Predictive modeling is one of the most critical components of predictive analytics. The latter uses historical data to predict future outcomes. Conversely, predictive modeling is a mathematical process allowing you to predict future activity, behavior, and trends through data analysis. This type of data analytics is based on current and historical data.
Predictive analytics, a branch of advanced analytics, is gaining more significance in enabling businesses to leverage big data to identify risks and opportunities. According to Statista, the global predictive analytics market revenue is expected to account for $41.52 billion in 2028, up from $5.29 billion in 2021.
Below we cover all the need-to-know information about predictive modeling, its pros, and the biggest assumption about its capabilities.
Predictive modeling is a tool and data mining technique in predictive analytics that can help businesses predict what might happen in the future. Data mining, also called knowledge discovery in data (KDD), is the process of extracting usable data from raw data.
A predictive modeling process uses current and historical data to create and validate a model that can help forecast future outcomes. A model represents a simplified version of reality, which is also true of data models.
More specifically, this type of modeling is a statistical technique that uses machine learning (ML) and data mining. Such predictive data can be time-based or related to your customers’ past behavior and characteristics.
Machine learning is one of the types of artificial intelligence (AI), that allows software applications to predict outcomes more accurately.
Data is increasing in terms of petabytes (PB). One petabyte holds 1,000 terabytes (TB) or 1,000,000,000,000,000 bytes. And it’s no wonder the number of businesses relying on predictive modeling is growing worldwide.
For example, businesses in the financial sector use this type of modeling for credit card fraud detection data. Credit card fraud is a global problem. According to Statista, the value of fraudulent transactions completed using payment cards worldwide is anticipated to make up $38.5 billion by 2027.
Companies in the banking space implement machine learning algorithms to observe and detect fraud in real-time. Specifically, they use ML algorithms to automate customer behavior analysis to identify abnormalities and find fraudulent activities in real time quickly. For instance, high transaction volumes within a short period are sometimes indicative of suspicious transaction activity.
This type of modeling can help businesses:
Data, statistics, and assumptions are the essential elements enabling data scientists to make predictions.
Specifically, data is a vital component necessary for analyses, and statistics help make conclusions. Since predictive analytics is future-oriented, making 100% accurate predictions is impossible, so businesses need to rely on assumptions.
You may ask, “Is predictive modeling the same as forecasting?” As a statistical technique, predictive modeling uses observations and statistics to provide possible outcomes. For example, since this type of modeling is about consumer behavior, it can tell a business why the consumer buys. And forecasting analyzes past data to identify future trends, so it’s more about numbers.
Predictive modeling is based on subjective considerations and provides multiple outcomes for the whole business. And forecasting takes past data or opinions as a base, so it’s more objective and provides insight into one specific question.
The goal of predictive modeling is to forecast the future and predict event outcomes. For example, it can tell you the probability of a fraudulent transaction.
Specifically, when you deal with an event that has already occurred, predictive business analytics can help you forecast whether the transaction is fraudulent.
Namely, companies use profile- and transaction-specific models to identify fraudulent activity. The first is a user-level model determining whether a user is fraudulent or not, and the second identifies fraudulent transactions rather than fraudulent users.
These models use key features for their predictions, such as the time of registration or the number of completed transactions, which are some of the primary signals of fraudulent activity. For instance, fraudulent users may make several transactions at a time, while regular users don’t typically complete multiple transactions in a short period.
In addition, this type of modeling can also facilitate what-if analysis, also called sensitivity analysis. A what-if analysis helps develop different scenarios, and test, and evaluate business assumptions.
Moreover, it helps determine possible outcomes when circumstances or measures change. As a result, companies can evaluate their strategies or tactical moves in advance and make wiser and quicker decisions for the future.
For example, a marketing team lead can use two variables to understand the impact on the bottom line. A variable also called a data item, is a measurable characteristic, number, or quantity.
Specifically, one of the variables can be “hiring 20 marketing employees,” and the second one can be “over the next six months.” So, the question will be, “What if I employ 20 marketing specialists over the next six months? Will it increase revenue, and if yes, by how much?
More and more companies rely on predictive analytics software for fast model building and quick decisions. This is because predictive analytics tools act as a real-time decision-making engine. Additionally, predictive software offers buildable and maintainable AI models that provide fast results and decisions without requiring coding experience.
To create models, you need to make assumptions. That’s why model assumptions underlie predictive modeling, and every model makes an assumption. For instance, some predictive models may be used to assume consumer product preferences based on past purchase data.
However, if an unexpected situation occurs, predictive models may have difficulty using past data to make accurate assumptions. In these instances, the predictive model may produce a false prediction. According to Harvard Business School professor Clayton Christensen, a significant disruption won’t allow the past to foreshadow future events properly.
For example, consumer behavior has undergone significant changes since March 2020 because of COVID-19. As a result, the demand for online purchases in almost every industry has grown exponentially. And if a company used data from 2019 to 2020, the predicted outcomes would be different from those associated with the first half of the COVID-19 period. Specifically, customers preferred online shopping during COVID-19.
Such inaccuracy can make it harder for companies to deliver relevant offers, especially now, when customers look for greater relevance than ever. As a result, companies can have difficulty creating successful loyalty marketing programs and retaining customers, which are now critical to business recovery.
Another example refers to the assumptions associated with the Great Recession or the Great Depression of 2008 to 2009. This financial crisis is often called the “subprime mortgage crisis.”
During those years, brokers and analysts built prediction models based on the assumption that people would always manage to pay their mortgages. However, their predictions didn’t work because the assumption was invalid.
Why? Those models excluded the possibility that housing prices might not go up or down. And when this happened, the models proved to be poor predictors of mortgage repayment.
So, predictive analytics assumptions are so significant that invalid assumptions can devastate economies. That’s why businesses and analysts need to continually monitor the critical factors associated with assumptions and their changes.
The biggest assumption in predictive modeling is that the future will follow past trends. As American journalist and non-fiction author Charles Duhigg writes in his book, “The Power of Habit,” people build strong behavior patterns that they usually retain over time.
However, sometimes, people change their behaviors, and the models used to predict them may become invalid. And time is the most common reason that can make assumptions invalid. For example, if a model was built several years ago, it may not accurately predict today’s customers’ behavior as it changes over time.
Additionally, other factors that contribute to invalid assumptions in predictive modeling include:
For example, if buying habits change, the predictions made on past practices will become invalid and potentially useless over time.
And why are assumptions important in modeling? Specifically, it’s vital to check model assumptions before creating a prediction model. The reason is that if assumptions aren’t met, the model won’t accurately reflect the data and will lead to inaccurate predictions.
Predictive modeling forecasts event outcomes using mathematical statistics based on current and historical data. And what is the biggest assumption in predictive modeling? It’s the assumption that the future will continue to resemble the past. Time and changing market circumstances are the top reasons why these assumptions might prove to be incorrect. So, it’s critical to gather the right data and be careful with statistics and assumptions to predict future outcomes more accurately.
