What is predictive analytics? Turn data into insights for the future

Predictive analytics technology can help your organization predict future outcomes based on historical data and analytical techniques such as machine learning.

What is predictive analytics?

Predictive analytics is a type of data analysis that aims to predict future outcomes based on historical data and analytical techniques such as statistical modeling and machine learning. Predictive analytics science can form insights into the future with great precision. With advanced predictive analytic tools and models, any organization can now use past and current data to reliably predict trends and behavior in the next few milliseconds, days, or years.

According to a report released by the market research institute of Zion Market Research in 2017, predictive analytics technology has been supported by many organizations and it is expected that by 2022, the global market will reach approximately US$ 10.95 billion, and the CAGR will be calculated from 2016 to 2022. About 21%.

How does predictive analytics work?

The power of predictive analytics technology stems from the adoption of various methods and technologies, including big data, data mining, statistical modeling, machine learning, and a variety of mathematical processes. Organizations can use predictive analytics techniques to screen current and historical data to detect certain trends based on the parameters provided and predict what should happen at a particular time.

With predictive analytics, organizations can discover and use certain patterns contained in data to discover risks and opportunities. For example, you can design a model to discover the relationships between various behavioral factors. These models can assess the prospects or risks associated with specific conditions and guide you in making informed decisions in various supply chain and procurement activities.

What are the benefits of predictive analytics?

For centuries, people have looked forward to the future with three basic goals: money, reputation, and ability. Predictive analytics technology does not really change the reason people want to know what will happen next week, next month, or next year, but it only allows us to look more accurately and reliably toward the future than we did before using tools.

Money: Predictive analytics can help users find ways to save money and make money. Retailers often use predictive models to predict inventory quantities, manage shipping schedules, and configure store layouts to maximize sales. Airlines often use predictive analytics to set ticket prices that reflect past travel trends. Participants in hotels, restaurants, and other hotel industries can use this technology to predict the number of guests for any night to maximize occupancy and income.

Reputation: No business will be successful due to its obscurity. By using predictive analytics to optimize marketing campaigns, organizations can gain new customer responses or purchases and promote cross-selling opportunities. Predictive models can help companies attract, retain, and cultivate the most valuable customers.

Capability: Predictive analysis can be used to detect and prevent all types of criminal activity before any serious damage occurs. By using predictive analytics to study user behavior and activities, organizations can detect abnormal activities, including credit card fraud, corporate espionage, and cyber attacks.

What is the difference between predictive analytics and traditional analytical techniques?

The difference between traditional analysis techniques and predictive analysis techniques is straightforward. Traditional analytic techniques often focus on insights that affect this moment, and predictive analytics techniques are designed to allow users to focus on the immediate and long-term future to identify possible trends and impending behavior.

How should an organization start using predictive analytics?

Although it is not a simple matter to start using predictive analytics, as long as you stick to this approach and are willing to invest the necessary time and money to start the project, almost any company can do it. Launching pilot projects in a limited area in key business areas is an excellent way to control start-up costs, while at the same time generating the fastest financial return. Once the model is put into use, it usually needs little maintenance, and it can bring about viable ideas for many years.

Predictive Analysis Technology Example

Today's organizations can use predictive analytics in almost endless ways. The technology can help users in many fields such as finance, medical, retail, hospitality, pharmaceuticals, automotive, aerospace and manufacturing.

Here are a few examples of how organizations use predictive analytics:

Aerospace: Predict the impact of certain maintenance operations on aircraft reliability, fuel usage, availability, and uptime.

? Automotive: Combining the robustness of parts and fault records with the upcoming automotive manufacturing plans. Researching driver behavior to develop better driver assistance technology, eventually developed a self-driving car.

? Energy Sector: Predicts long-term price and demand ratios. Determine the impact of weather events, equipment failures, regulations, and other variables on service costs.

? Financial services: Develop credit risk models, forecast financial market trends, and predict the impact of new policies, laws and regulations on companies and markets.

• Manufacturing: Predict the location and proportion of machine faults. Optimize raw material delivery based on predicted future demand.

• Enforcement: Use crime-trend data to make it clear that certain communities may need additional protective measures during specific periods of the year.

• Retail: Track online customers in real time to determine if providing more product information or incentives will increase the likelihood of completing a transaction.

Predictive analysis tools

Predictive analytics tools provide users with in-depth and real-time insights to help users master almost every type of business activity. These tools can be used to predict various types of behaviors and patterns, such as how to allocate resources at a particular time, when to replenish inventory, or to determine the best moments to start a marketing campaign based on analyzing and predicting data collected over time.

Nearly all users of predictive analytics use tools provided by one or more external developers. Many of these tools are tailor-made to meet the needs of specific companies and departments. The major predictive analysis software and service providers include:

Acxiom Corporation

IBM Corporation

Information Builders

Microsoft Corporation

SAP Corporation

SAS Institute

? Tableau Software


TIBCO Software Corporation

Predictive analysis model

Models are the basis for predictive analytics—these models allow users to turn past and current data into actionable insights that produce positive long-term results. Some typical prediction models include:

• Customer lifetime value model: Identify those customers who are most likely to purchase more products and services.

Customer segmentation model: Customers are grouped based on similar characteristics and purchasing behavior.

• Predictive maintenance model: Predict the probability of failure of important equipment.

• Quality Assurance Model: Discover and prevent defects when providing products or services to customers to avoid disappointing customers and incur additional costs.

Predictive modeling

Model users can use almost unlimited predictive modeling techniques. Many methods are unique to specific products and services, but the core of common technologies, including decision tree technology, regression technology, and even neural network technology, is now widely used to support various predictive analytics platforms.

The decision tree is one of the most popular technologies and it depends on the schematic and tree diagram used to determine the action plan or display statistical probabilities. The branching method can also show each possible outcome of a particular decision and how a choice leads to subsequent results.

Regression techniques are commonly used in banking, investment, and other financial-oriented models. Regression techniques help users predict the value of assets and understand the relationships between variables, such as commodity and stock prices.

The forefront of predictive analytics technology is neural networks - designed to identify potential relationships within a data set by mimicking the functions of the human brain and designing algorithms.

Predictive analysis algorithm

Predictive analytics users can easily use a variety of statistics, data mining, and machine learning algorithms that are designed for use in predictive analytic models. Algorithms are often used to solve specific business problems or a series of problems that can improve the performance of existing algorithms or provide some type of unique functionality.

For example, clustering algorithms are well suited for customer segmentation, community mining, and other social related tasks. In order to increase customer retention or develop recommendation systems, classification algorithms are often used. The regression algorithm is usually chosen to create a credit scoring system or to predict the outcome of many time-driven events.

Predictive analysis technology in the medical field

Medical institutions have become the most interested organizations using predictive analytics technology for the simple reason that the technology is helping them save money.

Healthcare organizations use predictive analytics in a number of different ways, including intelligently allocating facility resources based on past trends, optimizing employee work schedules, identifying at-risk patients for short-term expensive rehospitalization treatment, and supplying medicines Buy and manage to increase your level of intelligence.

A 2017 Actuaries Association report provided a trend in the medical industry's predictive analytics technology. It found that in organizations that already use predictive analytics technology, more than half of medical executives (57%) believe that the technology will be used in the next five years. Their total budget saves more than 15%. Another 26% of executives expect their budget to save 25% or more.

The study also showed that most healthcare executives (89%) work in organizations that are currently using predictive analytics technology or plan to adopt the technology over the next five years. Impressively, 93% of medical executives said that predictive analytics technology is very important for the future of their business.

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