Predictive models and their impact on business decision-making

Anticipating future situations in order to establish action protocols, moving forward with greater certainty when the time comes, and simultaneously identifying potential business opportunities: this is how predictive models contribute positively to business management.

Business management and administration have evolved thanks to the development of tools that facilitate decision-making and allow organizations to handle complex situations in the best possible way. One increasingly adopted system across all types of organizations—both SMEs and large corporations—is predictive models. Do you want to know what they are and how they can be applied to optimize business operations?

What is a predictive model?

The first question to address is: what is a predictive model? It is a system through which, by managing data, information, and relevant documents from a specific sector in which a company operates, reports can be generated that serve as a starting point for defining strategies, among other uses. The benefits of this approach are numerous:

• Forecasting demand levels for a product or service.
• Identifying new business opportunities.
• Anticipating market trends and proposing solutions before competitors.
• Establishing action protocols for emergencies, corporate crises, reputational issues, or sales declines.
• Supporting decision-making in international expansion or product development.
• Identifying potential opportunities to enter specific market niches.
• Preventing risks associated with market volatility or changes in political or economic conditions.

How do predictive models work?

Predictive models work in a fairly intuitive way. There are several steps that help understand how to apply this methodology in any business, regardless of its size or scope:

1. Define the project: what objectives are to be achieved with the predictive model?
2. Collect relevant data and information aligned with the goal.
3. Analyze the collected data to extract key insights aligned with the objective.
4. Develop statistical analyses to support interpretation.
5. Create the predictive model. There are different types of models, which will be discussed below, and selecting the right one is essential.
6. Develop the project using the selected model, combining data, analysis, and insights to draw conclusions.
7. Make decisions based on the predictive model’s results.

This methodology must be continuously adjusted. Markets and environments change constantly, making periodic updates essential to avoid being caught off guard in critical business situations requiring rapid decision-making.

Types of predictive models

There are several types of predictive models. Below are five of the most commonly used in business management and administration. These are often included in training programs for executives, entrepreneurs, and professionals aiming to develop careers in finance, such as EAE Barcelona’s MBA Track in AI.

• Classification model

This is the simplest model and is used for clearly defined problems or decisions. It works by answering a series of “yes” or “no” questions based on the objective. It is widely used due to its simplicity and applicability across various business scenarios.

• Regression model

One of the most widely used models in business environments. It allows predictions based on how multiple variables interact within a system. It is highly customizable depending on the company and objectives.

• Forecasting model

This model uses historical data to generate forecasts. By analyzing past trends and collected data, it helps support strategic corporate decision-making.

• Clustering model

This model groups elements with similar characteristics. It helps design group strategies or make small adjustments that improve efficiency at departmental or organizational levels.

• Time series model

This model provides a long-term perspective on business or departmental performance. It helps identify trends over time and supports both improvement and corrective decision-making.

Examples of predictive models

How are predictive models applied in practice? They are already being used across multiple industries and business areas. Here are some examples:

• Forecasting product sales: estimating demand, revenue potential, and pricing strategies.
• Supply chain management: identifying weaknesses, preventing stock shortages or overstocking, and managing logistics costs under changing conditions.
• International expansion: analyzing target markets, customer profiles, production costs abroad, and logistics expenses.

As can be seen, predictive models are highly versatile and increasingly used across management and administrative departments due to their practical applications and ease of use.