XA.net and Efficient Frontier don’t want you to overspend on Facebook ads, and they’ve recently launched tools to solve that exact problem.
Until recently advertisers used to say it’s almost impossible to quantify a return on investment in ad spending. The web has changed that pretty quickly with pay-per-click and the broader category known as performance advertising. That’s an area that Facebook has continued to improve upon. Last year the social network released to some developers and application programming interface that includes optimizing ad performance, and offerings that tap this API have been trickling out since then.
Enter XA and Efficient Frontier. Their new tools fine-tune bidding on ads and helps manage them as part of an entire campaign across different channels. Both remind me of the kind of software that a mutual fund manager uses to run an investment portfolio.
Efficient Frontier oversees more than $1 billion in annual ad spending for its clients. Founded in 2002, the company now incorporates Facebook ads into portfolios that include search engine marketing display ads. According to the press release touting the integration:
The new integration of Facebook’s APIs into Efficient Frontier’s platform helps marketers succeed by delivering automated optimization while removing much of the complexity from campaign management and reporting. The platform simplifies the creation of successful Facebook campaigns by allowing marketers to create thousands of ads in seconds. It also allows marketers to more easily target ads to new audiences using Facebook’s segmentation including interest, likes, age, gender, and geography.
XA, by contrast, formed in 2008 and has since optimized more than 60 billion worth of ad page views. The company calls its Facebook-integrated tool optim.al and describes it in a press release as:
A multivariate text and image advertising optimization platform which allows clients to quickly and efficiently test thousands of ad variations via a single interface. The platform gathers and analyzes data to define the best performing components of each advertisement, empowering advertisers to drive optimal return on investment utilizing either full factorial or fractional factorial multivariate testing. Fractional factorial testing allows for advertisers to test many ad components with far fewer tests – allowing advertisers to determine winning combinations of image and copy elements far more quickly than if they ran every possible combination.
Both companies’ product descriptions sound a lot more complicated than the software really is. I wonder about the extent to which this might affect adoption rates. Readers, what do you think about finer-tuning ad spending on Facebook? Do you think this trend might stir up privacy concerns?