What’s a PQL?
Let’s start with the basics. You’ve likely heard of or are identifying MQLs and SQLs, but what about Product Qualified Leads or PQLs? If your company is product-led, it’s an essential benchmark for qualifying your users and predicting your success.
“A product qualified lead, or PQL is a lead or user who has experienced meaningful value using your product through a free trial or freemium model.”
To begin building your PQL model, you first need to consider all the available data. While the typical definition of a PQL doesn’t make mention of demographic data, It’s recommended to use a mix of fit and engagement data for optimal targeting. Let’s break down these two types of data.
User Fit Data
Think of fit data as the “who”. In other words, does the person and/or company who is trying your product fit your Ideal customer profile (ICP)? Typically, there are two classifications of fit data.
Explicit Data – Explicit data is information that is provided by the user, usually through your products’ signup form or onboarding flow. At a minimum, it’s recommended to collect a user’s name and email address upon signup, but it’s also common to ask for a few more pieces of the puzzle such as company name, job title or phone number. This data will help you gather more information through the 2nd method – implicit data.
Implicit Data – Implicit data is information that is not provided intentionally but gathered from available data streams. This can be done manually or through a data enrichment service like Clearbit. Through data enrichment, you can further strengthen your data signal with pieces of information such as company revenue, what software they use, their location or industry.
User Engagement Data
Engagement data is created by monitoring the prospect’s user behavior inside of your product – either while they are participating in a free trial or using a free version of your product. While there is likely a myriad of data points to draw from when analyzing product usage, most companies start with a simple set of actions they deem valuable to a user gaining traction within their app. If you’ve already defined what user activation is within your product, that’s the perfect place to start.
If you’re just getting started, here are a few examples of early signals to utilize for an engagement score:
- Total number of logins
- Number of interactions with your support team
- Have they connected integrations or data sources?
- Total number of invited users in an account
- Did they complete the onboarding flow?
- Other key actions that are unique to your product. For instance, if you’re a survey company, has the user created a survey or collected responses.
Once you have determined your data points, it’s time to begin scoring your users. This step will help guide your team to effectively and efficiently convert more accounts by deploying different nurturing or sales tactics.
There are a number of ways you could approach this. For the sake of illustrating this as clearly as possible, we’ll keep it simple with a low amount of variables.
PQL Fit Scoring
Let’s say you’re product is aimed at marketing and sales professionals in the SaaS industry. In addition to SaaS, you also sell to agencies who service your target market. In general, you see the best results from small to mid-sized businesses with 0 – 250 employees. Let’s also assume you only have access to form data and are using an enrichment service. If you are able to compile the conversion data to inform your point values, that’s great. If not, just start somewhere. Perfection is the enemy of progress – you can always iterate.
PQL Activation or Engagement Scoring
Continuing with the theme of survey software, let’s add some key behaviors and start with some baseline values. You can add as many events or actions that you can track here, but I would recommend starting with at least three key actions that your users who convert to paying customers almost always take. As you increase in sophistication, you may want to consider a score decay action that removes points for lack of engagement over meaningful periods of time.
Calculating your PQL Score
Once you have your point values in place, it’s time to begin bucketing your users based on their fit and engagement scores. We started with five grade buckets and three scoring buckets, but you can certainly limit or expand these. For instance, you could limit the grade to only A, B & C.
We’ve used color to illustrate where you’re best prospects are in this scoring matrix. Green = good/likely to buy. Yellow = average chance of closing/needs attention or nurturing and Red = low value/unlikely to close.
Congrats! You’ve built your first PQL scoring model. Now that you’ve got your model in place, you can use these score bands to assign sales reps, deploy marketing automation campaigns and run tests.