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What is Predictive Analytics: Definition, Tools and More

What is Predictive Analytics: Definition, Tools and More
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Predictive Analytics

Today’s business organizations are working in an environment where there are threats from everywhere be it competitors, uncertain market conditions and so on. This is where they rely on predictive models for exploiting the patterns in transactional and historical data for forecasting the future with a certain degree of accuracy. Building the Predictive Analytics model helps to assess risks using a certain set of conditions and helps organizations move into uncharted territory in order to make the right decisions.

How Predictive Analytics compares with Prescriptive and Descriptive Analytics?

CriteriaPredictive AnalyticsPrescriptive AnalyticsDescriptive Analytics
Asking the right questionWhat will happen?What should be done?What happened and why?
Tools and methodologiesStatistical modeling and simulationHeuristics and optimization modelsData aggregation and mining
ApplicationMake informed decisions about futureMaking complex time -sensitive decisionsSummarizing business results

Businesses don’t have to just rely on gut feeling or intuition but can take data-driven decisions for getting more insights into their customers, market conditions and more. It is used in a diverse range of industries from banking, insurance, marketing, credit risk analysis, manufacturing, healthcare, governments and a whole host of industry verticals.

Predictive Analytics is more interested in getting the events of the future rather than of the past or the present. The accuracy of the Predictive Analytics greatly depends on the accuracy and usability of the data and also on the level of analysis and the quality of assumptions.

At a more granular level the Predictive Analytics can be thought as something that is related with creating predictive scores for an individual organizational element. The domain of Predictive Analytics is different from forecasting in the sense that the in Predictive Analytics there is learning from the data in order to predict the future so organizations can make better decisions.

Today’s businesses need to go beyond knowing what has happened to giving the right assessment of what will happen in the future. Though Predictive Analytics has been there for quite some time now it is only recently that it has got some serious clout thanks to the following reasons:

  • The availability of faster, cheaper computing systems
  • Software that is use to deploy and maintain
  • The need for a real competitive edge

Due to these developments that we are seeing it is clear that Predictive Analytics has not just remained the sole domain of mathematicians and statisticians but has been more wide-spread. Today business analysts and decision-makers are exploring this field to gain more insight into the mind of the customer.

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Understanding Predictive Analytics

Predictive Analytics has been developed in the last 50 years and today we are seeing a huge jump in the number of companies that are using Predictive Analytics. Today due to the incessant growth of big data and the need to make data-driven decisions, it is imperative on each and every organization to make use of Predictive Analytics. Predictive modeling based techniques help to work in a streamlined fashion and get the results delivered as per the specific framework.

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The process of Predictive Analytics includes the following steps :

process of Predictive Analytics includes the following

Defining the project : This is the first step of the Predictive Analytics model. Here you will have a clear-cut definition of the outcome of the project, the business objectives, the scope of the effort, identifying data sets and more.

Collecting the data : This is the second step of the process wherein you will be mining for the data from multiple sources and prepare the Predictive Analytics mode and provide a complete overview of the entire process.

Analyzing the data : This is the process that includes the various steps of inspection, cleaning, modelling of the data for discovering the objective and help to reach at a conclusion.

Deploying the statistics : Here you will be working on validating the assumptions and hypothesis and testing it using the standard statistical models.

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Data Modeling : This is the process that provides the ability to create automatic predictive models of the future. You can also create a set of models and choose the most optimal one.

Model Deployment : This is the step in which you will be deploying the analytical results into your everyday business operations helping to get results, reports and the output of the automated decisions.

Monitoring the Model :  The models are reviewed in order to ensure the performance of it is going in the right direction.

The Predictive models are the relation between the specific performance of a unit in the sample and other attributes in the unit. The model is designed in order to understand the possibility of a different sample that exhibits the same specific performance. It is used in many domains for the purpose of answering a whole set of questions in various areas like marketing, sales, customer service among other domains.

Predictive Analytics is used for various purposes like business segmentation, decision-making and other purposes like statistical techniques among other tasks.  There is a huge advancement in the speeds at which computing is done, the availability of modeling techniques to come up with valuable insights.

What can you do with Predictive Analytics?

What can you do with Predictive Analytics

There are a lot of aspects when it comes to Predictive Analytics. Organizations are using it in mission-critical applications to gain valuable insights and drive the business organizations forward. It is the process of extracting value from big data, applying the right algorithms to large data sets and using the Hadoop and Spark tools for coming up with insights in real-time. The sources of data may consist of transactional database, log files, video, image, sensor data and all other types of data which need to be analyzed and predictive models have to be built on it. A lot of organizations are today deploying machine learning techniques for finding the right patterns, including of linear, logistic and non-linear regressions, neural networks, decision trees, support vector machines and more.

Peter Levine, VC and General Partner at Andreessen Horowitz,

“For predictive analytics, we need an infrastructure that’s much more responsive to human-scale interactivity: What’s happening today that may influence what happens tomorrow? A lot of iteration needs to occur on a continual basis for the system to get smart, for the machine to “learn”Peter Levine, VC and General Partner at Andreessen Horowitz,

It is up to every individual organization to find out newer ways to deploy Predictive Analytics and uncovering of new opportunities in their pursuit of growth and revenues:

Targeted marketing campaigns – A data-driven approach to customizing the marketing campaigns, understanding customer response, designing the most appropriate approach to creating the right marketing campaign, measuring the key performance indicators and ensuring the campaigns are able to meet the business goals.

Improving operational efficiency – Streamlining the various operations of an organization, managing the supply chain, inventory management, deploying the right resources, promoting opportunities for cross-selling, optimizing the various processes.

Risk mitigationPredictive Analytics deployment for finding out more about the customer’s aversion to buying a product, the various factors which dissuade a customer from making the purchase decisions and finding out how to reduce the risks involved.

Fraud detection – Working with the various analytical tools to find out more about the pattern detection of the fraud transaction in banking and financial domains, preventing the criminal motives, deploying behavioral analytics to preclude fraud, researching about zero-day vulnerabilities and reducing the risks of advanced frauds.

Industries using Predictive Analytics model

Industries using Predictive Analytics model

Aerospace : The amount of data that is generated in the modern era aircrafts is phenomenal. Today due to the abundance of sensors, newer ways of storing the data and finding various ways in which that data can be useful, Predictive Analytics is suddenly taking a huge stride in the aerospace industry.

Automotive : Today’s automobiles are heavily invested when it comes to deploying the most cutting-edge gadgets, technologies, sensors for coming up with highly valuable analytical methods for ensuring the driving experience is simply phenomenal. In the not so distant future, most of the automobiles will be connected to the internet of things and due to this the role of Predictive Analytics will only grow stronger.

Energy & Utilities : This is another domain wherein the role of Predictive Analytics is again very significant. It helps to predict the demand and supply of electrical energy through the power grids. There are various sophisticated models that are used for monitoring the plant availability, impact of changing weather pattern, learning from historical trends, forecasting the optimal demand and supply balance among other things that can help the energy domain save huge amounts of money and resources.

Banking and Financial Services : This is one of the biggest domains that is currently deploying Predictive Analytics at scale. Due to the large amounts of data being generated and the extremely high stakes involved, banking and financial institutions are increasingly deploying Predictive Analytics for ensuring the customers get a world-class experience that is customer-friendly, secure and forward-looking. It is possible to tailor-make products and services depending on the profile built around the customer, opportunities for cross-selling and up-selling, find patterns of fraud and malpractices among a host of other things.

Retail : The retail industry is working with predictive analytical tools and technologies to get inside the mind of the customers. It includes the process of stocking the right products, promoting the right products to the right customers, providing the most optimal discounts to persuade sales, having the right strategy for marketing and advertising among a whole host of other aspects.

Oil & Gas : The industry of oil and gas is a big user of the domain of Predictive Analytics. it helps to save millions of dollars through better predicting equipment failure, need for future resources, ensuring safety and reliability measures are met, and so on. There are a lot of sensor data that needs to be monitored in order to take the right data-driven decision in the oil and gas industry.

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Governments : Since the data in a government department is humungous thanks to the all-encompassing nature of this domain, there is a huge untapped opportunity which can be aptly exploited using the right Predictive Analytics tools and technologies. It could be deployed for providing the right services to the citizens, monitoring the various welfare schemes are reaching the right audience, checking corruption and malpractices and so on.

Manufacturing : Even in today’s world of services-oriented economy the domain of manufacturing is still extremely important. The manufacturing industry can make use of Predictive Analytics in order to streamline the various processes, improve the quality of service, supply chain management, optimizing distribution and such other tasks for enhancing the overall business revenue and achieve bigger goals.

Learn about the working of Predictive Analytics in our detailed blog on Predictive Analytics in Crime Prevention.

Working with Predictive Analytics

Working with Predictive Analytics starts with a business goal. It is all about asking the right questions, what are processes that need streamlining, optimization, how to leverage data to come up with better decisions and such other aspects. It is about making a prediction that is represented by a probability of the target variable as per the significance of the input variable.

The goal of Predictive Analytics is being deployed by the mathematical and computational methods for predicting the outcome of an event or process. The process of the predictive modeling is based on the forecast of a certain future state that is based on the changes that happen to the model input. It uses an iterative process for developing the model using a training data set and then testing and validating it for determining the accuracy of the predictions to be made. There are a various set of machine learning methodologies also involved for finding the most effective model.

The difference between Predictive Analytics and prescriptive analytics is that Predictive Analytics lets you know what will happen next with a certain degree of accuracy while prescriptive analytics lets you react to the predicted situation in a certain manner for the optimal results.

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Advantages of Predictive Analytics

Organizations are increasingly working on directing, optimizing and automating the decisions for improving the business processes. Here are some of the advantages of Predictive Analytics framework:

  • Deploying analytics for analyzing past, present and projected future outcome
  • Choosing the right step to drive the action in the most optimal manner
  • Predictive Analytics includes both decision optimization and advanced analytics
  • Supporting action and recommended actions are sent to the decision-makers
  • It helps to take proactive risk management measures
  • Testing iterative actions for the intended and unintended consequences
  • Process improvement, cost reduction and revenue generation are all possible


Predictive Analytics is set to grow at a huge pace thanks to the need for making data-driven decisions on an ongoing basis in organizations regardless of the industry vertical. So the domain of Predictive Analytics will see a huge interest and this opens the door for professionals who are trained in Predictive Analytics to take up jobs for hefty salaries in some of the best organizations around the world.

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About the Author

Principal Data Scientist

Meet Akash, a Principal Data Scientist who worked as a Supply Chain professional with expertise in demand planning, inventory management, and network optimization. With a master’s degree from IIT Kanpur, his areas of interest include machine learning and operations research.