ABM Vendor Guide: What to Look for in External Data Sources

Last week’s posts introduced our new Raab Guide to ABM Vendors (buy it here) and introduced a framework four process ABM steps, six system functions, and six key sub-functions. The idea was that functions define major categories of systems, while the sub-functions differentiate systems within each category. The world isn’t really quite this simple, if only because many systems provide more than one function. But the sub-functions are still important for stack design and vendor selection.

My plan this week is to follow up with a sequence of posts that go through each sub-function in some depth.  Let’s start with the first sub-function, External Data. 

ABM Process	System Function	Sub-Function	Number of Vendors
Identify Target Accounts	Assemble Data	External Data	28
	Select Targets	Target Scoring	15
Plan Interactions	Assemble Messages	Customized Messages	6
	Select Messages	State-Based Flows	10
Execute Interactions	Deliver Messages	Execution	19
Analyze Results	Reporting	Result Analysis	16

Vendors that support this sub-function gather account and contact information from the Internet, private, and government sources and purchase it from other vendors. They may resell the data to marketers or use it themselves to support tasks such as account scoring or ad targeting. 

(To put things in a broader context, “external data” can be contrasted with “internal data”, which comes from a company’s own systems for CRM, marketing automation, Web analytics, order processing, customer support, etc. Internal data is most important later in the sales cycle, when prospects and customers are interacting with the company directly. External data is most important at the start, when the company hasn’t identified its target accounts or established direct relationships with them.)

External data may seem like a commodity – after all, all vendors have access to pretty much the same sources. Yet there’s probably more variety among the vendors in this category than any other. Some key differentiators identified in the ABM Guide include:

• types of data provided (companies, contacts, events, intent, technology used)
• number and types of data sources (company Web pages, publisher Web pages, ad exchanges and networks, job posting sites, social networks, IP directories, financial reports, government files, industry and professional directories, news feeds, etc.  Different sources provide different data types.)
• depth of data (to measure this, get a list of data elements)
• quality of data (harder to measure: review some sample records and have the vendor explain their quality methods)
• coverage by region, company size, industry, etc. (depends heavily on data types and sources)
• coverage by language (many systems extract data using natural language technology that can only read English)
• how often data is refreshed (which involves two issues: how often are sources revisited and how quickly do changes get communicated to clients)
• on-demand updates for individual accounts or contacts (to get up-to-the minute information on a new or existing account)
• add new data sources to meet specific client needs (e.g., reports of new research contracts in the client's industry)
• custom research to supplement public information (in particular, some vendors do custom research to identify the IP addresses used by target accounts)
• custom taxonomies for intent analysis (because standard taxonomies may not be precise enough for specialized client needs)
• maturity of data management processes (how long they’ve been evolving, size of team, etc.)
• data verification methods (phone call, test for email bounces, compare against other sources, etc.)
• special methods to associate personal and business emails, attach leads to accounts, find social media handles, etc. (vendors may do different kinds of “fuzzy” matching, machine learning, or natural language processing to uncover or infer relationships when exact matches are not available)
• load client data and match against it for enhancement (most vendors will do this but some require the client to do its own matching)
• continuous updates of client data (reporting on changes as the vendor learns about them; requires uploading a list of accounts or individuals to monitor)
• provide personal identifiers on contact records (name, address, phone, email address, social media handle, etc.; not all vendors do this, especially in countries with strict privacy laws; different identifiers are also treated differently)
• provide a complete universe of all companies in a target market (some vendors only enhance records already in the client database, others provide "net new" records as well.)
• find social connections between company employees and target account employees (make sure this is done without violating the social network terms of service).
• real-time processes to identify Web site visitors, auto-fill Web forms or verify form entries, show data to sales people, support ad targeting, etc.
• add ownership relationships to accounts (headquarters/branch, parent/subsidiary, brand/franchisee, etc.) in general and to the D-U-N-S Number in particular
• fee structure (most are vendors charge per record and/or based on the data types; some are performance-based)

Which of these are important will depend on your business needs and approach to ABM. For example, if you sell to small businesses, then coverage is critical because many vendors identify companies using IP address or Web domain – things many small businesses do not have. On the other hand, if you want to target Web messages to large enterprises, your critical need will be real-time identification of Web site visitors, something only a few vendors can support.

The issue hovering over all this is data quality. If quality is poor, then nothing else matters. Quality can be a somewhat tricky concept, since it’s not just accuracy or coverage or currency.  My personal favorite definition of quality is “fitness for purpose”, which makes the point that the quality of data (or anything else) is related to how you’re going to use it. But even assuming you know exactly what you need, you can’t predict quality based on a checklist of features or attributes. The only practical approach is to get some sample data and see how it performs, whether by comparing it to known correct data, testing it directly via phone calls or surveys, or using it in a marketing program and measuring the results. Experienced data-driven marketers have known this forever, but less experienced marketers may not realize that all data isn’t as good as they’d like to assume. There’s not much I can do in the ABM Guide to solve this issue, but smart marketers can use the Guide to identify data vendors who meet their other requirements, and then test those vendors' data to ensure the quality is what they need.

Data Plus MarTech: HubSpot and Demandbase Join the Race

There were two industry announcements this week that were unexpectedly related. The first was HubSpot’s announcement yesterday that its CRM offerings would now include access to a 19 million account prospecting database. The second was Demandbase’s acquisition of data-as-a-service vendor WhoToo, which offers its own set of 250 million profiles relating to 70 million business professionals.

The WhoToo acquisition marks a big step in the continued evolution of Demandbase, since it's a change from targeting companies to targeting individuals (although DemandBase still won’t sell you their names). More precisely, WhoToo aggregates audience data from multiple sources and makes it available for selections based on company and individual attributes. The company does know the identity of some individuals and will use these to target email and Web advertising to names you provide. It will also let you market to audiences in those channels without providing their names. This is a nice extension of Demandbase’s existing account-based marketing capabilities. What makes WhoToo really special is it has the technology to access its data with the split-second speed needed to purchase display and mobile ads in real time.

The addition of individual-level targeting puts Demandbase on a more even plane with LinkedIn, which of course already sells advertising to its own huge database of more than 350 million profiles. The WhoToo deal won’t fully close that gap, but it does help to keep Demandbase competitive. (I’m sure Demandbase would argue it has its own advantages over LinkedIn.)

In this context, the HubSpot announcement is interesting mostly because it too recognizes the importance of giving marketers audience lists without acquiring the names for themselves. You could argue this makes HubSpot a player in the super-hot Account Based Marketing category, although they didn't use the term.  If they are, it's ABM-lite, in the sense that HubSpot will give CRM users basic profile information, usually including a phone number, but doesn't offer contact names or email addresses. It also pulls recent news stories.  This is pretty consistent with HubSpot's historic aversion to unsolicited outbound contacts.  The company does approach the line by giving enterprise users an option to find other people in their company who have a contact at target accounts and ask for a warm introduction.

On the other hand, HubSpot also announced integration with LinkedIn for paid ad campaigns and said a Google AdWords integration is in beta, which are definitely in outbound territory. Naturally, HubSpot says its LinkedIn and Google campaigns will be giving potential buyers information they want, so they are not at all like that bad old interruptive advertising that HubSpot has always opposed. No, not one bit.

Anyway, the point here is that both HubSpot and Demandbase are adding data to their marketing technology, something we’ve seen in other deals like Oracle buying Datalogix. There are still plenty of stand-alone data vendors, especially when it comes to B2B prospecting lists. And there are plenty of vendors who combine prospect data with predictive – including LinkedIn itself since its recent FlipTop acquisition. But I think we can add “data plus tech” to the tote board of martech horse races.