Putting PQLs into Practice at Your Organization
More and more companies, especially at the seed stage, are building freemium and free trial offerings to acquire prospective users. Freemium can be an amazing acquisition engine, opening the top of the funnel and halving your customer acquisition costs (CAC) during a period where the industry as a whole sees CAC on the rise. That being said, freemium can be too much of a good thing. Sometimes, you’ll find that your organization has lots of users in the product who may not be a great fit for the paid version of your software, or you have more leads than your inside sales team can really handle, and they have a hard time honing in on the best prospects. This can lead to a lack of focus and probably a decrease in sales effectiveness over time.
Hopefully, your team is already leveraging sign up forms to identify and hunt the “whales” trying out your product, but those leads may still not be ready to engage with your sales team, or there may not be enough of them to feed the revenue machine.
“Lead scoring” is not the answer to your problems
Google “Lead Scoring Tools” and you’ll get ~60M results, and tons of ads that promise you the world in terms of predictive lead scoring and AI. Honestly, I’m a machine learning enthusiast in my spare time, and I have no inkling of what models, etc. these companies are using to produce effective prediction at scale for companies seeking to find “the perfect lead.”
Warning: Many of these tools may look like automated and scalable ways to help your team define and deliver leads who are engaged with your product, and aren’t just fulfilling the BANT framework (Budget, Authority, Need, Timeline). But, they’re typically only using surface-level information like website visits, Clearbit lead segmentation and some information about how prospects are interacting with your marketing. If you’re operating with a freemium or free trial model, this just isn’t enough. You owe it to your team to do better.
Enter: Product Qualified Leads (PQLs). Today, 55% of companies value PQLs, but only 23% of companies have actually begun to track PQLs through their acquisition funnel. What are PQLs? PQLs are leads that identify themselves as high-priority by their usage patterns within the product. Unfortunately, there’s no one-size-fits-all way to identify a PQL, but our beginner’s guide to PQLs should help you get started with an MVP.
Who owns PQLs?
PQL ownership can get a little confusing. Maybe because sales owns sales qualified leads (SQLs) and marketing owns marketing qualified leads (MQLs), so people tend to think that the product team is responsible for identifying and creating more PQLs. This could be true, depending on your organization, but if you have a growth function, that team is most likely the best equipped not only to identify the indicators that create a PQL, but also to own and manage experiments that increase the volume of PQLs over time.
But once I find these hot leads, how do I actually get them to my sales team?
This is possibly where the generic “lead scoring tools” have a leg up. Most of these tools have built numerous integrations to popular CRMs, and most CRMs have already acquired some sort of magical sounding lead scoring startup that they’ve integrated into their platform (I’m looking at you Salesforce Einstein), so it makes it simple to start feeding these leads.
I haven’t found many tools that take rich product analytics data (usually the forte of the nerds in another room, kept far away from sales) and deliver it directly into the tools that revenue teams live in every day without substantial engineering work or high costs from your CRM provider. But, in order to generate revenue from PQLs you have to deliver them to the sales team! So, I’ve put together a few cheap and easy ways that I’ve delivered PQLs to sales teams in the past, and some new products that seem promising.
A word of warning: Ideally, you’d meet with your Head of Sales Ops and your Head of Sales before starting this project. While I’m sure they’d love to start seeing better leads come through their funnel, a little ramp time for them to prepare their teams and think about PQL distribution will go a long way in proving out the success of your PQL program and making sure those leads convert.
Using the analytics tools you have
Typically, I use a data visualization tool to build out tables of my product qualified leads using all of the metrics I’ve identified that makes them so special. For example, if I found that a lead in my funnel is 5x more likely to convert if they’ve:
- Spent more than 30 minutes in the application
- Added 2+ users
- Visited the billing page
Then I would build a table pulling data from my sessions table (i.e. session_duration for a user_id >30 minutes), and joining it on my server-side data (i.e. user_id has >2 users and >1 visit to the billing page). Those would be the filters for my table, which would contain information relevant to the sales team like lead name, lead sign up date, marketing channel, phone number, email, etc.
I’ll use Looker as a BI tool for an example, but most BI tools have the ability to auto-export data in a CSV or email format, see below:
In this scenario, I’d set up an email with an attached CSV file every time a new lead comes in to my PQL table. Now, I could be lazy and just have these emails forward directly to some BDR responsible for checking out new leads, but if you want to take your PQL program to the next level (and put a smile on your sales team’s face) you can take this a step further by parsing this notification email in Zapier (it sounds hard, but it’s really easy) and creating or augmenting a lead in your CRM of choice.
Appcues is a tool known for its product guided walkthroughs, NPS, and checklists, not for its analytics tools, but I found a way that you can leverage Appcues to notify your team about PQLs. Appcues recently created a new feature called Segments. This allows you to define certain buyer segments based on actions they’re taking in your app (or how they’re interacting with your Appcues flows) and join a list. Once they’re in that list, you can leverage the free Appcues and Zapier integration to send those PQLs to the CRM of your choice, or pay up for the direct Salesforce integration.
New Tool Alert: Hull.io
I found Hull.io during my search for lead scoring software, but they caught my eye because they leverage product engagement data from your SQL-based warehouses, or via their Segment integration. Looks like they have a free trial (like any good PLG company these days) so it may be worth a signup if you’re looking for a more comprehensive solution.
Getting fancy with it
Some teams will want to build their own tools no matter what, maybe to meet customer security requirements, or to cut up-front costs. As a growth professional, this may be frustrating, as you’re going to need an engineer or two to help you set up your PQL pipeline to your sales team.
In the past, I’ve seen this done somewhat easily by engineers creating a Lamda function in your AWS instance, then taking your lovely PQL data table, listening for new row creation events, and sending data to a Salesforce endpoint to generate or update leads with qualifying information. Now, as a growth professional, I’d be upset with the amount of time that this would take to build, especially when those leads need to reach the sales team, but if you’re looking for scalability, this is the ticket.
Show me the money
Once you’ve begun to shower your sales team in PQLs, your job isn’t done. Growth is all about small, impactful experiments, and measuring the outcomes of your work on the business so you can continually optimize. Your next step is to build reporting in your CRM that helps your team to understand the velocity of PQLs through your sales funnel, PQL conversion rates, and expansion opportunities.
Try and review this information on a regular basis not only with the sales team, but also with your product organization, one of the best things about PQLs is they’re everyone’s job to create, convert, and grow, so they’re a useful tool to align the rest of the team on PLG.
We monitor these closely within our portfolio at OpenView.
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