Starting my career from a traditional B2B SaaS software background, Marketing Qualified Leads (MQL) were the bread and butter of how we defined success and buyer intent as a marketing organization.
I regularly looked at MQLs to gauge content and campaign impact and in turn who was sale-ready. While MQLs still have their place within marketing-led GTM motions, when shifting into a product-led motion, it didn’t take long to see that MQLs don’t offer nearly as much for Product-Led Sales (PLS).
Why marketing qualified leads aren’t relevant to the PLS motion
To levelset, an MQL score usually is made up of two factors: website or campaign activity (read blog; attended webinar) and firmographic data (company size; job title).
In a product-led world, MQLs become less relevant because the people you’re most interested in are already in your product trying it out.
At Calixa, we are seeing more growth and demand gen leaders asking how to define and prioritize Product Qualified Leads (PQL). Marketing has realized that they need a better picture of their buyer – and when set up and defined correctly, a product qualified lead can tell a much fuller story of a user's journey with your brand.
A PQL is made up of product insights (how they are using your product) and customer fit (are they your ICP) data, so sales can be better informed when selling. By contrast, MQLs have no relation to product usage, adoption, or conversion.
MQLs are failing PLG
In traditional B2B SaaS marketing funnels, deal cycles are notoriously long with the prospect not ever interacting with the product. As a result, marketing collateral is used as a proxy for buyer interest. As an MQL accumulates points in top-of-funnel activities, such as reading this blog article, downloading a report, or signing up for a webinar and that score reaches the defined MQL criteria, the prospect is then passed over to the sales team for qualification and outreach.
Downloading a white-paper isn’t buying intent. Setting up a scoring system that emphasizes lead generation on that type of qualifying is very dangerous for PLG sales reps.
But in Product-Led Growth, where users sign up to use the product on a free plan - or even paid - product-led marketers (and sales) can get ahold of far more user insights. They know which features a buyer is attracted to, and which high-value actions were taken, allowing marketers to nurture and educate product users more effectively and with a personal touch.
After all, think about what criteria makes for a stronger sales opportunity:
❌ A prospect who opened up two marketing emails and downloaded an eBook?
✅ Or a freemium user who created an account 2 weeks ago and added 10 people to their workspace last week?
Using PQLs for deeper, more meaningful lead scoring
Unlike MQLs, which qualifies buyer intent via website visits, PQLs measure usage behaviors of users already getting value from your product. As such, these lead scores are high-quality metrics based on product usage and behavioral patterns–not vanity metrics like content downloads or blog views that don’t translate to value with your product.
Because of this, PQLs also give marketing and sales teams deeper, more reliable context into how each lead engages and uncovers value within the product.
For example, common signals indicating users get value out of the product are engaging with specific features or adding new people to their plan. Combined with other PQL criteria (which we’ll cover below!), these types of activities indicate someone who may be ready to convert to a paying customer. What more product-led marketing teams are realizing–and what we see within our own customer base–is that PQLs drive higher conversion rates, generate more accurate lead scores, and give the sales team more valuable context for meaningful conversations. In fact, PQLs often convert 5x higher than traditional MQLs (source: Accenture).
What people get wrong about PQLs–and how to get them right
Not all PQLs are created equal. Done wrong, they can push arbitrary product data into your CRM or result in sales reaching out to folks who aren’t the right customer fit. We see this happen most when product-led teams over-index on product usage data without incorporating a more holistic customer view.
For example: a super user who’s a single, independent user without potential for a multiple-seat upsell or enterprise package. Or a power user at A Big Enterprise who’s active in the product but not a target buyer persona. If your PQLs only score product usage, without factoring in demographics or customer profile criteria, you risk higher chances of wasting your sales team’s time.
That said, coming up with a bulletproof PQL doesn’t have to be complex. For the most part, you need to account for two key factors: product usage/activity and customer fit.
- Product usage and activity: Here, the emphasis should be on activities that reflect points of value along the user journey with your product. Think: reaching the critical “a-ha!” moment. To do this, you’ll need to determine what activities best represent user value. This could be actions like adding more users to their plan, doing a certain high intent set of actions, or setting up a specific integration within your product.
- Ideal customer profile (ICP): This piece then should sound familiar to marketers..define who you want to sell to and define their profile criteria. In other words, who are your most valuable customers and what organization and persona within that organization benefits the most from using your product? You can include criteria such as job titles, company size, type, and industry. If relevant, you can also expand this to target specific regions or geographies and other considerations.
The magic is when you layer these two criteria together – they are using your product and are your ideal customer - bam! From there, you can go on to create multiple types of PQLs based on various conversion goals & pricing.
How to add PQLs into your sales process
Disclaimer: your engineers don’t have time.
Yet you need PQLs if you want to stop missing 50% of your opportunities as a product-led business 😱. For PQLs to actually turn to revenue, you need these four steps.
Step 1: Scoring
Find PQLs using product usage and ICP criteria.
Step 2: Visibility
Get context into the hands of your reps so they know who to engage. Set up playbooks for them to turn a PQL model into potential customers.
Step 3: Actionability
Automate actions so users convert by themselves and bigger opportunities get created for AEs.
(Bonus) Step 4: Machine learning
Find opportunities and correlations between product usage and revenue that you can’t see.
Steps 1-4 are built into Calixa’s platform, no code or product team required. Play around with the product and see for yourself!
Customer experience > revenue
Looking at MQLs vs PQLs from the point of view of your customer, one feels salesy and the other helpful.
By understanding what free users do in your product, your sales cycle is adapted to the self-service expectations of your prospects.
Going even further: your marketing strategy can help support it. Content can be used to facilitate a sale rather than lead qualification.
Start your product-led sales journey with Calixa today!