What is Predictive Analytics?

Understanding Predictive Analytics, How Businesses Use It, and How it Affects More Than You Think. 

 
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From the very beginning, businesses have wanted to see into the future. What do customers want to buy? How likely are people to pay back their debts? How can we be more efficient and make more money? The problem is that businesses want the answers to these questions before they start investing money in making any changes. It’s all about efficiency after all, no reason to throw a bunch of money at a problem if there’s no guarantee the money is coming back. Being able to predict what will happen and why is a very real problem that businesses want to solve, and for years, they have been attempting to be clairvoyant, to the best of their ability, through something called predictive analytics.

So what is predictive analytics? Predictive analytics is the science of predicting what will happen based on a wealth of good data on what people or even processes have done in the past. There are countless articles from leading institutions on the science and mathematics behind predictive analytic models, but what may be more interesting is exactly how businesses deploy these models and where you come across them in everyday life.

Has iTunes ever recommended a movie or a song to you? Well, that recommendation comes directly from analyzing the songs you like and the songs of people who like the songs you like to predict new artists you could be interested in. Amazon does the same with predictive recommendations for products, and Target might give you coupons for products similar to those you buy. This comes directly from the data provided every time you shop or listen or browse, collected in giant piles and used in predictive models created by data scientists., In these cases, data scientists are trying to understand specific buying habits of customers to predict what they may purchase in the future. However, predictive analytics works in a slightly different way than other measurements of data.

 
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With much of the Big Data analytics industry, the idea is to look at the data using text analytics tools such as AI-powered, natural language processing and sentiment analysis, processing structured and unstructured data, in order to understand how customers are currently feeling, if there is a problem, or if the business practices they have in place are working properly. Predictive analytics works the opposite way, in order to build the right predictive models and measure something, data scientists need to understand what they are trying to predict. Let’s say that customers are leaving your store and never coming back, this is something in the industry known as churn. So, the churn rate is high, and you not only want to know why, but you also want to know how likely customers will be to churn in the future. You’ve collected a wealth of great data on your customers such as their age, location, spending habits, how many complaints they’ve filed, etc. This is valuable data to form a predictive analytics model. Based on the information provided the model can look at how these aspects fit together, identify those individuals with similar traits who have churned, and predict if other customers, whose traits match those who are defecting, are also likely to churn.

These types of predictive models can help companies make forethought based decisions, applying the expertise in their industry, to change their business models, processes, or marketing to attract the right kinds of customers and keep the ones they already have. However, customer retention practices are not the only use of predictive analytics, it’s something we encounter every day whether we know it or not. Banking and financial institutions use predictive models to determine, based on your spending habits, whether or not a transaction on your card is fraudulent or not. Most banks run every transaction for every customer through this fraud prevention model within 40 milliseconds of every purchase. The U.S. Census Bureau uses predictive analytics to determine population trends based on their data, and manufacturers use these models to predict quality issues or production failures in their products in order to optimize expenditure. Perhaps the most familiar example is one that affects the most people directly, a credit score.

 
 
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This little 3 digit number between 400 and 800 determines your ability to accrue debt and the likelihood you’ll pay back that debt. It takes information such as your history of payments, amount of debt currently owed, how long your credit history is, what type of new credit you have, and the type of credit you use to provide the score. This predictive analysis is one that the vast majority of people encounter and use on a daily basis to buy a car or a house, acquire a credit card, or even a TV from Best Buy. Companies have been using this since 1989 to predict the behavior of individuals who owe them money and it’s one of the foundations that makes the economy move at a steady pace.

Businesses overall want to use predictive analysis to protect their bottom line, ensuring their resources are used as efficiently as possible and that they are identifying potential issues before they happen. Data analytics technology has grown at a rapid pace to keep up with demand, developing artificial intelligence to accumulate and analyze data to provide predictions in less time than traditional methods. Powerful tools from a variety of companies look to crack the prediction code and provide deeper insights to companies more readily, making use of the wealth of data collected every day and putting that information into action. So while businesses may never be able to see into the future, predictive analytics gives them the next best thing. Using solid collected data from a variety of sources, predictive models provide forward thinking insights into the big problems that businesses are trying to solve. It’s not quite clairvoyance, but it’s as close as we can get.