By Ali Azhar

In predictive modelling, future events are predicted based on statistical analysis. Read this guide to understand how predictive modelling works and how it can benefit your business.

The rapid adoption of digital products and services has created vaster volumes of data than we’ve ever seen before. As a result, an increasing number of organizations are using big data analytics and strategies to derive value from available data.

This data is often too complex and extensive for humans to analyse manually, especially for organizations that want to derive future insights from existing data sets. Organizations are instead relying on predictive modelling tools to connect data points and identify patterns in data. With the right predictive modelling tools and strategies, companies are able to make predictions about future events, customer behaviours and market trends.

Jump to:

• What is predictive modelling?
• Benefits of predictive modelling
• How to build a predictive model
• Predictive model examples

What is predictive modelling?

Predictive modelling, a component of predictive analysis, is a statistical process used to predict future outcomes or events using historical or real-time data. Businesses often use predictive modelling to forecast sales, understand customer behaviour and mitigate market risks. It is also used to determine what historical events are likely to occur again in the future.

Predictive modelling solutions frequently use data mining technologies to analyse large sets of data. Common steps in the predictive modelling process include gathering data, performing statistical analysis, making predictions, and validating or revising the model. These processes are repeated if additional input data becomes available.

Benefits of predictive modelling

Organizations use predictive modelling to reduce the time, effort and resources that are needed to forecast business outcomes. Here are the top benefits of using predictive modelling:

• Minimizing risk: Predictive modelling can predict an organization’s potential for cyberattacks, fraudulent transactions and other types of risks.
• Optimizing marketing campaigns: Using predictive modelling, organizations can uncover customer insights to tailor and recalibrate their marketing campaigns.
• Maximizing profit margins: Predictive modelling can be used to predict sales revenue, forecast inventory and create pricing strategies.
• Prioritizing resources: There are several ways predictive modelling can help prioritize resources for an organization. For example, sales teams can receive lists of expected leads to convert, allowing them to allocate more time and effort to these high-priority leads.

One of the challenges or limitations of predictive modelling is that the results are only as good as the data used to construct the model. To ensure predictive modelling is as effective as it can be, organizations should implement data quality tools to keep data accurate, safe and reliable. They should also prepare the data for business use by cleansing and formatting it for predictive modelling needs.

How to build a predictive model

There are various predictive modelling techniques. The two most prevalent techniques involve using neural networks and regression, respectively. In statistics, regression refers to establishing a relationship between input and output variables. The predictive model could be linear or nonlinear, depending on the variables.

In neural networks, predictive modelling tools use interconnected nodes in hierarchical levels, a model inspired by the human brain. These nodes create patterns and relationships between variables to establish future trends. Beyond these two most popular predictive modelling techniques, businesses also use clustering, outliers and classification models.

Traditionally, predictive modelling was handled manually by a data analytics team. But as the process has become more complex and data quality efforts have increased exponentially, using computer software for predictive modelling has become increasingly popular. As a result, most organizations use predictive modelling tools such as Oracle Crystal Ball, RapidMiner Studio and SAP Predictive Analytics.

Predictive model examples

Many industries rely on predictive modelling to help with key business decisions. These are some common use cases for predictive modelling:

Finance and banking

Predictive modelling is used in banking to identify fraud and illegal activities. For example, the amount and frequency of transactions are analysed to recognize patterns or trends in money laundering.

Supply chain management

In supply chain management, predictive modelling is used to forecast the impact of multiple variables on the inventory. Different risk factors can be plugged into the calculations to check their effect on the efficiency and reliability of the supply chain.

Digital marketing

In digital marketing, predictive modelling can help market research analysts improve their understanding of customer behaviour, which can reduce customer acquisition costs and improve sales conversion rates. This is done by modelling customer buying trends and online engagement based on historical data.

Feature Image Credit: doidam10/Adobe Stock

By Ali Azhar

Ali is a professional writer with diverse experience in content writing, technical writing, social media posts, SEO/SEM website optimization, and other types of projects. Ali has a background in engineering, allowing him to use his analytical skills and attention to detail for his writing projects.

Sourced from TechRepublic