Predictive Analytics: Its Significance to Business Managers
Prof. (Dr.) S. Shyam Prasad
Introduction
Business analytics, big data, and data science, are the most happening things in the business arena. Companies are collecting data from every customer who visit them at every turn and have accumulated piles of data at their disposal. Only those companies that develop analytical skills and exploit the data are going to have clear competitive advantage over others. Most of the companies have now evolved from working on simple Business Intelligence (BI) to extracting maximum knowledge from data by using the power of predictive analytics (Figure 1).
The term ‘predictive analytics’ is increasingly being used in business; something that was esoteric and remained in the domain of statisticians is now finding place in amongst business managers. The wide spread of internet technology and the availability of easy-to-use interactive softwares have enabled business analysts and managers to use predictive analytics for business purpose.
Predictive Analytics
Predictive analytics is the use of historical data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes.
Organizations are now increasingly using predictive analytics to increase their effectiveness and gain competitive advantage. Predictive analysis enables business to predict and respond to change before it happens and thus influence business outcomes. It is a scientific art. It is scientific because it uses sound mathematical and statistical techniques. It is an art because it depends on the person who analyses – what raw data is used and what techniques are applied to extract the prediction. The objective of the data analysis is to look beyond descriptive statistics and make the best assessment of what is likely to happen in future or how or what the customers are likely to do. Predictive models built on predictive analysis, analyze current data and historical facts in order to estimate the future probability of an event in contrast to Descriptive models built on descriptive analysis that tells what has happened (Figure 2). One word of caution; predictive analytics cannot be used for soothsaying, but it does tell what is likely to happen in future with a certain level of reliability under a given scenario.
Analytics may include high sounding topics like data mining, text mining, statistical modeling, machine learning, social network analysis, sentiment analysis, real time bidding, online campaign optimization, and so on. However, as mentioned earlier, the availability of interactive softwares that are simple to use have made the life of managers easy and have turned some them into business analysts.
Uses of Predictive Analytics
In fact, predictive analytics can be used by any company to increase the effectiveness of their operations and thereby the profits. It finds wide applicability; according to a recent TDWI report, the top five things predictive analytics used for is to:
- Identify trends.
- Understand customers.
- Improve business performance.
- Drive strategic decision making.
- Predict behavior.
Besides the above, a few other common usages of predictive analytics include fraud detection, cyber security and risk analysis to assess a buyer’s likelihood of default by looking at the credit scores.
Some examples of its uses are given below:
Insurance industry. Predictive Analytics is used by these companies to determine premium amounts, detect frauds, build customer relationships and retain them and optimize marketing campaigns.
Banking and Financial industry.Here it is used to determine credit risk, detect and reduce fraud, maximize cross-sell/up-sell opportunities, retain customers and optimize marketing campaigns.
Health Care industry.In health care industry predictive analytics is used to predict the effectiveness of new procedures, medical tests and medications, and improve services or outcomes by providing safe and effective patient care.
Sports industry. Sports industry too uses predictive analytics. Sports analytics is a used to find the trending and hot areas.
Retail industry. In Retail industry, it is used to decide on which products to stock, where and how to build brand loyalty. It is also used to calculate the effectiveness of promotional events and campaigns and determine which offers are most appropriate for consumers.
Conclusion
Predictive analytics has gained in popularity and continues to do so. One of the reasons is that it provides insights that can lead to better solutions. Though analytics finds place in marketing, finance, HR, operations and other areas of business, most of the data collected are related to the pages visited by the customers on internet, the products they buy, the quantity of purchase, place of purchase, time of purchase, the manner of purchase, the price they pay, the emails customers open, the banners they see to name a few. Thus about 90% of the data collected are related to customer behaviour and marketing activities. Hence it is prudent for modern marketing managers to be conversant in analytics. In fact, today’s managers are needed to be not only tech savvy but also data savvy in order to interpret results and make better decisions.
“Those who ignore statistics are condemned to reinvent it,” warns Bradley Efron of Stanford University.
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