Concept of Predictive Analysis | IVL Global
Concept of Predictive Analysis
Introduction
Predictive analysis is the use of large data sets and machine learning algorithms to identify the trends and likelihood of the future. In the current blog, we would like to aim for the existing market research and the need for predictive analytics, also the role of data and machine learning techniques for prediction.
Despite all the new technologies lining up in the current market, corporate business or workplace or even our home is very inefficient with the use of resources available. Engagements on different problems cost high to the organizations, with less accurate output and minimal customer satisfaction. Delay caused in the service provided by the Health care organizations, may affect the life of an individual. Critically, the health care sector could use all the prognostication it can get. The lows & highs in the financial market can cause institutions or organizations to be in the risk of debts. These effects could cost everyone heavily in various ways in our day to day life.
There is a need to work more efficiently in a functional society. The main goal of predictive analysis is to go beyond what is happening in the current world and provide the best assessment of what is going to happen in the future.
Background
The predictive analysis serves the very purpose of huge processing and predicts the future from the available data. Predicting customer behavior and their preferences is the trademark of top E-Commerce companies, but this technology is also being used and is relevant to smaller companies as well. With nearly every organization working on its own digital space, data is aggregable and accessible.
With available machine learning techniques and the data availability, we and/or the organization can find the trends in the behavior of customers at a time, in a specific region, or in any other aspect as well. Identifying these problems and using predictive analysis to solve them, benefits the organization with positives like improved customer satisfaction, cost reduction, efficient use of the inventory/resources available.
Data visualization is a valuable tool that not only appeals to the eye but can be used to inform, inspire, and guide actions based on customer behavior. An organization can get all the information available from the customer and using data visualization, we can see a different type of customer and their needs in a better way, which can be much helpful in predictive analytics.
Advantages
E-Commerce organizations can effectively forecast their inventory and the products with higher demands in a specific period, by improving the efficiency in the production. Telecom Domain companies can predict customer behavior and reduce the cost of the customer centers by sending them notifications prior. This can be done using predictive analysis, by analyzing the data and understanding customer behavior. Similarly, Competitive advantages can also be found by following the trends of other organizations.
Depending on the organization, the predictive analysis also comes into the picture when it comes to risk reduction. Organizations in the Finance sector or even the insurance sector, use predictive analysis to predict the condition of the market, to make sensible decisions to reduce the risk.
One of the most beneficial uses of predictive analysis is by using it for detecting fraud. Tracking changes in the behavior of the site or network can alert/warn about some fraud. Unusual behavior in the network can send notifications to the concerned authority about it detecting the fraud.
Millions of predictions a day can improve the world to move in a better manner by predicting the behavior like whom to call, the reason for the call, fraud analysis, diagnosis, warn, etc.
Use Case
Predictive Analytics in the Telecom domain is a plus point in aspects like cost reduction, analyzing their customers, customer satisfaction, etc. In our project, we will come across a similar use case. Telecom companies face a huge amount of money loss in customer call centers, as the organization must pay for the toll-free numbers. Customer calls regarding their issues like high bills, out of service, payment issues, and other scenarios. We can use this data to predict which customer is going to call and reason for the call and send the customers details of their bills stating the reason for the high bill. This can reduce the calls on the call centers reducing the cost and thereby increasing customer satisfaction.
Also, the Organization can get an insight into their customers and can work more efficiently to provide a better service to their customers. Technologies that can be used for this process are Spark, Spark ML algorithms, Hive, Tableau.
We can use previous data to train the ML model, to predict the next/current month customers, by comparing the patterns in the behavior of the customer and their details. Tableau dashboard is created to display the customers that are going to call.
Conclusion
The predictive analysis makes the world work smoothly and efficiently, reinventing every industry in its own possible way. It can be used for determining events or outcomes before they happen, simulation of a process to determine bottlenecks and risks as well as in “what-if” scenarios to determine the “best” course of action.
Originally published at https://www.ivlglobal.com on March 22, 2019.
