Serial entrepreneur Ric Calvillo had planned to stay away from enterprise customers with his newest venture. He’s now CEO of Nanigans, a Boston-based startup that offers a Facebook advertising platform for enterprise customers with a couple thousand dollars to spend a day on online ads. Oops.
“We’re scaling right now with large accounts,” he says. “That’s exactly what I didn’t want to do.”
Calvillo has twenty-plus years starting and running infrastructure software companies, like Conley, which sold to EMC in 1998. His last venture before Nanigans was Incipient, a company that sold its data storage virtualization and migration software largely to enterprise customers in the financial services space. “I picked financial services at the worst possible time,” says Calvillo. The startup, which raised $95 million in venture capital, struggled to gain traction and sold its intellectual property assets to Texas Memory Systems in 2009. Hence his resistance to the enterprise world.
Nanigans’ big customers aren’t huge financial firms, but companies in the fashion e-commerce, social gaming, and deal-a-day spaces, says Calvillo.
After getting out of Incipient, Calvillo says he spent time thinking about the next software space to play in. Cloud, SaaS, and social all came to mind, he says. Nanigans started building its ad platform code (off of the Facebook Ads application programming interface) in November 2009 and incorporated in 2010.
“There’s a lot of room for innovation in the advertising optimization area in social,” he says.
Part of that optimization is “closed-loop feedback,” says Calvillo. Online ads can be tracked to see how they lead to actions like attracting new fans for a brand’s Facebook page or prompting customer purchases. But that feedback wasn’t being used to inform and adjust future ad spend on Facebook, says Calvillo. And that’s what Nanigans is looking to change.
Nanigans’ product, called Ad Engine, first measures the success of an ad campaign, then uses that information to automate decision making, like how much to spend on an ad bid and which audiences to target. The software can use historical data to determine how much a click on an ad should be worth in the future. And it can decide which Facebook users to put ads in front of based on variables like age, location, and likes on users’ Facebook profiles.
Nanigans is looking to take the human grunt work out of monitoring and optimizing Facebook ads. The engine only requires a person to input the ad graphics, their total budget for a campaign and daily maximum spend, and a genre for their product. “The system knows similar campaigns we’ve run, what are good audiences, and what audiences do we have that are known to work for those genres,” says Calvillo. It uses this information to plug the ad into the feedback loop and constantly optimizes based on the information and predictions it generates.
And Nanigans isn’t just making those predictions and changes based on how many clicks an ad gets, but how
