It goes without saying that most ad agency business development managers need to persuade clients that the agency can really drive online advertising sales. But in a world where Big Data has become truly colossal, how do you back up those promises with convincing data? Digital ad technology startup Rocket Fuel may have the solution.
Big Data meets Artificial Intelligence
Launched in 2009, the company went public in September last year and now has offices in New York, Chicago, San Francisco, Los Angeles, London and Paris (it’s headquartered in Redwood, California). At a moment when other ad tech firms were seeing underwhelming IPOs (think YuMe and Tremor Media, for example), Rocket Fuel’s shares doubled on the first day of trading. Its market valuation then was $1.6 billion. And it’s attracted some big names in a crowded and competitive marketplace, including American express, Dell, Microsoft and Nike.
The more inquiring business development manager will want to know what Rocket Fuel has got that its numerous rivals lack. Everyone’s into Big Data these days, after all. According to its co-founder and VP of Engineering Abhinav Gupta, not only can the firm’s software analyze mind-bogglingly vast amounts of data in an instant, it learns from its analyses too.
“At the start, we may not have much idea about the market, but in a few days or weeks, the system starts learning by itself about what works for this campaign. This is our competitive advantage, leveraging machine learning and artificial intelligence, on petabytes of data.”
For any business development manager who’s uncertain exactly how much a petabyte is, it’s a vertigo-inducing one million gigabytes. That’s how big Big Data has become these days.
Dynamic campaign shaping
Rocket Fuel does on tracking data after an ad appears, keeping its finger on the pulse of positive outcomes (like purchases) for every advertising dollar spent, data which immediately gets fed in its intelligent, actively learning software. And that gives advertisers a very clear picture of the success of a campaign.
As Gupta puts it:
“If we learn from the confirmation page that the user has bought something, that informs our software: ‘Hey, good job, go find more users like this one!’ That’s machine learning. Look at each positive outcome, and do more of whatever produced that result.”