Do you recall the time you applied for a mortgage and were given a maximum price of what you qualified for to purchase a home? What about that time you inquired and received an insurance quote? There was also that time when you walked in the grocery store, and the bread you normally buy was conveniently stocked on the front end cap of the aisle, saving you precious time and steps going all the way down the aisle. There was also that time you were watching news coverage of a local election, with the television displaying a poll of who would win.
All of these are examples of predictive analytics at work. Mortgage and insurance quotes, product stocking strategies, and political analysis are all ways in which predictive analysis has been used historically. The concept in itself is nothing new, but it’s getting better.
Predictive analytics takes multiple concepts – data, data modeling, statistical analysis, machine learning, artificial intelligence – and combines them all to make predict the likelihood of future outcomes. With the use of technology by the average person increasingly becoming a necessity, how that data is used by industries is also a necessity. Today, predictive analytics has infiltrated and improved almost every sector. In sports, predictive analytics is looking at ways to predict and reduce the risk of injury, allowing professional athletes to have longer careers and perform at optimal levels. In business – especially retail – it’s being used to increase sales by giving customers the right messages at precisely right moments. In politics, it’s also used to give messages at the right moments, but to target and influence potential voters.
By continually evaluating the past, learning from it, and using that information to predict future outcomes, there are enormous benefits from predictive analytics. Not only does it help to better meet customer needs, but it provides a competitive advantage. It not only provides insight for how companies should execute future strategies but provides a blueprint for how to execute efficiently, optimizing resources. This doesn’t come without its own set of challenges. There are huge debates over the best ways to purchase and cultivate data. Additionally, privacy and ownership [of the data]concerns are barriers that must be overcome. At the root of all challenges to predictive analytics is human behavior. Since predicting human behavior is difficult at the least, it’s important to keep in mind that predictive analytics models are a tool and not the tool.
Despite these challenges, predictive analytics is not only the future of computing, it is the present way of staying relevant. As the world of predictive analytics becomes larger, with more companies adopting it, it will also become more specific, allowing companies to target niche groups and outcomes. As the technology of predictive analytics improves, also expect the data to be projected in more visual ways. In B2B scenarios, this allows predictive analytics companies to sell software that is more user-friendly.