Practical framework to finding & monetizing PQLs

A 3-part practical playbook to generate more revenue opportunities from your free users.

Fred Melanson

January 11, 2023
·
8
 min read

Welcome to PLS Deep Dives! This series walks you through how to do Product-Led Sales from a practical standpoint – from defining your first PQL all the way to executing wall-to-wall upsells.

In Part One, we’ll go over PQLs: How to find them and turn them into sales opportunities. 

Why should I read this? 

Simple: PQLs convert more often than other lead types for businesses with free trials or freemium plans. 

  • Managers: This framework explains the fundamentals of a PQL engine and 6 step process on how to set it up. 
  • Reps: This article explains how to leverage your company’s PQLs to prospect and close more business with less effort. 

Not convinced? We recently heard from the following an AE at a $4B SaaS company: “After just 3 days of leveraging PQLs, I sourced a $1M deal!”.

Let’s get to it 👇

What is a Product Qualified Lead? (Hint: Not an MQL)

Before we start, here’s a reminder of what a PQL is: 

PQLs, also known as Product Qualified Leads, are users within your product who are qualified for sales opportunities because they have reached specific usage milestones in the product and fit with your ideal customer profile.

Think of a PQL as a user with high INTENT (product usage and/or buying signals) + FIT (customer profile). 

Note that some companies prefer to find Product Qualified Accounts, as they don’t sell to individuals, but rather to companies, or teams. The framework below can be applied to both.

A PQL is better for your revenue stream than marketing qualified leads since the intent of someone getting self-serve value from your product is clearly higher than a potential customer who has downloaded content or another basic marketing qualifier.

Let’s go through the PQL framework so you can find more opportunities for your sales team 🎯! 

Part 1: Finding PQLs

Pick a conversion event

Do you do something without a goal in mind? 

That’s the equivalent of setting PQLs without a conversion point. 

Conversion points are ways for your business to generate more revenue from people using your product. 

Common conversion points for most PLG businesses are 👇

  • Free-to-paid: Converting free users into any of your paid tiers. 
  • Self-serve to enterprise: Converting those people that have hopped on your paid tier by themselves into an enterprise contract. 
  • Expansion: Converting paid accounts into more lucrative paid accounts (more seats, more usage, more features, etc.).
  • Account consolidation: Merging multiple workspaces or lower tier accounts into one centralized account on a higher tier paid plan. 

Start with a single conversion point 

Focus is important as you develop and iterate on your PQL workflows. Starting with one conversion event ensures that you can properly track your PQL efforts. 

Also, different teams might be working on your various conversion events. As we’ll explain below, the best product-led GTM teams get one PQL workflow working for a single conversion event and then build on from there. 

If you’re new to product-led sales (PLS), you probably have an untapped pool of free users to prospect from. If that’s the case, start with free-to-paid as your conversion event. 

Depending on your PLS maturity and the dynamics of your user base (amount of users, pricing strategy), you can assess where the biggest revenue opportunities lie. 

Examples

  • You have a narrow user base with volume-related pricing tiers (Reverse ETL tools) → Focus on expansion as your first conversion point. 
  • Massive user base with sharing virality (Miro) → Focus on free-to-paid as your first conversion point.
Lead jumping through paywalls

Define an intent threshold

Once you’ve identified your conversion point, you want to find what kind of intent (or behaviors) users need to demonstrate to be worthy of your sales efforts.

Product intent can include both product usage and buying signals. 

Let’s untangle both. 

Product usage 

The most famous one is the “aha moment”. The moment at which a user realizes the value that your product is providing. 

But in general, this one is about setting milestones at which you’re confident that your users have gotten value from your product, and can become even more successful on paid plans.

Self serve revenue vs PQL activated revenue

Here’s a simple formula for setting a product usage milestone: 

Step 1: Make a list of your product’s main actions (tasks created, storage used, integrations, etc). 

Step 2: Make a list of your highest-revenue customers.

Step 3: Go to your product analytics tool (Mixpanel, Amplitude). Filter usage dashboards by those customers you identified and the product’s main actions. (You can ask product folks or data science for help). 

Step 4: Look for the moment on the graph when your best customers upgraded plans (you can get this info from your CRM). 

Step 5: Compare the trends you find between customers and define a “ballpark” threshold. 

Real-world example: Slack’s team has observed that after 9,000 messages sent, their users have a high tendency to convert to paid plans. 

Buying signals 

Buying signals are actions taken by users that demonstrate a willingness to buy.

Tracking buying signals is crucial as you might have users who have not yet reached qualifying product usage thresholds but are still good opportunities for sales because they’re (hypothetically) raising their hand to buy.

Examples

  • Buying new seats
  • Going to your pricing page
  • Trying out premium features
  • Contacted sales
  • Hit plan limits

Note that you should not weigh buying signals equally. Users contacting sales are showing more willingness to buy than users passively looking at your pricing page. 

Buying signals to convert deals

Best practice: track as many buying signals as you can and prioritize sales actions on leads who have shown the strongest intent to purchase. 

How to use product usage and buying signals to step up your sales game

We find this matrix below helpful in crafting a sales strategy that capitalizes on the type of intent (product usage vs buying signals) you can consider acting on.

Graph representing how to act based on usage and buying signals

Let’s break down what you should do based on where your lead is in this matrix: 

Lots of buying signals + high product usage: Focus sales efforts on uncovering what’s blocking your prospect from becoming a paid customer. Engage decision-makers. 

No buying signals + high product usage: Focus sales efforts on pitching the added value of paid tiers. Leverage case studies and build an ROI simulation. 

Lots of buying signals + low product usage: Educate your prospect about how to become successful in the product. Share best practices, templates, etc. 

No buying signals + low product usage: Nurture users through automated campaigns and regular product experiences. Sales teams shouldn’t spend time on these leads. 

Note that intent isn’t intended to be product usage or buying signals. For many high-performing PLG companies, the PQL threshold is a mix of both! 

(Hypothetical) PQL thresholds from notable PLG companies: 

ClickUp logo

Created more than 50 tasks (product usage) and invited 10 users in the last 30 days (buying signal).

Miro logo

Created 10 boards (product usage) and have 20 daily active users (buying signal)

Twilio logo

Made > 100 API calls in the last 30 days (product usage) and bought a phone number (buying signal)

Prioritize PQLs with customer fit (ICP) data 

Deadly mistake: having a salesperson engage a lead with low revenue potential!

You now have your product signals in place. As mentioned, the other half of your PQL is customer fit. 

Not every highly active user has the ability or needs to purchase your product. Product-Led Sales is about talking to the right users at the right companies.

To do this, you need to filter PQLs who reach your usage thresholds with firmographic data like title, company size or industry. 

Example data on the account level: 

  • Company size 
  • Industry 
  • Revenue
  • Funding raised

Example data on the user level: 

  • Title
  • Size of the team they manage
  • Function 

Think of customer fit as putting glasses on that let you see through the haystack to find the needle! 

Before and after layering ICP data on PQLs

What firmographic data should I use? 

Again, if you’re thinking: “That’s all great, but where do I start?”, here’s how you define baselines for fit criteria 👇

Look at your current customer base 

Use the same formula as for usage thresholds! What do your best-paying customers have in common? 

Start there. 

Who’s delighted by your product?

Another trick is to look further than the most lucrative customers. 

Like:

  • Going to sites like G2, Capterra, Google reviews, etc. and look for your best reviews.
  • Talking to your Customer Advisory Board (if you have one). 
  • Looking for the most active customers in your community (again, if you have one). 
  • Paying attention to customers who do case studies or provide reference calls. 

What do those users have in common? Look at their titles, company sizes, etc. 

How to add firmographic data to your PQL engine

Most PLS platforms will have built-in enrichment so you can add fit conditions to your PQL prioritization process. 

Data-based filters in Calixa

Another option is to use tools like Clearbit to enrich data through your warehouse (Snowflake, Redshift) or Customer Data Platform (Segment).

What if it's not that simple? 

PLG companies like Jasper use fit thresholds that aren’t fixed. Meaning a user that is part of a fast-growing tech company may be more valuable for their SDRs to engage than a user at a bigger company that is stalling. 

For this very reason, advanced usage of fit data to identify PQLs is to look at data points through time. 

Another one that’s often forgotten is to route PQLs to the right workflows, which we break down next 👇

Part 2: Engage PQLs

Workflows! Route your PQLs correctly

After doing steps 1-3,  you might be tempted to jump straight to hunting 🐅 your fresh leads. 

DON’T! 

Not all PQLs should be touched on by your sales team. So it’s crucial to route them to the correct workflow. 

Here’s what we mean 👇

Workflow for PQL types

In a nutshell, you want to route PQLs to specific workflows depending on their ICP fit, like: 

  • Sending big accounts to enterprise AEs. 
  • Notifying SDRs of mid-market PQLs. 
  • Passing lower-tier PQLs to a nurture campaign where they can convert on their own. 

How to route users to the correct workflow

You can do it with custom integrations and complex back-end setups. 

But in most cases, sophisticated PLG companies (like Notion, ClickUp, Jasper, Zoom,  etc.) use a PLS system to handle settings up these workflows. 

With a PLS platform in place, you can: 

  1. Qualify leads based on intent & fit data. 
  2. Automate the creation of tasks and actions based on PQL type.
  3. Route hot PQLs to the systems your reps are already using.

How to get PQL data in the hands of sales 

🚨 Get your data right! 

If your data team is not tracking events in your warehouse or customer data platform, you won’t be able to set up an intent threshold from product data. 

Assuming this is not an issue, here are 3 ways to set signals up in your current workflow: 

Best data workflow for PLS: 

Data workflow for PLS

This setup allows your rep to visualize product-qualified accounts and self-sufficiently gain the context needed to take the right actions at the right time. 

Product-Led Sales platforms play a crucial role in making sure that your GTM teams understand what prospects are doing with the product and how they should take action. 

Without it, many reps are back to shooting in the dark, engaging every lead with the same “one-size-fits-all” approach. 

Good: 

Reverse ETL workflow for PLS

This setup requires reps to dig for information, which isn’t ideal, but can power automated workflows and get product data into your GTM systems. 

Worse

Manual data workflow for PLS

This one requires custom coding by your engineering team. Product insights do get to your GTM systems, but most often aren’t even leveraged by GTM teams and lack timeliness, resulting in reps having a rearview mirror understanding of their opportunities. 

Got it? 

NOW it’s time to hunt! 

Find a product narrative to pitch

Well, not quite. Almost!  

Let’s build a sales story first. Meaning let’s find an angle to pitch to our lead so that she understands what she’s losing out on by not upgrading to a higher-paid plan. 

Here’s the idea 👇

Product perception before and after sales

What the best PLG sales teams out there do is build a story that they can explain to decision-makers using numbers. 

Paint a bright future: “Here’s how you’re using us. If you upgrade, you get X functionalities that solve problem Y that’s worth Z dollars a month for your team.” 

Or 

Anchor to the main problem: “You started using our product to solve problem Y. By allowing your marketing team to collaborate with sales using X features of our product, you can gain a Z increase in output from your team”. 

Selling to end users is a different beast. A few tips here.

When & how to reach out to PQLs – with email examples

Speed-to-lead is crucial. Zoominfo (although not a PLG company) is a great example. 

Zoominfo reps call inbound prospects within 60 seconds of an intent signal, and that’s been a game-changer for their pipeline. 

We’re not saying that you need to call PQLs, but Speed-to-lead (independent of how you get in touch) has a big impact on conversion rates. The faster reps take action, the higher the chances of creating an opportunity. 

We recommend setting up alerts when new PQLs are found. 

To increase your chances of key users responding to your sales outreach, you need to use usage context + helpful insight. 

Usage context = You have done X actions in the product. 

Helpful insight = Similar case study, product documentation, how-to guides, etc. 

Here are a few email examples: 

Reached a milestone

Email example to a PQL

Close to a paywall

Email example to a PQL

Using specific features (or not)

Email example to a PQL

Part 3: Operationalizing your PQL engine

PQL ownership: Stories from MixPanel & 1Password 

When we interviewed Dan McKnight, Director of Sales for North America at Mixpanel, he shared with us his vision of what he called the PQL Council. After running Mixpanel’s Onboarding Specialists and Experiments teams on a high volume of users, he decided to get more intelligent about increasing conversion rates. The purpose of the PQL Council was to generate their own homegrown definition of a PQL and decide how to act on PQLs. 

Mixpanel’s PQL committee included::

  • The data science team first analyzes what combination of behavior leads to revenue-generating conversations.
  • The ops team institutes these changes into the lead flow and execution.
  • The sales team then provides feedback about what’s working qualitatively and how to enable reps.

Dan said the ideal state is to run a rapid feedback loop optimizing for PQLs. Their committee does the work of data analysis, PQL notifications, and implementing playbooks – while reps focus on selling. This collaboration allowed them to learn quickly from their large user base.

Every company’s PQL committee may look a bit different. For example, in our chat with Raj Sarkar, advisor and former CMO at 1Password, he stated that such cross-functional teams belong under a Chief Growth Officer. At the end of the day, what matters is having clear PQL ownership. Reps know which PQLs belong to them, and revenue leaders have a holistic view of their motions. 

How to experiment with PQLs 

→ Set a hypothesis and goal

We believe that our users with the most revenue potential are ________ because they use the product on average _______ per (month/week/day) and gain _______ value out of taking _______ actions for/to solve ______ use case.

→ Define success 

Set a baseline KPI for success after implementing PQLs. It can vary based on your business, and we get into what’s a desired end state for PQLs below, but for now, you can start with either of these: 

  • Change in conversion % (based on conversion point set in step 1).
  • Improvement in sales close rate.
  • Net new opportunities created.
  • Net new revenue.
  • Revenue per sales rep (AE or SDRs). 

→ Analyze the results after a given time period

Numbers don’t lie. But don’t forget to talk to your stakeholders. Results are often nuanced. Give yourself some time to iterate.

One powerful insight about experimentation came from our conversation with Ryan Milligan @QuotaPath: 

Don’t hold on to dead leads because you think that they’ll come back! Ryan recycles his PQAs every 90 days if they aren’t showing intentions to buy. 

How PandaDoc thinks about revisiting PQL criteria 

Depends on your business, of course. 

But according to Pavel Hayes, former Head of Global inbound sales at PandaDoc: 

“I found that two months was a reasonable timeframe to understand what’s working well and what’s not. Weekly syncs with stakeholders across product, data, ops, sales, success, and marketing to review performance helped us a lot. A week gave us plenty of time to see how things developed and decide whether to make tweaks or let it run. Anything longer than a week made it harder for us to pivot direction or course correct.” 

How do I know if I’m tracking the right actions? 

We’ll get into turning your PQL setup into a well-oiled machine further down. 

For now, here’s a simple formula: 

Step 1: Look at the sales team’s opportunities created last quarter. 

Step 2: Implement PQL tracking with the INTENT and FIT thresholds explained above. 

Step 3: After a quarter of implementing PQL tracking, evaluate your sales team’s opportunities created again. 

Notice a difference? 

More opportunities? Higher close rate? Faster sales cycles? 

Those are all side effects of good PQL activation. It’s time to level up 🚀. 

Level up: Why is improving PQLs important? 

If your product changes, does your website copy change? 

Same thing with PQLs. 

Your pricing tiers may change. New features will be added to your product, etc. 

Those require adjustments to your PQL tracking if you want to make sure that you’re always uncovering the best revenue opportunities for your sales org. 

Especially in 2023, when every SaaS company is trying to do more with less. Chances are, you need to reach the same revenue goals with fewer reps. Keeping your PQL engine effective makes that possible. 

Repeat for other conversion points 

Simple: You don’t sell the same way an enterprise deal as you would a team expansion.

Also, you probably will have different reps handle both conversion points. 

Different PQLs = New workflows. 

Therefore, the best PLG companies set up multiple “views” and “workflows” around their conversion points. 

What’s the desired end state? 

This google sheet helps you easily calculate whether your PQL engine is making an impact on your business. 

After a few months of utilizing PQLs, input your numbers in the sheet (deal size, close rate, new user sign-ups), and see how much revenue you’re generating per sales rep (SDR or AE). 

NOTE: You might have internal reports that are similar. The idea for an end state is seeing how your revenue closed per sales rep is increasing with PQLs embedded in your revenue strategy. 

Onwards 

We hope that this gives you a concrete starting point of how to implement a product-qualified lead strategy that generates a constant pipeline for your business. 

Hungry for more? Don’t forget to check back for Part 2: In-debt breakdown of the sales workflow you need to close PLS deals.

Learn more about how to prioritize PQLs with Calixa:

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Turn your self-serve funnel into a revenue engine.
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Product-Led Fundamentals

Practical framework to finding & monetizing PQLs

A 3-part practical playbook to generate more revenue opportunities from your free users.

Fred Melanson
|
Director of Content
|
Calixa

Subscribe to High Intent

Your PLG roundup in 5 minutes.

January 11, 2023
ReadTime

Welcome to PLS Deep Dives! This series walks you through how to do Product-Led Sales from a practical standpoint – from defining your first PQL all the way to executing wall-to-wall upsells.

In Part One, we’ll go over PQLs: How to find them and turn them into sales opportunities. 

Why should I read this? 

Simple: PQLs convert more often than other lead types for businesses with free trials or freemium plans. 

  • Managers: This framework explains the fundamentals of a PQL engine and 6 step process on how to set it up. 
  • Reps: This article explains how to leverage your company’s PQLs to prospect and close more business with less effort. 

Not convinced? We recently heard from the following an AE at a $4B SaaS company: “After just 3 days of leveraging PQLs, I sourced a $1M deal!”.

Let’s get to it 👇

What is a Product Qualified Lead? (Hint: Not an MQL)

Before we start, here’s a reminder of what a PQL is: 

PQLs, also known as Product Qualified Leads, are users within your product who are qualified for sales opportunities because they have reached specific usage milestones in the product and fit with your ideal customer profile.

Think of a PQL as a user with high INTENT (product usage and/or buying signals) + FIT (customer profile). 

Note that some companies prefer to find Product Qualified Accounts, as they don’t sell to individuals, but rather to companies, or teams. The framework below can be applied to both.

A PQL is better for your revenue stream than marketing qualified leads since the intent of someone getting self-serve value from your product is clearly higher than a potential customer who has downloaded content or another basic marketing qualifier.

Let’s go through the PQL framework so you can find more opportunities for your sales team 🎯! 

Part 1: Finding PQLs

Pick a conversion event

Do you do something without a goal in mind? 

That’s the equivalent of setting PQLs without a conversion point. 

Conversion points are ways for your business to generate more revenue from people using your product. 

Common conversion points for most PLG businesses are 👇

  • Free-to-paid: Converting free users into any of your paid tiers. 
  • Self-serve to enterprise: Converting those people that have hopped on your paid tier by themselves into an enterprise contract. 
  • Expansion: Converting paid accounts into more lucrative paid accounts (more seats, more usage, more features, etc.).
  • Account consolidation: Merging multiple workspaces or lower tier accounts into one centralized account on a higher tier paid plan. 

Start with a single conversion point 

Focus is important as you develop and iterate on your PQL workflows. Starting with one conversion event ensures that you can properly track your PQL efforts. 

Also, different teams might be working on your various conversion events. As we’ll explain below, the best product-led GTM teams get one PQL workflow working for a single conversion event and then build on from there. 

If you’re new to product-led sales (PLS), you probably have an untapped pool of free users to prospect from. If that’s the case, start with free-to-paid as your conversion event. 

Depending on your PLS maturity and the dynamics of your user base (amount of users, pricing strategy), you can assess where the biggest revenue opportunities lie. 

Examples

  • You have a narrow user base with volume-related pricing tiers (Reverse ETL tools) → Focus on expansion as your first conversion point. 
  • Massive user base with sharing virality (Miro) → Focus on free-to-paid as your first conversion point.
Lead jumping through paywalls

Define an intent threshold

Once you’ve identified your conversion point, you want to find what kind of intent (or behaviors) users need to demonstrate to be worthy of your sales efforts.

Product intent can include both product usage and buying signals. 

Let’s untangle both. 

Product usage 

The most famous one is the “aha moment”. The moment at which a user realizes the value that your product is providing. 

But in general, this one is about setting milestones at which you’re confident that your users have gotten value from your product, and can become even more successful on paid plans.

Self serve revenue vs PQL activated revenue

Here’s a simple formula for setting a product usage milestone: 

Step 1: Make a list of your product’s main actions (tasks created, storage used, integrations, etc). 

Step 2: Make a list of your highest-revenue customers.

Step 3: Go to your product analytics tool (Mixpanel, Amplitude). Filter usage dashboards by those customers you identified and the product’s main actions. (You can ask product folks or data science for help). 

Step 4: Look for the moment on the graph when your best customers upgraded plans (you can get this info from your CRM). 

Step 5: Compare the trends you find between customers and define a “ballpark” threshold. 

Real-world example: Slack’s team has observed that after 9,000 messages sent, their users have a high tendency to convert to paid plans. 

Buying signals 

Buying signals are actions taken by users that demonstrate a willingness to buy.

Tracking buying signals is crucial as you might have users who have not yet reached qualifying product usage thresholds but are still good opportunities for sales because they’re (hypothetically) raising their hand to buy.

Examples

  • Buying new seats
  • Going to your pricing page
  • Trying out premium features
  • Contacted sales
  • Hit plan limits

Note that you should not weigh buying signals equally. Users contacting sales are showing more willingness to buy than users passively looking at your pricing page. 

Buying signals to convert deals

Best practice: track as many buying signals as you can and prioritize sales actions on leads who have shown the strongest intent to purchase. 

How to use product usage and buying signals to step up your sales game

We find this matrix below helpful in crafting a sales strategy that capitalizes on the type of intent (product usage vs buying signals) you can consider acting on.

Graph representing how to act based on usage and buying signals

Let’s break down what you should do based on where your lead is in this matrix: 

Lots of buying signals + high product usage: Focus sales efforts on uncovering what’s blocking your prospect from becoming a paid customer. Engage decision-makers. 

No buying signals + high product usage: Focus sales efforts on pitching the added value of paid tiers. Leverage case studies and build an ROI simulation. 

Lots of buying signals + low product usage: Educate your prospect about how to become successful in the product. Share best practices, templates, etc. 

No buying signals + low product usage: Nurture users through automated campaigns and regular product experiences. Sales teams shouldn’t spend time on these leads. 

Note that intent isn’t intended to be product usage or buying signals. For many high-performing PLG companies, the PQL threshold is a mix of both! 

(Hypothetical) PQL thresholds from notable PLG companies: 

ClickUp logo

Created more than 50 tasks (product usage) and invited 10 users in the last 30 days (buying signal).

Miro logo

Created 10 boards (product usage) and have 20 daily active users (buying signal)

Twilio logo

Made > 100 API calls in the last 30 days (product usage) and bought a phone number (buying signal)

Prioritize PQLs with customer fit (ICP) data 

Deadly mistake: having a salesperson engage a lead with low revenue potential!

You now have your product signals in place. As mentioned, the other half of your PQL is customer fit. 

Not every highly active user has the ability or needs to purchase your product. Product-Led Sales is about talking to the right users at the right companies.

To do this, you need to filter PQLs who reach your usage thresholds with firmographic data like title, company size or industry. 

Example data on the account level: 

  • Company size 
  • Industry 
  • Revenue
  • Funding raised

Example data on the user level: 

  • Title
  • Size of the team they manage
  • Function 

Think of customer fit as putting glasses on that let you see through the haystack to find the needle! 

Before and after layering ICP data on PQLs

What firmographic data should I use? 

Again, if you’re thinking: “That’s all great, but where do I start?”, here’s how you define baselines for fit criteria 👇

Look at your current customer base 

Use the same formula as for usage thresholds! What do your best-paying customers have in common? 

Start there. 

Who’s delighted by your product?

Another trick is to look further than the most lucrative customers. 

Like:

  • Going to sites like G2, Capterra, Google reviews, etc. and look for your best reviews.
  • Talking to your Customer Advisory Board (if you have one). 
  • Looking for the most active customers in your community (again, if you have one). 
  • Paying attention to customers who do case studies or provide reference calls. 

What do those users have in common? Look at their titles, company sizes, etc. 

How to add firmographic data to your PQL engine

Most PLS platforms will have built-in enrichment so you can add fit conditions to your PQL prioritization process. 

Data-based filters in Calixa

Another option is to use tools like Clearbit to enrich data through your warehouse (Snowflake, Redshift) or Customer Data Platform (Segment).

What if it's not that simple? 

PLG companies like Jasper use fit thresholds that aren’t fixed. Meaning a user that is part of a fast-growing tech company may be more valuable for their SDRs to engage than a user at a bigger company that is stalling. 

For this very reason, advanced usage of fit data to identify PQLs is to look at data points through time. 

Another one that’s often forgotten is to route PQLs to the right workflows, which we break down next 👇

Part 2: Engage PQLs

Workflows! Route your PQLs correctly

After doing steps 1-3,  you might be tempted to jump straight to hunting 🐅 your fresh leads. 

DON’T! 

Not all PQLs should be touched on by your sales team. So it’s crucial to route them to the correct workflow. 

Here’s what we mean 👇

Workflow for PQL types

In a nutshell, you want to route PQLs to specific workflows depending on their ICP fit, like: 

  • Sending big accounts to enterprise AEs. 
  • Notifying SDRs of mid-market PQLs. 
  • Passing lower-tier PQLs to a nurture campaign where they can convert on their own. 

How to route users to the correct workflow

You can do it with custom integrations and complex back-end setups. 

But in most cases, sophisticated PLG companies (like Notion, ClickUp, Jasper, Zoom,  etc.) use a PLS system to handle settings up these workflows. 

With a PLS platform in place, you can: 

  1. Qualify leads based on intent & fit data. 
  2. Automate the creation of tasks and actions based on PQL type.
  3. Route hot PQLs to the systems your reps are already using.

How to get PQL data in the hands of sales 

🚨 Get your data right! 

If your data team is not tracking events in your warehouse or customer data platform, you won’t be able to set up an intent threshold from product data. 

Assuming this is not an issue, here are 3 ways to set signals up in your current workflow: 

Best data workflow for PLS: 

Data workflow for PLS

This setup allows your rep to visualize product-qualified accounts and self-sufficiently gain the context needed to take the right actions at the right time. 

Product-Led Sales platforms play a crucial role in making sure that your GTM teams understand what prospects are doing with the product and how they should take action. 

Without it, many reps are back to shooting in the dark, engaging every lead with the same “one-size-fits-all” approach. 

Good: 

Reverse ETL workflow for PLS

This setup requires reps to dig for information, which isn’t ideal, but can power automated workflows and get product data into your GTM systems. 

Worse

Manual data workflow for PLS

This one requires custom coding by your engineering team. Product insights do get to your GTM systems, but most often aren’t even leveraged by GTM teams and lack timeliness, resulting in reps having a rearview mirror understanding of their opportunities. 

Got it? 

NOW it’s time to hunt! 

Find a product narrative to pitch

Well, not quite. Almost!  

Let’s build a sales story first. Meaning let’s find an angle to pitch to our lead so that she understands what she’s losing out on by not upgrading to a higher-paid plan. 

Here’s the idea 👇

Product perception before and after sales

What the best PLG sales teams out there do is build a story that they can explain to decision-makers using numbers. 

Paint a bright future: “Here’s how you’re using us. If you upgrade, you get X functionalities that solve problem Y that’s worth Z dollars a month for your team.” 

Or 

Anchor to the main problem: “You started using our product to solve problem Y. By allowing your marketing team to collaborate with sales using X features of our product, you can gain a Z increase in output from your team”. 

Selling to end users is a different beast. A few tips here.

When & how to reach out to PQLs – with email examples

Speed-to-lead is crucial. Zoominfo (although not a PLG company) is a great example. 

Zoominfo reps call inbound prospects within 60 seconds of an intent signal, and that’s been a game-changer for their pipeline. 

We’re not saying that you need to call PQLs, but Speed-to-lead (independent of how you get in touch) has a big impact on conversion rates. The faster reps take action, the higher the chances of creating an opportunity. 

We recommend setting up alerts when new PQLs are found. 

To increase your chances of key users responding to your sales outreach, you need to use usage context + helpful insight. 

Usage context = You have done X actions in the product. 

Helpful insight = Similar case study, product documentation, how-to guides, etc. 

Here are a few email examples: 

Reached a milestone

Email example to a PQL

Close to a paywall

Email example to a PQL

Using specific features (or not)

Email example to a PQL

Part 3: Operationalizing your PQL engine

PQL ownership: Stories from MixPanel & 1Password 

When we interviewed Dan McKnight, Director of Sales for North America at Mixpanel, he shared with us his vision of what he called the PQL Council. After running Mixpanel’s Onboarding Specialists and Experiments teams on a high volume of users, he decided to get more intelligent about increasing conversion rates. The purpose of the PQL Council was to generate their own homegrown definition of a PQL and decide how to act on PQLs. 

Mixpanel’s PQL committee included::

  • The data science team first analyzes what combination of behavior leads to revenue-generating conversations.
  • The ops team institutes these changes into the lead flow and execution.
  • The sales team then provides feedback about what’s working qualitatively and how to enable reps.

Dan said the ideal state is to run a rapid feedback loop optimizing for PQLs. Their committee does the work of data analysis, PQL notifications, and implementing playbooks – while reps focus on selling. This collaboration allowed them to learn quickly from their large user base.

Every company’s PQL committee may look a bit different. For example, in our chat with Raj Sarkar, advisor and former CMO at 1Password, he stated that such cross-functional teams belong under a Chief Growth Officer. At the end of the day, what matters is having clear PQL ownership. Reps know which PQLs belong to them, and revenue leaders have a holistic view of their motions. 

How to experiment with PQLs 

→ Set a hypothesis and goal

We believe that our users with the most revenue potential are ________ because they use the product on average _______ per (month/week/day) and gain _______ value out of taking _______ actions for/to solve ______ use case.

→ Define success 

Set a baseline KPI for success after implementing PQLs. It can vary based on your business, and we get into what’s a desired end state for PQLs below, but for now, you can start with either of these: 

  • Change in conversion % (based on conversion point set in step 1).
  • Improvement in sales close rate.
  • Net new opportunities created.
  • Net new revenue.
  • Revenue per sales rep (AE or SDRs). 

→ Analyze the results after a given time period

Numbers don’t lie. But don’t forget to talk to your stakeholders. Results are often nuanced. Give yourself some time to iterate.

One powerful insight about experimentation came from our conversation with Ryan Milligan @QuotaPath: 

Don’t hold on to dead leads because you think that they’ll come back! Ryan recycles his PQAs every 90 days if they aren’t showing intentions to buy. 

How PandaDoc thinks about revisiting PQL criteria 

Depends on your business, of course. 

But according to Pavel Hayes, former Head of Global inbound sales at PandaDoc: 

“I found that two months was a reasonable timeframe to understand what’s working well and what’s not. Weekly syncs with stakeholders across product, data, ops, sales, success, and marketing to review performance helped us a lot. A week gave us plenty of time to see how things developed and decide whether to make tweaks or let it run. Anything longer than a week made it harder for us to pivot direction or course correct.” 

How do I know if I’m tracking the right actions? 

We’ll get into turning your PQL setup into a well-oiled machine further down. 

For now, here’s a simple formula: 

Step 1: Look at the sales team’s opportunities created last quarter. 

Step 2: Implement PQL tracking with the INTENT and FIT thresholds explained above. 

Step 3: After a quarter of implementing PQL tracking, evaluate your sales team’s opportunities created again. 

Notice a difference? 

More opportunities? Higher close rate? Faster sales cycles? 

Those are all side effects of good PQL activation. It’s time to level up 🚀. 

Level up: Why is improving PQLs important? 

If your product changes, does your website copy change? 

Same thing with PQLs. 

Your pricing tiers may change. New features will be added to your product, etc. 

Those require adjustments to your PQL tracking if you want to make sure that you’re always uncovering the best revenue opportunities for your sales org. 

Especially in 2023, when every SaaS company is trying to do more with less. Chances are, you need to reach the same revenue goals with fewer reps. Keeping your PQL engine effective makes that possible. 

Repeat for other conversion points 

Simple: You don’t sell the same way an enterprise deal as you would a team expansion.

Also, you probably will have different reps handle both conversion points. 

Different PQLs = New workflows. 

Therefore, the best PLG companies set up multiple “views” and “workflows” around their conversion points. 

What’s the desired end state? 

This google sheet helps you easily calculate whether your PQL engine is making an impact on your business. 

After a few months of utilizing PQLs, input your numbers in the sheet (deal size, close rate, new user sign-ups), and see how much revenue you’re generating per sales rep (SDR or AE). 

NOTE: You might have internal reports that are similar. The idea for an end state is seeing how your revenue closed per sales rep is increasing with PQLs embedded in your revenue strategy. 

Onwards 

We hope that this gives you a concrete starting point of how to implement a product-qualified lead strategy that generates a constant pipeline for your business. 

Hungry for more? Don’t forget to check back for Part 2: In-debt breakdown of the sales workflow you need to close PLS deals.

Learn more about how to prioritize PQLs with Calixa: