Predictive analysis is helpful when we use data, machine learning techniques, and other statistical algorithms to help us identify the likelihood of an outcome in the future, usually based on some of the data that we have from the past. The goal of using this is to go beyond just knowing what has happened so that we can then come up with a good assessment of what we think will happen in the future.
Of course, this is not always going to be accurate all of the time. Sometimes, the future doesn’t behave in the way that we would expect, or some big changes in the economy or the market have delivered results that no one is expecting. But the idea here is that we want to use this kind of analysis because often, the way the company has done in the past is like how they will do in the future, as long as all things remain constant.
This could be used when you are trying to figure out how to schedule employees based on sales, for example. If it is a cold winter, and you had lower sales the last few colder winters, then it is likely that you will not schedule as many people to handle all the shifts because there won’t be as much traffic. If you make some big sales around Christmas time, then you would want to make sure you schedule more shifts at the right times to handle this higher business level. Though the work of predictive analysis is something that we have seen around for decades, it is a technology that is starting to seem more valuable recently. There are a lot of organizations throughout all the different industries which are seeing the value of this kind of analysis, and they are turning to it to see how they can benefit. The point of doing this is because it provides the company with a big advantage over the competition, and it can even help their bottom line.
Why predictive analysis is so popular?
There are actually a couple of different reasons for this. Some of these reasons include:
- There is a growing amount of data types that companies are using and gathering now. In fact, many companies have more data now than they have ever had in the past, and they need to know how to use it. There is also a growing interest in using this data in an efficient way to produce insights that are valuable and can help the business to perform better.
- Computers that are faster and less expensive.
- Software that is easier than ever before to use on a variety of tasks.
- Economic conditions that are tougher, which means that each company – no matter what kind of industry they are in – needs to be able to differentiate themselves from the competition.
With software that is interactive and easier to use than ever before, and with the fact that this is becoming more prevalent all the time, predictive analytics is no longer just the domain of statisticians and mathematicians, as it was in the past. Line-of-business experts and business analysts are now opening and using a lot of these same technologies for their companies as well.
We can also take a look at why this predictive analytics is going to be so important. Organizations are going to turn to this analytics in order to help them solve some difficult problems, and uncover some new opportunities. This means that there is already a lot of opportunities out there for this kind of process already.
With this in mind, some of the common uses that we are going to see with predictive analysis will include the ability to detect fraud. Because predictive analysis is able to combine together multiple methods of analytics, it is able to improve the detection of patterns and prevent a lot of criminal behavior. As the world is on the lookout for more types and more advancements in the cyberattacks that come, being able to have the right kinds of analytics in place can make sure that all the abnormalities that are present can be caught ahead of time and can keep personal and financial information as safe as possible.
Another benefit is going to come in the form of optimizing the marketing campaigns. Predictive analytics will help companies and marketers determine the purchases or the responses of the customer, as well as help, promote some of the cross-selling opportunities as soon as they come up. Predictive models are going to be able to help with this because they are good for assisting a business in attracting, retaining, and growing some of their customers who are the most profitable.
You can also use these predictive models to help with improving operations. Many companies are going to use these predictive models to forecast inventory and manage the resources that they have. For example, this is something that you will see with airline tickets because they will use these models to ensure they set the ticket prices at the right point, based on the demand at that time of year. Hotels can do this by predicting how many guests are going to show up on any given night, maximizing occupancy and increasing their revenue at the same time. When predictive analytics is used properly, it is going to enable the organization to function in a manner that is more efficient, increasing their bottom line.
We can see how predictive analytics can come into play and reduce the amount of risk that the company is going to assume. For example, credit scores are often used because they can do an assessment of how likely it is a buyer will default on their purchases, and it is one of the best-known examples of how this predictive analysis is going to be able to work.
The score that you get on your credit is actually a number that has been generated through the use of predictive analysis, based on some of your past habits with making purchases, asking for and either receiving or being denied for a loan when you apply, whether you have made payments on time in the past and more. Other risk-related uses of this kind of model are going to include things like the claims and the collections that you try to do with your insurance company over time.
With this in mind, we also need to take a look at who is using this kind of model in order to gain a competitive advantage over others in the same industry. Pretty much any industry will be able to use this predictive analysis to help them to increase their revenue, optimize how they run the company, and to reduce the risks that they face.
Predictive analysis in industries
There are a few industries already jumping on board and seeing a lot of benefits in the process.
Banking and Financial Services
The financial industry is often going to handle a huge amount of money and data on a regular basis. In between people putting money into and taking money out of their accounts, the various loans they offer, and more, it is no wonder that this industry is going to use some of the predictive analytic models to help them out. They can use these models to help out with the detection and the reduction of fraud, to help measure the credit risk of various customers, to maximize the cross-sell or the upsell opportunities, and to make sure that they will retain valuable customers along the way.
One example of this is done with the Commonwealth Bank. They have been able to utilize the predictive analysis models in a way that they can figure out the likelihood of fraud pretty quickly. This company can predict how likely it is that fraud is occurring for any given transaction before that transaction has been authorized – within 40 milliseconds of the initiation of a particular transaction.
Think of how this can change the financial world. It builds up a lot of trust with the bank because it is more likely they will catch someone who is trying to make fraudulent purchases long before they can get away with anything. It helps to put the mind of the customer at rest because they know their money is safe, and often the check for fraud is done without anyone even noticing because it is that quick. The bank is also going to benefit. They can save billions of dollars in the process if they can stop fraudulent charges before they even happen, saving time doing investigations into the transactions, and the potential for losing the money because the person who did it was never caught will go down to a minimum as well.
Oil, Gas, and Utilities
Whether these companies are predicting when equipment will fail so they can get things fixed or replaced quickly without a bit interruption, or they are predicting the future needs of their resources, mitigating safety and reliability risks, or trying to improve their performance overall, the energy industry has really taken a liking to predictive analytics and using it on a daily basis.
A good example of how this has been used is with the Salt River Project. This is the second largest of the public power utilities in America, and one of the largest suppliers of water in Arizona. Analysis of machine sensor data is able to help this company predict when their turbines, the ones that give power, need some maintenance along the way.
Retail companies can benefit from this kind of analysis, as well. At one point, a study that is now infamous shows that men who go to the store to buy diapers also will purchase some kind of beer at the same time. And since this time, retailers have been using predictive analytics to help them plan out their merchandise and optimize the prices that they sell things to. This allows them to see how effective their promotional events can be, and will make it easier to determine which offers are the best to start.
While many retailers do this on a regular basis, we are going to take a look at Staples for a moment. Staples was able to use predictive analysis to gain customer insight. They analyzed behavior, which gave them a complete picture of their customers. When they were able to put this into practice, it resulted in a return on investment of 137 percent.
governments and the public sector
We can also see governments and the public sector using this kind of technology. Governments have already been one of the key players when it comes to the advancement of computer technologies. For example, the US Census Bureau has been analyzing data to help understand trends in the population for a long time. To add to this, governments now use predictive analytics like many other industries so that they can improve their performance and their service, so they can detect and then prevent fraud, and help them better understand the behavior they see with the consumer. They can often add in cybersecurity to their use of predictive analysis.
Health insurance industry
The health insurance industry uses predictive analysis as well. This can be a risky business because you want to provide the best service to customers, but you must balance out the costs that it takes to help patients with the amount that you are receiving each month. And predictive analysis can really help to make all this fit together.
First, predictive analysis is going to help when it is time to detect fraudulent claims. This industry is taking it a bit further, though, by taking steps to identify patients who are at most risk of chronic disease and then find out what kinds of interventions are going to be the best to help them out. This can help keep the patient healthier while saving the insurance company a lot of money in the long run.
For example, Scripts, who is a large pharmacy benefits company, has used predictive analysis. They use these analytics in order to identify those who are not adhering to their prescribed treatments, which has helped them to save somewhere between $ 1500 and $ 9000 per patient. This can help the insurance company lower costs while still giving their customers the healthcare and medicines that they need.
We can also see some of this predictive analysis show up in the world of manufacturing as well. For these companies, it is important for those running it to identify factors that could lead to identifying factors that could lead to reduced quality and even to failures in production. They also need to focus on optimizing the parts, the service resources, and the distribution.