Essec\Faculty\Model\Contribution {#2233
  #_index: "academ_contributions"
  #_id: "14864"
  #_source: array:26 [
    "id" => "14864"
    "slug" => "metrics-gone-wrong-what-managers-can-learn-from-the-2016-us-presidential-election"
    "yearMonth" => "2021-05"
    "year" => "2021"
    "title" => "Metrics Gone Wrong: What Managers Can Learn from the 2016 US Presidential Election"
    "description" => "KÜBLER, R. et PAUWELS, K. (2021). Metrics Gone Wrong: What Managers Can Learn from the 2016 US Presidential Election. <i>Nürnberg Institute of Marketing Intelligence </i>, 13(1), pp. 30-35."
    "authors" => array:2 [
      0 => array:3 [
        "name" => "KÜBLER Raoul"
        "bid" => "B00806952"
        "slug" => "kubler-raoul"
      ]
      1 => array:1 [
        "name" => "PAUWELS Koen"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:6 [
      0 => "Metrics"
      1 => "Dashboards"
      2 => "Decision-Making"
      3 => "Polls"
      4 => "Probabilistic Models"
      5 => "User-Generated Data"
    ]
    "updatedAt" => "2024-10-31 13:51:19"
    "publicationUrl" => "https://doi.org/10.2478/nimmir-2021-0005"
    "publicationInfo" => array:3 [
      "pages" => "30-35"
      "volume" => "13"
      "number" => "1"
    ]
    "type" => array:2 [
      "fr" => "Articles"
      "en" => "Journal articles"
    ]
    "support_type" => array:2 [
      "fr" => "Revue scientifique"
      "en" => "Scientific journal"
    ]
    "countries" => array:2 [
      "fr" => "Allemagne"
      "en" => "Germany"
    ]
    "abstract" => array:2 [
      "fr" => """
        In the 2016 presidential election, the vast majority of available polls showed a comfortable lead for Hillary Clinton throughout the whole race, but in the end, she lost. Campaign managers could have known better, if they had had a closer look at other data sources and variables that – like polls – show voter engagement and preferences. In the political arena, donations, media coverage, social media followership, engagement and sentiment may similarly indicate how well a candidate is doing, and most of these variables are available for free.\n
        Validating the bigger picture with alternative data sources is not limited to politics. The latest marketing research shows that online-consumer-behavior metrics can enrich, and sometimes replace, traditional funnel metrics. Trusting a single ‘silver bullet’ metric does not just lead to surprises, it can also mislead managerial decision-making. Econometric models can help disentangle a complex web of dynamic interactions and show immediate and lagged effects of marketing or political events.
        """
      "en" => """
        In the 2016 presidential election, the vast majority of available polls showed a comfortable lead for Hillary Clinton throughout the whole race, but in the end, she lost. Campaign managers could have known better, if they had had a closer look at other data sources and variables that – like polls – show voter engagement and preferences. In the political arena, donations, media coverage, social media followership, engagement and sentiment may similarly indicate how well a candidate is doing, and most of these variables are available for free.\n
        Validating the bigger picture with alternative data sources is not limited to politics. The latest marketing research shows that online-consumer-behavior metrics can enrich, and sometimes replace, traditional funnel metrics. Trusting a single ‘silver bullet’ metric does not just lead to surprises, it can also mislead managerial decision-making. Econometric models can help disentangle a complex web of dynamic interactions and show immediate and lagged effects of marketing or political events.
        """
    ]
    "authors_fields" => array:2 [
      "fr" => "Marketing"
      "en" => "Marketing"
    ]
    "indexedAt" => "2024-12-22T15:21:43.000Z"
    "docTitle" => "Metrics Gone Wrong: What Managers Can Learn from the 2016 US Presidential Election"
    "docSurtitle" => "Articles"
    "authorNames" => "<a href="/cv/kubler-raoul">KÜBLER Raoul</a>, PAUWELS Koen"
    "docDescription" => "<span class="document-property-authors">KÜBLER Raoul, PAUWELS Koen</span><br><span class="document-property-authors_fields">Marketing</span> | <span class="document-property-year">2021</span>"
    "keywordList" => "<a href="#">Metrics</a>, <a href="#">Dashboards</a>, <a href="#">Decision-Making</a>, <a href="#">Polls</a>, <a href="#">Probabilistic Models</a>, <a href="#">User-Generated Data</a>"
    "docPreview" => "<b>Metrics Gone Wrong: What Managers Can Learn from the 2016 US Presidential Election</b><br><span>2021-05 | Articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.2478/nimmir-2021-0005" target="_blank">Metrics Gone Wrong: What Managers Can Learn from the 2016 US Presidential Election</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.834279
  +"parent": null
}