Mitch Morando
Dec 31, 2016 · 5 min read

Attn: SaaS Revenue Leaders

Many of the fastest growing SaaS teams over the last 10 yrs have leveraged the concept of the PQL (Product Qualified Lead) but they rarely talk openly about it as it’s been a material competitive advantage for their Sales teams.

PQLs convert 10X+ more revenue than MQLs

PQL defined: Conceptually it’s based first on monitoring the user’s behavior (actions, clicks, etc) inside your SaaS product = “What (happened)”. Actions that match a pattern indicating readiness to buy.

Then you apply the “Who (is involved)” filter to see if they fit your Ideal Customer Profile

PQLs = What (indicative behavior) + Who (is involved)

These leads can then be prioritized immediately to the top of your Sales team’s queue because they Act and Look like customers you have previously converted. With each new conversion you have more data to reverse engineer the most optimal buying process.

Step 1: What (indicative behavior)

Monitor the prospect’s user behavior inside of your product either as a Trial, free version or self-service version (ie. paying small amounts via credit card $5-$500/mo).

Every product has particular Key Actions or usage patterns that correlate to being ready to convert. These typically are non-obvious, yet simple.

Conversion can either be:

1) Self-Service (no sales touch) manner via credit card ($5-$500/mo)

2) OR Enterprise level ($500/mo to $250k/yr) which requires an inside sales professional to engage and educate them through the sales process.

Step 2: Who (is involved)

It’s important to look at not only the individual but groups of users from the same company. In large 500+ employee orgs you’re looking for groups indicating a business unit.

Based on the user’s email only you should be able to determine both:

  • Demographic data: job title, Employer, # yrs of experience, location, skills,
  • Firmographic data: Company, # of employees, location, industry

If you are constantly monitoring the What and can accurately identify the Who, then you have the basis for determining when a user / account becomes a PQL in real-time. Now your Sales team is equipped with the proper timing, prioritization and context to convert to revenue quickly.


Before you get into fancy “machine learning” and complex modeling, an effective place to start is simply investigate who is logging into your product (let’s define this as “Engagement”). The only tool you’ll need is Excel.

PQL 101 level:

Step 1: Data Collection — To start you’ll at least need visibility when users are logging into your product. Keep the definition of “Engagement” simple to start by counting the cumulative # of times they have logged into your product.

Ask your product / engineering team if they can give you a weekly report of all users and their cumulative “Engagement” (# of logins) in a .csv. Many Product managers have this data readily available via various analytics tools they already use.

Or if your team can add one line of code, use a simple open source option to begin collecting Login data via Analytics.js and the IDENTIFY method

Step 2: via Excel, sort the users who have cumulatively logged into your product the most. Then take their email address and use any of the numerous data enrichment services available.

Note: this will only work for business domains, not @gmail (more on that later)

Step 3: Sort the Leads that have the highest total engagement. Then filter on the ones that have the proper demo/firmographic data (ie # of employees, location, etc)

A simple v1 PQL model is to apply these filters:

User logged in more than X times in total (ie 10)

User’s Company has > Y employees (ie 50 employees)

Company is located in North America

That’s it! If you can do this simple exercise even once a week manually for your sales team you’ll see an immediate impact in revenue and your sales team will want more. Tag these as Lead Source = PQL and see how they perform vs MQLs with regards to revenue.

You’ve just scratched the surface. Imagine what’s possible if you begin to:

  1. Use more usage / behavior data: ie extending to include more product usage data or other systems that touch your users — helpdesk, chat, marketing automation, etc
  2. Leverage Big Math brains: Gain access to a data science team that has specific domain expertise in how to build and evolve a predictive PQL model that is NOT a black box
  3. Access Unique Data: Leverage more robust demo/firmographic data even for those users who use personal (@gmail, @hotmail) emails
  4. Use a Prebuilt Machine: Leverage infrastructure specifically built to deliver PQLs in real-time, ingest endless sources of data and build new predictive PQL models daily (we’ve invested 10+ man yrs thus far in our architecture).
  5. Access Consultative Domain Expertise: We’ve been practitioners of PQLs 10+ yrs and you benefit for our collective “best practices” in how to operationalize this into your Salesforce workflow
  6. Measure Direct Revenue Impact Immediately: PQL → Revenue (do PQLs make an ongoing impact to revenue). We’ll get you operational and seeing Revenue impact in 1–2 wks regardless of your sales cycle.

This is why Whalr’s team + platform exist

Product Qualified Lead (PQL) Engine

We’re happy to teach the power of the PQL. Just ping me @

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Morando Method

Moneyball for Sales : PQL “Product Qualified Leads”. Using product usage behavior to identify the priority users that are ready for the sales conversation.

Mitch Morando

Written by

Quant SaaS Sales leader w/ real war wounds. I like math + reverse engineering sales. Founder @ Whalr : “Moneyball for Sales”

Morando Method

Moneyball for Sales : PQL “Product Qualified Leads”. Using product usage behavior to identify the priority users that are ready for the sales conversation.

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