Creating the Foundations for Using Intent Data

Creating the Foundations for Using Intent Data

Table of Contents

From a x6 Marketo Champion

They say variety is the spice of life. If that’s the case, the Marketing Ops industry is certainly a flavorful one. The data space holds a diverse multitude of companies, strategies, and tools—so it makes sense that best practices aren’t “one size fits all.” Just ask Jessica Kao, Senior Director of Demand Operations at F5 and 6x Marketo Champion.

We recently had the pleasure of hosting Jessica on our Data Heroes podcast. In this post, we’ll recap some of her advice on incorporating intent data into your company’s go-to-market strategy. Spoiler: One size does not fit all.

Jessica Kao

Meet Jessica Kao. A 6x Marketo Champion, Jessica works in Marketing Operations and Analytics. She is one of the primary thought leaders in the Marketo/MarTech space with 10+ years of experience. She was named Marketo Revvie: Champion of the Year in 2018.

Jessica studied at Stanford University, completing her PhD in cancer biology.

From Science to Marketing Ops: An A Priori Approach

The potential of data is endless. For Jessica’s PhD she broke down data to understand the signals of breast cancer in women. Using data, she was able to predict and determine the signals of cancer development.

How? Instead of focusing on individual data pieces, Jessica focused her efforts to see where the data was clustering to avoid bias. Jessica calls this the “a priori approach,” going in without assumptions, allowing the data to tell a story.

"Instead of asking; is it this piece? Or is it this piece? Let me look and see where the data is clustering, where I'm seeing a pattern and then I go there and then I dig deeper into that. 'Huh, that cluster looks interesting. Can I tell a story about these pieces?'"

- Jessica Kao

Jessica claims this approach can be directly applied to the B2B world.

"Marketers really have to start thinking like scientists, because we need to understand how to use all of that data."

- Jessica Kao

Intent Data

"Intent is like a New Toy"

- Jessica Kao

6x Marketo Champion

Intent Data is Hot and Getting Hotter

Intent Data is just taking off; not many marketers have a wide breadth of experience with it. Luckily, we’ve been able to speak with experienced consultants like Jessica Kao who have been to able try (and fail) to incorporate intent data into clients’ go-to-market strategies.

"Intent is a really hot topic and I see a lot of people getting on the bandwagon—rightfully so—but, it’s like a new toy that we haven’t had a lot of time to play with it. Luckily, as a consultant, I’ve had a couple of years’ worth of implementations at multiple companies to learn from and have been able to develop some best practice frameworks."

- Jessica Kao

What Is Intent Data Again?

Intent refers to data signals and points that can be used to empower data-driven marketers to determine when a prospect is likely or ready to buy based on their online behavior. While technically, any type of behavioral data is “intent” data (first or third-party), the hype we hear about is really on the topic of third-party intent data.

Third-Party Intent Data

Third-party intent data is information (typically collected via cookies at the IP level) that grants insight into a company’s behavior across other websites. This entails a visitor’s IP address being mapped to a particular company and occurs at the account level.

Many studies claim that 80% of the relevant user engagement is actually not with your company’s websites or properties.

This data is can be obtained from third-party vendors like Bombora, Zoominfo, and online review sites (G2 Crowd, Capterra)—all of which are accessible via RingLead’s DataExchange.

"Remember that intent data is collected on a company level."

- Jessica Kao

Why Third-Party Intent?

Third-party intent data helps you identify your potential customers before they have raised their hands or landed on your website so you can proactively coordinate sales and marketing activities around your target accounts, getting a jump on competitors.

There are various uses for intent data, but it depends on your business type and goals. Generally, it allows you to focus on specific accounts or prospects and, based on their behavior, determine relevant messaging.

Locating engaged prospects allows you to target them several times over without the risk of them becoming disengaged.

Companies effectively employing actionable intent can expect to see an increase in:

  • ICP Accuracy: By actively finding the targets who are most likely to buy.
  • Campaign Effectiveness: By using insights to drive informed conversations with relevant messaging at the right time.
  • Team Efficiency: Using interactions to capture intent reduces time spent sourcing and chasing new leads.
  • Positive Customer Experiences: Through personalization and relevant messaging you’re likely to create a long-term and loyal customer base. A better understanding of your ICP lets you know how to engage with relevant, targeted messaging.
  • Increased Revenue: Through an increase in sales and marketing productivity and accuracy.

How to Use Intent Data

No One Practice Fits All

Before diving into strategies, it’s important to understand that “no one size fits all” when it comes to intent data. Every business captures intent differently and applies it in different use cases. That said, there are certain thought processes and frameworks that companies should implement to create a foundation for intent data strategies.

"Every situation is going to be different. The people, personalities, skill sets, politics, everything is going to be different. There is no by step-by-step best practice; however, after participating in over 40 different intent data implementations, I've developed a thought process and framework that can set companies up for intent success."

- Jessica Kao

Incorporate Intent Data into Your Existing Account Prioritization Strategies

Successful companies create a comprehensive, buyer-centered journey by integrating intent with other first and third-party data sources to indicate an account’s total propensity to buy based on your ideal account profile.

After setting up scoring models that incorporate data point clusters, we can create triggers to activate sales and marketing activities and ensure the highest priority accounts showing the most propensity to buy are followed up with as quickly as possible.

"All of this data from Marketo, Salesforce, Tableau, and multiple third party vendors—it has to be aggregated in one place where we can write the logic rules for scoring."

- Jessica Kao

Intent data is time-dependent, so it is critical for sales to follow up quickly when an account is showing readiness to buy.

Creating Cluster Scores

As Jessica suggests, successful companies create an intelligent scoring system by weighting different data points into clusters (and sub-clusters) to yield score outputs like Behavioral Scores, Demographic Scores, Technographic Scores, or Intent Scores (i.e. an Intent Score for a specific product or cluster of keywords).

Jessica also suggests that to be used effectively, these clusters should be combined into an accumulative score.

"We have a technographic score, a demographic score, a behavioral score, and multiple intent clusters—what now? We can't just shove 15 scores at a sales or marketing person"

- Jessica Kao

In other words, companies should combine these demographic, first-party behavioral, and intent scores into a single score output so triggers can be created from a dynamic output that is always adjusting based on all factors that yield an account’s total propensity to buy.

An accumulative score makes it easier for operations teams to create triggers when that score value reaches a certain threshold. An accumulative score also allows teams to use an all-encompassing data point to visualize where the highest priority accounts fall from a geographic or demographic perspective, and prioritize them from there.

How Do I Execute Scoring?

In the next section we will dive into RingLead’s score modules, briefly showing how we incorporate the above framework internally by aggregating our first and third-party data sources to create individual score components, then aggregating those components into an accumulative multidimensional score model.

Step 1: Establish a Demographic Score that fits your Ideal Customer Profile

Based on Ideal Account Profile characteristics, create a scoring model that weighs different static Account attributes and rolls them into an overall Account Fit Score. Some data points used may include:

  • Firmographic points, i.e. company size
  • Technographic points, i.e. tools used
  • Geographic points, i.e. region

Creating the Foundations for Using Intent Data

Step 2: Create a Behavior Score Based on Identified First-Party Account Engagement

Behavioral Scoring (Engagement)

  • Refers to online behavior that indicates buyer interest.
  • Examples include content downloads and page visits.
  • Create an account behavioral score that incorporates the behavioral scores of all records related to the account. Prioritize high-value activity for the best results.
  • Keep in mind that behavioral scores decay over time.

Matching leads to accounts is a critical component to outputting an overall account behavioral score.

Creating the Foundations for Using Intent Data

Step 3: Make Meaning Out of Intent Data

"Then you take your clusters and then you start sub clustering there is generic terms and more like competitor terms, brand terms, and then you weigh the clusters a little differently and then you can combine it. And then you can start doing things like outputting a score based on weighted clusters."

- Jessica Kao

Create Intent Cluster Scores

  • Customize the weight of specific keywords in your intent cluster models.
  • And/or calculate an aggregate Intent Score/Value.

Creating the Foundations for Using Intent Data

Step 4: Continue Clustering with a Multidimensional Model

In this step, aggregate the Demographic Score and Behavioral Score into a Multidimensional Score that reflects both Fit and Engagement. This enables account prioritization based not just on a single attribute, but rather on a multitude of key facets.

Creating the Foundations for Using Intent Data

As you can see, successfully incorporating intent data into your company’s go-to-market strategy requires a sturdy framework built on scoring. Lean on demographic, first-party behavioral, and intent scores to generate informative clusters that can help you target your ICP at the right time. In our next blog post on intent data and scoring, we’ll cover in more detail how to aggregate and orchestrate data points, score them, and route them back to your CRM. Stay tuned!

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Creating the Foundations for Using Intent Data

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