Powerhouse Facebook Ad Platform Nanigans Raises $5.8M Series A.1 For Predictive Modeling R&D

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Do you target kids with cheap ads or more expensive adults? Nanigans has just raised a $5.85 million “Series A.1″ from Avalon Ventures to build SaaS technology that predicts which audiences earn advertisers more money. With revenue up 6x in 18 months, Nanigans hopes to keep up with Facebook’s progress by pouring its funding into R&D. It’s already discovered a surprising trick to supercharging ROI.

In advertising, there are three numbers that really matter. Cost per acquisition (CPA) – how much you pay for a customer; lifetime value (LTV) – how much you earn off that customer; and return on investment (ROI) – how much higher your LTV is than your CPA. Most advertisers don’t have the technology or data to accurately predict LTV, so they aim to get the lowest CPA so a smaller LTV will still produce ROI.

But Nanigans has found that doesn’t actually work so well, and it’s building the technology to prove it. Founded in 2010, the company had previously raised just $3 million in a mid-2011 Series A to build its licensable self-serve tool for buying better Facebook ads in exchange for monthly or yearly fees, or a percentage of spend. The tool lets businesses buy, intelligently target, and A/B test huge Facebook ad campaigns much more efficiently than Facebook’s basic tools. Nanigans’ focus on technology and delivering lifetime value over smaller immediate returns has been a hit with social game and e-commerce game companies.

Now it believes it’s No. 1 or at least in the top 3 Facebook adtech companies in terms of revenue, which grew 6X since its last funding round. It now boasts customers, including eBay, Fab.com, Rue La La, Zynga, GSN, Wooga, Kixeye and Kabam. It’s now running hundreds of millions of dollars of Facebook ad spend per year, and has grown from 15 employees to more than 100 in just a year with offices in San Francisco, New York, Boston and London.

The Nanigans secret sauce is its predictive modeling engine, which can both deduce and track how much a customer spends. This lets it determine that while it might cost an e-commerce company 3x as much to acquire a 25-year old Australian female, she’ll earn them 5x as much as a 15-year old Australian male. An advertiser may have to invest more up front, but in the long-term they’ll be richer for it.