Azalead Account Based Marketing Tracks Web Site Visitors, Orchestrates Outbound Messages, and Reports on Results

Account Based Marketing. Perhaps you’ve heard of it?

Okay, just kidding: ABM gets just slightly less attention than Donald Trump and arguably generates a similar amount of confusion. Many of the industry vendors are addressing that problem (the confusion, not Trump) by working together in the Account Based Marketing Consortium which, among other things, has published an excellent survey and nifty six-level functional framework. You can download them both here.

The purpose of the framework is to help marketers understand the differences between ABM vendors. Let’s apply it to ABM Consortium member Azalead, a Paris-based firm that is planning to enter the U.S. market.

We’ll start with an overview of what Azalead does. It lets marketers create lists of target accounts by identifying Web site visitors (at the company level) based on IP address, by reading data through a CRM system integration, or by uploading lists any source. Users can export account lists for retargeting, connect via API with the company Web site to personalize messages shown to visitors from target firms, or send lists to CRM. Azalead tags can be embedded in Web pages or emails to track response. Opportunities and revenues can also be imported from CRM. The system reports on impressions (i.e., messages sent), responses, opportunities and pipeline value for targeted accounts and gives salespeople lists of all identified Web site visitors or of visits from target accounts.  It can also rank accounts with a system-generated engagement score.

Now that you have a more or less coherent view of Azalead functions, we can map these to the Consortium framework.

Account Selection: users can select accounts from the list of identified Web site visitors, from the list of accounts imported from CRM, or from any other list uploaded to the system. There is no predictive scoring although users can build lists by filtering on whatever attributes have been attached to the records, such as industry, country, or company size. Azalead has created its own technology to identify site visitors whose IP address cannot be tied directly to a specific company. Results vary, but in Europe this can increase the percentage of identified visitors from the usual 30% to as high as 50%..

Insights: once a company has been identified, Azalead can present users – typically sales people – with a screen that shows the company name, basic information (industry, revenues, etc.) from an external database, engagement history as captured by Azalead’s Web and email tags, and contact names from the CRM system. 

Content: Azalead doesn’t create content.

Orchestration: Azalead can create lists based on engagement level, opportunity stage (imported from CRM), and other account attributes.  Different lists can be selected for different marketing treatments.

Delivery: Azalead tags on a company Web site can both identify visitors and return basic parameters including company name, industry and size. These parameters can drive personalized Web site messages. Messages in other channels are generated by sending lists to marketing automation, CRM, retargeting, or other external systems.

Measurement: Azalead offers a summary dashboard and more detailed information on retargeting, display ads, and Web site visitors. A pipeline impact report plots sales opportunity probability against marketing engagement for individual deals, helping marketers to see the impact of their efforts.

So, what information is missing here? The framework doesn’t explicitly cover integration, arguably because that’s a supporting technology rather than a functional capability. Azalead has API-level integration with Salesforce.com and Microsoft Dynamics CRM. A general purpose API allows custom integration with Web sites and other systems. The company is working on API integration with major marketing automation platforms, but currently uses its own tags to track response to emails sent by those systems.

The framework also doesn’t including pricing or vendor background, which are also not functional capabilities.  Azalead pricing is published on its Web site. A limited system starts at $1,000 per month for a 3,000 monthly Web visits and 400,000 ad impressions. A system with all features starts at $3,200 per month for 20,000 Web visits and 1.4 million impressions. The company was founded in 2013 and currently has about 80 customers, mostly tech companies in Europe. It plans to open a New York office later this year.

LinkedIn Buys Fliptop: Why Account Based Marketing and Predictive Analytics Are a Natural Fit

Predictive analytics vendor Fliptop today announced its acquisition by B2B social network LinkedIn.  It's an interesting piece of news but I'm personally disappointed at the timing because I have been planning all week to write a post about the relationship between predictive analytics and account-based marketing (ABM).  I would have looked so much more prescient had they announced the acquisition after I had published this post!

The original inspiration for the planned post was a set of three back-to-back conversations I had last Friday with one ABM vendor and two predictive analytics companies (none of which were Fliptop or LinkedIn).  The juxtaposition highlighted just how much predictive and ABM complement each other.  In fact, the relationship is so obvious that it almost seems unnecessary to lay it out: predictive vendors help marketers find accounts to target; ABM helps marketers reach target accounts.  You can safely assume that both sets of vendors have noticed the relationship and that many are working to combine the two techniques.  The Fliptop/LinkedIn deal is just more evidence of the connection.

To move past the very obvious, ABM vendors – whose basic business is selling ads targeted to specific companies – could also use predictive analytics to refine their ad targeting.  This could mean selecting the best people to reach within targeted accounts or selecting the most effective ad placements to reach those accounts.  This requires integration of predictive analytics within the ABM product, not just using predictive before ABM begins.  I expect LinkedIn will use Fliptop's capabilities in these ways among others.

But, getting back to last week's conversations, what really struck me was a less obvious connection of ABM and predictive to content.  Two of the vendors described using their systems to select which content to send to specific accounts or individuals.  These selections are based on previous behavior, something that certainly makes sense.  But I don't generally recall hearing ABM or predictive vendors discussing as one of their applications.  It's an important idea because it promises to improve results by delivering more relevant content for the same price.  The same data gives marketers insights into broader trends in the types of content that buyers find interesting.

Content analysis requires the ABM or predictive system to be aware of the topics of the content being consumed.  This is only possible if someone specifically goes to the trouble of tagging the content and capturing the tags.  So content analysis is not quite a natural byproduct of the ABM or predictive analytics: it takes some intentional effort.  A corollary is that not all ABM and predictive systems can deliver this benefit.  So it's something to specifically ask prospective vendors about if you think you'll want it.

To put things in a still broader perspective, targeting content with ABM and predictive systems is part of a broader trend of using advanced technology to help marketers create, manage, and optimize content.  This is something that vendors like Captora, Persado, and Olapic do in terms of content creation, and Jivox, OneSpot, Triblio, and BloomReach do in terms of personalized content creation.  I've been looking at a lot of those systems recently although I haven't written much about them here.  New targeting technologies create unprecedented demands for more content, which only new content technologies can meet.  So you can expect to hear more about technology-based content creation, whether I write about it or not.

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

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 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 Salesforce.com 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.

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* 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.