In the business world, organizations already see predictive analytics as a necessity and apply it on a daily basis without realizing it. Artificial intelligence, machine learning and deep learning algorithms are basically becoming your typical business tool.
As for predictive analytics, it has many use cases: predictions about customer behavior, competitors, the business itself, stock forecasts, etc. Predictions about what will happen next week, next month and next quarter are part of the DNA of companies’ data analytics and business intelligence strategies.
However, despite being used by many, few people know what lies behind predictive analytics or how it actually works.
What is predictive analytics?
IBM defines predictive analytics as “a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities.”
As a discipline, predictive analytics is part of data science. Data scientists are therefore responsible for developing predictive analytics models by developing machine learning and deep learning algorithms that find patterns in data sets.
Predictive analytics is often combined with prescriptive analytics, which takes advantage of the insights discovered by predictive analytics to generate recommendations and propose improvement actions.
Types of Predictive Analytics: Classification vs. Regression
We can differentiate between two types of predictive analytics: classification models and regression models. Both are part of supervised machine learning, but classification models discrete variables, while regression models continuous variables.
Classification models identify a class or category from a set of categories. That is, from among a set of established variables, the algorithm is able to recognize to which category a new variable belongs to.
Classification models applied to predictive analytics are often used to make binary predictions. There are two possible answers and the algorithm predicts which of the two options is more likely to happen.
- Will production go down in August? Yes / No.
- Will the price of electricity go up in December? Yes / No.
- Will this customer buy this product again? Yes / No.
However, we can also use classification models for non-binary predictions. Systems that recognize what is in an image from a set of set options, for example, use classification algorithms.
- Which product will this customer buy? [Sneakers / Waterproof coat / Gloves ].
In short, predictive analytics by classification always gives us a category or class as a result.
Regression models are typically more complex than classification models and are used to predict the performance of something, usually a product, a process or an individual.
One of the most simple ways to differentiate the two models is that predictive analytics by regression always give a number as a result. Unlike classification models, regression models are able to predict what will happen from an infinite number of possibilities.
Regression models are mostly used to predict the performance of something:
- The inflation in Spain in 2023
- The profits generated next year
- The number of products that a given customer will buy in a given month
- The company’s productivity in the first four months of the year
Predictive analysis techniques
Beyond the two types of predictive analysis mentioned above, there are several techniques for the application of these two models, which, in practice, are, again, mathematical and statistical algorithms.
Regression analysis techniques are those that associate and link variables with each other. Regression analyses can be logistic —they predict the outcome of a categorical variable with predictor variables— or linear —dependent variables, independent variables and random elements—.
Decision trees work with supervised learning and are formed by subsets of predetermined objective variables structured as a tree, since they start from a node that ramifies into multiple variables.
The neural network technique is structured in multiple layers that are related to other simple elements connected to each other. The name it receives is due to the similarity of the structure of the algorithm to the human brain’s neurons.
The purpose of artificial intelligence is to try to reproduce human intelligence. That is why neural network algorithims have evolved a lot in recent years.
In short, predictive analytics is complex to understand for non-specialists. However, its use is already common and many companies are using it to optimize processes and adapt to new customer needs and market trends.