Faculty of Economics and Business Administration Publications Database

The Ambiguous Identifier Clustering Technique: A Method for Unobserved Product Heterogeneity in Online Transaction Data

Scholz, Michael
Franz, Markus
Volume: 26
Number: 26
Pages: 143 - 156
ISSN-Print: 1019-6781
Link External Source: Online Version
Year: 2016
Keywords: Product heterogeneity; Clustering; Online transaction data; E-commerce

Investigations of online transaction data often face the problem that entries for identical products cannot be identified as such. There is, for example, typically no unique product identifier in online auctions; retailers make their offers at price comparison sites hardly comparable and online stores often use different identifiers for virtually equal products. Existing studies typically use data sets that are restricted to one or only a few products in order to avoid product heterogeneity if a unique product identifier is not available. We propose the Ambiguous Identifier Clustering Technique (AICT) that identifies online transaction data that refer to virtually the same product. Based on a data set of eBay auctions, we demonstrate that AICT clusters online transactions for identical products with high accuracy. We further show how researchers benefit from AICT and the reduced product heterogeneity when analyzing data with econometric models.