Essec\Faculty\Model\Contribution {#2233
  #_index: "academ_contributions"
  #_id: "4671"
  #_source: array:26 [
    "id" => "4671"
    "slug" => "how-to-profile-your-customers-using-collaborative-database-profiling-an-application-to-age-estimation"
    "yearMonth" => "2008-05"
    "year" => "2008"
    "title" => "How to Profile your Customers Using Collaborative Database Profiling: An Application to Age Estimation"
    "description" => "DE BRUYN, A. et TRAMONTE, N. (2008). How to Profile your Customers Using Collaborative Database Profiling: An Application to Age Estimation. Dans: <i>Proceedings of the 37th EMAC Conference</i>. European Marketing Academy (EMAC)."
    "authors" => array:2 [
      0 => array:3 [
        "name" => "DE BRUYN Arnaud"
        "bid" => "B00024825"
        "slug" => "de-bruyn-arnaud"
      ]
      1 => array:1 [
        "name" => "TRAMONTE N."
      ]
    ]
    "ouvrage" => "Proceedings of the 37th EMAC Conference"
    "keywords" => array:3 [
      0 => "Estimation de l'âge"
      1 => "Prénom"
      2 => "Profiling collaboratif"
    ]
    "updatedAt" => "2021-04-19 17:57:25"
    "publicationUrl" => null
    "publicationInfo" => array:3 [
      "pages" => null
      "volume" => null
      "number" => null
    ]
    "type" => array:2 [
      "fr" => "Actes d'une conférence"
      "en" => "Conference Proceedings"
    ]
    "support_type" => array:2 [
      "fr" => "Editeur"
      "en" => "Publisher"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "CD-Rom Les entreprises utilisent des bases de données commerciales externes pour enrichir la connaissance qu'elles ont de leurs clients et prospects. Cet article présente une méthodologie pour améliorer cette approche."
      "en" => "Firms use external data sources (e.g., Census Bureau data) to infer the most likely age, income, housing situation or marital status of their customers, and better portray their profiles and needs. In this paper, we show that the methodology commonly used in the industry is severely biased towards average national figures. We discuss a maximum likelihood approach to correct for these biases. Taking the example of first name analysis to infer customers'age, we show that the approach we suggest gives much more precise and unbiased estimates, allowing marketers to better profile their customers and tailor their marketing strategy more precisely to their customers' characteristics."
    ]
    "authors_fields" => array:2 [
      "fr" => "Marketing"
      "en" => "Marketing"
    ]
    "indexedAt" => "2024-11-21T10:21:50.000Z"
    "docTitle" => "How to Profile your Customers Using Collaborative Database Profiling: An Application to Age Estimation"
    "docSurtitle" => "Actes d'une conférence"
    "authorNames" => "<a href="/cv/de-bruyn-arnaud">DE BRUYN Arnaud</a>, TRAMONTE N."
    "docDescription" => "<span class="document-property-authors">DE BRUYN Arnaud, TRAMONTE N.</span><br><span class="document-property-authors_fields">Marketing</span> | <span class="document-property-year">2008</span>"
    "keywordList" => "<a href="#">Estimation de l'âge</a>, <a href="#">Prénom</a>, <a href="#">Profiling collaboratif</a>"
    "docPreview" => "<b>How to Profile your Customers Using Collaborative Database Profiling: An Application to Age Estimation</b><br><span>2008-05 | Actes d'une conférence </span>"
    "docType" => "research"
    "publicationLink" => "<a href="#" target="_blank">How to Profile your Customers Using Collaborative Database Profiling: An Application to Age Estimation</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 9.0310545
  +"parent": null
}