Cluster analysis refers to methods for discovering similarity structures in data sets. The groups of “similar” objects found in this way are called clusters, and the group assignment is called clustering. The similarity groups found can be graph theoretic, hierarchical, partitioning, or optimizing.
In innovation work, cluster analysis is used in the field of understanding phases of innovation projects. The aim of cluster analysis is to present important segments or characteristics of products or target groups in a structured way by forming groups and thus to uncover gaps within the product range.
New products can then be created to satisfy the potential demand within these gaps. The cluster analysis provides an overview of which customers are distributed in which way and which third-party products are comparable with the company’s own.
