How prevalent are suggestive brand names and Distinctive Assets? An AI-human approach

Abstract

Despite the concept of a suggestive brand name existing for over one hundred years (Viehoever, 1920), the prevalence of suggestive versus non-suggestive brand names has not been documented. Previously, to do so extensively would have taken considerable time and money. We now show that artificial intelligence can replace manual coding with increased accuracy. We found the coding performances of Chat GPT-4 are 34% more accurate than GPT-3.5 and 44% more accurate than human coders. Systematically expanding our research to over 4,600 brands from consumer goods, services, and durables in major English-speaking markets (United Kingdom, United States, and Australia), we find that overall, slightly more than a quarter of all brand names are suggestive - ranging from 10% of durables to 56% of service brands. Further, we expand the suggestiveness research to non-brand name elements of almost 600 Distinctive Assets (e.g., colours, logos) across consumer goods, services, durables, and retailers (in the same three countries), finding that two in five are suggestive. The brand name and Distinctive Asset prevalence distributions are positively skewed, with most categories falling beneath the respective averages. Furthermore, regarding performance, on average, suggestive Distinctive Assets display lower levels of Fame and Uniqueness than non-suggestive Distinctive Assets.