Customer Success - Key First & Third Party Intent Data Intersections You May Be Missing

Oct 27, 2020 | Author Ed Marsh

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.

Customer Lifecycle™ Intent Data - Overlooked Insights

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:

  • If my customers are putting in a lot of change orders, does that mean they’re dissatisfied with my service? If they’re not touching their subscriptions at all, does that mean everything is smooth sailing?
  • Are my customers happier with a usage-only model, a tiered model, or a combination of the two? If it is a combination, what does that ratio look like?
  • Are my customers always happier with more payment methods? Is there such a thing as too many?
  • Are my customers fine with subscriptions that renew on their own, or are they asking for more notifications and touchpoints?
  • If my customer is in danger of churning, then when does a downgrade offer make sense? What will it take to help them re-evaluate my service?"

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.

Barriers to Full Synthesis of First and Third Party Intent Data

There are four reasons most companies miss this.

  1. Third party data is owned and siloed in the marketing/demand gen group, while success KPIs and related data points are owned and siloed in the customer success group.
  2. There's a pervasive mindset that these are different functions - they are of course, but from the perspective of the customer lifecycle they're contiguous.
  3. The typical Martech stack, for all its sophisticated software solutions, is poorly suited to synthesize large volumes of related but dissimilar data.
  4. Most data is sold by companies with the same dissonance in #2 above. Reps want to close deals and move on. Success wants to renew. Nobody owns the hard work of incrementally helping a company socialize data capabilities internally, develop appropriate complimentary campaigns, onboard different departments, etc. Further, lots of data is really only designed for customer acquisition or is embedded in another application (e.g. account-based marketing/ABM software) and not applicable or available to other departments.

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.

Technographics + Third Party Intent Data + First Party Data = Success Superpower

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?


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:

  • there are no technographic signals (e.g. competitor tags installed on the website) and no third party indication of anyone from the company engaging with competitors or researching alternative solutions
  • you observe competitor's tags installed OR see various people from your customer team engaging with competitors or taking action online that indicates they're researching alternative middle or bottom of the funnel key terms/solutions
  • same as above but AND

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:

  • where you see troubling indicators in your first-party data, but no indication of competitor research in third party data
  • where low adoption is paired with online action indicating research around other (not directly competitive) solutions

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.

The Importance of Orchestration

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:

  1. Unify the data - Collect and integrate all the sources
  2. Analyze and segment at scale - dynamically observe key combinations of signals (e.g. a certain threshold of third party activity with key technographics signals and any first party data) on a rolling basis (maybe three weeks) over time.
  3. Orchestrate actions - when you work a full data stack at scale, you'll often find that the volume of signal, and the appropriate marketing, sales and success movements are potentially overwhelming. That leaves you with two choices - try to triage and ignore some (what a waste!) or figure out how to automate lots of actions with a scoring system. That's what an orchestration layer can do - to automatically send emails, trigger cadences, schedule calls, run custom audience ads, create custom chatbot experiences, etc.
  4. Enablement at scale - It's not enough to just trigger a call, email or other outreach for a customer service (or sales) rep. You also have to give them context and enough background on the confluence of circumstances and inferences you've drawn, that they can follow up with contextually appropriate actions and messages.

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.


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