How Trump defeated Clinton using analytics

Columnist Rob Enderle writes that by following the three rules of analytics Trump won the election.

By Rob Enderle
Nov. 14, 2016

In short, they used their realization that everyone was doing this wrong, and there had been substantial evidence of that during the primaries, to create a strategic weapon for their client and that significantly helped turn what was likely a certain loss into one of the biggest surprise victories in U.S. history.

Part of what likely worked for Trump is he didn’t want to believe the bad numbers he was seeing driving people to a better methodology. Clinton liked the numbers she was seeing so didn’t do the same. This also showcases a huge common mistake; people just don’t challenge results they like and that almost always leads to bad results.

Second example

There was a second company that popped up as a hero after the election. It was the Trafalgar Group out of Atlanta. This company particularly looked at the problem with Trump supporters being undercounted and came up with a creative way to mitigate the problem and their results were unusually accurate even though their methodology was challenged by the better-known and better-funded firms that got this wrong.

What is both fascinating and annoying, as I’ve been there myself, is that to cover up their own incompetence firms like this will say things like “they got it right but for the wrong reasons” or complaining about crappy data completely forgetting that the “got it right” part is actually the most important and excuses don’t win. Some of these folks need to review the difference between winners and losers and come to the conclusion that being expert at excuses isn’t a formula for continued success.

3 rules of analytics

This all comes down to three rules for any kind of analysis.

First, you have to assure your data source. If you don’t have a strong sampling methodology you won’t have accurate results and you’d likely be better off not making the effort than in giving decision-makers bad advice.

Second, you have to identify and eliminate bias. Bias will invalidate the result and if you can’t eliminate it, once again, you’ll give decision-makers bad advice.

And finally, decision-makers have to learn to challenge the analysis, especially when it tells them what they want to hear, because you don’t get off the hook when you royally screw up by blaming the analysts. Yes, you can fire them but you’ll likely follow them out the door.

So, assure the data, identify and mitigate the bias, and always challenge the analysis to assure the advice you do get leads to a positive outcome.  

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