We’re big believers in the power of research to establish a sound foundation for PR campaigns, employee engagement strategies and brand building opportunities.
We loved Volkswagen’s recent awareness campaign about the dangers of texting and driving for that very reason.
The same goes for a Trillium Gift of Life campaign spot that in two short minutes makes the case for needing more people to register as organ donors. http://www.youtube.com/watch?v=ECF_lyxkSIE
It’s pretty clear the people behind these campaigns have done their homework. They’ve researched the facts. They know the numbers. And the results of their research are compelling. We think this is a smart way to use data.
But while oftentimes numbers can spin a good story, they can also be used to grossly over-simply things. Take the announcement last week about an employee engagement strategy in which Apple and Facebook committed to paying for their female employees’ fertility treatments, ostensibly giving them the opportunity to delay having children if they chose to focus on their careers.http://www.reuters.com/article/2014/10/14/us-tech-fertility-idUSKCN0I32KQ20141014
Does this mean that Apple and Facebook actually polled their employees and found that a majority of childbearing-age women within their ranks wants children enough to deal with the roller coaster ride of IVF? Was this strategy the outcome of extensive research into work-life balance, or did they simply look at the average age of their female employees and make some assumptions about Millennials?
And speaking of the Millennial ‘demographic’, we note that today in Philadelphia, Forbes is hosting an Under 30 Summit, and that a new study released in AdWeek talks up the tremendous opportunities for financial institutions that target Millennials with content marketing campaigns. http://www.adweek.com/brandshare/finance-industry-marketers-are-missing-huge-opportunity-millennials-160794
Is this is what data mining comes down to? It sounds a bit like old-fashioned marketing segmentation. Are all people 18-25 really so similar that they can be grouped together under the Millennial moniker? And are all women so like-minded that they can be spoken to (‘targeted’) in the same way?
Has history taught us nothing? If you’re over 50, this kind of stereotyping is called age-ism. And if you’re out-of-touch enough to be making generalizations about women on the basis of their fertility and/or reproduction, it’s called paternalism or misogyny.
The point is, if we’re smart enough — and socially evolved enough — to be able to obtain big data in the first place, can we please be smart enough to use some discernment in the way we interpret and use that data? Let’s put it in a context that’s meaningful, thoughtful and respectful.
Numbers aren’t smart all by themselves. Our job as people, and as PR people, is to make the numbers make sense in an honest way.