The world of online advertising and online conversations meet in BuzzLogic’s media index and analytics platform, an all-in-one space for analyzing, buying and reporting on campaigns. Driving the tracking and extraction of relevant web conversations and the indexing and ranking of URLs to buy ads against is the company’s NLP and cognitive analysis capabilities.
The vendor last week the vendor updated its BuzzPlanner module for honing in on targets across its index of 60 million sites and 3 billion conversations based on advertising agencies’ queries around audience and campaign objectives. “All content is social over the course of time,” says CEO Dave Hills. “So targeting ads against a conversation vs. content improves the ability to improve brand metrics.”
In BuzzLogic’s view, anything from Facebook to Twitter to blog postings to HTML forms to comment widgets are nothing more than content management systems. And BuzzLogic’s job is going to be ingesting as many and as diverse a number of those fragmented CMSes as possible for its sprawling index, and for its NLP and cognitive analysis capabilities to grow their expertise. Those two capabilities are the key to letting BuzzLogic “pinpoint areas of influence where people are most likely receptive to a brand’s message and measure the effectiveness once we run [a campaign],” he says. “The insight and research is supported by using cognitive analysis on our index of conversations and understanding of the associated nature of words. So it’s not just looking at the similarities between ‘took a ride in a car’ and ‘took a ride in a car on the beach and had a great time,’ but we understand the latter has an emotive context to it where someone would like to have their brand associations reinforced.”
In practice, its technology starts with analyzing the advertiser’s brief and aligning it to conversational media; understanding which conversations are the best to target and then targeting the centers of influence in those conversations; measuring how an advertiser’s marketing efforts have changed those targeted conversations; and continuous optimization to meet campaign goals. “One different thing in highly targeted media is scale and the way our index operates – well, nothing can scale infinitely but we have a long way to go,” he says.
Hills gives the example of a telco that wanted to get buzz for a new mobile device, align with content related to consumer technology, and to those early adopting gadget lovers in the 18 to 49 age group. “We dumped that stuff into our system and with NLP and search defined our analytics and targeting,” he says. Its output included indications of higher-density conversation based on audience, incidence of keywords, in-linking, and so forth, as well as lower-density but larger conversations. The result was finding 400 discrete URLs to buy on a media plan across dozens of publishers. That scale is almost impossible to otherwise realize, he says, but BuzzLogic can do it because “we know where the URLs are connected to and we know how to access them through exchanges, DSPs, ad networks, so we buy the URLs,” he says, and throughout the campaign to measure and manipulate them as conversations within them shrink or grow to optimize brand metric performance. It is, he says, a closed loop both to place media and measure impact. “We are agnostic as to connectivity. We sell the conversation because we own our own data at the end of the day.”
BuzzLogic launched in mid-2010 and since then has handled some 130 or so campaigns for Fortune 1000 companies. “Our stack allows us to have the horizontal view of conversations rather than the standard channel/site contextual vertical view,” Hill says. “That allows us to track a conversation, optimize as we see it change and that is how we service our customers.”