Predictive Analytics

Predictive Analytics

Friday September 15, 2017

The technology that amazes us by predicting the future outcomes and trends by extracting the information and creating patterns from the existing historical sets is known as Predictive Analytics. It is basically branch of advanced analytics that is used to make prediction about the unknown future events. Predictive Analytics uses various techniques from data mining, statistics modelling, machine learning and artificial intelligence to analyze the current data to make prediction about future outcomes.

This blog post has been written as a part of the assignment for Predictive Analytics studied at Victoria University. By getting the practical hands-on experience of SAP product in the university has always been the best part of learning experience. We are very obliged to our lecturer Dr Shah Miah who helped in understanding the value and scope of Predictive Analytics.

As the competition is growing in the market and there are heaps of data that is kept in the database that is also known as Big Data. The Big Data made up of historical data sets can be used to produce some valuable information about the organization and processes. The information that is extracted is done with the help of Data Mining tools and predictive model is built. These models help company to take better decision.

In our task, we discussed, why more and more organization area turning into Predictive Analytics?

To increase the bottom line and competitive advantage, more and more organization are turning to predictive analytics because of the growing volumes and types of data, and more interest in using data to produce valuable insights, faster and cheap computers, easy to use software, due to tougher economic conditions and a need for competitive differentiation. 

The next question that we touched on is, who is using it?

There are various industry using Predictive Analytics to reduce risks, optimize their operation and increase revenue. For example, Banking and Financial Services, Manufacturing, Healthcare, production. Even the government is also getting the benefits of Predictive Analytics to get the insight data reports.

How this technology works, how do they do that?

Predictive Analytics models use known result to develop a model that can be used to predict values for different data. Modeling provides results in the form of prediction that represent a probability of the target variable for example, revenue based on estimated significance from a set of input variables. There are two different types of models that are classification model and Regression model that is used to predict the future outcomes.

What are the different types of models?

First model is Classification model is basically a Boolean approach that is 0 or 1, where you receive two answers like yes or no. For instance, when you try to classify whether someone is likely to leave, whether he will respond to a solicitation, whether he is a good or bad credit risk. Normally the model results are in the form of 0 or 1. Second model is Regression model that predict a number for example, how much revenue a customer will generate over the next year or number of months before a component will fail on a machine.

What are the importance of Predictive Analytics?

As already mentioned, more and more organization are turning to Predictive Analytics to increase their competitive advantage like detecting fraud, optimizing marketing campaign, improving operations, reducing risks these are some of the common uses that makes predictive analytics helpful. Now the question is how it works, for example in Fraud Detection, combining multiple analytics methods can improve pattern detection and prevent criminal behaviour.  Other use includes Operating Marketing Campaigns, for instance, Predictive Analytics are used to determine customer response or purchases, as well as promote cross sell opportunities. It helps businesses attract, retain and grow their most profitable customers. By Improving operations includes various companies nowadays use predictive models to forecast inventory and mange resources. For example, Hotels try to predict the number of guests for any given night to maximize occupancy and increase revenue. It helps organization to function more efficiently. Last one is by Reducing Risk includes Credit scores are used to assess a buyer’s likelihood of default for purchases and are a well-known example of predictive analytics. A credit score is a number generated by a predictive model that incorporates all data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims and collections.

Hence, it is inferred that Predictive Analytics has become crucial not only in the business but also for different purposes. There are several techniques and models that help organizations obtain insights from their data. However it is essential that companies interested in implementing Predictive Analytics develop deeper technical skills in order to acquire more profits by adapting this solution to their specific needs and requirements.  

Dhruv Arora / Daniel Velasco