<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
<p>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.</p>
</abstract>

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.
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.

Metrics Gone Wrong: What Managers Can Learn from the 2016 US Presidential Election

NIM Marketing Intelligence Review

This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.

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...

Metrics Gone Wrong: What Managers Can Learn from the 2016 US Presidential Election

Publié en ligne: 05 mai 2021

Pages: 30 - 35

DOI: https://doi.org/10.2478/nimmir-2021-0005

Mots clés
Metrics, Dashboards, Decision-Making, Polls, Probabilistic Models, User-Generated Data

© 2021 Raoul Kübler et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.

Metrics Gone Wrong: What Managers Can Learn from the 2016 US Presidential Election

Publié en ligne: 05 mai 2021

Pages: 30 - 35

DOI: https://doi.org/10.2478/nimmir-2021-0005

Mots clésMetrics, Dashboards, Decision-Making, Polls, Probabilistic Models, User-Generated Data

© 2021 Raoul Kübler et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.

Mots clés
Metrics, Dashboards, Decision-Making, Polls, Probabilistic Models, User-Generated Data