What Is Predictive Analytics? And Where Is It Used?

Radhika Madhavan

Director of Marketing

Table of Contents


Data has always been an integral part of organizations and their day-to-day operations. While the concept of predictive analytics came into existence as early as the 1950’s when ENIAC generated models to forecast weather, it gained mainstream reach only during the seventies. This was when the Black-Scholes model was developed to predict stock options. Later in 1998, Google applied predictive algorithms to web searches to optimize result relevance.

Today, predictive analytics has a far more widespread list of use cases. Given that 2.5 quintillion bytes of data are generated in a single day, the need for analyzing, optimizing and using that data is cardinal.

Leading companies such as Google, Amazon, Facebook, Oracle, and even Netflix use predictive analytics for targeted advertising and personalized marketing. Similarly, even medium and small scale enterprises are opening up to the idea of the technology.

So what is predictive analytics?

Broadly defined, it is a subset of advanced analytics that extensively uses data to derive predictions with respect to certain activities, trends or behavior in the form of numbers or scores.

In this article, we shall delve in detail about predictive modeling techniques and applications of predictive analytics in various verticals.

Predictive Modeling Techniques

Representation of the analyzed data can be in different forms depending on what kind of data the predictive model is based on and the variables that are used in the creation of that model.

Multiple variables from the data set are combined using automated machine learning algorithms and statistical analysis techniques to create a predictive model. This model then uses the data to predict the likelihood of occurrence of a particular event. The working of the predictive models relies heavily on advanced methodologies and algorithms.

Logistic Regression

It is a statistical analysis tool that analyses historical data and predicts a value based on that analysis. The more data fed to the algorithm, the better it gets at predicting. For example, predicting whether a political candidate is likely to win or lose an election.

Time Series Charts

Time series is a data visualization tool that is useful in identifying trends or a pattern or analyze how a key metric is performing over a period of time. It consists of a graph where regular time intervals are plotted on the horizontal axis and the variable is plotted on the vertical axis. Time series graphs are especially useful in weather forecasting.

Decision Tree

A decision tree is essentially a graph represented like a tree where the branches indicate all possible outcomes of a decision. Each branch of the tree represents a possible outcome and can have further branches based on the complexity of the problem statement that finally lead to a decision. For instance, if sending an immediate response to an email is the decision, then possible outcomes would branch out as yes or no. As a next step, the yes option could branch out into two more outcomes as immediate and save for later.

Neural Networks

Neural networks are used in pattern recognition and data classification. Neural networks analyze past and current data to predict an outcome. The advantage of using neural network algorithms is that it can identify even complex correlations in a vast amount of data, making them reliable and definitive.

Where Is Predictive Analytics Used?

Predictive analytics has long been associated with forecasting and prediction of weather. However, there are many more applications of this technology. One of the common applications in the present market scenario is in online advertising and marketing. Predictive analytics is used to identify what kind of products users are most likely to purchase, or what kind of URLs are likely to get maximum clicks and so on. It is also used in areas such as customer relationship management, E-commerce, human resources, and finance. Let us look at some of the areas where predictive analytics is being used:

Operations Management & Customer Support

Predictive analytics can be employed across major areas of operations such as IT operations, finance, marketing, customer service, and other internal processes.

Based on the data and domain of operations, predictive analysis can be done on two levels — quantitative and qualitative.

For instance, predicting the company’s customer retention average would be quantitative analysis, and predicting application or network downtime would be qualitative analysis.

Customer support and service is not just your average business requirement anymore. It is a business standard that will determine who hit the jackpot: you or your competition. Knowing what makes your customers want to keep coming back to you well in advance not only makes it easier to make that happen, but it also helps you strategize a plan to achieve it. This is where predictive analytics becomes useful.

It uses historical and current data to predict market trends, product performance, and customer insights about a particular product. This aids organizations in developing sound strategies to ensure good customer experiences. For instance, predictive analytics can be used to predict whether a customer will make a purchase on a website or not by analyzing browsing patterns.


The insurance sector is one that deals with a large number of data sets, owing to the nature and complexity of the insurance process. Data gathered is in various formats and usually unstructured, and hence, predictive analytics becomes useful here.

Primarily, predictive analytics is used in insurance for pricing and risk selection, underwriting and claims. With advancement, the technology can identify the risk of cancellation, risk of fraud, risks relating to outlier claims. Further, predictive analytics is also used to understand and predict the behavior of insureds by analyzing gathered data.


E-commerce companies in today’s digital world face huge competition to stay relevant in the marketplace and maintain credibility at the same time. E-commerce businesses generate large quantities of data on a daily basis, which can be coherently used to gain insights about market trends and consumer behavior. One of the challenges that these businesses face is convincing customers that their product is the only product the customer needs. Even with the most ingenious marketing campaigns, companies sometimes fail to achieve their objectives, owing to rigid brand loyalty of customers.

Predictive analytics can be used to improve personalization and customer engagement to identify what affects buying behaviors, to determine the effectiveness of promotional events, and to decide product pricing strategies. It can also be used to understand which offers would be most suitable for consumers and gauge consumer satisfaction levels, otherwise known as behavior analytics.

For instance, Target, a leading chain of supermarket stores in the U.S., uses predictive analytics to identify a customer in the initial months of her pregnancy. The company gathers various demographics like age, marital status, ethnicity, job history, browsing habits, etc., from its customers. This data is then used to incentivize the customer into shopping for essential items from vitamins to maternity clothing. Whether this is an appropriate method to enhance customer service and retention is a question that is left to the ethical committee.

Finance and Banking

The finance sector is a volatile one with rapidly changing trends and ever-evolving competition. Predictive analytics allows financial firms and banks to keep track of progress in various aspects by using relevant data to make predictions. One of the major areas where predictive analytics is being used today is to predict the customer lifetime value for a customer by observing certain patterns and trends and analyzing if that customer will be retained for a long period.

Other significant areas of banking and finance where predictive analytics is actively being used are to improve efficiency of the supply-chain process, for customer feedback management, reevaluating labor costs, fraud detection, and alleviating overhead expenses.

For instance, Citibank uses predictive analytics and artificial intelligence to tackle cyber attacks and fraud.

Fraud Detection and Risk Assessment

Companies are under constant pressure to protect their assets and avoid fraudulent mishaps. Companies face competition from adversaries both outside, and within the organization. This makes it imperative to include data security as a part of the organization’s infrastructure. In such a scenario, preventive measures like fraud detection become necessary. Predictive analytics can be used to recognize patterns and trends in data that could depict fraudulent activities. The predictive model analyzes data and provides actionable information that can help organizations predict and prevent the possibility of fraud.

DBS Bank, a leading bank in South East Asia, employs a programme that uses predictive analytics to facilitate fraud prevention and detect trade anomalies

Risk assessment for an organization hinges on a bevy of factors. More often than not, these risks are uncontrollable and organizations find themselves unprepared to face the consequences.

Data plays a major role in risk assessment by predictive analytics because the predictive models are built on historical and current data. Predictive analytics uses the predictive model built using the data to help the organization decide on various precautionary actions to mitigate foreseeable risks and/or alleviate losses already incurred by the organization.

Human Resources

Human resource functions encompass collecting and analyzing data to improve operations and workforce productivity in the organization. The objective of using predictive analytics in human resources is to enable the organization to make informed business decisions by analyzing data and turning it into actionable information.

Predictive analytics can help improve employee retention rates, predict the impact of introducing new company policies on employees, gauge employee satisfaction, and revise hiring strategies by analyzing past data.

Companies like Google and Hewlett Packard use predictive analytics for employee engagement. For instance, Hewlett Packard created a predictive model called ‘Flight Risk’ that could predict the possibility of each of its employees resigning from their position.

Weather Forecasting

Meteorology is one of the few initial fields where predictive analytics was extensively put to use. Weather forecasting affects both organizations and individuals across the globe, and hence its accuracy is indispensable.

Predictive analytics uses past and current data to forecast daily, weekly or even annual weather and climate respectively. Current advancements in technology have also made it possible for meteorologists to predict the occurrence of natural and man-made calamities such as earthquakes and tsunamis.

Marketing and Ad Campaigns

In a largely competitive market scenario, companies prefer being prepared rather than waiting for the ball to drop. Companies can design their ad campaigns using predictive analytics taking into consideration key metrics and requirements that will get them guaranteed results.

For instance, Caesar’s Palace, a leading hotel and casino in Las Vegas, collects data on its guests and analyses that data to predict what kind of upgrades would be suitable for the guest.

In addition to having multiple uses for businesses, predictive analytics is also used in other arenas like politics and sports. Specifically, it is used by political parties to reach out to probable voters and send out more personalized messages to them as a strategy to gain their confidence and votes during the election. In the sports industry, predictive analytics is used to predict scores by a team or an individual player.


Nearly every industry today finds a use for predictive analytics in one domain or another. What makes this technology so dynamic is its ability to use data to provide solutions, figures, trends and in turn help an organization make important decisions.

Predictive analytics as a technology is growing rapidly, especially in the development of self-driven cars. Companies like Renault, Tesla, General Motors, and many others are using predictive analytics to develop cars with automated and assisted driving capabilities.

In addition to predictive analytics, users will soon be using anticipatory analytics to make everyday decisions in healthcare, finance, and education. From being able to cure rare diseases to predict the next president of America, predictive analytics is going to make its way into businesses predominantly.

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