Finance teams in product-led companies look at a tough problem. They need to figure out how to forecast revenue when customers qualify themselves through product usage and not sales calls. The approach is in product qualified leads, or PQLs.
PQLs are users who've tried your product, found value, and show clear signs they want to buy. For finance folks building models, PQLs are a strong leading indicator. They convert 5-10 times more often than marketing qualified leads (MQLs) and move quicker through your pipeline. They also stick around.
This guide explains how to define, find, and track PQLs. You’ll get the formulas, benchmarks, and a simple step-by-step process for setting up PQL tracking in Runway.
Defining and understanding the product qualified lead (PQL)
A PQL is a user who’s active in your product, hits clear milestones, and looks ready to become a paying customer. MQLs rely on things like downloaded ebooks or email clicks. PQLs stand out because they qualify through their actions.
This has financial implications. MQLs usually convert at 5-15%. PQLs at 25-30%. That extra lift compounds through your whole go-to-market model. Accurate planning, CAC estimates, and forecasts all get better when you use PQLs instead of MQLs.
In product-led businesses, tracking PQLs becomes essential. Spotting which free users will convert lets you model bookings accurately, set real targets, and direct resources with confidence. In our pipeline generation guide, we show how qualified opportunities create momentum. PQLs are your highest-quality opportunities in a product-led approach.
Finance teams use PQL data to answer big questions:
- how many sales reps do we need?
- what’s our true bookings capacity?
- how long will our cash last if our conversion rate holds?
These are financial planning questions. You need clean, predictive data to answer them.
The methodology behind identifying and tracking PQLs
There’s no single formula for PQLs. Every product, price model, and customer set needs its own approach. The key: pick a method tied to actual purchase behavior in your business.
Most finance teams use one of the five core approaches, or blend a few. Each has pros, cons, and its own level of complexity.
Standard usage threshold
The simplest method sets clear engagement numbers. A user is a PQL if they meet your thresholds, for example 10 logins, 5 features used, 30 minutes in-session.
Example formula:
PQL status = if(logins ≥ threshold and features_used ≥ threshold and time_spent ≥ threshold, “qualified”, “not qualified”)
This is easy to roll out and explain. But you’ll need to check that your thresholds actually predict conversion. Change them if your product changes. A curious user hitting 10 logins isn’t the same as a user solving real business problems.
Activation milestones
This approach looks at key “aha” moments. Slack tracks teams that exchange 2,000 messages. Expensify looks at invoices sent in the first month.
Example formula:
PQL status = if(aha_moment_completed = true and days_since_signup ≤ window, “qualified”, “not qualified”)
This works best when you’ve found your value moments and have data showing these link to conversion. Choose actions that really predict long-term success. You’ll likely need to dig into your history to find the right ones.
Behavioral scoring model
Scoring models give points to different user actions. High-intent moves add more points. Low-intent or negative signs pull points away.
Sample formula:
PQL score = (feature_usage × 20) + (pricing_page_visits × 25) + (team_invites × 50) + (integration_setup × 30) - (personal_email × 20) - (inactive_days × 5)
PQL status = if(pql_score ≥ threshold, “qualified”, “not qualified”)
This lets you use more nuance. You can up-weight executives, down-weight negative activity, and evolve the model over time. But it’s more work to maintain—set clear weights and review them often.
Firmographic-enhanced method
Add company details to your product data. A user at a 10-person company may score high on engagement, but if you only sell to large teams, that’s not a PQL.
Formula looks like:
PQL status = if(usage_score ≥ threshold and company_size in target_range and industry in target_list, “qualified”, “not qualified”)
This narrows your list to real prospects. It’s useful for B2B products with clear customer segments. The challenge is getting good data, especially from free users who may not give you the info right away.
Predictive PQL approach
Machine learning models study your historical conversion data. They spot usage patterns that lead to revenue. You don’t need to manually set every rule, the model learns as your users and product change.
How it works:
PQL probability = ml_model(usage_features, firmographic_features, temporal_features)
PQL status = if(pql_probability ≥ threshold, “qualified”, “not qualified”)
Predictive models find patterns manual rules miss. As you grow, this can be a powerful option. You’ll need enough data and technical skill to support it. Most teams start simple, then switch when they’re ready.
Key components and considerations
Make sure to get your PQL definition right. If you don’t, your data turns into noise and your planning stalls.
Start by deciding which user actions signal intent, not just curiosity. For many products, a user who nails one key workflow is more likely to buy than someone who pokes around. Engage frequency tells you more than total time spent. Daily 10-minute sessions show more intent than two-hour marathons once a month.
Pick your thresholds carefully:
- Absolute metrics: simple (10+ logins) but may not scale with complexity
- Percentile-based: top 20% of engaged users, adapts as your base grows
- Accelerating usage: tracks momentum instead of pure volume, but takes more tooling
Pick your observation window:
- 7 days = fast buyers
- 30 days = more methodical buyers or complex sales
- rolling periods = more balance (7 of last 30 days active)
Think about accounts with multiple users. In B2B, a team’s collective usage may matter more than any one person’s. Decide if you’ll track individual PQLs, account-level PQLs, or both. Account-level is usually better for complex products. An example: 3+ active users from one company plus strong team usage.
Trial users move with urgency. Freemium users take their time. Adjust your PQL setup for their different journeys.
Buyer personas differ. Analysts need your product every day. Managers might check in weekly. Executives might never log in, but still buy. Use persona-specific criteria so you don’t miss decision makers.
Watch for reactivated users. Someone who churned before acts differently than a new face. Some teams treat a return as a separate path to PQL status.
Longer time-to-value products face a unique test. When it takes weeks to reach “aha,” early signals may not predict value. Focus on what leads to activation, like onboarding steps or integration setup.
Power users aren’t always buyers. Some love your free tier and never plan to upgrade. High activity alone doesn’t equal intent. Look for true signals, like hitting limits, visiting pricing pages, or asking about advanced features.
The impact on go-to-market performance
A smart PQL system improves every part of your planning. For finance, this drives better forecasts and resource decisions.
When sales teams focus on PQLs, productivity climbs. PQL deals close 30-50% faster than cold leads. The sales cycle shortens. Reps spend more time closing and less time educating.
Your customer acquisition cost drops by 30-50% with a product-led approach. Users qualify themselves before sales gets involved, so your cost per win goes down. Track PQL conversion as a key input for CAC. Check out our pipeline coverage guide for how this fits into bigger pipeline goals.
Better PQL accuracy lifts trial-to-paid conversion. Get the right users at the right time, and conversion ticks up. Loose definitions flood sales with low-intent leads and undercut your forecast. Finance should make PQL-to-customer conversion a lead indicator of revenue health.
Retention tags along with PQLs. Customers who buy after real product use stick around longer. Some teams see 10% higher first-year retention from PQL-sourced deals. That means higher customer lifetime value and stronger revenue projections.
Expansion is more predictable when you watch team PQLs. Accounts adding users, exploring features, or growing fast are ready for upsell. Finance teams can add these signals to their expansion forecast.
Teams align when everyone uses the same PQL definition. Product, marketing, sales, and finance can collaborate using the same data and dashboards. Attribution gets sharper. Forecasting gets easier. Our approach to shared financial platforms works because everyone stays on the same page.
Benchmarks and rules of thumb
PQL results vary by product and price model, but these numbers give you a ballpark.
- Free to PQL conversion: 5-15%, top teams hit 20-40%. If it’s under 5%, fix onboarding or revisit your product’s fit
- PQL to customer: 15-30%. PQL-led companies can hit 25-40% conversion and fast sales cycles. Below 15%? Tighten your PQL criteria
- Time from signup to PQL: 3-14 days for most. Under 30 days is ideal. Longer means friction or an unclear value story
- Freemium models: lower PQL rates (2-5%) but stronger buying intent
- Fast sales response: PQLs responded to within 24 hours convert better. Plan for enough reps to work your volume
- PQL definitions often need 2-4 rounds of tweaking before things stabilize
These are in line with pipeline coverage ratios for top-performing teams. SMB inbound wins at 45-60% often need 1.7-2.5x coverage. PQL-driven companies often sit at the higher end due to stronger qualification.
Common pitfalls to avoid
- set the bar too low and sales gets overwhelmed fast, conversion drops, trust in the model slips
- set thresholds too high and real buyers slip through the cracks; low volume means you’ll probably miss targets
- don’t use vanity metrics; focus on value-driven actions like integrations, feature usage, invites, pricing page views
- adapt PQL criteria by customer segment; enterprise versus SMB behave differently
- track both individual and account-level signals in B2B; a team champion and the economic buyer may act differently
- weight high-intent actions more heavily; inviting teammates or integrating is worth more than changing a setting
- watch out for negative signals, like multiple support tickets or declining activity; take action quickly
- review PQL criteria quarterly, make changes as needed, and tie it back to actual conversions
- make sure your sales capacity matches PQL flow and don’t waste opportunities due to bottlenecks
- distinguish real business use from curiosity, look for urgency and frequency
- track job titles and seniority so you don’t miss decision makers
- analyze PQL performance by channel to focus your marketing spend
- qualify on sustained usage, not just spikes; look for steady patterns
- refresh engagement signals; don’t let PQL lists go stale
- align the PQL definition across teams to keep attribution and forecasting smooth
How to track product qualified leads in Runway
Runway pulls product usage and financial data together. Here’s how to set up PQL tracking in a few steps.
Step 1: Connect your product and CRM data
Bring usage data into Runway. Connect your source using our Salesforce, HubSpot, SQL, Fivetran, or our API.
Step 2: Create a users database
Build a Runway database for your users. Segment by signup date, company size, industry, user role, and channel. Add drivers for:
total_logins(number)features_used(number)aha_moment_completed(true/false)days_since_signup(number)pricing_page_visits(number)
Step 3: Define your PQL scoring logic
Add a driver called pql_score with your chosen method. Example behavioral scoring:
pql_score = (features_used * 20) + (pricing_page_visits * 25) + (team_invites * 50) + if(aha_moment_completed, 30, 0) - (days_inactive * 5)
For a simple threshold model:
pql_status = if(and(total_logins >= 10, features_used >= 5, days_since_signup <= 30), "qualified", "not qualified")
Runway lets everyone see how each score is calculated.
Step 4: Build aggregate PQL metrics
Pull aggregate metrics for your user base:
total_pqls_this_month: count of users withpql_status = "qualified"this monthpql_conversion_rate: PQLs who convert to customers / total PQLsfree_to_pql_rate: PQLs in a period / total signups in period
These plug straight into your revenue forecast. Track them over time and adjust as needed.
Step 5: Connect PQLs to pipeline and bookings
Match PQLs to deals in your sales database. Tag each deal with source = "PQL". Report on PQL-sourced pipeline just like in our pipeline coverage guide.
pql_pipeline = sum(deal_amount where source = "PQL" and stage not in ["Closed Won", "Closed Lost"] and close_date.month = this month)
Compare PQL pipeline to bookings targets. If you need 3x coverage and PQLs deliver 1.5x, focus on activation or grow your top-of-funnel.
Step 6: Model scenarios and capacity
Use Runway’s scenario tools to see how changes in PQL criteria ripple through your business. Test things like:
- tighter PQL: bump score from 100 to 150 and see how volume and conversion change
- faster activation: improve free-to-PQL from 10% to 15% and track bookings impact
- sales team growth: add more reps, check if there’s enough PQL volume to keep them productive
PQL numbers plug into bookings, revenue, staffing plans, and cashflow automatically. In our cohorts guide, we show how cohort tracking helps pattern match for future forecasting.
Step 7: Set up alerts and reporting
Track if your free-to-PQL rate dips below 8% or PQL-to-customer conversion falls below 20%. Set up alerts for those shifts. Build dashboards for finance, product, and sales to keep everyone aligned and decisions fast. Read more in our guide to cross-functional financial planning.
Start monitoring PQLs
A focused PQL setup transforms finance teams from firefighting to leading. You’ll see your top prospects in real-time and forecast with confidence.
The best product-led growth companies use PQL data in every forecast and scenario. Pipeline, CAC, bookings, and cashflow all depend on accurate PQLs.
Start simple. Track cohort results. Test your assumptions each quarter. Keep your model moving with your product and market. Aim for progress and always back up changes with data.
Runway puts all this power in reach. Model PQLs, run what-if scenarios, and keep every team member working with real numbers, no matter your company size.
Want to try PQL tracking in your own financial model? Schedule a demo today and see how Runway helps you turn usage data into better forecasts and decisions.