The Sources of Early Demand Data

Prologue

Over these last months, my focus has been on omnichannel and its implications for demand gen. Today, I return for a bit to a topic on which I’ve held forth for far longer: early demand.

(This post will shortly delve into the various data sources of early demand. First, though, the briefest primer for those just joining our exploration of the topic.)

Early demand is comprised of various types of digital signals. They occur in multiple channels, can be identified in specific ways, and can be captured by various means.

In November, I published a prior post related to this topic, entitled “Why Does Demand Start Here?”. One of my key points is that with the advent of buying groups and prospect self-education, early demand is identifiable and can be captured much sooner than a down-funnel form completion. I referred in that article to early demand as it is expressed digitally through clicks and intent data. Lastly, I mentioned early demand (be it intent data, clicks, ad views, etc.) could be “poured” into the top of the “waterfall” to accelerate Forrester’s Target Demand segment.

So, to return to today’s subject:

  • What are the different sources of early demand?
  • How can they be identified? How can they be captured?
  • How can they be leveraged for business outcomes?

Granted, a deep dive into this area would comprise a book, and this cursory exploration should still prove helpful – especially for the “newbies” among us, as well as the unconvinced. Let’s begin.

The Sources of Early Demand Data

The most efficient way to dive into early demand is to first look at where this digital behavior is expressed. Here’s my shortlist:

  • Opens and clicks on emails
  • Page impressions and clicks on digital ads
  • Views and clicks on social media ads
  • Intent data

Interestingly, intent data is the aggregated product of captured early demand activity (at the domain level) and a leverageable tool to drive additional early demand.

Identifying Early Demand Data

Now that I’ve named the sources of early demand, you can see that where the behavior occurs is important to understand. While we refer to all of those digital signals as early demand, it’s the where that dictates the how of identifying and capturing it. In other words, each channeltargeted outbound, inbound web activity, and community-centric social media interaction – has its own means and methods of doing so.

Regarding email, opens and clicks are identified by your email service provider or your marketing automation tool. This information is generally available by generating reports in the respective dashboards and exporting the data or directly extracting the data using a separate database tool. The fundamental goal is to match the behavior to the existing contact record.

Opens (the mere opening of an email) are largely analogous to page impressions or ad views. As in advertising, they help understand the overall behavior of your audience. What’s different with email compared to ads or intent data is that you know who you sent to (targeted outbound), and therefore – if you have the right toolset – you know precisely who opened your email. And who clicked.

This is not the case with ads or intent data. Whether capturing impressions or appending data, this information is only available at the domain level. That means not the specific person who behaved in a certain way but just the domain. Not an email address but just Microsoft.com and so on.

That said, ad networks and social media apps generate rich demand intelligence. Most ad networks have sophisticated onboard tools that enable the dissection of their ad data.

Most social media apps tell you much more than ad networks because ad interactions in these communities are related to the individual profiles of those engaging with the ads. The catch, though, is almost none of them – certainly not LinkedIn and Facebook – expose that personal information. While they can tell you how much activity occurred in an industry, or at companies of a specific size, or in certain job levels, they will not tell you who specifically drove that behavior.

Capturing Early Demand Data

Once identified, early demand behavior must be consistently gathered and stored for subsequent leveraging of this data efficiently later.

Here, again, be it a marketing automation tool, an ESP, an ad network, or a social media community, you generally have onboard capabilities to capture and store this information. Most enterprises, ultimately, prefer to bring contact data into their CRM for maximum analysis.

Page impressions and ad views are useful for studying the general audience’s behavior but do not reveal individual behavior at the contact level. This is primarily true of email opens too. Again unidentifiable clicks are not useful when generated from ads, if without cookies or past progressive profiling.

The paradox is that most marketing automation tools require opted-in contacts only to be used. So, even if you use email and know in advance whose behavior you’re trying to drive unless they click with consent, it’s preferred that you not import them.

💡 TIP: When using email to drive behavior, you only need a click to identify the individual behind it. However, to meet the opt-in requirement, the individual must provide consent. It’s important to remember that a click with a disclaimer (accompanied by a check box or something like it) accomplishes that with a minimum of friction.

Leveraging Early Demand Data

Our efforts to run campaigns and then identify and capture signals are mostly for naught if the resulting data can’t be leveraged. In my opinion, leveraging early demand data falls into two broad categories:

  • Audience-level behavior data, and
  • Individual behavior data

By audience-level behavior data, I mean the aggregated ad view and page impressions that reveal how the audience, overall, engaged and specific segments such as those I mentioned earlier. While more than anecdotal, most of this data remains within the channel it was created and is available by query or report. In other words, audience-level behavior data is generally not accommodated in CRMs and marketing automation, as you would need to attribute said behavior to an individual contact record.

The early demand data that is most leverageable for business outcomes is that of individual contacts. Said demand data is most easily captured using the outbound email channel, but any channel that enables you to identify the individual is valuable. That means short of a form completion which essentially means identifiable clicks.

Why? Because you have to know who they specifically are to fully leverage. When you can tie early demand behavior to a contact record, you can instantly activate it through the multiple channels that a full contact record contains – email address, phone number, physical address, and maybe even a social media profile URL.

If you enable a click with consent business rule for your digital demand gen efforts, you can incorporate click-driven behavior into your marketing automation or CRM. Then the simple adjustment and then application of lead scoring to these early engagers enables you to apply nurture to their activity and drive them further into their buying cycle. All without a form completion.

Of course, if you’ve invested in an ABM tool or are simply having intent data appended to your customer/prospect domains, you’re applying an example of early demand to your demand gen efforts. While it’s aggregated, third-party data, it can still power campaigns that are seeking to foster and capture individual engagement at those domains.

In Closing

For years, I’ve argued for the value and use of early demand data. Identifying, capturing, and leveraging early demand data is crucial for gaining a competitive advantage when considering how many marketers wait until a form is completed. In the days of buying groups, self-education, and privacy concerns, eliminating the form is, in my opinion, a necessity and is in keeping with content marketing best practices. With the ratio of clicks to form completions being 2x+, leveraging early demand data can power a much greater demand gen scale.

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