The number one problem we hear from salespeople at product-led companies is: 'I have so many users I could talk to. Where do I focus?'

Enter the Product Qualified Lead (PQL). The PQL combines signals from both what the lead is doing in your SaaS product AND who they are.

• Intent → how is the user or account using your product? (product usage)
• Fit → are they the right buyer for our product? (customer profile / ICP)

By leveraging product usage signals, you're talking to prospects ready to talk to the sales team. As a result, PQLs convert 5X better than Marketing Qualified Leads (MQLs) and drive growth in a Product Led Growth business model.

PQLs can be used to find both:

• Freemium or trial accounts ready to convert to paying customers
• Paid accounts who are expanding / ready for enterprise plan

Here are some tips for combining product usage data and customer fit to develop PQLs and prioritize Sales activity to drive revenue.

Product Qualified Leads ≠ Marketing Qualified Leads

Traditionally, B2B selling relied on Marketing Qualified Leads to fill the top of the sales funnel. From an analytics perspective, the data MQLs generate are fairly basic:

• How many emails did people open?
• What whitepapers or other content did people download?
• Which web pages did they visit, and how frequently?

The narrow range of actions tracked in this MQL process limits the value a sales team gets from MQLs. Among the weaknesses of the marketing-led approach:

• Metrics are based on what marketing thinks, not what the customer does.
• MQLs measure customer intent indirectly—and weakly.
• Sales get few insights into customer wants or needs.
• Sales can only touch a fraction of the MQLs marketing generates.

Most importantly, potential customers entering a traditional sales funnel do not live in the SaaS product until after the deal closes. Most of the Sales effort goes into convincing customers of the software's business value—which only gets confirmed after the customer commits.

Let products generate leads

A Product Led Growth (PLG) model takes the opposite approach by getting potential customers to use the product from the beginning. Free plans let these users discover the product's value and take action for themselves.

With potential customers already convinced, Sales can take consultative approaches to guide PQLs along the customer journey. This head start makes PQLs easier and faster to convert while reducing churn.

A challenge PLG and traditional Sales share is time. Reps can only contact so many customers in a day. Since a PLG business model generates even higher volumes of potential customers, how do you filter the best PQLs so Sales can maximize your revenue potential?

The key is to get the right PQL definitions.

Product Usage = Real Intent

A core component of your PQL definition is product usage. Product-led companies are sitting on a treasure trove of usage data that is a valuable signal. How an account is using your product provides insight into how real their intent is. It answers questions like:

• How intensely are they using the product?
• Have they uncovered the core value of our product?
• Are they using the product in a way that indicates they need a higher tier plan?

Answers to these questions give Sales a foundation on which to have a conversation with the customer—a massive advantage for PLG companies. Sales can use PQLs to identify who to talk to and leverage the usage itself as the conversation starter for their sales engagement. It makes the outreach relevant.

Data reveals usage patterns

Because companies are different, so too are the product usage criteria defining a PQL. Here are some examples that products might have:

Created more than 50 tasks and invited 10 users in the last 30 days

Created 10 boards and have 20 daily active users

Made > 100 API calls in the last 30 days and bought a phone number

Usage-informed criteria are where the Product Led Growth advantage kicks in. Because you have thousands of people using your product every day, you have all the data you need to understand the patterns that indicate:

• Free users are ready for a paid plan.
• Teams of users need the features in a business plan.
• Accounts are poised to become enterprise accounts.

When figuring out your own product usage criteria, it's essential to look at:

• What do existing successful customers look like?
• What are the key milestones in the customer journey? (e.g., what's the aha moment)
• What volume of leads can our Sales team handle today?
• What customer size is worth Sales involvement?

You don't need to have one and only one PQL definition. You can further segment into levels of product intent via multiple definitions. In the beginning, we generally recommend starting with one PQL definition. Once you've mastered that, you can add more definitions.

PQLs = Fit + Intent

While product usage is a powerful indicator of intent, usage alone doesn't make someone a PQL. Your PQL definition needs to combine both intent and fit to ensure you're talking to the right leads.

• Intent → are they going to use our product?
• Fit → are they the right buyer/persona for our product?

To figure out Fit, companies often leverage firmographic data (e.g., role, industry, company size, annual revenue, etc.) from providers like Clearbit, Zoominfo, or PeopleDataLabs. Using data providers to enrich your signups will give you deeper insights. For example:

• Industry → Is this company in the right industry?
• Funding → Do they have enough funding such that they'd be willing to pay?
• Company Size → Is the business big enough to have this problem / willing to pay?
• Title → Which of the users has the buying authority for the business?

If you don't want to rely on data providers alone, it's increasingly common for companies to have quick questionnaires (5 questions or less) when customers sign up. It will ask about things like industry, use cases, etc. as part of the signup flow.

As customers use your product and you earn the right to ask more questions, you can round out your information about each user and their business. This data is a great way to augment or get around the need for data providers.

Tiering your PQLs

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These PQL tiers group accounts by their revenue potential: the size and probability of closing the deal. PQL tiers change how sales teams should approach the leads.

Tier 1: This group is most likely to buy because they use your product intensely and have a good ICP Fit. Prioritize these accounts since Sales can close deals with them faster than any others.

Tier 2: With a good Fit, this group could convert into higher revenue accounts, but they don't use your product as much as they could. Sales can improve engagement by helping users get so much from the product they become evangelists within their business.

Tier 3: Since this group's product usage shows they value your product, they may be worth spending time on even if their revenue potential is smaller or they are not an ICP Fit. Keep in mind that customers often find new ways to use your product that you don't expect. A few tweaks to packaging and pricing, or a new PQL definition, could make these users worth Sales' attention. The question with Tier 3 is whether Sales should engage or should you let marketing campaigns encourage the account to self-serve?

Revenue Growth = Intent + Fit + Tiering

Rather than relying on a pure, self-serve business model, PQLs let you add an effective Product-Led Sales motion. Product usage data generated by thousands of free and paid accounts let you identify the accounts ready to advance along the customer journey. Filtering these accounts by their Fit to your Ideal Customer Profiles gives Sales the high-quality Product Qualified Leads they need to prioritize their time.

PQLs are customers who already see the value of your product because they use it every day. At the same time, PQLs give Sales the usage-driven insights they need to open consultative conversations that reinforce your product's value and accelerate conversions.

PQLs are the heart of Product-Led Sales motions, driving account growth and revenue while delighting customers.

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