(…and how to stop failure from happening)

Big Data

Radius offers a marketing platform built on top of a database that sorts through billions of data about U.S. businesses each day. Because our platform uses big data to help marketers unlock key customer segments, we’ve thrown our hat into the overcrowded online world of marketing about big data solutions. Online search traffic for big data has surged in the last couple of years.

However, growing search traffic does not necessarily correlate with increasing understanding of big data. Big data has become an important conversation in the boardroom, and as a result, thousands of marketers have undertaken big data projects in the last year–more than in each year of the previous decade combined. IBM recently released the results of a report that found only one-fifth of CMOs feel prepared to deal with big data. Why do so many CMOs lack the confidence to tackle big data, and how can we turn executive enthusiasm for big data into modern marketing campaigns that drive real results?

Let’s take a look at a couple high profile marketing teams whose failed big data projects made unmistakably public headlines last year.

Duck Dynasty

Duck Dynasty is an A&E television show that chronicles a family that grew its wealth from a line of duck hunting products in the marshy backwaters of Louisiana. The family patriarch, Phil Robertson, recently made headlines for his anti-gay comments in an interview for GQ. The media received his stance with outrage and criticism. A&E suspended the show’s star indefinitely as a response to the huge outpouring of negative attention he brought, only to allow him back on the show after more than 250,000 faithful fans signed an online petition for his reinstatement. The fourth series premiere of Duck Dynasty last summer amassed more viewers than any non-fiction cable series that came before it with hugely successful ratings in its target demographic of adults 18-49. Some speculate that the dip in viewership during the fifth season opener posted stems from Robertson’s negative publicity.

The Phil Robertson suspension and reinstatement reveals a powerful story about the consequences of misreading big data. Networks gauge TV success by viewers and ratings amongst their target demographic. They also track volume and content of social media conversation. Regardless of A&E’s moral stance, by all measures Phil Robertson absolutely should have been removed from the show. However, as a result of vague success metrics, A&E missed the signal amidst all of the noise. Duck Dynasty may collect millions of views, but of those millions, over 250,000 die hard fans love Phil Robertson. These are the fans that will watch the show religiously, regardless of controversy. These are Duck Dynasty’s best customers. Had A&E known a little more about Duck Dynasty’s most loyal audience, the network may never have suspended the show’s star in the first place, as the dip was not due to the negative publicity rather than his absence from the show. We can find big data about our audience online today, and we need to leverage that data to cater to our target customers.


Now let’s look at a brand that does count social statistics in their data analysis. For this year’s Super Bowl, Esurance ditched the traditional high budget ad for a more creative distribution of marketing budget. The brand purchased the first ad after the game and gave away the $1.5 million (a 30% discount from the game time going rate, and also the rate Esurance saves customers on insurance) they saved in media spend to a lucky viewer who tweeted #esurancesave30. After the giveaway, Esurance stamped the campaign an indisputable victory. They tracked over 200,000 entries in the first minute of the giveaway–a number that grew to 5.4 million by the time they announced the winner. Their Twitter following grew an impressive 3000% to incorporate 261,000 new followers.

Today, nearly two months after 5.4 million people interacted with Esurance on Twitter, the brand only has 143,000 followers. Somehow, less than two months after running their Twitter contest, Esurance lost nearly half of its new followers. Even if this counts as a hugely successful growth of their Twitter following, the giveaway would cost over $10 per follower. Would you pay that rate for a Twitter following?

The metrics that Esurance used to gauge the success of its Super Bowl campaign don’t tell us anything about how viewers of the commercial feel about the brand, let alone how Esurance’s target audience feels about the brand. The commercial generated enormous conversation, which is an admirable marketing goal, but it didn’t generate a specific conversation. Without the right analytics and a targeted campaign, all they’ve got is a huge number of eyes.


Both of these examples come from B2C marketers, so now let’s turn our attention on a B2B company that botched their sales results because of poor data. When Groupon expanded into China to sell deal offers to small businesses, they duplicated an approach that had worked well for them in other international markets–mostly in Europe. The high volume, low touch cold call model was Groupon’s secret to instant success, and the numbers proved it. The brand entered large markets, bought out smaller companies with customer lists, and launched their call strategy. Sales soared. But then Groupon moved into China with the same approach. The sales results suggested their strategy worked fine, but suddenly, in China, the same strategy stopped working. And then European sales started to slow. Groupon had gauged success solely on sales; they didn’t take market differentiators into account when expanding.

Big Data & Marketing

All of these examples demonstrate valuable examples of industry-leading brands with huge budgets and accessible resources that do not understand how to derive real value from their marketing campaigns. A&E didn’t understand the attributes of Duck Dynasty’s biggest fans; Esurance didn’t even consider their customers’ wants, let alone the attributes that define them; Groupon incorrectly assumed every prospect in every market had the same motivations. Before we get caught up in the hype that surrounds big data, we need to understand what we want big data to accomplish.

We don’t typically refer to the results of our marketing campaigns as big data, but we should. As marketers, we face insurmountable volumes of data about our prospects and customers. As more social media innovations develop and grow to bring more information online and allow marketers new channels through which to reach our customers, we will all face new and difficult reporting problems. Most marketers also face cost obstacles, data quality issues, and tired vendors that don’t understand how new data sources impact unique revenue challenges. Big data undertakings have become a new burden for marketers, but they also offer tremendous opportunity. Groupon had a massive database of small business customers from around the globe. Imagine what they could have learned about buying patterns had they spent less money cold calling and more money on customer segmentation. Both A&E and Esurance sparked worldwide conversation online. Imagine the sorts of campaigns their marketing teams could have run with these captive audiences had they understood their best customers better instead of simply sitting back and counting the voices buzzing about their brands. The data possibilities are endless, and modern marketers have to get past the hype to explore them.