Clustering Distribution Contacts for Adaptive Campaign Visualization

The patent segments the large database of distribution contacts into multiple clusters, where each cluster contains a subset of contacts with similar characteristics. Instead of analyzing all contacts individually, the system performs one-by-one analysis only on representative contacts within each cluster, then applies the learned features to the entire cluster. This segmentation approach maintains analysis completeness while dramatically reducing computing resource consumption and analysis time.

The patent merges multiple individual contact entries into consolidated cluster visualizations that display aggregate information for groups of contacts. Instead of requiring users to scroll through countless individual contact screens, the system presents merged views showing common characteristics, demographics, and behaviors of contact clusters. This merging approach preserves all contact information while dramatically improving ease of operation by reducing navigation requirements.

The patent adds a new dimension to the visualization by organizing contacts into clusters that span multiple feature categories simultaneously. Instead of isolated breakdowns by single categories (e.g., geography alone or demographics alone), the system creates multi-dimensional cluster views where contacts are grouped based on combinations of features across different categories. This enables detailed feature analysis while also facilitating cross-category comparison, as users can see how different features interact within each cluster.