Michael Benisch, an analyst at Rocket Fuel with a Ph.D. in computer science, adds that many computer experts are looking to the ad space because the sheer amount of data makes it “the ultimate test.” For quants, advertising is like the Wild West of quantitative analysis: the horizon is limitless, and the rules are always changing.
Madison Avenue and Wall Street have more in common than you may think. Referencing the real-time bidding wars for digital display ads, Christopher Steiner, author of Automate This: How Algorithms Came to Rule Our World, says, “I would say advertising is the closest thing we have to high-frequency trading on Wall Street—it’s almost the exact same thing.” Meanwhile, ChoiceStream’s Liang compares his company to a hedge fund and himself to a portfolio manager. “We use our algorithms to figure out which people are most useful to our clients, which is basically what a hedge fund does,” he explains.
When a brand or agency approaches a company like ChoiceStream, it is looking to either tap a specific market or, in certain cases, determine what its market might be. The ability to do this essentially comes down to two factors: the quality of the data set and the algorithm itself. An algorithm is, at its most basic, a set of instructions for performing some kind of calculation and achieving an ideal result. Algorithms can be used for calculation, data processing and automated reasoning. On Wall Street, such methods were first used in the ’70s, when savvy investors began using them to price stocks and bonds. In advertising, algorithms grew in importance as the amount of data on consumer behavior swelled.
How exactly does a mathematical equation tell a marketer how best to market its minivan to a suburban New Jersey mother of three? Quants compose algorithms that can take in thousands and thousands of inputs (in this case, data) and spit out hundreds of possible solutions (who to target an ad to). As Steiner writes in his book, “Algorithms can be looked at as giant decision trees composed of one binary decision after another.”
Last year, the shopping site Zappos approached ChoiceStream about targeting consumers based not only on their age, location and other typical demographic data, but also what the weather was like. ChoiceStream designed a display ad that linked a three-day, location-based weather forecast with climate-appropriate merchandise that also took into account factors like age and gender. If a forecast called for showers, for example, an ad might display rain boots or slickers. In the bidding wars for online ad space, algorithms also determined how much Zappos might be willing to pay for ad space. Choice-Stream built the ad in three days.
The project was a test for Zappos, and of ChoiceStream’s own capabilities. The cost of a conversion—in this case, how much money must be invested on average to get a new customer to buy something on the site—declined significantly. Not surprisingly, Zappos decided to continue with the campaign. “The conversion rate was strong enough to realize a nice healthy return on investment,” says Lisa Archambault, manager of display marketing at Zappos. “[The ad] was much more specific to the consumer, which is really our goal.”
Meanwhile, Rocket Fuel’s data-modeling campaign last year for auto brand BMW was such a success that it was credited with helping boost North American second-quarter sales by 40 percent. Similarly, a campaign for tire manufacturer Bridgestone’s retail sites using Rocket Fuel’s Audience Booster and Insights Booster products contributed to a 45 percent bump in store sales after specific markets were targeted. “[Rocket Fuel] has an ability to drill down to something that’s very niche,” says Steve Parker Jr., co-founder of the digital ad agency Levelwing, which brought Rocket Fuel onto the Bridgestone account.
While brands like BMW and Zappos claim success from data-rich, algorithm-driven online ads, there’s no shortage of skepticism out there. VC firms have poured millions into the ad-tech sector, as have scores of companies with dubious claims of super-precise targeting. Algorithms work for Google, but still at issue are the real prospects of delivering the perfect ad to the right person at the right time.