Wondering how to identify the PQL for your SaaS business?
In this guide, you’ll find:
Most B2B companies have a sales funnel to represent the journey of a potential customer (a lead), ideally culminating into a purchase. This sales funnel is instrumental in filtering out leads as they go further through the funnel. This is where we get the Marketing Qualified Leads (MQLs), Sales Qualified Leads (SQLs), and more recently, the Product Qualified Leads from.
Earlier, companies would base their strategies on MQLs – which determined the buying intent based on steps like filling up a form, downloading some content, signing up for a newsletter, or actually adding stuff to the cart. These would then be vetted by the sales team, further qualifying them into SQLs.
However, with the dynamic changes in the industry today, how do we accurately qualify an interested individual as a lead simply based on a few emails they clicked open, or a few forms they filled?
Not to forget the CAC (Customer Acquisition Cost) involved in this whole process!
PQLs are slowly replacing MQLs and SQLs when it comes to acquiring customers. Let’s take a detailed look at what PQLs are, and how they can be optimized…
What Are PQLs?
With the rise of SaaS companies, customers are increasingly inclined towards trying the product before investing in the premium version. Thus, an interested individual who has tried your product, usually by means of a free trial or a try-before-buying model, and seen the true value addition that the product has to offer, can be termed a PQL.
While MQLs and SQLs can help you understand how successful your marketing and sales leads are, they may not be able to justify the actual value the product offers to the users. They aren’t irrelevant. It’s just that they are more relevant to achieving business objectives, than giving the user a great product experience!
PQL approach uses in-product insights to predict when a lead is ready to make a serious commitment, post the trial or limited-time offer period.
When you use a PQL system, you will observe the users demonstrating buying behaviors related to ‘bottom-of-the-funnel’ criteria, rather than ‘middle-of-the-funnel’ ones. It will provide insights on how interested the user is in your product, the number of features actually being used extensively, spending patterns, and the usage patterns too.
Thus, a PQL can also be a great metric to understand whether or not a potential customer is qualified to use your product.
How Does One Identify A PQL?
If you wish to convert trial users to paying customers, you need to know who your users are, what are the goals they are trying to achieve with your product, and what made them sign up for the product in the first place.
You then dig further, to narrow down your objective – what actions are the engaged users taking that others aren’t, do the ideal users share any traits, which actions or traits indicate long-term usage, how did they discover you, why did they trust your product, what motivated them to take the plunge, etc.
This is just the first step. As mentioned earlier, you must analyze the various buying behaviors, such as product interest, number of users, features used, spending patterns, usage patterns, and how fast a user is adopting the product. You need to have all your product-related information at hand, to be able to correlate these behaviors with your product, and eventually identify a PQL.
Let’s take a look at some of the benefits of using this approach:
- The customer comes for ‘free’. If the product sells itself, then you don’t need to bear any additional costs, except those meant for product development.
- PQLs are highly scalable since they don’t require much of a ‘human touch’, as compared to SQLs which would need a sales rep to guide the whole buying process.
- It’s easier to ‘unqualify’ leads if sales reps take the PQL approach. Just as the product usage data helps them find leads that will most likely convert, they can drill down further (like Appcues did using ‘Hull’) to find the ones that will ‘most definitely’ convert. Their efforts can be focused on leads that are fruitful rather than wasting time on other potentially dead leads.
- The engineering and product teams are focused on a single goal – product development. They may not be aware of the revenue-oriented implications of the product, which sort of puts them at odds with the revenue-generating units (sales and marketing). A PQL-oriented approach essentially unites them both into a single, effective force, since all further actions are based on the user’s experience with the product.
To create a sustainable PQL system, it is essential to have a clear understanding of what you need to achieve, and measurable ways to achieve it. One way to do that is to use OKRs, a goal system introduced by John Doerr. Let’s take a look at how this can be done.
Define Your Vision and OKRs (Objectives & Key Results)
As SaaS organizations grow and become more complex, it becomes important to make strategic goals a lot more tangible with clearly defined objectives for different teams to achieve. OKRs act as a roadmap to steer the company and its products to the next level.
Essentially, you need to define the objective that needs to be achieved and have measurable key results that will help you achieve it. For instance, in the case of establishing a PQL system, the most fundamental objective would be to have all your product-related information at your disposal, in a structured manner. The measurable key results, in this case, would be:
- To identify the kind of data required for product-qualified leads (product usage data, sales activity, stages of the life cycle)
- Identify where each type of data is stored (product database, analytics tools, CRM)
- Channels used to send marketing messages (newsletters, live chat, ads)
- Tools used by the sales team (CRM, sales cadence tools, research tools)
- Integrating product usage data with other tools (one-click integrations, manual import/export, workflow tools, customer data platforms, custom-coded integrations)
It is important to note that visions and OKRs might change as per the company’s maturity; they might align to a certain goal during the company’s infancy, and evolve into more complex, more specific milestones to be achieved as the company matures.
Defining your vision and OKR and having proper metrics in place to measure them is a vast topic in itself, and we’ve barely scratched the surface in this article. You can check out this guide for more in-depth information.
In addition to these metrics, you need to zero down on one ‘super-metric’ that helps you iterate effectively on product value. This would be the metric you go to when you want to know if the number of people benefiting from your product is growing. In simpler words, you need something that lets you know if you are headed in the right direction.
This is what is called the ‘North Star Metric’.
Identify What Can Be Your ‘North Star Metric’
The North Star Metric (NSM) is an excellent tool to capture your product’s specific benefit that potential customers are looking for and deliver it to them. Thus, NSM can help teams go beyond seemingly attractive short-term growth to focus their efforts on building a growth strategy that is long-term, customer-retention-oriented, and sustainable.
The right NSM should take into consideration your product’s vision mapped to a metric that accurately measures your current product strategy. Any mismatch indicates that the product will not fulfill the customer’s needs, thus taking your team back to the drawing board to iron out the creases.
For instance, a good NSM for Facebook is perhaps the number of users active on the platform daily, for WhatsApp it could be how many messages a user sends. Similarly, AirBnB’s NSM would be the number of nights booked. With these metrics mapped to product strategy, Facebook could look at ways to optimize timelines, embed new features, etc. so that it remains a relevant and attractive platform for its loyal users in the long run, amid fierce competition for user attention in the same space.
When it comes to identifying PQLs, Hubspot realized that their NSM was weekly active teams. The more weekly active teams they had, the better their freemium business would function.
It is important to note that, much like the vision and OKR, NSM too changes over time, aligned with where the company is in its lifecycle. Similarly, the NSM can also help in communicating to your employees your product’s exact offering as well as growth aspirations, eliminating ambiguity and therefore reducing wastage of resources on non-important aspects that don’t directly align with the NSM.
Again, we’re just looking at NSM basics in this article, but here’s an interesting, in-depth take on how NSM can drive growth.
The NSM may not be actionable on its own – you will need to break down the metric into further sub-metrics that influence it directly.
Find Associated Metrics That Support the NSM
Having arrived at your North Star Metric – the single most important metric that captures your product’s core value – you need to complement it with various sub metrics that help you build a well-rounded case for a better, more effective product- and business growth strategy. These sub metrics might not mean anything by themselves, but they contribute to your NSM to enrich it.
Every NSM can have 4 dimensions which can act as the sub metrics mentioned above:
- Efficiency of engagement.
Breadth relates to the number of users required to make a significant impact on the NSM, while depth could be understood as to how invested your users are in your brand or product. Frequency is the number of times a user needs to repeat his/her action so as to make an impact on your NSM, while efficiency is the ideal manner in which user behavior should impact the NSM.
Let’s understand this in detail, using Spotify, a digital music service, as an example. Their NSM would be the time subscribers spend listening to music. This would be further broken down into identifying the number of trial users and premium subscribers (breadth), the hours per session (depth of engagement), and the number of sessions per week (frequency).
Similarly, for Instacart, the NSM is the ‘items received’. The supporting metrics would be the users placing orders (breadth), the items within each order (depth), the total number of orders completed (frequency), and the percentage of items delivered on time (efficiency).
Hubspot focused on the inputs that contribute to the weekly active teams – increase the number of users who import their data or close more deals using Hubspot’s sales tools. This way, they weren’t focusing on their success by changes in happening in the NSM, but on creating a measurable impact on the different inputs that contributed to the super metric.
Read this article for more clarity on these sub metrics that eventually contribute to your NSM.
How To Identify The PQL Using NSM And The Sub Metrics
These sub metrics that influence the NSM could actually help you identify the PQL – perhaps not in their absolute form, but as the first sign of what the ideal user would look like. Follow the steps below to find your PQL:
Step 1: Match The Metrics To Events
These metrics should be linked to the actual events within your product. For instance, if the number of new users is the sub metric you’ve identified, then the event corresponding to that would be the verified sign-ups.
Step 2: Grade The Events Based On Importance
A PQL could be someone who has performed, say 5 actions. However, not all of them would have the same level of importance when it comes to making a decision about the product. Grade the events based on their importance, to narrow down on your ideal user.
Once you’ve determined a grading system, download a list of users with all the events each one of them has performed, and calculate their score based on the grades you’ve assigned to each event.
Step 3: Normalize These Scores And Make A List Of Users
Once you have the scores, normalize them. Choose a base score of 10, 20 or 100, based on your requirements. Once you do this, you will have a huge list of users, with their respective scores, which can help you determine how well each user has performed with respect to the product.
To simplify this, let’s take an example. Let’s say the NSM for a video creation tool for marketers is, “How many videos did the user send to his/her Buffer account?” In this case, the events could be the number of media assets the person uploaded into the video, the number of templates he/she tried, whether he/she connected to the Buffer account, etc.
The weightage or score assigned to each event would vary – connecting to Buffer would be more important for the product (5/10) as against the number of media files uploaded (2/10).
Additionally, you could have another column for the marketing channels which brought the user to the product, to know which of them is giving you the best results.
Derek Skaletsky, the CEO of Sherlock, has developed a very simple, but effective way for product engagement scoring. Learn more about it here…
Step 4: Further Qualify Your Users Using Appcues’ Flywheel
An interesting framework in this context is the Product-Led Growth Flywheel by Appcues. It makes a strong case for investing in a product-led user experience in order to achieve business growth organically.
The list of users and the corresponding scores can be further qualified using the flywheel. Based on the scores, categorize your users into evaluators, beginners, regulars, champions, and. Let’s take a quick look at each of these user categories:
Evaluators: These are the ones you need to ‘wow’ with an incredible onboarding process! They are interested in how your product will help solve their problem (not so much in the gamut of features your product has). With them, it’s all about how fast they reach the solution – and can then be led on from there.
Beginners: These are the users who are ‘still learning’ – they are exploring everything your product has to offer, and are enthusiastic about using your product to solve their problem. To involve them further and keep them engaged, one action would be to introduce them to new functionalities after onboarding. This will slowly take them to the next stage.
Regulars: These are the ones who know your product thoroughly, have used it to solve their problems, and are now probing if there’s anything else the product has to offer. These users are the goldmine, if you want to add brand new features to your product that are in demand. Ask them what they need from the product, and enhance your product with these features.
Champions: These are the ones who have developed an emotional connection with your product and brand, and recommend it to others. These are the ones you should be asking to leave reviews of your product on sites like Capterra, G2, and the like.
The champions would be your ideal users, drawing maximum use from your product, and that’s where you’d want all your users to be. Additionally, you’d want to let your champion users know that their investment into your product is appreciated.
For instance, HubSpot selects its ‘Champion Users’ based on the Marketing Hub and Sales Hub users’ product usage activity each quarter, and also annually. The activities (or events) they use to measure the user’s success are the number of marketing emails sent, the workflows activated, social media posts, landing pages created, and the like.
These users are then awarded the ‘HubSpot Champion User’ certificates to reward their prowess.
It is important to note that you shouldn’t rush into converting all your users into champion users at once. A ‘one-size-fits-all’ approach won’t work here! Take actions that help users at each stage of the wheel graduate to the next one, gracefully, and seamlessly.
The way to achieve this is by using Eric Ries’ ‘Build, Measure, Learn’ framework. This should be your next step.
Step 5: Use The ‘Build, Measure, Learn’ Framework To Help Users Move Along The Flywheel
You can experiment with the product, at various stages of the flywheel, to figure out ways to help the user graduate to the next level. You then measure the outcomes of these experiments, in terms of how the user is progressing in terms of success with the product.
Much like your vision, OKRs, and NSM, the tools that you need during different stages of your product, and your business, lifecycle too change. For instance, during the initial phases, your product might need analytical insights fleetingly. It is only when your product has a steady customer base that analytics can actually help you evolve your approach. Here’s a handy guide to choosing the right analytical tools to get the desired results.
Based on the results of the analysis, you learn something new that will move the product forward and form the hypothesis for the next step/experiment. This is a continuous process, until you arrive at the point where all your users are moving to become your product champions!
What you have thus developed is an effective PQL system, that can be iterated constantly based on the changing needs of the users.
Here’s a quick summary of the stages in the journey we’ve covered above:
- What are Product Qualified Leads (PQLs) and why are they more effective than MQLs and SQLs?
- How to identify a PQL?
- Define your vision and OKRs to set up a PQL system.
- Identify your NSM (North Star Metric)
- Find the sub metrics that influence the NSM
- How to identify your PQL using the NSM and sub metrics
- Match the metrics to real events
- Grade the events based on importance
- Normalize the subsequent scores and make a list of users
- Qualify your users using Appcues’ Flywheel and find the ideal user
- Use Eric Ries’ ‘Build, Measure, Learn’ Framework to gradually convert all users into the ideal user
A PQL-oriented mindset can reduce time and energy spent acquiring potential customers, and target those who are more likely to become paying customers.
It’s not just the free trial that will make them convert, it is the focus your products put into evolving itself and meeting the customer’s needs, the one-track vision and NSM that keeps your company on track to constantly innovating while staying true to your customer and their requirements, that is what will eventually win them over.
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