At Bombora’s second annual Intent Event in September, I began noticing a developing trend among several successful B2B sales and marketing teams. No fewer than six presentations shared a common way of gathering, organizing, and perhaps most importantly, valuing all their sources and types of data.
This intrigued me. However, it wasn’t until I put this trend into the larger context of the industry’s move from automation to orchestration that the importance of this new data framework became clear.
The following outlines the data framework from a high level. Of course, a framework is one thing. How you use that framework is another. Due to length (and attention-span) constraints, I'll address using the framework at a later time.
B2B Marketing’s Shift From Automation to Orchestration Is Affecting Data in a Big Way
Several influential B2B marketers have recently written about this shift. As Marc Johnson, CMO at Bombora, put it in his recent byline, “Marketing Automation Must Give Way To Marketing Orchestration”: "Orchestration means moving away from automating single tools and components, toward having all of the different marketing and sales elements running off of the same data and talking to each other …. It’s about actually aligning around what the consumer needs and what the prospect is interested in — while resisting the extraneous.”
Elaborating on the same shortfalls of automation, Scott Vaughan, CGO at Integrate, recently wrote in “B2B Marketing Shifts from Automation to Orchestration”: "The overabundance of disconnected tech and tools to automate key processes is often creating more work than it’s worth to implement and use .... Orchestration ... builds off basic automation and offers the ability to orchestrate data, systems, programs and experiences and adds new levels of intelligence."
The key point: automation means little unless all the tools you use to automate your processes — as well as the data that fuels these tools — are properly aligned and coordinated; that is, orchestrated. And as the sources and types of data we use to drive our increasingly sophisticated initiatives proliferate (not to mention the growing number of data-privacy regulations), so too must we orchestrate this data accordingly.
(For more information on marketing orchestration specifically, read Jon Miller’s, “I Predicted Marketing Automation and it Changed Everything – Here’s My Next Big Prediction,” in which he dives into the many reasons why orchestration will become critical.)
Related Article: The 4 Factors Defining Marketing's Future
Top B2B Teams Are Independently Developing a Common Data Framework
The growing need to logically gather, organize, visualize and act on data as a critical element of marketing orchestration became clear after reviewing all the Intent Event presentations. The vast majority of presentations comprised nearly identical data-framework elements. The terminology used by each team varied to some extent, but the types of data and how they were organized were strikingly similar. To be clear, each presentation was created independently of one another.
What’s more, the teams weren’t even asked to provide a data framework. The teams all shared their frameworks because, presumably, they were vital to the success of their respective programs. This makes sense. In his recent CMSWire article, “Data Management Is the Key to Success – or Failure – in Customer Journey Orchestration,” Mark Smith writes: "To operationalize your data, or, frankly, to have a proper data strategy at all, you need a framework. The appropriate data framework will not only help you leverage data to inform specific marketing activities, but it will provide you with a standard for measuring the effectiveness of your customer journey initiatives."
So, how are successful B2B teams building their data frameworks?
The Elements of a B2B Data Framework
Slight variations exist, but each framework generally organized data into what’s becoming known as the F.I.R.E model (credit to EverString for coining the phrase):
This category includes data that identifies whether an account (and/or persona) fits within your ideal customer profile (ICP). The types and quantity of data included in the fit category — and the way it’s leveraged — can vary significantly, from straightforward firmographic info (e.g., company size, geographic location, annual revenue, etc.) to a sophisticated composite of various data (e.g., firmographic info, social data, current tech investments, recent funding, hiring trends, etc.) weighted in a predictive analytics model. Moreover, it often includes a mix of first- and third-party data.
Engagement data highlights accounts or leads that are actively engaged with your marketing or sales team. Perhaps several individuals from a target account filled out a lead form on your website or responded to a business development rep’s (BDR) outreach. In some way, the business is showing interest in your brand, product or service, though they may not be a fit. This category is most often made up of first-party data culled from marketing automation platforms, however, it can include third-party data by way of content syndication or other third-party campaigns.
The intent category includes data that’s collected about a business’s online behavior, which provides insight into the extent to which that business is likely to purchase your product or service. For example, an intent data provider may monitor a business’s whitepaper downloads, search topics, attended webinars, online subscriptions, read and shared articles, and more to assess whether that company is “in-market” to buy a specific solution. (It should be noted that this is done on an account level, not an individual level.) Unlike engagement data, intent data is almost exclusively collected from third-party providers.
Recency tells you when important activities and events occurred. If a target account downloaded a lot of your whitepapers and had productive conversation with your BDRs 18 months ago, but have gone silent the last year, that engagement probably doesn’t mean much. Similarly, intent data may identify that a company is consuming a ton of content related to your offerings, but if that level of content consumption has been steady for several years, is it really indicative of intent to buy? Trends are important. Recency data highlights these trends, allowing you to understand the importance of the other data types.
Related Article: Good Personalization Hinges on Good Data
How to Use the Data Types in the Framework
This venn diagram, while simplistic, illustrates how the four categories work with one another to help better orchestrate marketing and sales resources around clear objectives.
|Data categories (all include Recency)||Priority||Notes|
|Fit + Intent + Engagement||A||Highest priority. BDRs should focus time/efforts on these accounts.|
|Fit + Intent||B||High priority. These businesses fall under ICP and their online behaviors are indicative of purchase intent. Yet, they haven’t engaged with your company. Good idea to allocate targeted advertising budget/efforts to these accounts to raise awareness. Possibly, BDR resources too, if available.|
|Fit + Engagement||B||High priority. These businesses fall under ICP and have engaged with your company in some way, though those engagements may be superficial. Good idea to nurture them via email and targeted advertising, and allocate BDR resources, if available.|
|Fit (alone)||C||Priority. Fall under ICP, however, they haven’t yet shown intent to buy or engaged with your company. If budget is available, you may want to run targeted advertising to raise awareness.|
|Intent + Engagement||D||Possible value. If these companies fall clearly outside your fit, it’s probably not worth focusing on — even if they buy, they’re more likely to churn later. On the other hand, they can help identify gaps in your fit model, so they shouldn’t be ignored. It’s good to think of these accounts as new sources of info with which to expand and/or refine your fit criteria.|
|Intent (alone)||E||Possible value. Similar to “Intent + Engagement.” Accounts in this category may help identify gaps in your fit model, so allocating some effort to analyze these businesses isn’t a terrible idea.|
|Engagement (alone)||E||Possible value. Same as “Intent (alone).” Accounts in this category may help identify gaps in your fit model, so allocating some effort to analyze these businesses isn’t a terrible idea. Further, it might tell you you’re spending your marketing dollars in the wrong place.|
Data Orchestration Starts With a Strong Data Framework
The rapid expansion of tech and data is requiring marketing and sales organizations to think more holistically. Rather than focusing on automating individual processes, successful businesses are shifting to an orchestration mindset. Orchestrating technology (the tools, systems and platforms) is vital to this effort. Just as important — if not more important, due to emerging data-privacy regulations — is the way in which we orchestrate the various types of data that fuel these systems. Developing your own data framework is a key step to orchestrating your data.