7 Tips for Weaving Intent Data Across the Customer Lifecycle and Enterprise

Sep 17, 2019 | Author Ed Marsh

This article originally appeared on the OpenView blog.

tl/dr: Intent data is generating lots of buzz. Demand generation marketers are increasingly relying on data to identify active buyers to quickly nurture into sales qualified leads. This helps to reduce sales cycles and increase close rates. But there are frequently overlooked enterprise-wide opportunities to use this powerful data. Fully operationalizing intent data requires strategic vision, explicit goals and deliberate execution.

Understanding Intent Data

Talk to someone in SaaS demand generation and they’re likely either using intent data or considering it. Growth marketers may see intent data as the Rosetta Stone of new logo sales. Knowing which 3% of the potential market is active at any given time allows marketing and sales to focus resources on likely, active buyers.

Third-party intent data has received the most recent attention. It reports on actions that indicate research and purchase intent, taken by individuals, around the web. It complements zero-party data (what we own and control about ourselves), first-party data (what we observe others doing across our own digital footprint) and second-party data (leads from another’s first-party data – often publishers).

The third-party data market is chaotic, however. Terminology and definitions aren’t harmonized, and a profusion of overlapping products has confused the space. Sales databases, website deanonymizers, data enrichment and predictive analytics are all sold as intent related. Further, differing methods of intent data collection deliver different results, capabilities, accuracy and potential exposure.

A Demand Gen Foothold and Fizzle

The most common application for intent data is in demand generation. So that’s where it’s often first implemented. It’s also where it may first disappoint and therefore stop.

Excitement inevitably accompanies project kick-offs. Data starts to roll in and marketing teams get moving. They customize outbound sales sequences and marketing messaging. But gradually excitement wanes for several reasons.

First, sales pushes back on intent data leads because they don’t identify the actual contact. You can’t sell to a middle-market or enterprise customer without knowing who, among hundreds or thousands of employees, is the active buyer(s).

Second, marketing begins to question the value when they struggle to segment activity. Without contextual detail, often obscured by opaque “topics,” there’s likely no way to gauge the stage in buying journey, or infer the problem to be solved, outcome sought or even competitor engagement. Further, without specific contacts, one can’t evaluate seniority and job function which provide important insight into the context of certain actions and interests.

Third, although companies always start out eager for maximum data, it can quickly become overwhelming. There are practical concerns like contact count/cost in marketing automation platforms, and implementation concerns around dynamically prioritizing leads.

Increasingly intent data is delivered as an integration plugin to account-based marketing (ABM) applications. That inherently restricts the opportunities for activation in other use cases.

The bottom line is often limited implementation. Salespeople are skeptical, activation is incomplete and the data isn’t fully operationalized.

Missed Opportunities

As a result, common use cases across the customer lifecycle are completely or partially missed.

Intent data can help to power a number of marketing, sales and success functions. These include:



  • Target account sales
  • Sales intelligence for account and opportunity management


  • Churn reduction
  • Upsell/cross-sell

To activate multiple use cases, companies have to share and collaborate with the data. That typically means providing multiple access points and analysis views. It also requires robust playbooks for tactical activation (each use case) and strategic orchestration (across the enterprise in support of key objectives).

Data limitations (limited imports, missing aggregation, limited ETL capability, etc.) can be an initial barrier. Further, most data is either sold as an integration with inherently little flexibility or extensibility, or with little framework and coaching for best practice implementation.

Collectively these contribute to the opportunity cost of incompletely operationalized intent data. However, it’s hardly inevitable.

7 Tips

For most companies, fully operationalizing the data potential is within reach. It just won’t happen automatically as a result of subscribing to data. Companies should resist long-term subscriptions, instead opting to run pilots for proof of concept in their environment, and evaluating not only the quality/impact of data source but their ability to successfully operationalize it as well. Companies need to recognize that fully using the data will require adaptations to established processes, as well as creating new processes. For super lean teams, that can be daunting.

When it comes to successful activation and orchestration, we’ve observed seven common best practices across companies that most effectively operationalize intent data.

  1. Find a C-suite sponsor – there are numerous use cases across the enterprise. Easy ones are missed when they cross org chart divisions. A C-suite sponsor that socializes and strategically views the potential of data will help to ensure it’s fully adopted.
  2. Activate data for multiple use cases – while data is typically bought for a single use case, often demand generation, it can be simultaneously or serially activated for ABM, remarketing/retargeting of paid social and display ads, competitive intelligence, event marketing, sales intelligence, target account sales, churn reduction and upsell/cross-sell. Too often the value is locked up in one application.
  3. Explicitly share via reports and alerts – we all suffer from data overload. Even amazing data will be lost in the flow if it’s not explicitly highlighted to the right people. Ideally, alerts, reports and even automated actions are used to drive awareness and prompt contextually appropriate activity. Notifying sales of target account activity with suggested outbound messaging, and prompting success teams with upsell suggestions are examples.
  4. Treat the data as a valuable, but raw asset – volumes of contact level intent data can obscure important signals. Intersections of third-party data with a company’s own first-party data is a simple example that’s often missed. Fully realizing the value of data requires thoughtfully designed ETL data practices.
  5. Proactively build alignment and collaboration around the data – an overlooked but significant benefit of data is the opportunity to align departmental efforts. A marketing department that subscribes to data to support event marketing can create powerful collaboration by proactively sharing insights and activation advice with colleagues in sales and success. The reality, though, is that enterprise-wide orchestration rarely occurs without active c-suite sponsorship.
  6. Develop additional use cases – the standard list of marketing, sales and success use cases is easy to envision. But the power doesn’t stop there. Corporate development might use data to identify potential suitors and receptive acquisition targets. Operations might keep an eye on key suppliers who are courting competitors, and PR can use data to discover media trends and cultivate relationships based on current activity vs. traditional beat. With c-suite engagement, you’ll certainly find others.
  7. Assign resources to overcome operational barriers – despite the buzz around intent data and the sexy use cases, it takes work. Turning data into qualified sales leads requires a combination of data management, paid ad nurturing and active outbound sales. It doesn’t happen on its own. Successful activation requires creativity and resources.

Activate and Orchestrate Intentionally

Companies can implement intent data in various ways. It can be applied to specific, limited use cases or broadly across the enterprise.

There’s no right answer, but it’s important to recognize that it won’t happen casually.

The most practical approach is to bring it in for a specific requirement, with executive engagement and awareness of opportunities for expansion once fundamental processes are in place.


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