Sales and marketing teams know the pain all too well: marketing, tasked with hitting an ambitious MQL quota, tries anything and everything to get leads in the door.
And when it’s time for sales teams to step in and nurture those leads to close…only a fraction of them have any true interest in the product.
The result? Wasted time and ongoing tension between teams — teams whose efforts should complement each other, not leave each other in a tight spot.
MQLs to SQLs: outdated and broken
While setting ambitious monthly and quarterly quotas might motivate marketers and sales reps to act quickly, the pressure to generate high volumes of leads often causes those team members to focus less on quality opportunities, and more on merely hitting numbers.
This is not only detrimental to the sales team’s pipeline; it’s also increasingly out of sync with the natural adoption process of SaaS products, where the bottoms-up approach (having a sales-ready product that requires little to no hand-holding for a user to sign up and start seeing results) has become the norm for SMB solutions.
In the words of Scott Irwin, General Partner, Rembrandt Venture Partners:
“In today’s Bottoms-Up SaaS era, companies like Zendesk and Autopilot are gaining traction and growing quickly by winning over many successful customers – those who can get results without lots of hand-holding, as opposed to high-priced or happy customers. Assisted by in-product tips and guides, personalized automated communications, and online shopping carts, self-service applications have made it simpler and faster for customers to adopt applications than ever before, while making the aggressive outbound sales tactics and endless customization requirements which worked well a decade ago obsolete.”
Product Qualified Leads: the much-needed middle ground
If an MQL is qualified based on their interaction with marketing materials — e.g., whether they downloaded an ebook, visited a pricing page, etc — then a PQL (Product Qualified Lead) is the next natural step in the buying journey: they’re someone who’s qualified based on their interaction with your product.
Of course, the most relevant product usage data will vary from company to company, but common data points include a lead’s number of potential users, features used, spending patterns, and other in-app indicators.
Emmanuelle Skala, VP Customer Success at Toast, explains MQLs vs PQLs using this image:
“PQLs, as the name indicates, requires that the user (or lead) in question is actually using your product. Some people therefore assume this is only relevant to companies with Freemium or Self-Service products. Clearly, this makes a ton of sense for these companies — but they can also be an incredibly useful tool for identifying customers with the highest growth potential and also anticipating churn.
PQLs is what will bond sales, marketing and product teams together in the same way that MQLs bonded sales and marketing.”
Contentful is a prime example of a company using the PQL model. Contentful is a content management system, and as an API product, they look for accounts with more than 500 API calls. This is key usage data for Contentful: for them, an account with over 500 API calls signifies a high-value potential customer.
According to Chris Schagen, Contentful CMO:
“If somebody is doing 500 delivery API calls, that’s a good indicator that they’re going to use us for real. If you cross that 500 threshold, chances are you set something up. That POC’s up and running, and you delivered some things of worth. Before a user has crossed that threshold, it’s more likely that they’re just playing around, testing things out.’ After 500 API calls, there’s a good chance that they become a legitimate user.”
By now, you may be wondering: what makes PQLs so much more powerful than MQLs? We’ve identified 4 key reasons:
PQLs are “free”
If a product sells itself, and no hand-holding is required to convert a PQL into a paying user, then the company wins a “free” customer — one with little to no labor cost involved (apart from product development costs). You yourself are probably a “free” customer of many SaaS solutions — e.g., Netflix or Amazon Instant Video, your project management platform (Asana, Basecamp, etc), your doc service (Google Drive, Dropbox Paper), and the like.
PQLs are infinitely scalable
Because PQLs require little to no human touch to convert, they’re scalable in a way SQLs — which require a dedicated rep to guide the buying process — can never be.
PQLs are highly qualified
Redpoint VC Tomasz Tunguz found that when a human touch is needed, and sales reps do make calls to PQLs, those customers “typically convert at about 25-30%.”
This may happen because of the equally useful reverse effect: when sales reps qualify leads based on product usage data, they can unqualify leads much more effectively — as Appcues did, which allowed them to concentrate their small sales team only on the accounts most likely to close.
PQLs align the entire company
Finally, PQLs align outward-facing and inward-facing teams around the same goal: revenue.
As Tomasz Tunguz puts it:
“Typically, the product and engineering teams don’t have goals tied to revenue, which bisects a team into revenue generating components (sales and marketing) and cost centers (eng and product). Aside from potentially creating cultural challenges, this structure is less effective than it could be. PQLs pull product and engineering into the fray. Everyone in the company has the same goal.”
Getting started with PQLs: a process of gathering data
Designing an effective process for converting PQLs to customers requires a deep understanding of:
- Who your users are
- What they’re doing on your marketing site + inside your product
- Why they were motivated to sign up
- Which big goals they’re trying to achieve with your product
Without a clear understanding of these details, you’ll struggle to effectively personalize, segment, and craft experiences that convert trial users to paying customers.
To identify these details and begin setting up a strong PQL system, gathering and leveraging quantitative and qualitative customer data is key.
Quantitative data: trends in HOW people use your product
By collecting and leveraging quantitative customer data, you’ll be able to answer questions like:
- Are your most successful users taking certain actions, while less successful users aren’t?
- Do ideal users share common traits — goal with using your product, job title, industry, company size, stage of growth, etc?
- Which actions and traits indicate long-term usage? Which indicate high value spend?
For example, Mixmax team knows that for a new trial user to get value from the platform — and eventually upgrade — she has to do more than simply click around her account, exploring the features available to her. To get value from MixMax, she has to actually integrate at least one of those features into her daily workflow.
“What we’re looking for,” says Olof Mathé, Mixmax CEO, “Is whether a user has actually used calendaring. Have they actually set up an automated email sequence? Are they actively using any email templates? Did they co-opt into Salesforce, or set up our Slack integration?”
Beyond this first action, a new user might take additional actions that hint at greater account value — like inviting team members to join Mixmax with her.
Qualitative data: trends in WHY people use your product
By collecting and leveraging qualitative customer data, you’ll be able to answer questions like:
- What was so painful about users’ old lives that pushed them to seek out a solution like yours?
- How did they discover you in their search?
- Why did they trust your product, as opposed to other options?
- What happened in their early days of use that convinced them “Yes, this is exactly what I needed”?
- What event motivated them to take out their credit card?
This knowledge allows you to perfect your communication with PQLs, so your team can be as relevant and helpful as possible. As Emmaneulle Skala says:
“NEVER offer help if it’s not needed – There’s really nothing worse than ‘fake help.’ Please do not just say ‘I see you downloaded and want to learn more about your needs.’ If you don’t have any relevant context or reason to call, then don’t! In my experience, when there is no context it means the person is likely not going to convert or they’ll reach out on their own when they are ready.”
Bringing it all together
When you identify the trends in how and why people use your product, you’ll have a clear view of the ideal experience your highest-value prospects need to convert to paying customers.
But gathering customer data isn’t a one-and-done activity, and neither is designing a PQL process. By defining PQLs and outlining what types of data to collect, we’ve just scratched the surface. This much more to dive into — and without some guidance, designing a PQL process can be quite overwhelming.
Having watched countless marketers struggle to collect the right customer data and plot out high-converting PQL communication, we reached out to leaders in the SaaS community to ask how they’ve designed their PQL processes (and what lessons they’ve learned along the way).
We’ve collected those leaders’ lessons into a step-by-step PQLs guide, and made the entire thing available for free.
If you’d like to get an in-depth PQLs framework — plus stories from leaders at companies like DigitalOcean, Moz, AdEspresso, Typeform, and more — you’ll want to download our Complete Guide to PQLs today.
