Tl;dr - Many companies are taking sophisticated approaches to using third-party intent data for demand generation and ABM - OR using first-party data for customer success. Few are working at the intersection of these approaches, and there's huge opportunity in this space.
Too often intent data is wasted on demand generation.
Don't get me wrong - that's a common, fruitful and important use case.
But it's not the end of the important intent data use cases that any company can implement.
Churn reduction (as companies tighten marketing expenses during the pandemic) and up/cross sell (as companies seek to deepen relationships and generate more revenue with loyal customers during the pandemic) are two examples of customer lifecycle intent data that are easy and important to consider.
Critical insights lie in a company's first-party data, as outlined in these Interesting observations from Zuora CEO and author of Subscribed, Tien Tzuo's (@tientzuo) recent post on Twilio's acquisition of CDP company Segment.
"Here are some questions that subscription revenue analytics can answer:
Some of this is qualitative, and some quantitative.
It's all outside the normal range of first party marketing data that most companies think of in the context of intent. That's typically limited to page visits, form fills, email opens and clicks and related marketing metric related data.
But what about these metrics that Tzuo suggests? This is another form of first party data - like in app usage - that adds an important element to a company's full data stack.
There are four reasons most companies miss this.
Let's unpack these.
First, although intent data best practice calls for executive sponsorship and involvement across the enterprise, that rarely happens. Marketing (or sometimes Sales) takes the lead on the intent data initiative. It's hard enough to build the required collaboration between marketing and sales around data (you have to look at it, analyze it, parse it and enable it differently for different use cases), but rarely is the success team even aware of it, much less trained on how to interpret it and act on it. That's reality.
Second, although the customer experience is a continuum, the internal structure isn't. That means that there's little internal effort to coordinate efforts beyond the new transaction hand-off via onboarding.
Third, CRM is focused on sales pipeline. Marketing automation is generally contact centric, and the procurement contacts are often different than the renewal decision maker, much less day-to-day users. Pulling data from multiple sources (CRM, marketing automation, in app usage, service desk, chat, website visitor identification, 2nd party intent data like G2 and TechTarget, 3rd party intent data, enrichment sources, and technographics sources), unifying it, and then analyzing and enabling at scale often takes customer data platform (CDP) capability.
Fourth, if the data isn't granular enough, or contact level, then it realistically won't benefit success teams much. Further, using it for multiple use cases, across the enterprise, requires iterative onboarding, training, campaign development and enablement. Sadly, most data companies are built on a model of maximum subscription sales. Sell it and move on, not sticking around to help one department after another gradually build on it.
Therefore, absent an explicit and deliberate effort to help teams understand data availability, potential applications are normally missed.
But...there's another barrier. A missing ingredient. The integration of collection tactics and methodologies.
A. If a customer has broad adoption and frequent usage, does that mean they won't churn?
Of course not.
B. If they have negligible usage and little engagement with your training resources, is that an indicator of likely churn?
Perhaps.
But now let's look at each of these in the context of Contextual™ Technographic Data.
Let's imagine three different versions of scenario A where:
Obviously, you'd react differently in each case. Early awareness and proactive engagement, even absent indications of churn from usage data, might well save the account.
Similarly we can consider versions of scenario B as well:
There are similar up/cross sell scenarios as well. Common examples include various solutions for complimentary cyber security products, or related modules in suites of products. For instance if you look at the independent software vs. Salesforce campaign of earlier this year, you'll see some clear examples.
The point is that you have very different understandings of the dynamics at play based on the combination of first party data, third party intent data and contextual technographics.
That's all good news.
There's huge power in third party purchase intent data and loads of customer success insights which are likely available, but as yet untapped in your data.
The bad news is that, as noted above, your SalesTech and MarTech stacks may be unable to handle the work required. There are several required steps:
Bottom line is that the full range of intent data benefits for customer success, sales, and marketing is only unlocked with a systemic approach and largely automated customer data orchestration.
Can you extract value with a la carte approaches? For sure. It's important to be realistic, though, in your expectations.