Predictive models predict future events based on statistical analysis. Read this guide to understand how predictive modeling works and how it can benefit your business.
The rapid adoption of digital products and services has led to greater amounts of data than we have ever seen. As a result, more and more organizations are using big data analytics and strategies to extract value from available data.
TO SEE: Job description: Big data modeller (Tech Republic Premium)
This data is often too complex and extensive for people to analyze manually, especially for organizations that want to derive future insight from existing data sets. Organizations instead rely on predictive modeling tools to connect data points and identify patterns in data. With the right predictive modeling tools and strategies, companies can make predictions about future events, customer behavior and market trends.
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What is predictive modeling?
Predictive modelling, a part of predictive analytics, is a statistical process used to predict future outcomes or events using historical or real-time data. Companies often use predictive models to forecast sales, understand customer behavior and mitigate market risks. It is also used to determine which historical events are likely to recur in the future.
Predictive modeling solutions often use data mining technologies to analyze large data sets. Common steps in the predictive modeling process include collecting data, performing statistical analyses, making predictions, and validating or revising the model. These processes are repeated as additional input data becomes available.
Advantages of predictive modeling
Organizations use predictive models to reduce the time, effort, and resources needed to predict business outcomes. Here are the main benefits of using predictive modeling:
- Minimize risk: Predictive modeling can predict an organization’s potential for cyber-attacks, fraudulent transactions, and other types of risk.
- Optimizing Marketing Campaigns: Using predictive models, organizations can discover customer insights to tailor and recalibrate their marketing campaigns.
- Maximize Profit Margins: Predictive modeling can be used to forecast sales revenue, forecast inventory, and create pricing strategies.
- Prioritize resources: There are several ways that predictive models can help prioritize resources for an organization. For example, sales teams can receive lists of expected leads to convert, allowing them to spend more time and effort on those high-priority leads.
TO SEE: Top data quality tools (TechRepublic)
One of the challenges or limitations of predictive modeling is that the results are only as good as the data used to construct the model. To ensure predictive modeling is as effective as possible, organizations must implement data quality tools to keep data accurate, secure, and reliable. They also need to prepare the data for business use by cleaning and formatting it for predictive modeling needs.
How to build a predictive model
There are several predictive modeling techniques. The two most common techniques are using neural networks and regression, respectively. In statistics, regression refers to establishing a relationship between input and output variables. The predictive model can be linear or non-linear, depending on the variables.
In neural networks, predictive modeling tools use interconnected nodes at hierarchical levels, a model inspired by the human brain. These nodes create patterns and relationships between variables to determine future trends. In addition to these two most popular predictive modeling techniques, companies also use clustering, outlier, and classification models.
Traditionally, predictive modeling was handled manually by a data analytics team. But as the process has become more complex and data quality efforts have grown exponentially, the use of computer software for predictive modeling has become increasingly popular. As a result, most organizations use predictive modeling tools such as Oracle Crystal Ball, RapidMiner Studio, and SAP Predictive Analytics.
Examples of predictive models
Many industries rely on predictive models to help with important business decisions. Here are some common use cases for predictive modeling:
Finance and banking
Predictive modeling is used in banking to identify fraud and illegal activity. For example, the amount and frequency of transactions are analyzed to identify patterns or trends in money laundering.
Supply Chain Management
In supply chain management, predictive modeling is used to predict the impact of multiple variables on inventory. Various risk factors can be plugged into the calculations to control their effect on supply chain efficiency and reliability.
Digital Marketing
Predictive modeling can help with digital marketing market research analysts improve their understanding of customer behavior, which can lower customer acquisition costs and improve sales conversion rates. This is done by modeling customer buying trends and online engagement based on historical data.
Read next: Top data modeling tools (TechRepublic)
