What is Predictive analytics

What is Predictive Analysis?

Whether you own a business or have a startup, you will always have a need for a data analyst who can analyse the data related to your work and guide you about the performance of your company or Business. This process of inspecting, transforming and modelling data in order to retrieve some helpful information out of it is known as data analysis. Data analysis is a general form analysis whereas Predictive Analysis is a specialized form of analysis which is used to predict the future outcomes of Businesses. It involves the use of both new and historical data to predict future activities and trends. By predictive analysis, you will not know what will happen in the future but you can know what may happen based on the current scenarios.

The Importance of Predictive Analysis

Predictive Analysis is used by several organisations to solve several difficult problems and to discover new opportunities. Some of the common uses of predictive analysis are as follows

  • Fraud Detection

Cybersecurity becomes a major concern with the growing technology. With the help of predictive analysis, online behaviours of an individual can be analysed with real-time detection which helps in the detection of abnormal actions which may indicate vulnerabilities and frauds.

  • Marketing campaign optimization

Predictive analysis is used to determine the response of potential customers on the marketing campaign and helps Businesses in preparing a proper marketing strategy in order to promote and cross-sell their products or services. It helps Business in attracting new customers and growing their business.

  • Risk Reduction

Predictive Analysis helps Businesses and organisations reduce the risk by analysing the previous patterns. A customer’s risk is calculated by assigning him a credit score which is completely based on the previous activities. The credit score shows creditworthiness of the customer which can be used to reduce risk while making any transaction. Insurance claims and collections are some of the risk related uses of Predictive Analysis.

  • Improved Services and Operations

Predictive Analysis allows organizations in the management of resources and to work more efficiently. Predictive models are used by several companies to forecast inventory and serve customers more efficiently. Hotels use it to predict the number of guests they can accommodate on a particular night which enables them to generate more revenue. Similarly, Airlines use predictive analysis to set the prices of tickets.

The Working of Predictive Analysis:

Predictive Analysis makes use of existing data to predict what may happen in the future based on several different techniques. Predictive models are created by feeding the existing data. These models provide results that represent the probability of activities that may happen in the future based on the set of input data. There are two types of Predictive model. A classification model helps you in predicting class membership and a regression model helps you predict a number.

To start with data analysis, the first step is to define the problem you need to solve which includes the answers to questions like what do you need to predict based on the past? What do you want to understand? and several other questions that describe the problem. Once the problem is defined, you need data from the past and current situations in order to build a model for prediction. The data must be taken from different places and sources which may include transactional system, third party information, weblogs and much more. After the collection of data, the data must be prepared for predictive modelling which requires someone who can understand both the data and the Business.

After the preparation of data for predictive analysis, the process of Predictive model building begins. There are several easy to use software available which makes it easier for several people to prepare a predictive model to understand and predict future outcomes. Though there is much software out there, it is still important to have a data analyst in order to help you refine the data model and an IT specialist who has specialization in intelligence course as he can help you deploy the model. Predictive modelling is a team approach and hence you will need a good team of people who can handle the data and deploy the model along with the understanding of the Business.

The most widely used predictive techniques are decision trees, neural networks and regression.

Decision Trees:

Decision trees are basically a classification model that help you partition data into subsets on the basis of input variables categories. A decision is similar to a tree, where the branches represent a number of alternatives and the leaves represent a classification or a decision. The Decision Tree model analyzes data in order to find out one variable that will split up the data into different logical groups. The decision tree is one of the most widely used and popular classification models because of its ease in understanding and interpreting.

Regression:

The most popular method in statistics is Regression. It allows you to analyse and estimate relationships among the variables. This predictive model is intended for continuous data which is assumed to follow a normal distribution. The Regression model finds key patterns in large data sets which in most cases is used to determine the impact of specific factors like price, on the movement of an asset. In the regression model if you need to predict a variable, say it as response variable then the prediction can be done in three ways. Linear regression makes use of one independent variable to predict the outcome of a response variable and Multiple regression makes use of Multiple different variables to predict response variable outcome. The regression model also helps you predict unknown variables of a discrete variable with the help of known values of other variables. This is known as logistics regression. In binary logistics, the response variable will have only two values i.e 0 and 1 at the same time, multiple logistics regression allows the variable to have several different values.

Neural Networks:

The more sophisticated model of the predictive model is Neural Networks. The Neural Networks model is capable of modelling relationships that are extremely complex. It is one of the most powerful and flexible models which makes it a powerful predictive model as well. With the amount of data being collected, it is becoming much more difficult to handle nonlinear relationships. Neural networks are capable of handling such data and that’s what makes it a powerful predictive model. Neural Networks are often used for the confirmation of the findings from other simple techniques like regression and decision trees. It makes use of some artificial intelligence and pattern recognition in order to model the parameters graphically. When there is no mathematical formula to relate the inputs to outputs, Neural network works fine. In the prediction model, the prediction is given more importance rather than the explanation as there is a lot of data to train the model.

How Predictive Analysis and Models are applied to Business and Enterprises?

Typically, Predictive Models and predictive analysis are used in order to forecast future possibilities. Prior knowledge of future activities and trends can help businesses grow significantly and serve customers in the right way. The historical facts and current data are analysed by the predictive model which helps the Business to understand their customers in a better way. It also allows you to choose the right product by looking at the trends. Choosing a business partner can involve risk but with the help of predictive analysis, you can identify the opportunities and potential risk involved with the Business. There are a number of techniques used by Predictive models which involves statistical modelling, data mining, machine learning and much more.

A vast number of data is collected by Businesses which is used by predictive analysis in combination with the historical data and customer insights. A number of companies now offer solutions to predictive analysis and data mining. The software for predictive analysis can be deployed on-premises or over the cloud based on the type of business or enterprise.

The software for predictive analysis uses the variables that can be measured and analyzed in order to predict the future activities and behaviour of individuals, machinery or other entities. The Predictive model is capable of assessing reliability at an acceptable level by combining multiple variables. Several advanced algorithms and different methodologies are used by the software which includes logistics regression models, decision trees and time series analysis. With the emergence of big data systems, predictive analysis has sprung up in prominence. The opportunities for data mining have also increased as several enterprises are now collecting huge amounts of data through various sources. The capabilities of predictive analytics have been expanded with the increase in development and commercialization of ML tools by IT vendors.

The insurance companies, marketing companies and financial services are adopting predictable analytics since early days to leverage the use of online service providers and large search engines. Industries such as healthcare, retail and manufacturing are also commonly using Predictive Analysis. The main objective to use predictive analytics in Business is to improve targeting audience online for advertisements, analysing the shopping pattern and identifying potential risks for the Business.

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