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Demandbase adds AI-powered DemandGraph to generate lists of targeted accounts
Demandbase makes its living by identifying if a visitor to a client’s website is from a targeted account and, if so, showing them specific content. And it delivers ads to users from targeted accounts, across the web and in walled environments, like Facebook.
But which companies should be on the target list?
Previously, that list of targeted accounts could be generated via lookalike modeling to find accounts similar to current customers. The list could also come from a client’s sales department, or it could be generated by outside vendors that specialize in predicting which companies are possible leads.
Today, the San Francisco-based Demandbase is launching a business relationship engine so it can find potential account targets for its clients by using artificial intelligence to create profiles based on inferred needs, interests, and connections. The new offering, called DemandGraph, is built on technology from data science firm Spiderbook, which Demandbase acquired several months ago .
The company is describing DemandGraph as “B2B’s first AI-driven business graph.” It ingests massive amounts of structured and unstructured data for half a million of the world’s largest companies, including Security and Exchange Commission (SEC) filings, annual reports, corporate hierarchies, white papers that have been downloaded and web content pages visited, press releases, social media, company websites, individuals involved in deals, relationships among vendors, customers and partners, and more.
By mapping all of this data, DemandGraph generates lists of companies based on their apparent interest or spending on specific products or services.
The idea, CMO Paul Isaacson told me, is to provide answers to the two biggest questions: how do you identify companies that might be interested in what you sell, and how can you close the deal?
The New York Times?
DemandGraph, he noted, provides answers that are not just based on similar or lookalike accounts, or on predictions, but on patterns of relationships. It delivers a target list, as well as talking points for sales efforts.
The results are not always obvious, Isaacson said. As an example, he recalled that when Demandbase itself used DemandGraph to identify accounts, one that came back was The New York Times.
Since Demandbase is focused on B2B, he said, it had not considered the Times because it seemed to be a consumer-oriented company. But, as it turns out, the Times also has significant B2B efforts, such as selling its publications into hotels.
Overall, he said, DemandGraph generated a list of about 250 accounts of interest, which led to 57 specific opportunities after three or four months.
While a variety of companies like Dun & Bradstreet offer detailed business profiles, Demandbase senior vice president of technology Aman Naimat — the co-founder and former CTO of Spiderbook — told me that DemandGraph now offers, for the first time as a single dataset, “a systematic understanding of the B2B world.”
DemandGraph is Demandbase’s latest effort to become a powerhouse of B2B data analytics. In November of last year, for instance, itannounced a new B2B Data Cloud, based on its acquisition of data provider WhoToo a few months earlier.
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