By analyzing historical usage data, you can look for patterns that indicate your best-fit customers' actions and the "aha moments" that lead them to unlock value. These signals, or combination of signals, will help you craft scoring models to surface the best opportunities from your existing pipeline of users.
🔮 Or use a tool like Pocus to speed things up :) 🔮
Here’s a helpful framework you can use to start defining PQLs. Ask yourself these questions, write down answers and then go validate it with your data team.
How closely does the account and/or user match your Ideal Customer Profile (ICP)? (eg. industry, geography, company size, user role/title).
What actions or events in the product (at both user and account level) correlate with retention and conversion?
How do you weigh certain activities, whether in product or off-product (visited pricing page, clicked “talk to sales,” added new seats to account, etc) to gauge buying intent?
These categories help you decide how to identify and prioritize various scenarios of leads. You’ll ideally look for customers with great customer fit and high product usage. But, you should also be aware of customers with great fit and low product usage, and vice-versa so that you can nurture those users or accounts to ensure they are realizing value from the product.
We find this matrix below helpful in understanding the various scenarios and mapping your PQL definition (hint: it’s up and to the right).
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These are PQLs/PQAs for your sales team to prioritize!