Fresh Off The Press: Lisa Arthur’s new book, Big Data Marketing: Engage Your Customers More Effectively And Drive Value paves the way to a bright, enlightened world of customer experience.
Flash back with me to late 2008-09. Ashton Kutcher is the first person on earth to reach 1 million Twitter followers, MySpace has more members than LinkedIn, Yammer is less than a year old, a company called Clearspace has just changed its name to Jive and everyone is lugging around laptops because iPads … (iPad?)
When marketers tried to send messages to you, they usually came via email or banner ads on your computer screen — and their relevance was no more targeted than the circulars that showed up in your physical mailbox.
Customer experience expert and author Lisa Arthur calls it “spray and pray” marketing. To the consumer it’s electronic junk mail, aka SPAM. But Arthur contends the world has changed and is about to change even more.
At an O’Reilly Web 2.0 Conference in New York City an enthusiastic red head runs out onto a big stage and hails the crowd “Sawubona.” There’s a giant Twitter screen behind him. “Sawu-what-a?” someone tweets.
Apparently the person who sent the tweet — and everyone else who doesn’t speak Zulu — is wondering what the word means.
“Sawubona means I see you,” social media marketing maven Chris Brogan explained. It’s different than hello. It means that I recognize there’s another person present.
The “person” Brogan refers to isn’t just anyone. It’s you, wherever you are, with all your particular “likes,” “friends,” “connections,” ”followers,” memberships, hangouts, schedules, interests and so on…
As you sit there and think about it, the world suddenly becomes a little more friendly and intimate. You tune into social media to discover that the world is populated by “people like us,” “people we’d like to meet” and “people we’d like to be like.” We can engage with or eavesdrop on most of them, openly and with their permission, whenever we want.
We are Empowered
And the world suddenly becomes full of “ordinary” guy or gal experts as well, who, for the most part, have no reason to tell us anything other than their honest opinions about where we should eat or go on vacation, what plumber we should call or how we might make the gadget we just brought home work most effectively.
Our dependence on advertising for awareness diminishes. We don’t want to fill out forms on the web so we bypass vendors. This makes it harder for them to figure out who we are. And if there’s something we need to buy, like a baby stroller, we can pre-shop on the web — there’s no need to drive to Costco, Sears or Babies"R"Us.
Come to think of it, there might not be much of a need to even read a review. It might be planted by a marketer, after all. So we turn to our e-Communities, which have summaries of real world experiences, answers and advice before we’re done combing our hair. We can learn exactly what stroller to buy without having stroller manufacturers flood us with information and ads for days. We love our Internet friends.
I see you. Sawubona. It’s a beautiful thing.
At least this is the way it can be, if you’re a consumer or a pioneering data-driven company like Google, Facebook, eBay, Amazon or Netflix.
Those companies were born in the Internet era. You began your relationship with them in e-mode. They've been — and still are — collecting, processing and analyzing huge volumes of data (the more the merrier) and making predictions about you.
They use commodity hardware, so what they’re doing isn’t really that expensive. To figure out what they should pitch to you, they write algorithms like “people who buy this also need that” (say batteries for a flashlight), “people who bought this often buy that” (baby bottles or bibs) as well as social graphs (people who stay at Ritz-Calrlton hotels have friends who stay at Ritz-Carlton hotels) and interest graphs (people who like to ski Aspen and live in New York City also like champagne, or people who like to ski Aspen and live in Pittsburgh like to drink Scotch.)
They know things about you they couldn't learn from a questionnaire because the questionnaire would have to be never-ending.
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