As many companies make the transition towards a product led growth strategy, the introduction of the product qualified lead (PQL) has been essential to prioritizing sales outreach. Let’s breakdown the marketing qualified lead (MQL) and product qualified lead (PQL), along with a peek into how some of the leading product-led companies are solving this today.
What is a Marketing Qualified Lead (MQL)?
Marketing qualified leads (MQLs) combine together marketing data (like website visits, events attended, and ebook downloads) along with demographic and firmographic data (like a person’s title, company size, ICP fit, etc). MQLs have been the primary way for companies to prioritize who they reach out to within their existing CRM database to find potential customers. But in the era of Product Led Growth (PLG), many of the people in your CRM are existing users of your platform. They may be free users or on a lower tier of your product. So who should you prioritize reaching out to? In walks the product qualified lead (PQL).
What is a Product Qualified Lead (PQL)?
Unlike MQLs, there are often many versions of PQLs (i.e. you might have 5+ PQL definitions). Similar to an MQL, this is usually some combination of demographic and firmographic data (like a person’s title, company size, ICP fit, etc) but instead of marketing data, you’re adding in product usage data (like number of users in an account, number of logins, certain features used, etc). Trying to figure out which product usage metrics correlate with conversion, cross-sell, or upsell makes for an added complexity for PQLs over MQLs, which is why companies like Correlated have started using machine learning to automatically uncover PQLs for your team. But - enough definitions, let’s dive into how companies are uncovering product qualified leads from some of the leads in PLG.
How to create PQLs at your company
We’ve been lucky enough to bring together a vibrant community of growth, product, sales, marketing and CS leaders at product led growth (PLG) companies like Monday.com, Slack, Twilio, MongoDB and more. We’re highlighting some of the great conversations and learnings that are happening in our bi-weekly chat and Slack Connect group. Join the PLR chat invite here and if you’d like to request to be added to the Slack Connect group join here.
PQL question from Sarah at Predictive Index:
Hey all! We've been experimenting with sending handraisers to AEs right away in our sales led and product led funnels. They book time straight away for the 1 hour demo, with a pre-qual survey on their interest areas. We also have SDRs booking one hour meetings with AEs for a typical sales led process. My question to you all, is what criteria have you set for what defines a SQO in a PLG world? We noticed originally, our threshold was too high where anyone that had interest, got basic understanding of pricing, agreed to continue forward was a SQO and our close rates were crazy high and tough to compare to historic rates for meaningful insights. So we tried to say anyone that meets with us for an hour is a SQO and/or shows buying intent by inquiring about pricing next steps, which was closer to our typical BAU sales led process where an SDR had a pre-qual call and then booked time with the AE. However, now I fear we've over indexed in the other direction with crazy SQO conversion rates and not all really would be considered SQOs had they gone through our previous process. Ideas? What are you using?
How Monday.com uses MQLs and PQLs
The following is an edited transcript from a conversation with Aron Vuijsje (thank you!).
- The first funnel is the MQL where we look at data easy to identify without a conversation (Company size, region, segment, usage if on free trial etc.). This allows us to start segmenting and prioritize the best leads that come in via self-serve. For example, last year sales didn't touch any companies below 100 employees (today with SMB team that is 20 employees). Anything below that threshold by definition doesn't go to sales, but is instead handled by customer success. For us, 20-100 is handled by the SMB team, 100-1500 by mid-market and 1500+ by enterprise.
If you are lucky enough to have more leads than you can handle, filter it down further by (titles that signed up, usage, areas that show interest for larger deals like white paper, clicking on enterprise pricing, work email vs. personal etc).
- From here we have two routes: SDR or AE. SDR's will handle a much larger volume and mainly qualify around how many users they want to start with, timeline and available budget where sales gets between 3-5 leads daily and use CHAMP (Challenge, Authority, money and Priority).
- It turns into a SQL when A. the SDR set up a meeting and after the AE qualified the opportunity or B. a lead that the AE qualified himself as an opportunity based on CHAMP.
Finally, I don't believe there is one golden model. We consistently make adjustments based on changes in environment and data visibility. For example, you want to check the conversion rates of AE vs. SDR, When your team grows you might decide to lower qualification criteria or when you have an overload of leads you either make criteria heavier or higher more reps. For large companies that start small we often still want reps involved vs. small companies where this is not the best investment right now compared to the opportunities out there.
Why send smaller leads to CS at Monday.com?
We did an A/B test comparing self-service vs. sales to see where we make the most impact, so we can lean into that area.
Because we see the smaller companies converting well with the Self-serve funnel and if need help usually more CS oriented then sales (like more around feature questions then complex company set up, legal, security processes etc.)
Luxury problem: We don't have enough reps to focus on all the leads, so we prioritize accordingly based on potential and triggers. In short it is not worth (financially) to have a salesperson on an account lower than 20 employees, but of course maintaining quality service is still crucial hence CS.
We do make changes to the model all the time to keep it up to date, so for example opening the SMB team allowed us to focus as well on the 20-100 employee sized companies.
We play in SMB, MM and ENT space, so that also has an impact of course vs. only in SMB
Is CS compensated on conversion at Monday.com?
We don't comp CS on conversion from free to paid, as the main focus for CS is on retention, customer satisfaction and usage and that's what they are incentivized for. Now it does happen to be that all those connect very well and improve conversions regardless.
Additionally in CS we have different roles like Dedicated CS (connected to larger accounts) IC (Implementation consultants focused on onboarding and implementation parts), CS focused on 24/7 ticket support (This is where below 20 companies usually go), CX (technical CS) etc.
How do you do MQLs vs PQLs at Monday.com?
We use it on top of it. The MQL is more around data you get from enrichment tools (zoominfo, full-contact, linked in, clearbit, banner they came from each has different score based on conversion rate etc.) and the PQL enrichment is more around data we receive when using free trial (usage, features they use, titles of users in account, how many people they invite in first few days, how much they set up during trial, etc.)
Today both factors roll into one “score” together, but that could be a great topic for one of next PLG sessions. Def would be interesting to hear benefits and disadvantages from organizations that separate it vs. combine.
I believe that because we started our journey from the beginning as a PLG company and always focussed heavily on the buyer's journey, the product, and how to consistently improve the self-service funnel and enable growth through the platform itself. It came very naturally to focus as the sales team on the areas where we can add value on top of self-service and PLG. Which we identified by running tests. It’s not so much having different funnels, as identifying where, when and for what accounts in their journey can we add more value.
For example: with testing we could see that for companies with X employees the self-service works great and adding a salesperson in the mix didn’t add a ton of value compared to alternative options.
However for larger companies we see that with sales involved we shorten the sales-cycle, start significantly bigger with first buy, grow quicker, improve retention and customer satisfaction score and are able to bring on multiple departments or the whole company vs. 1 team which is more comment on self-serve
Do you test PQL playbooks?
Yes. We absolutely still test and will continue doing that. There are so many aspects that can impact the scoring algorithm. Any new feature or product, any new client segment you enter or even changes in overall market conditions can impact it. I believe that by consistently measuring and testing we keep ourselves accountable to always focus on how we can further improve.
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