Abstract
This paper explores the age and gender distributions of the bearers of British forenames and identifies key trends in British naming conventions. Age and gender characteristics are known to greatly influence consumption behaviour, and so extracting and using names to indicate these characteristics from consumer datasets is of clear value to the retail and marketing industries. Data representing over 17 million individuals sourced from birth certificates and market data have been modelled to estimate the total age and gender distributions of 32,000 unique forenames in Britain. When aggregated into five year age bands for each gender, the data reveal distinctive age profiles for different names, which are largely a product of the rise and decline in popularity of different baby names over the past 90 years. The names database produced can be used to infer the expected age and gender structures of many consumer datasets, as well as to anticipate key characteristics of consumers at the level of the individual.
Highlights
Introduction and overviewThe advent of new sources of consumer data, such as those arising from the use of social media, online shopping and customer loyalty databases, present new opportunities to measure and model the activity patterns of individuals
This paper has demonstrated that it is feasible to identify the age-gender distributions of forenames and thence to ascribe demographic characteristics to data where such information is not otherwise available
The modelled name data provide a suitable means of estimating age and gender distributions from British forenames because of trends in the popularity of baby names
Summary
The advent of new sources of consumer data, such as those arising from the use of social media, online shopping and customer loyalty databases, present new opportunities to measure and model the activity patterns of individuals. There has been no commensurate improvement in the detail with which we are able to characterise the individuals themselves, and ascertain how representative they are of consumer segments or the population at large In this context, our own attempts to understand consumer behaviour have become focused upon the task of front-loading the inferences that may be drawn from consumer names and social media user identifiers, in order to relate new Big Data sources to the wider populations from which they are drawn. Retailers have benefitted greatly from geodemographic datasets, made available from the government or other businesses, as a means of segmenting and understanding consumers Such data allow retailers to plan their stock and marketing to the local population characteristics (Mitchell and McGoldrick, 1994; O'Malley et al, 1997). Inferring demographic traits from names data could allow analysts to harness more consumer insight from many data sources
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