By Paul Wiefels
A few thoughts on what is to be learned from this past presidential election and how it might apply to our commercial endeavors. Political analysts of every persuasion have been in full-throat over these past weeks parsing the numbers. 62+ million people voted for Mr. Trump garnering 306 electoral votes. 64+ million opted for Mrs. Clinton tallying 232. Discouragingly, only 55% of registered voters actually voted. Perhaps this is understandable considering the one, perhaps only, metric that pollsters apparently got right. For many, the two names at the top of the 2016 ticket were so undesirable, so flawed, that they chose not to vote for president at all. They instead focused on down-ballot races. In 14 states, more people voted for the senate races than voted for the presidency (ref: Business Insider, Nov 14, 2016).
But, there is also another element to this election that intrigues me. Here’s why.
In the run-up to the election of Barack Obama in 2008, the Democratic National Committee (DNC) unleashed a voter segmentation apparatus which could identify in great detail, various voting blocs otherwise known as target market segments. Key to Obama’s victory in ’08 was utilizing this data to aggregate groups who might not otherwise identify with or reference each other, around a central rallying theme: Hope. In 2008, this turned out to be a simple and very compelling message to a very discouraged electorate. By direct exposition and implication, many Americans were then “not hopeful” and identified as such. Obama received 69.5 million votes, the highest total of any presidential candidate ever. The Republican National Committee (RNC) retreated to learn its lessons, publicly stating that they had wrong-footed themselves by segmentation and voter outreach strategies that proved very deficient.
Starting in early 2015, Democrats once again trained their sights on repeating the same targeting strategy. The DNC had further refined their ability to slice, dice, and julienne virtually every conceivable voter bloc into definable, quantifiable, and media-reachable targets. Male and female. Urban and rural. Black, white, brown, yellow and anyone in between. Gay, straight, old, young, happy, pissed-off or somewhere in-between. A Rubik’s Cube that could accommodate the most granular of segmentation variables – yet all of them described by their boundaries. For every one you’re a part of, you’re not in another. Not to worry. We will appeal to all. We will be “Stronger Together.”
Meanwhile, RNC leaders chafed under a candidate that seemed to be, ahem, far less interested in these same voter segments save for one: rural, modestly educated white working-class voters. “Make America Great Again” was the call. Again, by direct exposition and implication, America was currently “not great.” Rightly or wrongly, this resonated with the one group that the DNC’s analysis had largely ignored or dismissed.
What happened? The DNC identified their target audiences by segmentation schemes that emphasized the boundary conditions of each segment. Each segment was then appealed to relative to those conditions. Each received their own message, heard what Hillary Clinton and the DNC thought they wanted to hear. She and the message became all things to all people. Social media exacerbated this as we’re now capable of creating our own echo chambers, only hearing what we ourselves are saying.
By contrast, Trump and a foot-dragging RNC aimed their appeal at the very center point of the target they thought they could win. Those who were a bit outside of that center point – the other rings in the bullseye – found enough common ground to identify, at least sufficiently, with the center. Trump, a rich, privileged businessman and reality TV show host who lives in a penthouse in Manhattan could become the herald of the common working man (or, it turns out, woman). Once assembled, several groups not large enough to win an election based on popular vote but plenty sufficient to win one based on the Electoral College system, had prevailed.
Segmentation schemes based solely on boundary conditions are appealing because they’re typically highly quantifiable. They lend themselves to the great “machines” now in front of us including big data engines, next-gen analytics, machine learning, and AI. In practice, it takes much more work to identify such groups by the very heart of their own interests. Successful marketers know that the former is useful but no substitute for understanding that people if given the correct provocation, tend to self-select themselves into a segment rather than out when they can identify with the talked-about segment’s conditions or travails. Commit to the bullseye, not just hitting the board.