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Deriving age and gender from forenames for consumer analytics

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  • Lansley, Guy
  • Longley, Paul

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.

Suggested Citation

  • Lansley, Guy & Longley, Paul, 2016. "Deriving age and gender from forenames for consumer analytics," Journal of Retailing and Consumer Services, Elsevier, vol. 30(C), pages 271-278.
    • Handle: RePEc:eee:joreco:v:30:y:2016:i:c:p:271-278
    DOI: 10.1016/j.jretconser.2016.02.007
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    References listed on IDEAS

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    Cited by:

    1. Pedota, Mattia, 2023. "Big data and dynamic capabilities in the digital revolution: The hidden role of source variety," Research Policy, Elsevier, vol. 52(7).
    2. Justin T. van Dijk & Guy Lansley & Paul A. Longley, 2021. "Using linked consumer registers to estimate residential moves in the United Kingdom," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1452-1474, October.
    3. Xiaodong Cao & Piers MacNaughton & Zhengyi Deng & Jie Yin & Xi Zhang & Joseph G. Allen, 2018. "Using Twitter to Better Understand the Spatiotemporal Patterns of Public Sentiment: A Case Study in Massachusetts, USA," IJERPH, MDPI, vol. 15(2), pages 1-15, February.
    4. Sestino, Andrea & Prete, Maria Irene & Piper, Luigi & Guido, Gianluigi, 2020. "Internet of Things and Big Data as enablers for business digitalization strategies," Technovation, Elsevier, vol. 98(C).

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