Website Optimization Using Network Analysis for Customer Segmentation

The patent replaces traditional statistical modelling techniques with network analysis methods. Instead of using conventional statistical algorithms that require specifying segment numbers beforehand, the invention constructs a network graph where customers are nodes and affinities are edges, then uses graph theory algorithms (like community detection) to automatically identify segments. This substitution eliminates the need for iterative statistical modeling and manual segment number specification, significantly reducing processing time while maintaining or improving segmentation accuracy.

The invention changes the fundamental parameters of the segmentation approach by transitioning from statistical parameters (mean, variance, distribution assumptions) to network parameters (node degrees, betweenness centrality, community structure). This parameter transformation allows the system to handle large datasets more efficiently by leveraging the scalable nature of graph algorithms compared to traditional statistical methods that become computationally intensive with large n.

The patent extracts the underlying customer affinity relationships directly from behavioral data without imposing external assumptions. By constructing the network graph from observed customer interactions and preferences, the method removes the need for assumptions about segment numbers, segment characteristics, or customer behavior patterns. The segmentation emerges naturally from the data structure itself, preserving all objective behavioral information without filtering through preconceived models.

Instead of starting with assumptions about customer segments and fitting data to those assumptions (traditional approach), the invention inverts the process by starting with the raw data to construct the network, then letting the segment structure emerge from the network analysis. This inversion eliminates assumption-driven bias and allows the data to speak for itself, revealing genuine customer segments that may not fit traditional marketing categories.