Using signals to identify sales-ready leads (going beyond mqls & pqls)

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As go-to-market operators, it’s our job to create the systems and processes that make our GTM teams effective. One of those fundamental features (and one of the most fun) is building a system to identify a sales-ready lead (or CS-ready, in some cases).

Acronyms can get messy. Leave behind your preconceived notions of MQLs, and PQLs, and SQLs, and LMNOPs. What we want to build are the rules and processes to help our GTM teams know which of our prospects and customers need a human led moment.

Keep reading if you’re thinking about:

  • Building a smarter lead handoff process for top of funnel that goes beyond an MQL score
  • Turning your PLS strategy into a system
  • Leveraging your product data to identify churn early
  • Enabling your sales & cs team with better insights to have meaningful moments

What we’ll cover here:

  1. Source data: the raw data
  2. Signals: collection of raw data to identify a signal

It always starts with ICP definition

For any marketers thinking about top of funnel lead identification, you are going to want to leverage your ICP definition in any of your logic, no matter your use case.

This is typically done with Firmographic Data.

Do not build a lead handoff process, or expansion identification process without first ensuring you have all the data points (ideally normalized) to know whether or not you WANT to do business with them. (see below for examples of firmographic data)

There is nothing that will erode your GTM team’s trust in you like sending over terrible leads. This is especially true in PLG.

Lots of people who sign up for your free trials and tiers will not convert into customers. They may have high usage, but if they are not ICPs, exclude them.

1. Source Data

Let’s give it up for our friends at Forrester. Their research shows that data-driven organizations are 162% more likely to significantly surpass revenue goals than their data-ignoring counterparts.

That’s why we’re all going to invest in collecting and structuring our data so that it is democratized across our organization.

Source data, to us, is just the raw data points that we collect. It comes in many forms:

Firmographic: These are the data points that will helps us understand our persona (and exclude our ICP). We may be collecting these through forms and other lead gen efforts, but more likely through 3rd party tools like ZoomInfo, Apollo, Clearbit and Cognism.

  • Company attributes: Industry, Employee Size, Location/Region
  • Employee attributes: Title, Department, Seniority
  • Company events: Funding/Stage, Acquisition, Public Goal, New Hire, Job Posting
  • Technographic: Tools in their existing tech stack

Online/Marketing engagement: This is typically first party data (you’re tracking it) showing how your prospects and customers are engaging with you. This is primarily marketing engagement, but could be with other teams. This can tell us what the individual is interested in or can infer intent.

  • Website engagement: Viewing pricing pages, viewing specific product pages, downloading content.
  • Event engagement: Both in person and online, webinar attendance, event attendance, exec dinners, conference/booth attendance
  • Hand-raising engagement: This one’s almost too easy 😉 Contact Us and Demo Requests
  • Email engagement: Treating this one differently as this could be part of sales or marketing campaigns (especially if you’re using tools like Outreach & Salesloft). These can be useful though be wary of email opens as intent.
  • Dark funnel: Harder to track in an automated way. Podcast listens, community engagement, social engagement.

Intent: You can think of this category as third party intent. Intent can be measured through marketing engagement or product engagement, as well. Think: G2, 6Sense, Propensity, ZoomInfo, and likely some industry specific sources.

  • Product Comparison: Are your prospects comparing you to anyone specific? Even scarier, are your existing customers?
  • Category Search: Key term searches, reviews.
  • Complementary Product Search: Searching for features or problems you solve. This is especially important for companies with large product suites where customers may not be aware of all features.

Usage/product: Saved the best for last. Understanding how your users use your product is very powerful. These will be very custom to your features.

  • Frequency & Volume: Raw logins, daily active users, time spent, recency, invites sent
  • Threshold: Usage thresholds (Have they exceeded their current tier or contracted amount?)

2. Signals

This is the fun part – we get to turn all of the source data above into signals.

What’s the difference between data/usage & signals? Here’s an example. Usage might be that we have 30 active users. A signal might be that active users has grown by 25% in the last 30 days.

You can also mix and match lots of different data points to come up with signals. And you should be testing these often. And don’t stop at product data.

Here are some examples

Remember, ICP is always in play.

Conversion PlayCHURN PREVEntion playEXPANSION PLAY
Free to Paid PlayNew Champion PlayEnterprise Tier Play
Free Trial
Invited 5 + users
Completed 3 tasks
Attended new feature webinar
Champion deactivated2+ users engaged
titles in 2 different departments
Route to SDRRoute to CSMRoute to AM

Up Next:

Once you’ve ironed out your signals, you’ll need to create the system to route them to the right rep. That’s coming up next. Submit the below form to be notified when we post it.

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Upcoming topics include:
PLG/PLS, RevOps Intake Process, and CRM Data Infrastructure. 

For now, read how we did it for Intercom.