Customer Synergy Analysis in M&A: A Semi-Supervised Learning Approach

This research introduces a novel methodology for analyzing customer synergies in mergers and acquisitions (M&A) using graph theory and machine learning. We propose a semi-supervised graph neural network model that represents customer bases as graphs, with customers or segments as nodes and their opportunity relationships as edges. The model incorporates two key components: (1) a Customer Segmentation System (CSS) employing an unsupervised learning for customer segmentation, and (2) a Synergy Potential Score (SPS) quantifying cross-selling potential between the segments. Across a number of case studies in the software and technology industry, our approach identified high-synergy customer segments overlooked by traditional methods, leading to a higher adoption rate of new services in targeted customer segments, compared to non-merged companies. Through analyzing customer synergies, this methodology enables more precise M&A valuations and improved post-merger integrations.