How to match entities in secondary datasets in marketing strategy: Three approaches

Marketing strategy research increasingly relies on merged multi-source datasets spanning firms, brands, and products. Yet such entity matching remains a largely undocumented research design choice, despite its potential to alter samples, estimates, and replicability. This research note develops a cascading entity-matching workflow for marketing strategy research. We outline three progressively intensive matching approaches: deterministic joins using shared identifiers and crosswalks, probabilistic name-based matching with transparent similarity rules, and screening assisted by machine learning (ML) or large language models (LLMs). Finally, we translate this workflow into a practical audit framework specifying what researchers should record, report, and archive. The goal is to establish transparent and auditable matching standards that improve precision, replicability, and cumulative knowledge development in merged-dataset research.