Predictive Analytics

Using data to predict future trends, behaviors, and outcomes through analysis, algorithms, and machine learning.

What is Predictive Analytics?

Predictive analytics is a technique to understand future outcome and take informed decisions based on the findings. Predictive analytics uses various data, machine learning technique, statistical algorithms and past data to produce future scenarios.

The aim here is to move towards a more certain future by making better decisions and uncovering new stories from the data that leads to improved actions.

Predictive analytics help organizations to move beyond descriptive models (used to understand what happened) and diagnostic models (used to identify key relationships and reason for something happened). Predictive models factors in past results to develop a model that can often be used to predict the result from different data sets.

Organizations that are already using predictive analytics are able to make right decision at the right time and gain competitive advantage in the market.

Why does it make sense to use predictive analytics?

To be certain in uncertain economic conditions

To gain competitive advantage in the market

Availability of easy, fast and cheaper technology

Gain better insight from the data and take right decisions

Increase in the volume and type of data that can help in uncovering stories, align marketing activities, produce better product, deliver better care, and enhance customer experience and more.

Predictive analytics is used for?

As per a TDWI report, top five use of predictive analytics are:

Understand customers

Drive strategic decision making

Predict customer behavior


Identify business trends

Improve Business Performance

Application of predictive analytics across industries

Every industry uses predictive analytics in a specific way, few of them are explained below.


Manufacturing industry use Predictive analytics to gain significant insight into the factors that reduces the quality of products, processes, yield, machine and operation failures, also to improve quality, forecast profitable demand, maximize equipment value and increase equipment uptime.

Banking and Finance

Predictive analytics is crucial in banking and finance, identifying risks, boosting sales, retaining customers, detecting fraud, and ensuring liquidity planning. It aids companies in making informed decisions, ultimately enhancing overall performance and competitiveness in the industry.

Media and Entertainment

Predictive analytics aids media by understanding audience behavior, preferences, trends, and predicting engagement. It integrates social media data to evaluate performance and forecast opportunities, optimizing distribution channels, vendor performance, and cost control.

Insurance companies

Predictive analytics help insurance companies in detecting claims fraud, determining premium rates, identify high risk patients and provide the most appropriate policy per business rules, retain clients, attract customers, optimize claims processes,  better marketing  and improve performance and increase profitability.


Predictive analytics can change healthcare globally. It helps doctors diagnose accurately and prevent diseases by suggesting lifestyle changes. It also identifies at-risk patients early, enabling timely intervention. This benefits patients by ensuring appropriate care and helps pharmaceutical companies improve medicines.

Government and Public Sector

The public sector has vast amounts of data. Governments use predictive analytics for fraud detection, tax collection, cybersecurity, crime prediction, and more to make informed decisions and improve efficiency in various areas like operations and workforce management.

Oil and Gas

This industry is among the first to adopt predictive analytics due to its nature. Involved in extraction, refining, exploration, transportation, and marketing petroleum, predictive analytics aids in forecasting, analysis, energy trading, maintenance, risk management, and optimization, revolutionizing operations.


In retail, predictive analytics analyzes customer behavior and buying patterns. Retailers use this data to offer preferred products, enhance marketing campaigns, improve sales, and provide personalized shopping experiences. Operationally, it helps maintain inventory, control costs, ensure timely delivery, and optimize profits.

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