Bizo and DemandBase Lead B2B Marketing Automation to Web Advertising and Beyond


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I had a fascinating chat earlier this week with a client who described his vision for using DemandBase to tailor messages to Web site visitors from target accounts, using Bizo to further tailor messages to individuals by title, using all this data to synch inbound and outbound campaigns in Eloqua, and eventually driving everything with predictive model scores from a tool like Lattice Engines. That could serve as a pretty complete summary of the state of the art for B2B marketing today, especially if you consider “content marketing” as implicitly included. Equally helpful to me personally, it reinforced my intention to write about Bizo and DemandBase, both of which have recently briefed me on their latest product extensions.

Let’s start with DemandBase. Astonishingly, four years have passed since I last wrote about them. In that time, they’ve continued to build applications that exploit their core technology for identifying Web site visitors by company based on their IP address. This started by providing visitor lists and real-time alerts to sales people who were interested in specific accounts. It later extended to returning visitor attributes in real time so companies could pre-fill forms and personalize Web pages to match visitor interests.  The most recent expansion went beyond a company’s own Web site to the much larger world of online advertising.

To reach that market, the company had to build its own version of “data management platform” (DMP) systems that manage lists of known entities, recognizes them when they appear on an external Web site, and delivers them an appropriate advertisement. The big difference is that DemandBase entities are companies identified by IP address, while traditional DMP entities are cookies attached to browsers (and assumed to relate to individual human beings). DemandBase had to build its own engines for real time bidding (RTB) and ad serving (Demand Side Platform or DSP) to support its approach. These can integrate with Demandbase’s own network of Web publishers that will accept its ads and with other ad exchanges that connect to their own, larger publisher networks).

Data in the DemandBase DMP comes from both DemandBase and clients. The DemandBase data are the company-level attributes that DemandBase has long assembled: company name, industry, revenue, employees, technologies used, etc. Some of this, such as DUNS Number, is purchased from external sources and requires extra payment. The client data, which of course is available only to the client who provided it, could be anything but is usually attributes such as account type, buying stage, and sales territory. The system doesn’t store any information about individuals. Marketing automation, Web analytics, and Web content management systems can all access this data via API calls for analytics and as inputs to their own selection and treatment rules. Outside the DMP itself, DemandBase can store content and decision rules to guide bidding and select which ad is displayed to each account.

So much for the mechanics. The business value is that DemandBase is allowing marketers to tailor messages to target accounts even before they engage directly with the company, thereby (hopefully) luring new prospects into the top of the funnel and engaging them if they accounts don’t respond. This is a major extension beyond traditional marketing automation, which works mostly through email to known prospects.  It also goes beyond Web site personalization, which requires people to at least visit your Web site and in most cases actively provide information about themselves. As you might imagine, DemandBase offers many case studies to show how much this improves performance.

Bizo comes at Web advertising from the traditional route of building a pool of cookies and assembling them into audiences based on the attributes of the individuals they represent.  The pool was originally used to target display advertising and retarget site visitors by sending them ads on other sites. The company says it has pulled data from 4,200 publishers and other sources to identify about 120 million individuals worldwide, including 85 million within the U.S. The number of actual cookies is higher still.* Profiles contain titles and business demographics such as industry, but no personally identifiable information such as names or addresses.

Like DemandBase, Bizo has found many applications for its core data asset. These now extend beyond display ads to social media advertising through Facebook and LinkedIn, Web site personalization through Adobe, Web analytics through Google Analytics and Adobe, and integration with CRM, BlueKai DMP, and Eloqua marketing automation. Other partners will be added over time.

I’ll assume the Eloqua integration is most interesting to readers of this blog. Basically, it lets Bizo read audience segments created by Eloqua.  Bizo then matches segment members to Bizo identities and delivers Web site, advertising or social messages tailored to each segment. Because Eloqua captures such detailed information about prospect behaviors, this allows highly tailored advertising that is tightly synchronized with prospects’ progress through buying stages and marketing automation campaigns. Since it’s driven by cookies, it can send messages to anonymous as well as identified prospects – a huge expansion in marketing automation’s reach. Bizo can even allocate advertising spend across the different media to achieve reach and frequency targets as efficiently as possible. To encourage this approach, its pricing is based on the number of unique individuals that marketers manage in its system, rather than impressions or ad budget.

The business value offered by Bizo is similar to DemandBase: reaching prospects that haven’t yet engaged with a company directly or retargeting them when they don’t respond. The different technical approaches have their own strengths and weaknesses: IP-based identification is relatively stable but works only at the company level and doesn’t identify small businesses that lack their own stable IP address; cookies identify individuals but are often deleted, miss some people, and result in multiple, fragmented identities for others. Like the client I mentioned at the start of this article, you can probably think of them as complementary rather than competing components of a complete B2B marketing solution.

* Given that the total employed U.S. workforce is about 145 million, I suspect that 85 million contains quite a few duplicates, meaning any one profile captures just a fragment of an individual’s activity. But that’s the nature of this sort of thing; the business question is how well the data works even in its imperfect state.

Republished with author's permission from original post.


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