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The "yes" vote on Brexit and the election of Donald Trump surprised many media outlets, pollsters, analysts, and political experts. Those that predicted a different result emerged as the biggest losers of public confidence in their forecasting ability. Meanwhile, the U.S. election added the memes of fake news, fauxtos, and foreign interference to the discourse. Commentators across the political spectrum now debate a "post-fact" or "post-truth" world that would be familiar to the fictional character Winston Smith in George Orwell's master work.
What went wrong? News reports cited a variety of problems, including data sampling errors, flawed survey methodologies and designs, and voters who either wouldn't reveal their choices or lied about their decision to pollsters. Predicting the U.S. presidential race was further complicated by the Electoral College, an indirect election process in which voters in each state actually elect representatives who in turn vote for the president. This indirection means that the victor isn't necessarily the winner of the popular vote.
What really struck us in the field of market research and a lot of other people elsewhere – was the failure of the polls to predict the margins that gave Trump his Electoral College victory. Since we began tracking the language sector in 2002, CSA Research has used the same tried-and-true market research tools such as surveys and interviews that political pollsters and analysts employ to call the election results.
Because we rely on these quantitative and qualitative methods to calculate market size and identify behaviors, preferences, and trends, we reviewed the Brexit and presidential campaigns to determine what effect their outcomes might have on this market research model. Our Annual Survey of the language services market shows what we do differently:
The methodology, supplier database, representative sample, and the data collection process are the foundation of our market research – and show that our approach is strong and reliable. We do not assume correct results, but instead apply tested methods to deliver valid and reliable data-driven research that our clients can trust.
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