According to Gartner, almost every company out there is either saying that they have Artificial Intelligence (AI) capabilities integrated into their businesses, or they are planning to embrace it soon. And AI is directly linked to other concepts such as predictive modeling, predictive data, forecasting, and training models for machine learning.
These all sound like buzzwords, and although they come up a lot alongside definitions of what the future in technology may look like, they are often not correctly defined. Here I’ll focus on one of those concepts: predictive modeling, a part of predictive analytics, which also includes other statistics processes, data warehousing, decision optimization, and more.
What is predictive modeling, and why is it so important? How can we move from theory and adapt it to real-life scenarios? I’ll point out more its practical applicability and less the intricate mathematical formulas and definitions, providing answers to the main questions around this concept.
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.
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
The first and most prominent benefit of predictive modeling is that you’ll be able to support your decision-making process better since all the relevant information about past use cases will be compiled to enhance your future choices. Which takes us to another benefit, finding hidden trends and patterns in unstructured or sparse data. But that’s not the end of it.
When a high volume of data needs to be manually reviewed (e.g., tickets, banking), predictive modeling will help you to more approve and decline events automatically. It’s also a valuable tool that can identify patterns, drastically reducing the time it takes to process vast amounts of data.
It can also be used for image recognition and classification and text translation and classification. And even better, given the right tools and environment, the live model’s performance can be continuously improved to consider new and reviewed data, so that it never gets outdated.
In a more high-level view, you’ll also notice a boost in operational efficiency and process modernization, which translates to improved business performance, increased revenue, and better cost optimization.
However, behind all the success stories that you may read, there are also some risks often left unmentioned. Take into consideration that:
- Not all data consists of useful data, and a model may give you incorrect predictions based on irrelevant data.
- The history of a given event doesn’t always predict its future trends, and correlation doesn’t always imply causation.
- There are some ethical concerns. These may involve the way data is handled, its inherent bias, the algorithmic models, and its intellectual property.
To cope with these risks and limitations, monitoring the model results and comparing them with reality is vital. Additionally, models must be fed with updated data and retrained when their performance deteriorates. It’s easy to predict what the future might look like, but it’s just as easy to get it wrong.
What is it Like in the Real World?
Let’s get past definitions and benefits and move to what predictive modeling looks like when applied to real-world situations. Most companies don’t know it yet, but it’s more practical than farfetched digital humanoids or technical applications that will shape the future of human interactions.
Based on Gartner's report, we can see that the lack of understanding of how AI works is unsurprisingly one of the top three challenges this field still has to tackle. To reduce the fear factor and provide a set of great starting points for any business, let’s go through some of the most common use cases in which predictive modeling can play a crucial role.
Predictive modeling can be applied to predict customer behavior at any given point in the product’s journey. You can, for instance, use it to determine the probability that an upselling or cross-sell offer will be a success. Detecting spam messages in publicly available communication channels is another excellent example of how you can improve your customer relationship processes with AI. In a way, all the following use cases all come back to this one point of enhancing the relationship with the customer.
For most companies, it’s critical to know their customers, their habits, and behaviors. And it’s often hard to process the vast amount of data that comes from clickstream flows, web and mobile applications usage, and from many other behavioral and analytics tools. 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.
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 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 the 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.