Or, which of the folks in your Eloqua database are most intensively researching the topic of the white paper you’re about to blast out — this week?
Or, best of all: Which Fortune 1000 companies are intent about buying something you have on offer — ASAP?
I recently learned these things actually are knowable, today, given just a few machine-learning algorithms and a database the size of a small outer planet.
As a former editor-in-chief and current content marketer, such news makes my heart race. Until now, even with focus groups and surveys, the best we could really do was guess what topics mattered to our audiences and what they’d like to learn. Now we have actual behavioral data.
And it turns out that Erik Matlick, a fellow I know from my past life as a Web publisher, is among those making it happen.
I’ve done business with Matlick over the years, despite which he still takes my calls. So I called.
Over many years, Matlick built a series of companies (MediaBrains, IndustryBrains and Madison Logic) that increasingly leveraged Web data to deliver value to Web publishers and their customers. Those companies were a series of learning experiences leading to his latest, Bombora — a kind of data cooperative among roughly 3,500 B2B publisher sites.
Bombora tracks the billions of interactions readers have with all those sites’ content every month. At the moment, Bombora is tracking between 700 million and 1.2 billion interactions every day, and can identify from which of roughly 1.8 million corporate entities those interactions emanate.
To make sense of it all, Bombora developed a single uniform taxonomy of B2B topics with about 2,800 leafs, which it applies to all the content in the cooperative, according to Rob Armstrong, the company’s head of product. “Imagine a leaf like ‘intrusion detection’ on a branch called ‘security’ off a major limb like ‘technology,’” Armstrong says.
The company maintains readers’ privacy by not tying personally identifiable information to its anonymous tracking identifier, but has otherwise instrumented its dataset to yield insights on readers’ intent — at the company level. For example, if you have a set of personas that map to specific job titles in a given industry, Bombora can tell you what topics those personas were reading most last month, or last week. As a content marketing strategist, knowing what my targets were reading about over the course of the last two to four months would be powerful content ideation input, indeed.
Now think about this: if you know the total composition of topics that your target audience has been reading over the course of the last four months, as well as the last two years, you also know how that composition has changed over time. You can see peaks, troughs and spikes. You can discover which topics are top-of-mind today, which are trending up, and which are old news. You can create of-the-moment content that truly reflects what your target audience is concerned about most, right now — especially if you have a newsroom-as-a-service real-time content marketing capability.
Now, think beyond content creation (which is admittedly a challenge for me). Because Bombora does entity extraction, it can feed insights into account-based marketing (ABM) and sales. “Say you’re tracking a company like Boeing, and you know that on an average month readers from Boeing consume X articles on cloud storage,” says Matlick. “But, this month, that number spiked to 3X. It’s not a big leap to conclude that Boeing is undergoing a research surge on that very granular topic. When that happens, everything you do — every email you send, every white paper, or banner ad you present — is going to perform far, far better.”
Recently, Bombora automated that function and packaged it into something called “surges” that marketers can analyze through the self-serve Bombora.com site. “We create models with a baseline of historical consumption for a key; a key may be a company and a topic, but could also add location or industry,” explains Armstrong. “We track the average consumption for every key set over weeks and months, and when we see higher activity — whether in the form of more users reading content, or more content being read, or increased engagement with the content, or some combination — we know there’s a related ‘surge’ happening at that company.”
With surge, marketers don’t have to know what companies to track; you can find the entities whose interest is surging in what you have to sell, or the content topics with which you’re going to market. Having tracked a sufficient number of customer experiences from surge to sale, Armstrong says that the likelihood of closing a deal increases anywhere from 2x to 5x when Bombora detects that a company is surging for a relevant topic.
While Bombora’s analyses demand a lot of data, it’s not a sci-fi big data dream. It’s available now, through partners such as BlueKai (part of Oracle’s marketing cloud), Outbrain, Taboola and Adobe’s marketing cloud. Marc Johnson, Bombora CMO and general manager, added that the company’s data will feed into Einstein, the AI platform that Salesforce.com announced in September would be embedded in its cloud (not to be confused with Frederik Pohl's Einstein AI, described in my “Chatbots Rising” post).
Finally, I had to ask: What’s in the name? It turns out Bombora is an indigenous Australian word for a really powerful wave (a “bommie,” in Australian slang). “We view our data as powerful and creating a sea of change in the marketing and marketing stack that it sits in,” says Matlick. “Also, the derivative of our data – the ‘surge’ — is the wave of demand at the company level. Also very powerful.”
Here’s hoping marketers will be surfing these waves soon.