Simply put, predictive modeling, also known as predictive analytics, machine learning, or data mining, is a practice used to analyze current and past behaviors and trends in order to predict future facts or unknown events. It is based on the concept that past behavior or outcomes predict future behavior or outcomes. The goal of predictive modeling is to stretch beyond what is known to provide the best assessment of what could happen in the future. In this fast-paced, technology-filled world, predictive modeling is necessary to keep up.
A wide variety of entities and companies use predictive modeling to calculate the risks and benefits of certain decision making. With predictive analytics, a score or probability is used to influence a company or organization in their processes that relate to a large group of individuals. Although predictive modeling is most frequently utilized in meteorology and weather forecasting, it is used in many other areas as well.
Why Predictive Modeling?
Predictive analytics is no crystal ball, however, it can provide deep insights and help us make favorable decisions. Nearly any company in business today, whether public or private, can benefit from using some form of predictive modeling. The most common uses are:
A credit score is probably the most common or well-known type of predictive analytics. By looking at a consumer’s credit and financial past, a sound decision can be made by companies as to what type of credit to extend now and into the future.
To function as efficiently as possible, predictive analytics must be utilized. For instance, airlines use this method in order to determine ticket prices and hotels use it to predict the number of guests they will have on a certain night, allowing them to maximize their occupancy and make more money.
Criminal behavior can be detected and prevented when different analytical methods are used in conjunction with each other.
When businesses use predictive modeling in their marketing campaigns they can better grow and retain their customer base. Predictive analytics can help determine purchases, cross-sell opportunities, and customer responses.
The Growth of Predictive Modeling
For decades, predictive analytics has taken a back seat to other methods for businesses to increase their bottom line and their competitive advantage. However, predictive analytics is now on the rise. This is due to computers that are both faster and cheaper, software that is more user-friendly, the need for competitive differentiation, and harder economic times. Thanks to better technologies that are easier to use, predictive analysis is now a tool that not only mathematicians and statisticians can use but nearly anyone in any business can use.
“[…] use analytics to make decisions. I always thought you needed a clear answer before you made a decision and the thing is that you’ve got to use analytics directionally…and never worry whether they are 100% sure. Just try to get them to point you in the right direction.” – Mitch Lowe, Co-founder of Netflix
Advances in data, technology, and information storing have helped predictive modeling to grow into a bigger market. It has also grown in popularity and use due to its nearly limitless applications. For instance, Netflix uses this type of data to help suggest new movies or programs for you to watch based on what you have watched or searched for in the past. Amazon uses it to recommend products you might want or need based on your previous searches or purchases. Dating sites such as Match.com or eHarmony use predictive modeling to help pair romantic matches. Various websites use it to help determine what ads you should see that you will likely click on.
Predictive Modeling: The Future
The predictive analytics market is expected to be a $3.6 billion market in the United States by the year 2020 and a 42 percent increase in spending is predicted for business intelligence. Businesses are willing to spend top dollar in this area in order to have more certainty in their business forecasting and make sound business decisions.
Although predictive modeling is not a fool-proof method, it produces more results than when not used. Gone are the days of guessing at customer wants and needs or extending credit to nearly anyone who walked into a business. With a need to stretch their dollar further, to protect their own assets, and to increase their customer base, predictive modeling is a welcome tool for most businesses that will continue to grow far into the future.
Predictive modeling has increased the success of many industries. The internet’s rise to popular use and better accessibility has also made predictive modeling easier to use and apply in one’s field. However, just with any other advanced technology, we must not become solely reliable on predictive analytics. This method has its own role but it is not the final solution. Predictive modeling should be a part of a bigger and more comprehensive strategy.
There is no doubt that in the future, the range and volume of data to be collected will grow, increasing the need for more analytical techniques. This is especially true when it comes to the phase of exploratory analysis. Companies should have a clear path for managing, using, and implementing the models that they produce. In order to be successful, expert interpretation of the analyzed patterns and a clear understanding of the issues at hand are of utmost importance.