• by Yaffa Finkelstein , March 22, 2016

Today, in a world where everyone talks about breaking down silos, data is still bought, sold and analyzed in a fragmented fashion. Data-driven technology enables advertisers and marketers to target with greater granularity than ever before more viable customers, using virtually unlimited optimization methods. 

But what if this power actually added up to becoming short-sighted about the way we perceive marketing for boomers? What if marketers and advertisers, in their rush to target specific segments, are overlooking alternative routes for better targeting? What if they are inadvertently creating their own blind spots, and ignoring the true value of data-driven mobile advertising for older audiences?

Data-driven advertising can come at the cost of scale. By narrowly defining campaign success in line with very specific objectives, advertisers may miss the returns and refinements to be found in corners we hadn’t thought to look. Unfortunately, this is a result of the way data is currently sold and priced. 

When an advertiser wants to advertise healthcare plans to homeowners over 50, the target audience is comprised of three separate data sets: people believed to be in the target demographic, people whose devices are located near doctor’s offices or pharmacies, and a third set of data that provides a “flu index” for areas that have the highest reported cases of the flu.

But even if advertisers use these data sets alone, they may be missing out on customers that may be an even better fit. An active senior may have a digital footprint that partially identifies him or her as a travel-loving hipster, and, despite their need for healthcare as they age, they may not receive the ad because their static digital profile doesn't match the target demographic definition. The value in measuring against unsuspecting data sets only becomes clear when you look at real-life audiences responding to mobile ad campaigns. 

A luxury automotive brand, focused on traditional auto audiences, used location data to discover that audiences in specific locations were much more receptive to their ads (e.g., audiences at home or enjoying leisurely activities like golfing were more likely to engage with the ad). Considering this unexpected effect of location on their ad engagements allowed the advertiser to use dynamic ads for each real-life location, creating a more optimized campaign.

Working with a large amount of data is a complex and costly undertaking, and in most cases, advertisers will partner with a number of data providers. In order to use this audience data to reach “homeowners at the doctor’s office,” the advertiser will also have to work with an additional vendor to match cookies with device IDs, if they’re going to use their purchased audience segments on mobile. This is an immensely complex undertaking and in the best case scenario might yield 20-30% accuracy.

When it comes to measuring campaign outcomes, the process becomes even more complex. Campaign results can only be analyzed using the prepackaged targeting data, with no insights into other factors. If advertisers had access to additional data — other than the data used at the campaign’s outset — they’d be able to continue learning about the unexpected factors which impact ad engagement rates, and optimize their campaigns accordingly.

Many brands are unaware that the audience data segments they buy are restrictive, and to reduce costs occasionally cut the number of purchased data sets. But this strategy can go against quality campaign outcomes, because it forces buyers down a funnel where they cannot see outcomes they may not have otherwise anticipated. 

Brands will only get to benefit from unexpected returns from boomers — or any other specific demographic — if two changes are made. 

First, they must adopt a more curious mindset, and experiment with targeting boomers using more than desktop-era static digital profiles. Second, the data industry should loosen up to allow advertisers to buy and measure against data sets they hadn’t anticipated requiring.

Data is infinite and illuminating but, today, you can only see where your flashlight is shining. In order to fully understand where messages are resonating, and to capitalize on the true value of mobile targeting without compromising on scale, advertisers must operate with a full, daylight view of data to reach all of their audiences.