You’ve probably read that Product Led Growth (PLG) was one of the top trends in 2021. And in 2022, it shows no signs of slowing down. As enterprise software companies like Snowflake, Twilio, Slack, Shopify and others have IPO’d and attained $10 billion+ valuations, they have attributed this success to combining a self-service PLG motion with enterprise sales.
These companies all have Net Dollar Retention (NDR) percentages at or above 130%, which are primarily the result of a “land and expand” go-to-market motion. That motion combines a frictionless, self-service product that allows an end user to get to value quickly with an ability to upgrade seamlessly by adding more seats, products, data consumption etc. Enterprise sales is then layered in to go “top down” and sell into accounts where there is often substantial self-service paid usage. We call this combined motion Product Led Revenue and we think it’s the predominant model software revenue teams will deploy over the next 5-10 years.
Product Qualified Leads (PQLs) enter the scene
Running this product led revenue playbook is how decacorns ($10B+ valuation) are made, so how can an aspiring hyper-growth sales team replicate this model? Most teams so far have been employing a lead scoring model that’s meant to be tailored to PLG companies. We’ve written about Product Qualified Leads (PQLs) before so head over to that post for the full overview. In short, a PQL combines product behavior with firmographic (company industry, employee count) and demographic (title, seniority) signals to arrive at an aggregate score. When a self-service user performs actions in the product and matches the right scoring criteria, they’ll be routed to sales as a lead to reach out to.
Common PQL Mistakes
There are many challenges with only using a PQL score to run a Product Led Revenue playbook, here are the top three we see most frequently:
- A single score is a “black box” and doesn’t provide context or instructions to sales and leads to lower usage by the sales team.
- PQL creation is often owned by the data team which makes it hard for sales teams to make changes to the score based on what’s working or not working. We’ve talked to leading PLG data teams that have spent 12-18 months(!) creating and rolling out their PQL scoring system.
- Scores don’t make it easy for sales teams to craft personalized messages, they have to dig through multiple dashboards or reports to understand what to say. If you’re using sales engagement platforms like Outreach, it’s hard to automate AND personalize your cadences just using PQL scores.
How to do PQLs the right way
So what does an ideal Product Led Revenue strategy look like? How should you incorporate PQLs to drive higher self-serve conversion rates and expand revenue faster? Here are some of the top recommendations we’ve put together from working with some of the leading SaaS sales and revenue teams.
1. Utilize Signals and Playbooks
Rather than a “one size fits all” lead score, we recommend breaking down how accounts and individual users are using your product and the context of their usage to create customized Signals.
These Signals can then have playbooks attached. Here are some examples:
- Let’s say you are on the sales team at a popular messaging application product. You’d want to know when a self-service Account has seen week over week growth in messages sent and focus your messaging on the most active users within that account that have relevant titles.
- When a user tries to use Feature X, immediately send them an email letting them know how they can unlock that feature and offer to jump on a call to understand use cases for the feature. Not only was your message personalized, but it was timely too.
- When 3+ users at the same company reach a high usage threshold, send an email to the manager/potential buyer at that account and let them know the value their team is getting, offer a discounted team price and a team training session.
2. Segment who responds to PQLs
Many top PLG SaaS companies will have specialized teams, in addition to or instead of SDRs, that helps run PQL playbooks. Titles can include sales assist, product specialists or Growth AE. Often the goal of these roles is to help paid, self-serve customers expand their usage, ultimately spending more.
These roles utilize PQLs that indicate when customers could use additional assistance such as the fact that they tried a new feature, filed a ticket or started a new workspace utilizing a new product. Even in companies without these specific roles/titles, we often see collaboration between sales and CS when identifying customers who are ready for expansion based on their product engagement.
3. How should companies tackle building PQLs?
This is an entire blog post in its own right, as evidenced by the example of the company that spent over a year building the PQL data model. The summary is that in order to build a process and set of playbooks that leverage PQLs, you need clean customer data. Whether that’s coming from your product, your CRM, your billing system or any of the hundreds of SaaS tools you might use that touches your customers you need access to much of that data in a usable way. Today that often means utilizing a data warehouse like Snowflake, BigQuery or Redshift to centrally manage your data. Here's another post with more info.
I’m ready to run a PQL playbook!
Ok, you have your customer data ready to go, your sales team is armed with playbooks and you’ve built out a set of potential signals that will comprise your PQL strategy. Now how do you put it into practice? Well that’s where Correlated comes in. If you’re interested in learning more about how the best PLG SaaS teams use Correlated to build PQL playbooks, get started for free here.