What Is Predictive Modeling?

Predictive modeling is a data-mining solution that helps predicting future outcomes by analyzing historical data and current data. Predictive modeling involves collecting data, formulating a statistical model, predicting, and validating (or revising) that model.

Predictive modeling overview

A Definition of Predictive Modeling

Predictive modeling has been around for decades, but only recently was it considered a subset of AI, often linked to machine learning. It’s used to predict the likelihood of specific outcomes based on data collected from similar past and present events.

For example, with predictive modeling, you can calculate the probability that a customer will churn (unsubscribe or stop buying products in favor of a competitor’s). To achieve it, the model uses available data from customers who have churned before and from those who haven’t. This is done through patterns identified by machine learning algorithms to predict future trends.

While these predictions are commonly used for future events, they also apply to other conditions. Imagine that you want to classify the priority of a support ticket, based on its description text. After collecting data from similar tickets, you’ll be able to predict the priority of others with an accuracy rate that’ll increase with each prediction made.

Why Is Predictive Modeling Important?

The multitude of scenarios in which you can apply predictive modeling is one of the reasons its potential is so clear. But what type of benefits can you get from it?

Predict the Best Outcomes

Predictive modeling's first and most prominent benefit is that you will be able to better support your decision-making process, since all the relevant information about past use cases will be compiled, to enhance your future choices.

If a high volume of data needs to be reviewed manually (e.g., tickets, banking), predictive modeling makes it easier to approve or decline events automatically. This will also enable you to detect patterns, thereby reducing the amount of time required to process massive amounts of data.

Gain Operational Efficiency

As well as image recognition and classification, predictive modeling can also be applied to text translation and classification. In addition, the live model's performance can be continuously improved by using the right tools and environment so that it never becomes outdated.

You will also see improvement in operational efficiency and process modernization, which will lead to improved revenue and cost optimization.

Predictive Modeling Challenges and Common Use Cases

In spite of the success stories you may read, there are also some risks that are often left unmentioned. You should bear in mind that: Not all data is useful, and a model may give you inaccurate predictions based on irrelevant data.

It is not always possible to predict the future of an event by its history, and correlation does not always imply causation.

When it comes to predictive modeling, there are some ethical issues to consider. The handling of the data, its inherent bias, the algorithms, and the intellectual property rights may all come into play.

To reduce these risks, it is essential to monitor model results and compare them with reality. It is also necessary to feed the models with updated data and retrain them as their performance deteriorates. Making predictions is easy, but getting them wrong is even easier.

Challenges to AI ML adoption 

According to Gartner's report, we see that a lack of understanding of AI is unsurprisingly one of the top three challenges still to be faced by this field. As a way to reduce the fear factor and provide a set of solid starting points for any business, let's explore a few of the most common scenarios where predictive modeling can be extremely effective.

Customer Relationship

Customer behavior can be predicted based on predictive modeling at any point in the journey of a product. It can, for example, be used to forecast the success of an upsell or cross-sell offer. You can improve your customer relationship processes with AI by detecting spam in public communication channels. All of the following use cases ultimately come back to this one point of strengthening customer relationships.


Companies need to know their customers, their habits, and their behaviors in order to be successful. The vast amounts of data generated by clickstream flows, web, and mobile app usage, as well as many other behavioral analytics tools make it difficult to process. In this scenario, a predictive model helps by enabling customers’ classification and segmentation, making it easier to analyze relevant data, like detecting new revenue streams and planning actions accordingly. It can also help determine the success of display advertising and predict click-through rates.

There are already a good amount of practical use cases with excellent results. For instance, a leading bank was able to increase new account activity by 33% by targeting new movers relocating near bank branches and classifying them according to the likelihood of their response to a new bank account creation.

Process Automation

By identifying patterns in human behavior, models can infer, with high degrees of certainty, a human decision and categorize it. This can be useful, for instance, in manual triage and the prioritization of cases or tickets or workforce scheduling and optimization. With swift processes, everyone can focus on more critical and non-repetitive tasks.

Automation can be further enhanced with the Internet of Things (IoT), which consists of a system of computing devices with the ability to transfer data over a network without human interaction. For example, Ricoh improved operational and cost efficiency by up to 10% by automatically scheduling repairs to minimize downtime. All possible by predicting when a machine is more likely to fail.

Risk Assessment

Risk evaluation is essential for any company, especially when making significant investments. With predictive modeling, you’ll be able to anticipate the risk of payment default or fraud, avoiding potentially critical scenarios. Take the case of BBVA, as they were able to reduce the number of false positives related to fraudulent credit card transactions by 54% by predicting that risk.

These use cases are among the most common examples of how AI and predictive modeling are already making a big difference. It can now be applied to a vast number of sectors, from financial services to government, from retail to healthcare, and so many others. AI is not just the future. It’s already out there making a difference.

Take the Lead With Artificial Intelligence

If you’re looking for a competitive advantage that allows you to stay ahead of the competition, machine learning and predictive modeling are it. It’s more than just marketing hype, there’s real value out there, and it’s far easier to access than it was ever before.