Using Contact Level Intent Data for Programmatic ABM Personalization

Sep 26, 2019 | Author Ed Marsh

Tokens Are Not Personalization!

Engagio's recent The B2B Marketer's Guide to ABM Personalization at Scale starts with some eye opening statistics regarding user expectations for personalization of their buying and internet experience - eMarketer says 85% expect it! It acknowledges the challenge of highly personalizing content at scale as is required, and lays out a playbook for segmenting account-based marketing campaigns according to the three tier model they've traditionally recommended.

  • One-to-One - high touch personalization, inefficient but effective
  • One-to-Few - customized (general segments with adapted content and automation driven customization)
  • One-to-Many - passive approaches and "programmatic" ABM (content syndication, paid social, dynamic web content and experiences)

Engagio suggests establishing resource commitment levels for accounts in each tier to ensure that resources are distributed and enable multi-channel, multi-touch campaigns (and avoid SPAM) for each account. They outline the work that companies should do to understand and map buyer personas, buying journey's common challenges, etc. Finally they lay out some examples of campaigns for each tier.

This is good ABM blocking and tackling. And it's appropriate for a world in which sellers have to build their messaging around what they guess will resonate with prospects.

GUESS is the operative term here, and the primary limiting factor in personalizing even one-to-many programmatic ABM.

Customization Is Built on Assumptions

The customization Engagio suggests is built on those best guesses. Research around personas and prospect businesses and industries can certainly help marketers make informed assumptions about the challenges and goals of their ABM prospects by industry, seniority, job function and company size.

Building on that, companies can create content that takes a Challenger Sale approach to establish credibility with prospects and trigger dialog. And then they hope that volume generates results.

We've been conditioned to accept that this may be the best we can expect since only 3% of a potential market is currently researching and "in market."

Observing Trigger Situations

The corollary to the 3% rule (according to Vorsight) is that 40% are "poised to begin." While ABM to create opportunities among the 56% (sic) of key target accounts currently in purchasing stasis is prudent and appropriate business, the best approach is to find those that are poised to begin.

How can you do that? Much less do it at scale?

ABM is a common use case of intent data for marketing, and savvy use of 3rd party intent data can help. At this stage even account level intent data (like surge type data) can help. The playbook calls for understanding the related products/services/situations that a company would encounter which doesn't directly trigger active interest in your product or service, but which would logically and reasonably lead there.

For example, if a company announces a search for a new CEO, it's too late to sell them retained search but the board may value some compensation consulting. Similarly, a company considering an ecommerce solution is going to have heightened awareness of data security obligations to protect consumers. And apropos to this article, a company considering ABM software like Engagio, Terminus or Triblio is going to likely realize the need for intent data (and once they've contracted for account level data, realize what they're missing without contact level™ intent data.)

In each of those situations savvy marketers and sellers can prioritize their ABM accounts to foster engagement at opportune times when there's a window of interest but not yet intense competitive interaction.

Pro tip - If you think of intent data as a tool just to observe competitive interactions you're missing a huge opportunity! If you're limited to a handful of "topics" you'll feel constrained. That's part of the power of a bespoke algorithm.

Programmatic Personalization Built on Granular, Contextual Understanding

Here's where it gets interesting. Up to this point:

  • personalization was limited by resources (your time and attention to individually infer what's happening and incorporate that into your approach)
  • customization was based on assumptions
  • ABM targeting was based on firmographic fit
  • ABM prioritization was based on firmographic intent

Let's really blow it open.

Contact level intent data completely changes the ABM playbook. It can tell you who is taking action, and includes granular, contextual information on the action they're taking. Let's unpack that.

Instead of just knowing about something brewing, specifically in your main product/solution/competitor space, at some you'll know:

  • who - including job title - from which you can parse seniority and function - location and company
  • how - type of interaction (e.g. with a competitor, an article, etc.)
  • what - key term (problem, outcome, feature, solution, etc.) with which they've engaged - from which you can parse their theme of focus and stage in buying journey

This is the key to personalized programmatic ABM. Savvy companies create segments based on these observable attributes. For instance:

  • which competitors has an individual engaged with
  • their job function
  • seniority
  • stage in buying journey
  • theme/topic of their focus
  • velocity of their engagement

This provides insight into individual fit and intent which compliments the account level observations. And at the intersections of these attributes it's possible to create very personalized messaging matrix (for sales outreach, paid social nurturing, 3D direct mail, etc.) and deliver it at scale.

Further, because the contact level data is specific, you can determine the breadth of engagement in aggregate from the company AND who the various key players are and how they're approaching the process. That enriches the scoring of firmographic intent with "surge" type data, and can help to identify active players (vs. simply importing a bunch of contacts from a static database based on job title.) And we know from complex sales that the various stakeholders often have different priorities and perspectives, but we often guess or rely on a champion for insight.

Directly observable data is powerful.

Climbing the Slope of Enlightenment

Scott Brinker (@ChiefMarTech) kicked off the recent Boston MarTech event with a keynote which predicted that this will be the year that marketers make martech real, climbing the slope of enlightenment.

The use of intent data, particularly its integration into other marketing functions like ABM, retargeting, event marketing and CI, is an opportunity to climb that slope. Although new adopters of some intent data technologies may find themselves tumbling into the trough of disillusionment, experienced and savvy users of intent data who are discerning in their data selection and thoughtful in their orchestration will find that Brinker is right.

Taking ABM from a beautiful theory to an actionable and feasible tactic is a perfect example.


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