Learn All About Predictive Analytics Software

Starring Michael J Fox, Back To The Future 2 was a box office smash. Even though it killed at the box office, its predictions weren’t perfect. After all, it predicted the Cubs would end their long title less drought by winning the 2015 World Series.

The movie got it pretty close though as the Chicago Cubs won the World Series in 2016 instead. Even though they were off by a year, the Back to the Future Twitter account excused their failed prediction by blaming the ‘94 MLB strike. (couldn’t predict that, could they?)

Unlike the popular 1989 movie that failed to predict the future of baseball accurately, predictive analytics is often successful at using historical trends to uncover real-time insights and predicting future events. Let’s imagine you're Marty McFly and hear it from the Doc himself.
 

You’re About To Tell Me What Predictive Analytics Software Is, Right?

Good prediction, Marty McFly! You’ll need to learn what Predictive Analytics Software is before you’ll have a chance to save your future children. Predictive Analytics Software is an analyzation tool collect data from various source and make predictions based on past events. You can feed data into a computer that uses Machine Learning, statistical algorithms and other techniques to make predictions or forecasts of events. While predictive analytics is used across various fields to predict future events, it still could not predict Jordan Belfort's antics from The Wolf of Wall Street. That guy is a loose cannon! Coincidentally, finance and stock market are areas where Predictive Analytics Software excels. There are many aspects that this software can help with, like predicting credit scores or loan default rates.

The software is good for more than just finance though. We could even use predictive analysis to predict when we need to change the carburetor out on the DeLorean. Those things don’t last forever and they’re expensive!

The History Of Predictive Analytics Software

You could say that predictive analytics has been around as long as people have wanted to know the future. From reading star charts to studying palm lines, people have always wanted to know what happens next. We’ll mostly focus on the computational side of predictive analytics to keep things brief.

Predictive analytics began to enter the mainstream in the late ‘90s when Google hit the scene and started using algorithms to filter web searches, making them more relevant to the user. Then, Big Data became a thing, as 2.5 quintillion bytes of data was created each day starting in the 2000s. Analytics got better and better throughout the 2000s — they could even predict which team was going to win baseball games, Marty! Next, Machine Learning (ML) and Artificial Intelligence (AI) hit the scene and it all blew up from there.

Once the computers learned how to learn we really were in trouble. We already know how advanced today’s Predictive Analytics Software is, if you don’t, let’s teach you how this futuristic software works.

How Does It Work?

Predictive analytics uses a variety of techniques and methods to predict what will happen in the future based on past results. Unlike you, since you never learn to not eat that jelly donut right before a hoverboard ride.

Data modeling, Machine Learning, Artificial Intelligence, statistical algorithms, Neural Networks and data mining are all used by Predictive Analytics Software to derive predictive insights. While it's mostly used to predict future events (since that's where the money is) it can also be used with data sets to predict unknown parts of the past and present, like identifying stock market fluctuations and patterns. (That's also where the money is). The foundation of predictive analytics is statistical analysis and how well your predictive algorithms relate to the actual outcomes. If the software is predicting the future of a predictive analytics journey regularly, then it's doing its job.

Businesses create models based on past events to predict the future or other unknown variables. Basically, predictive analytics tries to establish a pattern, then uses that pattern to predict what will happen next. More sophisticated software use advanced algorithms and machine learning models to become more efficient.
 

Here’s Why We Need To Know What’s Next

Predictive Analytic Software can save the world. No, really! It’s used in a huge variety of industries and can save money in manufacturing, healthcare and retail services. The more we can predict the future, the more money we can save. That’s just good business.

Just think of how important it is to predict the stock market. If you can predict even a single stock market fluctuation in the future using predictive analytics, then you can make money. Investors can plan investments based on predictive analytics. That’s the core of predictive analytics — using past data to predict the future.

It’s good for more than just predicting the stock market though — credit scores can also protect lenders and predict which individuals are more likely to go through foreclosure. This saves lender’s and bank’s time because they can filter out a lot of potential mortgage applicants based on credit history.

Time is money — one of the big benefits of prediction is saving time. Even if you can’t make a correct prediction, narrowing the field can make a huge difference too. It can even be used to protect communities as well. Police stations can predict which areas are more likely to see criminal activity and proactively send officers to those areas.

What Are The Types Of Predictive Models?

Glad you asked! Predictive models are usually seen as a way to forecast what’s to come. Imagine a line graph telling you stock prices year by year. That’s a simple form of a predictive model.

Different predictive models are used depending on what you need to predict:
 

• Descriptive Models

  Group customers by common features and can predict what customers will purchase a specific product/ service or help determine an individual's potential credit score.

• Customer Segmentation Models

  Sort customers by common traits. For example, customers can be sorted by how much they spend on each shopping trip. One group is $0-10, another $10-20, another $20-30, etc., which helps know what to market to whom.

• Predictive Maintenance Models

  Try to predict how long manufacturing machines or their components will last, which helps in scheduling their maintenance and planning for replacements.

• Quality Assurance Models

  Look out for defects as new products come off the line. These models help make use of Machine Learning but not in an evil way like the Terminator. (Wait, did that movie come out before or after we saved your kids, Marty?) Machine Learning helps the software make better and better predictions and not kill people like a crazed robot. Predictive Analytics Software is not just useful for predicting stock market fluctuations or the World Series winners. Here are some more industries that use the software. There's a lot of stuff here, so make sure you keep up.

• Automotive

  Models can learn from human drivers to make autonomous (self-driving) cars possible. Thanks for the lift, robot-car! You can also predict how long parts will last and which parts are more durable than others.

• Retail

  Observe online customers to see what effect ads or page layouts have on shopping preferences. We all love getting those weekly ads in the mail, right Marty?

• Banking

  Collect historical credit data to predict how likely a person is to make payments.

• Consumer Behavior

  Analytical Customer Relationship Management (CRM) is a descriptive model used in the retail industry. It collects customers’ purchasing data and helps market products and services to target audiences.

• Legal

  Legal outcomes can also be predicted by software in the legal industry. This helps lawyers determine which cases to take and how much money to invest in a particular case.

Why Is Predictive Analytics Required?

If Luke Skywalker could have made better predictions, he’d still have his hand at the end of Star Wars. Man, that movie was great, Marty, wasn’t it? Companies can save their “hands” by using Predictive Analytics Software. Here’s 4 reasons predictions are essential:
 

• Predicts problems. Helps you prepare for problems in advance

• Gain advantages and beat  rival companies

• Analyze how successful marketing campaigns are

• Reduce risks. Lenders love predictive analytics because it can help them decide whom to give loans to, which reduces risk and saves time.

Predictive analytics can be applied to almost anything. If you can collect data on something and make a model, you can use that model to predict the future. It’s not a perfect process but it can narrow down the possible outcomes and help save lives.
 

Can You Predict The Future Of Predictive Analytics Software?

This software will only become more essential to businesses in the future. The more data companies deal with in the future, the more they can benefit from predictive software. Algorithms will be more advanced in the future as computers get more powerful and more detailed data is collected.

While credit scoring is already common throughout the world, including in the US, there’s a new kind of credit scoring that could emerge in the future: social credit scoring. This new scoring system not only uses financial records, it also uses everyday life to create a social credit score. For example, in Beijing, playing loud music in public or eating on rapid transits can result in a lower social credit score.

There’s also many other scary implications about the future. Software predicting our movements or spending patterns could be used maliciously to control or manipulate people. Not all predictions are evil though. Pretty soon we may be able to correctly predict a World Series winner. My prediction: The Indians win it all in 2049! Don’t bet all my dough, Marty!
 

Conclusion

Prediction will never be an exact science — the human element will always prevent 100% prediction from being possible but the rapid growth of computers could make predictions eerily accurate.

It’s not all doom and gloom though. Predictive analytics have also opened a lot of doors for people and made the world a safer place. Credit scores can be good too — they can reward good credit history by letting people purchase homes and cars on credit. Like most things tech, predictive analytics can be good or bad depending on the user. The technology will get more sophisticated, Marty! It’s up to us to make sure we use it for good and not evil!