The Risk of Overselling Intent Data

Aug 21, 2019 | Author Ed Marsh

This article originally appeared on the Intent Data publication on Medium.

Intent Data is a wonderful business tool. It supports various creative revenue related tactics.

Contact level™ intent data is an incredible business tool which enables a fascinating range of strategies and tactics. One of the most compelling is the opportunity to really foster alignment across the marketing, sales and success departments.

Both are tools. Both must be sold responsibly and used expertly.

Sadly, intent data is often oversold. Its capabilities are sometimes overstated and reasonable expectations inflated. And often, as a result, frustration and friction between marketing and sales are exacerbated.

The concept of IQOs (intent qualified opportunities) — a term recently coined by an independent research house cum 3rd party intent data provider is an example.

What’s an IQO? What’s an opportunity?

Every sales organization has some qualification criteria to establish the difference between an inquiry and an “opportunity” or “deal.”

They vary in rigor but almost always include some opportunity specific criteria (e.g. BANT) in addition to basic firmographic information.

Intent data can’t confirm budget. It might be able to infer timing; infer authority with contact level data (but certainly not account level); and possibly infer need, based on velocity and range of intent signals. But sales managers are rightly critical of salespeople who draw lazy inferences about qualification criteria. So inferring isn’t adequate.

An opportunity is when there’s a real project underway.

Intent data can’t discern that. To assert otherwise is to irresponsibly oversell it.

An IQO is fiction and will inflame the tensions between demand gen and revenue gen folks.


What’s a lead? An IDQL?

While most organizations have some definition of what constitutes an opportunity, fewer are careful and consistent in their use of “lead.”

Just ask marketing and sales about how many “sales qualified leads” are passed along and you’ll get two very different answers.

Let’s agree that a lead is some precursor to an opportunity. The rate at which they become opportunities, and customers — both in time (sales cycle) and closed/won (close rate) will vary. Theoretically, we can stratify leads into layers which reflect the amount we know and their likelihood to become opportunities and customers.

And here intent data, particularly contact level intent data, can help.

An IDQL (intent data qualified lead) is an entirely appropriate, realistic and helpful categorization with very practical applications.

(But first a quick note. Why not IQL for intent qualified lead? Simply because most use IQL as an early stage “information qualified lead.” Why not DQL for data qualified lead? Because every lead (IQL, MQL, SQL and now IDQL) better be data qualified. The actions that are observed (1st party data of page visits, form fills, downloads, email opens & clicks, etc.) are the basis for the scoring that determines the qualification level. Therefore the concept of a data qualified lead is pointless.)

An IDQL is a person, who based on contact level 3rd-party intent data is engaged with content, competitors, and events in ways that indicate they’re likely in the market to buy products or services.

Often companies establish criteria (e.g. the number of individual engagements observed, a combination of both general information and competitor engagements, their role in an ABM target account, or even multiple engaged contacts from the same company as Kerry Cunningham emphasizes is an important differentiating factor in evaluating leads.)

How to work with IDQLs

An intent data qualified lead is inherently different than an intent data prospect. While the latter might be ignored, observed for future engagement, nurtured with paid social ads as part of a targeted custom audience or some other gentle touch, a qualified lead will get more specific marketing and sales attention.

Depending on the intent data use case, stage in buying journey and other factors, they might be nurtured by email and content syndication, a BDR could reach out, or an AE could add them to a formal ABM playbook. Sales reps might add to their understanding of the complex buying team, and success teams can preempt churn.

Here’s the bottom line. Unlike the traditional lead segmentation categories which rely on 1st party intent observations, IDQLs can be identified using 3rd party data. That adds an enormous potential volume — not of random individuals, but of known and named prospects who have demonstrated intent.

As with MQL and SQL each company can define them according to their own process. And noting the 1st party engagements of IDQLs can provide a rich source of alerts to salespeople working traditional leads.

Are you ready to make an IDQL part of your marketing operations workflows and lexicon?


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