Is Predictive Modeling “Future Prediction Software”?Reading time: 4 minutes
In recent years, predictive modeling has had an increased public profile. It’s been implemented and discussed in every context from politics to pandemics. It’s often thought of as a “future prediction software”. While this descriptor captures some of what predictive analytics can do, it doesn’t paint a full picture of the technology’s capabilities, particularly for businesses and organizations that want to incorporate predictive modeling into their decision-making process.
In this post, we’ll explore if “predicting the future” is what predictive modeling actually does. We’ll also take a look at some of its applications in business and industry.
What is Predictive Modeling?
First things first, let’s talk about what a predictive model actually is.
Essentially, a predictive model is an algorithm or mathematical formula that uses trends in historical data to forecast outcomes. In simple terms, this means that predictive modeling uses information about the past to make a prediction about the future.
Technically, predictive models don’t even require computers; you could make the calculations by hand if you have a lot of time to work through the math. In fact, predictive modeling was in use as far back as the 17th century, when calculations were used to estimate the level of risk involved in sea voyages.
When you’re working with just a few data points, a predictive model can be fairly straightforward. But for most organizations today, the amount of data to incorporate into a model is vast. That necessitates an advanced predictive model that can process lots of data.
Automated predictive modeling has opened up the tool to people of all skill levels in every type of work to perform advanced predictive analytics with large datasets, and the technology becomes more and more accessible and affordable, broadening its implementation.
So It Does Predict The Future?
Well, not exactly. To say that predictive analytics predicts the future is actually a limited description of its capabilities.
Rather, predictive analytics uses trends in historical data to forecast likely outcomes based on changing variables. It’s best to approach predictive modeling as a “what-if” machine. Essentially, you can create a model to predict how things are likely to turn out based on changes you make in your strategic approach. You can test these changes before actually implementing them.
For example, let’s say you’re coordinating an outreach campaign. Before spending money on direct mail, it would be helpful to get a sense for how likely sending each of those mailers is to lead to your desired outcome – whether that’s a donation, a visit to your office, or a phone call.
Predictive analytics lets you compare the effectiveness of physical mailers versus email based on the success rates of past campaigns you’ve run. You may have a hunch for which type of outreach was more successful in the past; predictive analytics can help you make a more accurate decision. Perhaps people of a certain age respond better to direct mail than others. With predictive analytics, you can make that deduction before taking action. Then, send mailers only to zip codes that contain a certain proportion of older residents, rather than spending money less effectively by blanketing the entire city with mailers.
In that sense, predictive modeling is more than a future prediction software. What predictive modeling does is open up a “multiple worlds” approach to the future. With a data-informed perspective of multiple possible futures, you can make changes in the present to influence the future.
What Are Some Common Applications Of Predictive Modeling?
One of the incredible things about predictive modeling is how versatile it is. If you have good data – meaning clean, well-structured, accurate data – you can apply it to more or less any business or organizational challenge.
In business, predictive modeling can be used to make revenue projections based on past years’ data. This can help you plan for the coming year by expanding or contracting your operations correspondingly. You can estimate staffing or inventory needs (and scale up or down as needed) by comparing past years’ seasonal data with this year’s trends, as well as factors like weather forecasts and wider economic trends.
In education, particularly higher education, predictive analytics has seen a huge rise in implementation over the past several years. Enrollment managers can shape their incoming classes to meet KPI’s by modeling the impact of different forms of prospect outreach and recruitment. Retention and Student Success professionals can create more effective student intervention plans by flagging students who are at risk of failing and establishing success coaching protocols. And fundraisers can model the most effective ways of keeping alumni engaged and donating, streamlining donor outreach and cutting unnecessary costs.
Healthcare is another industry that has seen a rise in the application of predictive modeling. Predictive analytics improves patient treatment options and safety standards. Healthcare professionals can also integrate social determinants into patient treatment with analytics, granting greater insight into treatment options and appointment scheduling. These capabilities grant healthcare professionals and administrators a tool to reach better outcomes while cutting costs.
A wide range of other industries, like real estate, insurance, and supply chain management, see benefits from predictive analytics. For more information on industry use cases, check out our recent blog post on the topic.
So, Is Predictive Modeling A “Future Prediction Software?”
The bottom line is, it’s best not to look at predictive analytics as a future prediction software. Predictive modeling is most often accurate in predicting how things will play out. But simply looking forward to see the future is actually a very limited description of what predictive modeling can do. Rather, using it to test various strategies to get an idea of how they will shape the future you want to create is predictive analytics’s best and highest use.
Rapid Insight makes a predictive modeling solution called Predict which automates the process of building a predictive model by mining your data for the most statistically significant variables with a single click. It then creates a model that you can examine, put to use, and tweak as needed. It’s designed with users of every skill level in mind.
Predict can help your organization craft data-informed strategies for more efficient and effective operations. Click the button below for a demo!