Intent Data Prophesies Fulfilled: Predictions on Intent Data From 2014

I recently stumbled across a 2014 video on YouTube and thought it was a good opportunity to reflect on the progress and advances made in the operationalization of buyer intent data for sales and marketing operations. Below are my thoughts on the video “Buyer Intent and the Data Revolution.”

“What is a signal?”

In the video, Trip Kucera, then VP of Client Strategy at Harte Hanks Data & Content Solutions, highlights the fact that in-market buyers give off a lot of signals as they go through the buying process. But what exactly constitutes a signal? More importantly, he asks,“How do I interpret these interactions as a hand raise? How do I interpret this as, ‘Put more resources towards it’?”

The smartest human minds cannot compute the massive volumes of buyer intent signals that are captured by algorithms today, and for that reason, my response to Kucera’s earnest questions is: Analytics, and predictive analytics, too.

These days (five years after this video was uploaded) marketers know the power of analytics is matched only by its results: performance boosts, more efficient and improved decision making, increased numbers of closed deals, improved quality of leads, insight into customer buying journeys, higher likelihood of converting qualified prospects into clients — the list goes on.

Marketers can certainly assess buyer behavior and intent signals, but we rely on predictive analytics to tell us when the behavior captured online constitutes that “hand raise” above the normal noise of online activity. And because machine learning powers contemporary predictive capabilities, allowing these solutions to predict future behavior (and recommend next best actions) based on past behavior, these capabilities can tell us when to “put more resources towards” the intent signals it captures.

Target messages based on intent signals

As mentioned above, predictive analytics can improve a marketing organization’s performance, spanning account-based marketing programs, targeted advertising, demand generation, content marketing, and more.

Back in 2014, marketers were hip to the power of predictive. In this video, Julie Zadow, then-VP of Marketing at Globoforce, said the ability to “craft a targeted message for the right buyer at the right time” is “a crucial competitive differentiator, and it starts with data.”

I will not dispute that.

Today’s predictive analytics have so mastered buyer intent data that they can tell us which kind of resource to push at a prospect emitting signals of buying intent, and can even display targeted ads to identified personas of qualified prospects.

Using machine learning, predictive analytics parse volumes of buyer journey data, and can therefore predict where in the buying journey a targeted prospect is. Marketing and sales organizations can choose to act upon these insights with manually personalized engagement, or, as is a popular option in 2019, let their automated marketing programs act on those insights.

In either case, the “next steps” taken on a buyer intent signal have been informed by an intelligent analytics engine and are therefore a smarter and more efficient use of resources.

Beyond Mere Demographics

“The future of interpreting intent is prediction,” according to a transitional frame in the video.

Adding context, Zadow said, “There’s an opportunity to not just leverage what we know about customer data from a demographic perspective, but what we know about customer data from a predictive perspective.”

She was right five years ago, and she is right in 2019. Buyer intent data enables the most accurate way to predict which customers are actually in-market to buy.

Specifically, predictive analytics use machine learning of buyer journeys to identify and translate buying intent signals to predict purchase intent, and can even project which opportunities will likely result in wins, and which will probably end in losses, because there are countless volumes of past buyer intent data to analyze and learn from.

The future referred to in this 2014 video has arrived, and knowing there’s value in intent data is but a piece of the brilliant puzzle that’s being modeled by predictive analytics.

Here’s the video in its entirety (in case you don’t want to visit YouTube):

[Full disclosure: Aberdeen was formerly a Harte Hanks company and Trip a former Aberdeen employee.]

What do you think? Let me know in the comments section.