This is part 2 of a 3 part series where we explore product-qualified leads (PQLs). We discuss what PQLs are, why you should be using PQLs, and how to define them to supercharge your sales team. The purpose of this guide is to help product-led growth (PLG) companies develop, establish, and operationalize their product qualified leads (PQL).
Read the entire series:
- Part 1: How to define and Identify Product-Qualified Leads
- Part 2: How to develop your Product-Qualified Lead Engine
- Part 3: Advanced PQL lead scoring concepts
Aaron Geller, Director of Sales at Cypress and Joe Krawiec, Analytics Engineer at Cypress have built, operationalized, and scaled PQL engines at not one but two organizations - first at DigitalOcean and now working together to re-create the PQL magic at Cypress.
Needless to say, these two know what they’re doing and have some invaluable insights to share when it comes to PQLs.
Keep reading to learn:
- Real world strategies for experimenting with PQLs
- The exact process Aaron and team followed to launch PQLs
- How to start automating your PQL process
Picking the right time to launch product-qualified leads (PQLs)
Working at DigitalOcean, Aaron and Joe saw firsthand the power of a bottoms-up, self-serve model. As Aaron described, they had a “golden funnel” of sign ups on a weekly basis so there was a lot of data to analyze and patterns to uncover. They were beginning to see how users convert and naturally move toward expansion.
So how did Aaron and Joe know it was the right time to get started with PQLs?
Based on their experiences at both DigitalOcean and Cypress, Aaron and Joe recommend starting to think about PQLs when you are ready to be proactive rather than reactive. In describing the feeling, it sounds more like art than science, “when I joined, we were very, very reactive...we saw hand raisers left and right, we were closing deals left and right” according to Aaron. But, no one was proactively seeking out these accounts that were ready for expansion.
As we mentioned in last week’s blog, when your self-serve user growth reaches a critical mass you will begin to see buying intent signals that indicate a user is ready to do more with your product.
If you see this, there’s a pretty good chance you’re ready to start exploring a sales motion to complement self-serve, but in Aaron’s experience, you need to first answer a few key questions of what you are trying to accomplish with PQLs:
- How fast does your company want to accelerate revenue?
- If you have a sales team, can they handle more right now?
- Can we increase the average selling price (ASP) of these hand raisers?
- Can we convert users to higher ASPs?
- Can we increase the Net Dollar Retention (NDR) of these customers in the long term?
- Can we get them to adopt multiple products faster?
Key Takeaways on When to Implement PQLs:
#1 Don’t start thinking about PQLs until you have a few “hand raisers” and can find at least 10 other accounts in the data with similar patterns
#2 If you decide to start thinking about PQLs, make sure you avoid being a blocker to your users as you start this process. Don’t throw additional friction into the process if you don’t need to.
Getting buy-in on the PQL project
For Aaron, five plus years ago at DigitalOcean was a totally different world. Product-led growth wasn’t quite the industry buzzword yet, product-led sales was certainly not in the vernacular, and the playbooks did not exist for sales teams at PLG.
As he recalls, “Five years ago, I had to do a lot of convincing...I tried to go to the data team, and they're like, oh, we're really busy. Let me send you this list.” But then Aaron began uncovering patterns, which he took back to the data team, got buy-in that he was onto something and suddenly he had the initial case for building PQLs. Once Aaron was able to show the team the proof was in the numbers, the team bought in. Those early believers on the data side like Joe, helped Aaron get lift off for product-led sales at DigitalOcean.
Fast forward five years later, PLG is well known and many more teams are attempting to layer on sales. This time around buy-in wasn’t about education, it was all about experimentation with a small team and showing real results.
It also didn’t hurt to have leadership who understood just how important expansion revenue was to the company's overall goals. Aaron and Joe credit that understanding for making it easy for them to hit the ground running.
Key Takeaways on Buy-In:
# 1 If you can show rather than tell, try and hit the ground running with a lightweight experiment to gain buy-in for the larger PQL project
#2 Build consensus by partnering with other teams, like the data team (in Aaron and Joe’s case), marketing, or growth to validate your assumptions around PQLs and use their support to sell the project to leadership
Experiment first, then iterate
Aaron and Joe follow a simple formula for their PQL testing framework:
Step 1: Formulate a hypothesis for your PQL definition (see part 1 for more details)
- Try writing a mad-libs style statement: “We believe our best fit customer is ___________ because they use the product on average ____ days per ______ (week/month/year), for __________ use cases, their company profile matches ________ and they say the product adds _________ value to their company/workflow/workday.”
- Work backwards from the data and pick a key metric (i.e. usage spiked XX% in the last month)
Step 2: Pick a duration for the experiment
Don’t let your experiments run forever. Aaron recommends 30 to 60 days for an experiment, but this will also depend on your sales cycle.
Step 3: Define success metrics
You need clearly defined KPIs for any PQL experiment. Trying conversion or win rate is a good place to start, but don’t forget the softer metrics you may care about like overall efficiency of the process i.e. does the PQL make sales reps life easier?
Step 4: Pick your control and test groups (50/50 split or 75/25 - it's up to you)
This step is best done through a randomized process so you don’t create any bias in the groups.
Step 5: Analyze the results
Sometimes the results aren’t entirely black and white. According to Aaron and Joe, you may need to talk to your sales team and get qualitative feedback in order to decide whether the PQL is worth it. The data doesn’t always have the answers so don’t be afraid to talk to your sales reps and then make a decision.
Key Takeaways on Experimentation:
#1 Be really clear on your KPIs for experiments
#2 Don’t be afraid to dig into inconclusive or “failed” experiments by talking to the team. You may not want to throw the baby out with the bath water necessarily
Once the PQL is proven successful through an experiment, and the team is ready to review them on a regular cadence, it’s time to operationalize the PQL!
At Cypress, Aaron and team have devised a pretty easy to follow but detailed process for operationalizing new PQLs. It starts with this PQL template, a brief, and sometimes a lucidchart to keep everything organized.
Essentially, each section of the template informs a piece of the operationalization flow.
- Name + describe the PQL
- Anticipated launch data
- Team(s) involved and identifying their roles
- Assets required
- Data/tools required
- A/B testing details - choosing split, duration, test and control group assignment
- Messaging and sales playbook recommendations
- Metrics impacted
- What to measure against control group
Assembling the team + sharing the playbook
Operationalizing PQLs can be a tricky process and requires buy-in from the broader organization, as mentioned above. Aaron and Joe strongly emphasize the need to gather the right team. In their case, growth was a critical role for getting broad alignment across their customer-facing teams, product team, and data team.
At Cypress, Aaron and Joe facilitated very detailed education with the sales team (their primary consumers of PQLs) but also extended these enablement sessions to the broader team.
Beyond enablement, it’s important to meet on a regular basis and review PQL progress. At Cypress, Aaron and Joe held “PQL Monthly”, a meeting dedicated to reviewing their PQLs, conversion rates, and ideate on iterations.
Picking the tools
Perhaps the biggest lift when operationalizing PQLs comes down to the data infrastructure. Investing in the right tools for data infrastructure will pay off in the long haul. Currently at Cypress, they are using a variety of tools to ensure that the data can be understood and delivered to the right endpoints, such as Hightouch and Looker.
Further up the stack, the operationalizing of PQLs currently happens by writing queries with Looker/Metabase and inevitability pushing those to Salesforce for the sales team to engage with.
There are a variety of tools out there that can be used to achieve this motion. We’ve written in more depth about the PLG tech stack in a previous post, read it here.
Automation and iteration
Automation in the PQL process is all about making sure it’s easy for your team to view the latest PQLs and take the next best action given the data. Whether it’s creating new lists in your CRM or a weekly report that gets refreshed in Looker - Aaron and Joe suggest making this easy and streamlined for your sales team.
Joe and Aaron use the reverse ETL tool, Hightouch, to pipe product data into Saleforce. They will be implementing Pocus as the product-led sales platform to help further operationalize PQLs down the line and eliminate some of the manual efforts in the background. Pocus will help Aaron and the team identify & prioritize their top accounts and top users within those accounts, send alerts to their sales reps when certain thresholds are met, and provide insights so Cypress can take the next best action… all in one platform.
Once you have one PQL operationalized and automated, it’s time to iterate on new definitions. You’ll want to start the experimentation process over to see if there are different types of PQLs you should be capturing.
During Aaron and Joe’s tenure at DigitalOcean, they went from 0 to 10+ PQLs that were actioned daily by the sales team. This was built up over time as their product-led sales function matured (hint: you don’t need that many to get value from PQLs).
We’ll be diving deeper next week on how to go beyond sales for PQLs, how to scale up to multiple PQL types, and composite scoring.
Key Takeaways on Operationalizing:
#1 Documentation and organized process are your friends
#2 Regular cadences help keep everyone on track and keep your PQLs fresh
PQLs can be incredibly powerful tool for your entire organization. The process of experimenting and operationalizing PQLs fosters cross functional collaboration, refocuses teams on the right things, and leverages valuable data you already have.
If you have questions about PQLs and how to operationalize them within your organization, join the product-led sales community slack channel. You can ask folks like Aaron and Joe questions about their experience, what tools they use and more.
What’s next? In the Part 3 this series we'll dive into taking PQLs to the next level. Once you have one PQL, how and when should you add more definitions, what are composite scores and how to use PQLs beyond the sales team.