Customer Retention: Advanced Data-Matching Algorithms Improve Customer Recognition

These dynamic factors make it difficult to correctly identify and value each customer with traditional identity consolidation processes. Most merge/purge processes were developed more than 20 years ago and were never conceived to recognize the fluidity of movement, name change and channels in which customers interact today. Even the most advanced de-duplication processes use character-based logic and look-up tables that are ill-equipped to assess the totality of a customer’s name and address permutations that accumulate through multiple customer interaction channels. These processes easily are deceived by minor variations in the name and address elements such as married/maiden names, nicknames, typos and mis-keys. A typical file will contain 2 percent to 5 percent unidentified duplicate customers after a standard merge/purge process.