You have to know the past to understand the present

This article is written by the amazing Shauna Blackmon.

Sure, Carl Sagan was probably talking about politics and social conflicts rather than AI when he said this in 1980, but it works just as well. Predictive analytics use of data points to find historical or situational patterns can help give us clues about the future. Nothing is absolute, but finding patterns through statistics improves our ability to predict the future and situate ourselves to be better prepared for the oncoming days, weeks, or even years.

In short, predictive analytics affords companies the ability to focus on development and advancement, rather than being reactionary and putting out fires.

Predictive analytics uses a myriad of different technologies, including data mining, statistics, modeling, machine learning, and artificial intelligence. Together these technologies build a mathematical model to identify and capture different trends.

Say week to week your company sells X of any given product, but on certain days or times of the year, that number goes up or down. Predictive analytics can not only tell you how much to order based on that specific time period but even automatically process the order. By combining different technologies, most of the difficulties or aspects of running a retail company are fully automated.

Because predictive analytics is a machine learning formula, its versatility lies in its ability to provide predictions on any topic, as long as you have the initial data. Obtaining that data is sometimes a bit of a more difficult subject, depending on what you are hoping to accomplish and what data already exists. This is something that is easily determinable through an exploratory workshop with EnterpriseAI data specialists.  

The versatility of predictive analytics also enables the technology to be used across more industries than other forms of AI or ML. Health care, sales, marketing, insurance, business management, telecommunications, and even fantasy sports teams all rely on some form of predictive analytics to improve both daily and annual functions. Obama was even famous for using predictive analytics, earning him the nickname The Data President. 

Why predictive analytics are everywhere

In an ever increasingly competitive market, even small missteps can have out weighted and rippling consequences.

In the end, all companies have the same root goal: to provide ever increasingly better output, faster, with less waste, and at a better profit margin. Data collecting and processing allow us to take a fine-tooth comb through every aspect of a business’s operations and find every opportunity to increase value without increasing spending.

Unlike other forms of behind-the-scenes technology, we see the effects of predictive behavior modeling all around us in our everyday life. In the end, most models of predictive behavior aim to predict *human* behavior. We want to think we are the creators of our own destiny, but really, most of us are more predictable than we would ever want to admit. Companies all around us are using these technologies to understand us better than we understand ourselves. Every time Netflix suggests another true crime story that you can’t help but binge or Spotify gives you a new song that stays in your head for weeks, or a dating website matches you with that cutie that is totally your type, the companies behind those suggestions are learning how to better understand you. As each one of us clicks or swipes, predictive behavior modeling is learning how to understand all of us.

We all want to better understand our customers. What convinces someone to be a frequent buyer and not a one-time purchaser? Did the new interface design have the intended effect on users? What are the main drivers of engagement and retention?

To know when a product might be in high demand, or when a machine is likely to break, or even when a client is likely to default on a loan, we can gain the upper hand on chance.

By 2027, the global predictive analytics market is expected to reach 35.45 billion dollars (29.37 billion Euros). The technology and the accuracy will only increase, making it an even more powerful tool for companies to streamline their processes.