs. In that case, they can use predictive analytics to help reduce the need for expensive emergency repairs and do maintenance to extend the life of expensive machinery.

A report made by Forbes stated that the primary benefits of predictive analytics are that it allowed for a far higher level of accuracy, which included “the ability to identify better market opportunities, better ad targeting, improved nurture programs, and more targeted accounts.”

This is why predictive analytics is essential. It gives companies the ability to take action before problems occur, find opportunities, optimize processes, cut expenses, and improve customer satisfaction—all before they actually happen. By utilizing data and predictive models, businesses may make better decisions, lower uncertainty, and build an organization that is prepared for the future.

So how does it work? In order to estimate future events, predictive analytics uses statistical algorithms, machine learning techniques, and historical data to find patterns, trends, and linkages. It uses a number of processes to produce accurate forecasts.

Predictive Analytics Techniques and Modeling

Statistical models and techniques based on machine learning are used in predictive analytics to forecast future events from past data. Some popular predictive analytics methods are listed below:

1. Regression

A statistical method called linear regression predicts the relationship between one or more independent variables (predictors) and a dependent variable (target). Because it represents the relationship as a straight line, it is referred to as “linear.” Regression is commonly used to forecast customer loss using variables like account usage, engagement, etc.

2. Neural networks

A collection of algorithms called neural networks is based on the structure of the human brain and is designed to identify patterns. They process input data and provide predictions using layers of nodes or neurons. Neural networks are commonly used to estimate the lifetime worth of a customer by looking at their past transactions and shopping behavior.

3. Decision trees

A decision tree is a structure that simulates a flowchart, with branches representing the results and nodes representing decisions based on feature values. Using a set of rules to categorize or forecast values is helpful. A decision tree is commonly used to predict loan approval based on attributes like credit score, income, and loan amount.

Predictive analytics modeling is a process of developing a model that can forecast future events or trends by utilizing statistical methods, machine learning algorithms, and historical data. The steps in the predictive model are listed as follows:

• Define your goal
  The first step is to set a goal to define the problem you are trying to solve. The goal is to predict a future event or behavior based on historical data.
• Data collection
  Gather historical data, which may include customer behavior, sales history, sensor readings, or demographic data, depending on the domain. Also, don’t forget to clean the data, as it is essential to supporting the quality and accuracy of the model.
• Model selection
  Choose a modeling technique based on the nature of the problem (regression, classification, etc.) and the available data; you select an appropriate predictive modeling technique, as already mentioned in the previous paragraph
• Model deploying
  Once the model is trained and evaluated, it can be deployed to make predictions on new and unseen data. The model can be integrated into business systems for real-time predictions or batch predictions.
• Monitor and maintain model
  After deployment, the model’s prediction needs to be continually monitored and periodically updated to ensure it adapts to any changes in trends or patterns.

Analytics vs Machine Learning

While both analytics and machine learning (ML) are crucial instruments for obtaining information from data, their methods, goals, and approaches are different. Let’s take a look at the table for the main distinctions and points of similarity:

Different ERP software has unique capabilities for predicting consumer behavior based on company data and requirements. Take a look at our price scheme and see how ERP software from HashMicro offers advanced features to become your solution for predictive analytics.

How HashMicro ERP Software Can Support Your Business

The use of predictive analytics tools can help maintain your business for more efficiency, especially for your supply chain needs. With the help of supply chain software, your business’s supply chain will be more efficient and cost-friendly.

The term “supply chain software” describes a category of software created to assist companies in organizing and streamlining the various tasks that make up the supply chain process. From locating raw resources to shipping completed goods to clients, this program unifies several supply chain operations. The objectives are to enhance the supply chain’s overall performance, cut expenses, and increase efficiency.

HashMicro ERP Software Is presenting Hashy AI as your predictive AI a bright solution for your supply chain problems? With Hashy AI, your company can easily track raw materials, monitor production progress, ensure timely shipments, and optimize inventory levels. This helps the company minimize delays, avoid stockouts, and meet customer demand more effectively

Conclusions

To compete in a very high-paced world, you, as a business owner, must take innovative action and use advanced technology to support that action. Predictive analytics is a powerful tool that can help you make future decisions and future actions. Hashmicro is here to assist you in making those future decisions and future actions.

With the help of HashMicro, as one of the best ERP software in Singapore, you can easily determine the best data-driven decisions and actions for your business operation and your company’s future.

Ready to overcome this challenge and prepare your company for the future? Try HashMicro ERP software with a free demo now and unlock your business’s unlimited potential.

FAQ about Predictive Analytics

• • What is meant by predictive analytics?

    Predictive analytics is the process of using data to forecast future outcomes. The process uses data analysis, machine learning, artificial intelligence, and statistical models to find patterns that might predict future behavior.

  • Which type of data is used for predictive analytics?

    Predictive analytics is the use of data to predict future trends and events. It uses historical data to forecast potential scenarios that can help drive strategic decisions.

  • Is predictive analytics part of AI?

    ML and predictive analytics are both sub-areas within the broader category of AI, and utilize it in their operations.