The Definitive Guide to PQLs

Learn why should care about PQLs and how you can leverage them to prioritize high-value leads, create lifecycle marketing campaigns, and automate sales processes.

Customer acquisition is the backbone of every business. With so many acquisition channels today, qualifying leads and identifying which prospects are most likely to translate into paying customers is a challenge; and figuring out how to convert those leads by moving them from the top of the funnel to the bottom of the funnel while providing a positive user experience is even more difficult.

For a long time, SaaS companies have operated in the same way, using MQLs (marketing qualified leads) and SQLs (sales qualified leads) to determine the quality and real buying intent of prospects. MQLs represent leads who have interacted with specific marketing content (i.e. page view, form submission, ebook download, webinar signup, email open, etc.) and SQLs represent leads where active conversations are ongoing in the sales process. In general, marketing teams generate MQLs, and sales teams work to convert MQLs to SQLs.

The problem is, these models are based on leads who are not actually using the product yet. For PLG (product-led-growth) SaaS companies letting users “try before they buy”, the old model doesn’t work. The PLG business model hooks the user by providing real value before asking for anything in return and this is why PQLs or product-qualified leads are so much more reliable (they create a self-serve model where users are not reliant on sales teams).

Whereas MQLs consist of leads who have had interactions with marketing content and SQLs represent prospects in the sales pipeline, PQLs are actual users who are actively engaging with the product. PQLs focus on systematically identifying leads with high product usage who are likely to convert into paying customers. A product-qualified lead is a much better indicator of buying intent because it captures users who are already leveraging a freemium model or free trial of a product. Streaming services like Netflix and Disney+ are great examples of this; both products offer trial users an entire month completely free of charge.

Likewise, product-led-growth companies like DocuSign, Calendly, Grammarly, Slack, etc. have completely revolutionized the business ecosystem by offering trial periods or freemium features where additional features are locked behind a paywall. PQLs have a major advantage thanks to this new self-serve model where users are not forced to sit through a demo given by a salesperson or engineer. Whereas MQLs and SQLs are defined by marketing and sales teams, PQLs are unbiased because they are based on product usage. In addition to this, PQLs receive a better customer experience and this translates into more happy customers overall. Prospective customers or freemium users sell themselves on why they should purchase the full product. In general, the highest buying intent PQLs or most successful users tend to be frequent customers who navigate many product features and ask multiple product-related questions. Product teams can then use the product behaviors collected from PQLs to add new key features and experiences to their end-users.

Aside from reaching the highest-value leads at the perfect time, PQLs remove the guessing work that often comes with conventional MQLs and SQLs because the information is immediately evident and can easily be paired with other existing customer data to create a 360-degree view of the customer. There are several reasons why PQLs should be prioritized over other types of leads:

In addition to this, PQLs provide several benefits because they enable sales reps and marketing teams to create custom audiences based on specific product usage or other key metrics. These custom audiences can be tested with various forms of messaging to determine what actions lead to a higher conversion rate. Additionally, the results of these types of tests and experiments can be made available with attribution so that business teams can optimize successful campaigns and strategies.

Since every company has a unique product, the actions that individual users take while using that product will be different, so figuring out what key metrics are most closely correlated to a higher user adoption rate is extremely important. For example, a communications company like Slack is based around active workspaces, so its activation process is linked to metrics like messages sent, active users, invites, number of channels, etc. On the other hand, a company like Grammarly which helps users improve their writing likely tracks activation progress based on factors like words typed, spelling errors, words checked, writing style, etc.

While PQLs should largely be based around product usage, the customer profile and overall purchasing intent should not be discarded. There are some cases where a potential customer might closely match a company’s ICP (ex: job title, company size, geography, etc.) but not be using the product much after signing up. Alternatively, there could be a scenario where a user has expressed buying intent by visiting the pricing page and trying to schedule a meeting with sales. While neither of these prospects is actively using the product, they are both still giving off key indicators which would make them ideal PQLs for sales and marketing to target. With all PQLs, it is important to evaluate the overall customer profile, product usage, and buying intent to determine how an account or prospect should be nurtured through the product activation funnel.

Defining what exactly a PQL is, within the context of a given company will vary drastically. To answer this question, it is best to work backwards and search for common similarities across customers. One way to do this is by having conversations with customer-facing teams like sales, marketing, customer success, and customer support since they are often on the frontlines, engaging with product users on a daily basis. Another way to do this is by analyzing specific customer behavior to determine what actions closely align with the buying process and specific qualifying actions:

The goal of this is to identify solid PQL definitions to shorten the customer journey. For some companies, there may be multiple variations of PQL, all with different qualification levels.

These are generic examples, but more granularity into the PQL definition allows for more personalization

Since SQLs and MQLs typically only exist in a CRM (i.e. Hubspot or Salesforce), it can often be challenging to figure out how to implement PQLs on top of these other models, especially since product usage is not something that is natively captured in these platforms. For most organizations, product usage data is stored and collected in a data warehouse like Snowflake or BigQuery.

The easiest way to begin leveraging PQLs is to leverage the existing data models within the warehouse to create an engagement strategy. This can be done by building custom audience segments (ex: users who signed up in the last week and created an active workspace). However, a more efficient method that many companies use when it comes to PQLs is lead and account scoring. This is typically done by assigning qualification numbers to contacts or accounts based on specific criteria (ex: users who are actively using a product and maxing freemium usage would be rated higher than prospects who signed up but have never used the product.

Typically, these types of data models already exist in the data warehouse. However, getting access to this information is always difficult because the data is only accessible to technical users who know how to write SQL. In order to operationalize this data and move it out of the warehouse, data teams are either forced to download specific data sets as CSV files or create custom data pipelines and integrations for various SaaS tools. Neither of these solutions are preferable because CSVs are static, becoming unusable really quickly; and custom integrations are time-consuming to build, prone to failure, and difficult to maintain. In both scenarios, defining a PQL is solely dependent upon the data team.

Once a PQL has been defined and synced to the appropriate SaaS tool, it is relatively easy to start acting on this data. For example, Hubspot has a workflow feature that enables users to create if/then branches that kick off specific actions or touch points defined by the user (ex: sending a welcome email to new trial users or calling a prospect after a certain amount of product usage has been met). This same type of workflow could also be applied from a lifecycle marketing standpoint (ex: sending a resource to users who have not used the product in the last 30 days). These workflows can also be tailored with multiple touchpoints to push the customer through the marketing funnel (ex: send promotional offer if no response in X number days). PQLs allow for near unlimited personalization and create successful customers. Companies not leveraging PQLs will always be stuck doing generic outreach.

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