Duplicate Record Detection in Relational Customer Databases

Abstract

Duplicate record detection is important in relational customer databases because enterprises need accurate and unified customer information for sales, billing, service delivery, marketing, and reporting. Customer databases often contain repeated records caused by spelling variations, multiple registration channels, incomplete contact details, address changes, and inconsistent customer identifiers. Existing literature highlights exact matching, fuzzy matching, phonetic matching, similarity scoring, rule-based comparison, entity resolution, and record linkage as major methods for detecting duplicate customer records. However, many organizations still face challenges such as inconsistent name formats, missing phone numbers, different address structures, false matches, duplicate customer IDs, and weak validation during data entry. This research is important because duplicate records can reduce CRM accuracy, increase communication errors, distort customer analytics, and affect business decision-making. This article discusses duplicate record detection in relational customer databases, focusing on data profiling, standardization, matching rule design, similarity measurement, duplicate clustering, manual review, and merge validation. The study concludes that effective duplicate detection improves customer data quality, reduces redundancy, strengthens database reliability, and supports accurate enterprise customer management.