Marketers have been in uproar over iOS 9 ad blockers. But ad blockers aren’t an impediment to marketers; they’re a wakeup call to use data to create better consumer experiences.
By Michael Endler, Marketing Content Manager, Quantifind
It’s no surprise ad blockers dominated discussion at Advertising Week, factoring into dozens of panels and eliciting commentary from heavyweight speakers such as Sheryl Sandberg and Marissa Mayer. Marketers have been suffering a collective anxiety attack since June, when Apple revealed that iOS 9 would support ad blocking apps. This anxiety bloomed into full-blown hypertension during September, as iOS 9 adoption soared and ad blockers rose to the top of Apple’s app charts.
But if we look at this differently, ad blocking is actually an important wakeup call for digital marketers. After all, modern data methodologies and natural language processing technologies empower marketers to fix the problems that make ad blockers popular in the first place. Rather than objecting to the rejection of our content, why not get curious about why’re getting rejected… and change it?
Most Ads Are Obnoxious
As marketers, we shouldn’t whine about ad blocking. It’s not like we can blame consumers for not wanting to see most of the stuff they’re subjected to. Like it or not, not all advertising rises to the level of the award-winning campaigns we celebrate at Cannes.
That’s not say consumers are adverse to advertising in principal; some watch the Super Bowl just to see marketers’ best efforts, others get cranky if they don’t get to the movies early enough to watch the trailers, and others still seek out their favorite ads on brands’ YouTube channels so they can watch them again and again. Many consumers appreciate the role ads play in larger projects. If you’re excited about Google’s autonomous cars or enjoy the Android OS, you basically have ads to thank for these advances.
The problem is, even if ads aren’t bad on an intrinsic level, they’re frequently aggravating on a practical one. Everyone knows what I’m talking about: the modals that pop up; the videos that block us from the content we actually want to see; how all of it bogs down browsers, increases data usage, and drains battery life; etc. Even if the substance of the ad is clever, the presentation is so ostentatious and intrusive, it’s no wonder millions of iOS users have begun installing ad blockers.
Industry pundits and analysts are currently debating the scope of the ad blocking dilemma. The estimated damage ranges from about $1 billion in lost ad revenue, which is substantial but not catastrophic, all the way to a punch-to-the-gut $22 billion, enough to take down a few ad-reliant companies. The true damage will involve several factors–how ad blockers affect consumer consumption habits on browsers versus on blocker-free social media, for example. But really, the answer is simple: The more regularly advertisers piss off consumers, the more regularly those consumers will use technology to eliminate the ads.
Data + Creativity: An End to Ad Blockers
Obviously, most marketers would love to create content that delights and persuades consumers. But doing so is easier said than done. And perhaps counter-intuitively, the secret is to redesign the alliance between creativity and data.
Yes, we often treat data like some magic marketing elixir that will turn everything to gold, telling us whom to target, when and where to target them, what platform to use, and what to say. It hasn’t worked out yet, but that’s because the marriage between creativity and data is a young one. Just as consumers want to eliminate annoying ads from their experience, marketers must work to remove potentially misleading metrics from their analysis. We need to block bad data, if you will.
When assessing social media data, for example, many of us judge our success according to retweets, shares, followers, likes, clicks and so on. This is fatal, because without a lot more information, these metrics offer virtually zero explanatory value–that is, they don’t explain why certain creative decisions will actually lead to more revenue or a lift in sales.
For example, suppose you’re a Swiss watchmaker that recently observed an uptick in social media activity: more organic mentions, more people sharing content that mentions the brand, etc. If you measure your success according to “buzz,” you’ve done a great job. But have you really helped your brand? Have you created experiences that make consumers want to see your ad instead of download a blocker? It’s impossible to say. Buzz might have ticked up simply because consumer discussion about Apple Watches led to increased chatter about watches in general, with few of the conversations actually reflecting consumer purchase intent.
How do we get closer to actionable intent data? As the adage goes, consumers vote with their wallets. Ultimately, a click or a like costs a consumer nothing, so the consumer’s not very discriminating–far less than he or she would be with the real spending dollars that brands want. For that reason, any worthwhile marketing metric has to tie to revenue. A page view by itself means nothing–but a cluster of page views that correlate to movement in revenue can mean a great deal.
Revenue-driven metrics keep us accountable to what customers actually care about, in other words. Counting likes and clicks leads to superficial consumer insights and ads that consumers want to block, but using revenue-based data that reveals true consumer motivation helps to create dialogues that consumers actually want to be a part of.
Still, revenue-based analysis is only part of the equation; marketers also need to know why certain data trends align with revenue movement. The recent rise in sentiment analysis products in a small step in this direction, but it’s not sufficient.
Suppose a beer brand partners with a music icon for an ad campaign, the resulting social buzz is “positive,” but little of the new chatter aligns with revenue, which has been falling. In this case, the sentiment analysis is misleading and the revenue analysis is incomplete. Did revenue fall because core customers don’t care about the musician? Did the musicians’ fans drive buzz without driving beer sales? Is music a bad partnership bet, or was the problem just this genre or artist? And when people do buy the beer, what’s actually driving them, and how can it be maximized? A revenue-based sentiment analysis model can’t explain how to answer these questions.
Several of the Advertising Week panelists, such as Mars Chocolate brand director Kerry Cavanaugh and marketing legend Jim Stengel, noted that successful ads stand for something that people can feel– a principle, an experience, and so on. How does this relate to data? By correlating consumers’ organic, unaided online language patterns to movements in revenue, brands can understand which specific language trends are driving sales. Whereas a consumer says very little with a retweet or like, he or she can say a great deal in his or her own posts. Unfortunately, many of us look for the retweets and ignore the actual substance of what consumers say. This has to change.
When it does change, the shift will empower marketers to tell the stories consumers want to hear– the ones that resonate, rather than annoy, and that are delivered where consumers want them, rather than where advertisers think they can force their way in. Marketers will discover unexpected ways to stand for things their customers care about. It might be a wine brand that discovers its customers care more about how the wine fits into family gatherings than about the wine’s Napa heritage. Or it might be a beverage brand that’s been pouring its money into football sponsorships but learns that soccer offers more resonance with fans and more revenue upside. Whatever the insight, an explanatory data approach that ties to revenue is the key to better storytelling– and to a world in which marketers and consumers are in engaged conversations, rather than some adversarial stare-down on opposite sides of an ad blocker.
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