In 2004, jars of honey were delivered to apparently random people around the world. A code inside the jars revealed a website. When visited, the website appeared to have been hacked; gibberish code appeared along with a countdown to a date: August 24th. Curiosity was roused. The address to the website spread by word of mouth, text, and social media.
By the time the date arrived, more than a quarter million people had seen the website: ilovebees.com. The site launched an addictive online game that was played on computers and mobile devices. Over the course of the next 3 months, over 30,000 people a day would visit, playing the alternate reality game leading up to—and publicizing—the release of … Halo 2.
- Grand Canyon University - B.S. in Business Information Systems and M.S. in Data Science
- SMU - Master of Science in Data Science - No GRE Required.
- Syracuse University - M.S. in Applied Data Science: GRE Waivers available
- UC Berkeley - Master of Information and Data Science Online - Bachelor's Degree Required.
- Syracuse University - Master of Information Management Online
Every single one of those visits was tracked.
The “I Love Bees” campaign epitomized a new golden age of marketing- an age that would be defined by data science.
New Insights Reveal Which Half of the Marketing Budget Not to Bet
The man often credited for creating the field of marketing, department store magnate John Wanamaker, once famously said, “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.”
Today, with careful planning and design at the hands of the most talented master’s-educated data scientists, this is no longer true. Every advertising dollar spent can now be tracked against outcomes.
But that’s a small fraction of the work that data scientists are performing in the marketing space today. Not only are data scientists in marketing devising ad campaigns with verifiable metrics built-in, they also:
- Deploy search engine optimization protocols to make products easy to find based on relevant search queries
- Predict the relevance of content for various demographic targets to place the right products in front of the right customers
- Predict customer churn, segmentation, and clustering
According to research and advisory firm Gartner, more than 80 percent of companies generating more than $500 million in annual revenue now employ a “Chief Marketing Technologist.”
Gartner’s report goes on to say that, “Every aspect of marketing—from brand, experience design and demand generation, to content marketing, analytics and measurement—is affected by technology.”
How the “Internet of Things” Tracks Advertising Dollars
It’s no surprise that 81 percent of shoppers now research purchases online before buying. And every single one of those queries is logged as a data point, both with the search engine and at the sites consumers visit to conduct their research.
Michael Gethen, managing director of mobile marketing firm Sprooki, has been quoted as saying, “It is no longer sufficient for businesses to be 24X7 responsive to customer needs – they need to anticipate their needs in pre-journey, proximate, in-location and post-visit phases.”
With the ubiquity of Internet access, including through once-mundane appliances like thermostats and coffee brewers (the “Internet of Things”), the information about an individual that is required to anticipate those needs is all out there for the taking.
Data scientists are responsible for isolating that information and predicatively modeling consumer behavior in ways that allow marketers to reach customers, both overtly and in more subtle and innocuous ways.
Mining Marketing Gold from Social Media Networks
As transformative as the Internet has already been for marketing, the next big marketing revolution is now taking place on social media. Digital Tonto, a website that follows media, marketing, and technology trends, cited how the same algorithms used for social network science and predicting are launching a renaissance in digital marketing.
Using Social Media Users to Sell to Each Other
A suggestion to purchase that comes from a trusted friend or family member can outweigh any glib tagline or celebrity endorsement.
The most powerful form of marketing is customers marketing to one another, and social media networks are uniquely well-suited to this task. The ability to encourage happy customers to share their experiences directly with others in their network is powerful leverage for marketing departments.
It has always been the goal of marketers to encourage customers to share their success stories. The ease of sharing these messages is what sets social media marketing apart. A single successful campaign can echo through dozens, then hundreds, then thousands of social networks as it is shared and re-shared in a span of hours or even minutes. The corporate mindshare snared by these viral campaigns can be enormous.
Old Spice’s “The Man Your Man Could Smell Like” campaign leveraged YouTube and social media sharing to reach 1.4 billion views in the first six months and Old Spice’s sales rose 27 percent. Although the commercials were traditional in format and also aired on television, the additional effort to extend the reach to social media networks paid huge dividends for the company.
Social Networks are a Live Wire That Can Power a Company… or Burn it Down
Network virality has a potential downside, too.
The New York City Police Department learned this the hard way when they encouraged Twitter users to post pictures of themselves interacting with cops using the hashtag #myNYPD.
People did exactly that… but the interactions were far from positive. Instead, hundreds of pictures of police brutality were posted and dominated the news instead of the friendly PR outreach that planners had imagined.
Here, too, data scientists can make a contribution. By analyzing marketing data thoroughly, certain classes of mistakes may be avoided.
In 2014, for example, McDonald’s launched a Twitter campaign purporting that fictional spokesperson and all around scary clown Ronald Mcdonald would be personally typing out tweets. The campaign quickly fell apart under a deluge of anti-fast-food responses, some comparing the clown to the Marlboro Man.
A better analysis of the Twitter audience and trends relating to fast-food might have waved off the idea for not being well suited to the medium.
Where You Are Helps Determine What You Buy: Location-Based Advertising Pays Big Dividends
On a sunny day in the hot streets of New York, a sweating mother pushes a stroller and tries to corral an irritable toddler at the same time. Suddenly, her phone buzzes with a text. She pulls it out and looks at the screen. An offer for twenty percent off ice cream cones has just popped up, and the screen helpfully points out directions to a Van Leeuwen’s ice cream parlor half a block away. With the press of a button, she can pay for three ice cream cones right out of her PayPal account. Boom- like bat-signaling the ice cream man.
This location-based advertising system isn’t a marketing fantasy, in fact, it’s already pushing five percent of Van Leeuwen’s revenues. Such successes are drawing marketers to location-based advertising systems like kids to sugar.
As the most specific sort of direct marketing, location-based ads engage only the customers that are in a position to make an immediate purchase. When they are properly integrated with other demographic data—increasingly available from the Internet of Things—they can send out ads laser-fast to likely prospects.
Targeting Likely Buyers, Not Just Potential Customers
This doesn’t just allow marketers to avoid the cost of advertising to prospects they are unlikely to convert; it also allows them to avoid irritating people with unwanted or unusable advertising. The last thing marketers want is to annoy a prospective customer who might buy from them under other circumstances, but who simply isn’t in the right frame to make a purchase at the moment.
Location-based advertising has become so effective in some areas that it has superseded traditional marketing. In Singapore, for example, Coca Cola has entirely done away with print advertising in favor of mobile ads. Elsewhere around the Pacific Rim, Coke vending machines act as their own marketers, identifying their location and tempting customers that may be near by.
Some location-based marketing efforts are based on physical detection of phones, using magnetometers similar to what you might find when passing through airport security. Others use apps installed on the mobile devices themselves, piggybacking off the GPS and cellular location data already available.
In every scenario, data scientists are ultimately responsible for creating algorithms that tie location data to other demographic and product information that decides when and how to present advertising messages.