Research Topics:

Classification Tree Analysis | Data Analysis | Quantitative Research

Content Type:

Glossary

Classification Tree Analysis Definition

Classification Tree Analysis is used to predict membership of cases or objects in the classes of a categorical dependent variable from their measurements on one or more predictor variables. Classification tree analysis is one of the main techniques used in data mining.

Classification tree analysis, also known as decision tree analysis, is one of the most-used data mining techniques. Employed to analyze and predict consumer behavior, it involves developing a tree-like model to segment and categorize customers based on various attributes and variables. This technique uncovers patterns and relationships within the data that might not be visible through traditional methods. True to its name, classification tree analysis develops a visual representation of decision-making processes, which permits marketing professionals to act based on the most influential variables. 

Who relies on classification tree analysis?

Marketing analysts, researchers and data scientists use classification tree analysis to learn about their target audiences, optimize marketing strategies and develop marketing campaigns based on customer segments.

Why should I care about classification tree analysis?

Classification tree analysis provides insights into customer preferences, identifies key purchasing factors and creates more focused marketing strategies. The analysis helps bolster customer engagement, increase conversion rates and improve return on investment.