How to Rescue Your Failing Freemium Startup with Product Qualified Leads

Eric Siu by

As a freemium startup, you live and die by your ability to convert free users into paying customers. But thanks to higher customer expectations and intense competition, freemium conversion rates have been slowly declining. One reason for this is the widespread use of Marketing Qualified Leads (MQLs) to qualify and score leads. This is a concept that works for conventional products, but doesn’t fit the freemium business model.

Product Qualified Leads (PQLs) are an antidote to this problem. By separating lead qualification from marketing behavior and aligning it with product behavior, PQLs promise to greatly increase your conversion rates.

In this article, I’ll help you understand this revolutionary concept and how you can use it in your freemium startup.

What Are Product Qualified Leads?

In a nutshell, PQLs are qualified leads based on product-level data such as app usage frequency, engagement metrics, etc. The best way to understand PQLs is to see them in opposition to conventional marketing metrics – namely, MQLs.

A conventional marketing funnel using MQLs looks like this:

This funnel is linear. Prospects learn more about the product as they move further along the funnel. Apart from sales demos, they don’t get a chance to actually use the software until after the sale is finalized.

If you’re selling a freemium product, however, your prospects’ usage pattern is strikingly different. Instead of a linear progression from “lead” to “qualified lead,” prospects move in and out of the funnel as they use (or abandon) your free product.

In other words, freemium customers engage with your business at a product level. Thus selling to such customers requires a product-focused sales strategy.

This is where product qualified leads come into the picture. A PQL funnel looks something like this:

This model recognizes the non-linear nature of freemium software sales. It gives leads – and your sales team – the room to recycle, move and nurture potential customers towards a purchase.

Essentially, this means monitoring your product usage and identifying trigger behaviors that indicate a potential conversion. If this behavior aligns with the user’s known demographics, you can pass on the lead to your sales team. In case of cheaper products, it is even possible to convert users without any sales involvement (i.e. no-touch sales).

Why PQLs Work Better than MQLs for Freemium Products

The concept of PQLs emerged as a response to the challenge of selling freemium SaaS products. Freemium products have an abundance of product data and a deficiency of conventional sales teams. In this context, pursuing a streamlined, no-touch sales process improves conversion rates and profit margins.

Broadly speaking, there are two reasons why freemium products should adopt PQLs over MQLs:

1. CRM-Focused vs. Product-Focused

An MQL is essentially a record in a database. This record is based on marketing behavior such as downloaded content, webpages visited, etc. This data exists outside of your product.

A PQL, on the other hand, is a record of your product usage. This record includes data on how someone engages with your product, how frequently they log in, etc.

Consequently, PQL gives you far more product-level insight into your leads. This insight is a lot more relevant for a freemium startup that already has a large number of users.

2. High-Touch vs. Low-Touch

The MQL model is designed for a high-touch sales process. Once qualified, leads move to sales to be further qualified (SQL). This works as long as the lead volume is limited and the product price substantially high.

With a freemium product, however, this model breaks down. Higher lead volume and lower price make a high-touch sales approach unfeasible. SDRs are expensive; you can’t just call all potential customers and still turn a profit.

The PQL model is perfectly aligned with the low-touch freemium sales process. In fact, highly qualified leads can even be sold to without involving sales at all.

How to Use Product Qualified Leads in Your Freemium Startup

The PQL model pushes low-touch or even no-touch sales. This works best for products in the $5-$500 per month price range with limited customization requirements. It also works mostly for small businesses; enterprise deals are generally too complex to rely on a low-touch model.

Here’s a process for using PQLs in your freemium startup:

1. Have a Lead-Scoring Process

Although conceptually different, both MQLs and PQLs work the same way: you evaluate a lead and assign it a score based on available data.

To generate PQLs, therefore, you need to have a lead-scoring process in place. To develop this scoring process, you need:

  • Paid users’ behavior and conversion data. Analyze your paid users and make note of their in-app behavior and activity leading up to conversion. You can then use this data to identify conversion-oriented behavior in non-paid users. The more such data you have, the better.
  • A clearly identified target market. You should know what your ideal customers look like (including their demographic and firmographic data). It also helps if you’ve clearly identified your economic buyers, technical buyers and user buyers.
  • A streamlined sales process. If you want no-touch sales, you should remove all bottlenecks from the sales process. An existing user should be able to upgrade to a paid plan without any sales involvement. Focus on streamlining the upgrade user experience, especially payments.

Once you have a lead scoring process, you can start tracking product usage and score leads based on how they interact with your app.

2. Track In-App Behavior

Which behavioral metrics you need to track will vary from product to product. A B2C product might want to track usage frequency, while a B2B product might want to track the number of users per account.

At the very least, make sure to track the following:

  • Engagement metrics, such as visit duration, number of actions performed, retention rate, etc.
  • Usage metrics, such as how often a user uses a particular feature.
  • Login frequency, i.e. how often a user logs in to use the product.
  • Freemium account limits, i.e. which users are nearing or have exceeded freemium plan limits (on storage, number of users, etc.).
  • Social and support metrics, i.e. users who have engaged with you on social platforms (especially if the engagement was positive) and support desk.

Besides the above, also keep track of any users who’ve contacted you via the support desk. These are customers who have “raised their hands,” so to speak, and are willing to learn more about your product.

Also take special note of users who have come to you with a problem that can be solved with a paid plan feature.

3. Identify High-Converting Usage Behaviors

Your top priority should be to identify users with a high likelihood of conversion.

Mostly, these are users who have “adopted” the software and are using it frequently in their workflow. Look to data from your existing paid users to identify metrics that signal strong adoption.

Some common indicators of possible conversion are:

  • Tripping free plan account limits
  • Inviting other users, especially those on the same domain (i.e. team members)
  • Engaging with support or inquiring about a paid plan feature

Identify users who fit such criteria and push them towards sales. You can even send them offers or discounts if you want to pursue a no-touch sales process.

4. Use Demographic and Firmographic Data

Product-level metrics tell you what your users do in your app. But for true effectiveness, you still need to know who your users are. For this, you need demographics and firmographics data.

You should already have clearly identified customer personas. If not, look to your successful users and take note of their demographics:

  • Country/region of origin
  • Gender and age
  • Education level

Along with firmographics data (company size, revenues, number of employees, etc.) this will give you a detailed overview of who your customers are. Use databases like Clearbit and Datanyze to supplement your data.

When combined with product-level metrics, this can help you zero in on your highest potential leads.

As an example, here’s a simple product + demographics model for filtering leads:

User logs in a minimum of 10 times
Average duration of each visit is >3 minutes
User’s company has 20-50 employees
User is located in North America

Leads that meet the above criteria can be labeled as “product qualified leads” based on your data.

The PQL concept isn’t the panacea to all your freemium startup woes, but it comes close. By reimagining the sales funnel as a function of product behavior (instead of marketing behavior), it gives freemium startups the ability to find their best converting leads.

There’s a lot to learn about PQLs, but the above process is a good place to start. Focus on collecting data and identifying behaviors that align with successful conversions. Combine it with demographics data to develop a robust, product-focused lead-scoring process.