- May 25, 2019
- Posted by: admin
- Category: Predictive Analytics
Predictive Analytics Overview
Of all the different kinds of analytics that data science has given rise to, predictive analytics is the riskiest one perhaps. Why, because it is basically a very sophisticated form of predicting the future of enterprises, and is laced with technological intricacies.
Ever since the concept of Big Data came into being, there has been an offshoot of specialized streams and one of them is predictive analytics. So the modern trends in Big Data are reflected in predictive analytics which functions primarily on the Big Data tools and technologies.
If we take a step backward just prior to the arrival of predictive analytics, we find Business Intelligence. BI is like the precursor of predictive analytics. But where predictive analytics is all about looking ahead into the future in trying to fathom what lies there, BI is like taking a look at the past. To be more precise, BI is all about dealing with historical data to find out details about sales and related customer information.
However, there is no reason to think that predictive analytics and BI are antagonistic, because insights derived from historical data, are used to predict a company’s fortune. Various technicalities like statistical analyses, data mining, artificial intelligence, and machine learning go into predictive modelling. And then these models are applied to the data processed by BI, to predict potential market trends, risks, buying patterns and possible opportunities in the future.
Today, predictive analytics is used in all industries. Although each domain uses predictive analytics in different ways, it is ultimately for the same purpose. Now, let us take a look at examples of predictive analytics in different industries.
One sector that has massively benefited from predictive analytics is definitely Retail. The essence of the retail sector is to boost sales and improve customer experience at every level, for which predictive analytics is just unavoidable!
Take the example of shopping online from Amazon. Whenever we add a product to the cart with the intention of buying, there are automatic suggestions about similar items that other customers bought. Now, this is all because of predictive analytics, wherein analyses have revealed buying trends of customers. A deep study of the market along with segmentation of different customers by factoring in various parameters can only lead to such insightful results. As a result of using big data predictive analytics, retail giants like Amazon have successfully reduced the number of product returns, and have strengthened their bond with existing customers.
The use of predictive analytics in the world of sports is also very significant. There has been instances where national sports teams have banked on predictive analytics and data based on evidence, to curate a winning team.
But the most prominent example of predictive analytics in sports is Bind Predict, from the house of Microsoft Corporation. Using statistical information and sentiments displayed on social media, this search engine has fared laudably in predicting results for the race between the US House and Senate and has even scored an 80 percentile in predicting the winner for reality shows such as American Idol.
Predictive analytics is of utmost importance in the healthcare industry and has been used to predict public health problems and epidemics many times. Such is the accuracy that predictive analytics can also precisely determine whether an individual with some diagnosed ailment is susceptible to a relapse or not. Whether a particular patient needs behavioral therapy as well or not, can also be forecast using predictive analytics techniques.
Mention of Google Flu Trends that provided the estimates of influenza activity for more than 25 countries must be made in this context.
One of the best uses of predictive analytics lies in the assessment and mitigation of risks. And since risk is a major formidable factor in case of the insurance sector, there has been amazing outcomes after applying predictive analytics. Insurance companies can estimate the chances of losses in the future and even detect fraudulent activities. Moreover, they can even formulate winning business campaigns and make much better-informed decisions related to business strategies.
Manufacturing and Energy Domains
There has been a significant improvement in sectors like manufacturing and energy, with the advent of predictive analytics tools. These industries involve mega investment in procuring and maintenance of advanced complicated machinery. And being able to predict the failure of any equipment or even a partial malfunctioning can save major expenses and delays in production. This not only prevents sudden major expenditure but also smoothens out business operations. Moreover, it allows a scope to plan out further strategies for boosting productivity and sales in the longer run.
The scope of predictive analytics is not just limited to these few industries. It has proven useful for every kind of business, and empowered them with intelligent decision-making abilities, by gathering accurate insights from daily user-generated data.