The Product-Led Growth (PLG) way of making and selling B2B software has really changed things. Big names like GitLab, Slack, Miro, and Notion show how well it works. There's a lot of talk about using PLG in different parts of a business, especially in day-to-day operations and data analysis. In this article, we'll look at how using data about your product can help make PLG even better.
But first, a heads-up: if you're just starting out and your business isn't making money yet, focusing too much on data might not be helpful. In the beginning, it's more important to understand your customers and their needs through direct feedback. So, if you're still in the early stages, this article might not be very useful for you.
This article is best for businesses that have already found their place in the market, have a lot of people visiting their website, and are thinking about adding a sales strategy to their PLG approach. Remember, you can't have a good Product-Led Sales (PLS) strategy without a good PLG base to start from.
In this article, we will show you how to make product usage the backbone of your Sales strategy for your PLG company. The beauty of this approach is that it will help you maximise your revenue per account and grow your company non-linearly.
Let’s dive in!
No Product-Led Sales without Product-Led Growth
Product-led growth is all about letting customers try out a product and then decide to buy more features or better plans on their own. It's like the product does the work itself – it draws people in, gets them interested, helps them start using it, and even takes care of making money and keeping users around. Customers usually move through the buying process and make payments without needing to talk to a person.
In PLG, sales teams step in based on how much a customer uses the product. They focus on selling to the customers who seem most likely to buy, according to the company's ideal customer profile. If a customer is thinking about buying more or upgrading, someone from the team helps them out.
This is different from traditional sales where you often have to pay before really getting to use the product. We call this approach Product-Led Sales (PLS).
Now that we have clarified the requirement for Product-Led Sales, the big question is “How do you put it into practice?”.
There are basically 3 steps:
- Divide your product into different stages of its lifecycle
- Score the accounts
- Use these scores to help you make more sales
Putting PLS into practice
Break down your product into lifecycle stages
Okay, so enough talking about the theory, now the practice.
The first thing to do before establishing which customers a salesperson should pick, is to break down your product into different stages of its lifecycle
The ideal stages look like this:
- New Free Plan User: this is where the customer's journey begins. They've just signed up for the free version of your product.
- Activated Team: this stage is reached when a team or account has finished the onboarding process and has used the main features of your product for the first time. It means at least one person in the team has done what's needed to start getting real value from the product.
- Experienced the 'Aha' Moment: here, the user or team has used the product enough to realize its value – this is their 'aha' moment when they see how the product can help them.
- Reached the Habit Moment: at this point, a user regularly uses the app and has incorporated it into their routine. The 'habit moment' is measured by how often and how long they engage with the app, showing they've made a habit of using your product's key features.
- Team with an Active Trial: this is when a team starts a trial period to explore more features, moving beyond the free plan.
- Self-served Customer: this customer has purchased the product on their own, without needing a lot of help or a salesperson guiding them through the process.
- Churned Customer: this is a user or team that has stopped using the app and isn't active anymore. They've moved on from the product.
There are many different ways to measure or compute the stages mentioned above. In “set up,” we’ll discuss the three main approaches used these days by modern B2B SaaS.
💡 Note
In this article, I assume that your product fundamentals for PLG are done. The design of activation and monetization directly influences PLS motion, so a lack of self-served onboarding, inadequate high-touch approach, or poor monetization hinders behavioral signal design for the Sales team. Ensure the entire value chain is carefully managed.
Define your PQA
Our product starts its journey with 1,000 different customers who use our product by themselves, without much help from us. Out of these, 200 customers really stand out because they fit exactly what our sales team thinks is the perfect customer for us. We're focusing on a special part of the market: the offline retail industry, and we're especially interested in the people who lead the HR departments in medium-sized companies (with 201-1000 employees) in North America.
Among these 200 special customers, our goal is to find those who are not just using our product but are really getting into it. This is where our sales team comes in. They have special knowledge and plans to connect with these customers. The big question we're asking is: Are these customers who are really into our product ready to take the next step? Are they open to talking about more than just the product, maybe about working together in a way that's good for both of us in the long run?
To facilitate the work of the sales team, we have to come up with a way to score accounts to that the ones that are ready to be sold stand out.
💡 Note:
In the context of Product-Led Growth (PLG) articles, the concept of a Product Qualified Lead (PQL) is introduced. PQLs are defined as individual users within an Ideal Customer Profile (ICP) account who demonstrate both buying intent and authority.
Transforming Engagement into Revenue: Mastering PQA Scoring
After setting up your analytical tools and defining the stages of your product's lifecycle, it's time to focus on scoring your Product-Qualified Accounts (PQA). PQA scoring is all about timing – it helps you decide the best moment for sales to step in. This decision is based on a mix of signals from how the product is used, like how much and how fast it's used, and which features are popular.
With this kind of scoring, you can make educated guesses. For example, you might say that Account X, which has N users and has been actively using certain features for the past M weeks, is more likely to start paying for the product.
Calculating precise numbers can be tough because B2B companies often overlook a lot of their data. To start, try to understand what kind of activity happens in an account before it closes. At first, you can use simple rules based on what you learn from talking to customers. For example, use X as a sign of getting started and Y as a sign of growth.
Also, it's important to keep track of marketing-related activities, like who attends your webinars, asks for a sales demo, or visits your Terms and Conditions page.
Remember, there's a strong link between how you score accounts and the triggers you set. When you set up these triggers, you create chances to let your team know about important customer actions. This helps them respond quickly and lets you add extra points to your scoring system. But try to do this efficiently without making it too complicated.
Scoring offers a big advantage: it lets you focus less on reacting to every single product event and more on guiding sales teams to work with the most promising accounts or individual users, based on their likelihood to buy, or what we call the propensity score.
Tools like HubSpot CRM are really helpful here. They provide a user-friendly platform to set up these scoring systems for leads. This way, you can adjust the lead's stage in their journey with your product, based on how they use it. The best part? You get alerts that tell you the perfect time to reach out to your most promising leads. This makes it easier to spot and act on the opportunities that matter most.
Behavioral signals are external clues indicating that a customer is ready to talk about buying your product. These signals allow your sales team to focus on actions that show a customer's interest, rather than just looking at how they use the product.
💡 Remember: Your CRM system can get swamped with too many behavioral events. Also, just because a team meets your Product-Qualified Account (PQA) criteria doesn't mean they're ready for a sales chat right away. You can include this in your scoring model and combine it with the behavioral signals. This helps you better understand a customer's buying intent.
The 3 technical options to implement PLS
To push your data in your CRM ("activate" it is a more fancy word), you have 3 options today:
- the low-code approach: CDP (ex: Segment premium integrates w/ Hubspot)
- the no-code approach: June's native integration
- the data-intensive setup: Data warehouse + reverse ETL
You can use different approaches on their own or together to achieve the same goal. The choice depends on factors like your time, budget, team size, how complex your business is, and the amount of data you have.
CDP-Native Approach
In the last 20 years, digital products have become very common, leading to a lot of data on user behavior. Businesses now collect customer data from many places, like app databases, CRMs, marketing tools, offline interactions, and digital products. Customer Data Platforms (CDPs) have become important for managing this data. They gather customer information from various sources to create a complete profile.
CDPs are great for bringing together data about both anonymous and known users, giving a full view of each customer's actions and likes. They also allow for real-time creation of target audiences, letting marketers reach specific groups based on their unique features. When you use a CDP, you're trusting the software vendor to handle all aspects of data management, from collecting and transforming to modeling and storing.
While CDPs address many challenges associated with fragmented customer data, they aren't, by definition, a standalone solution for all data-related needs. That is, they are limited to data aggregation and management. They are great for creating a shared customer profile. Still, for B2B use cases, extra flexibility is needed, especially for tasks like recognizing a champion user-buyer, anonymous sessions, and advanced attribution modeling.
Product Analytics native
As the landscape of data management changes, product analytics platforms are increasingly integrating with or transforming into Customer Data Platforms (CDPs). This shift is particularly beneficial for startups and smaller companies, as it eliminates the need for repetitive processes and reduces extra work. The focus is on efficiently utilizing existing data for analysis.
Notable in this trend is Amplitude's recent launch of its own CDP, signaling a broader move among analytics platforms. Posthog is also on the path to developing a CDP, indicating a growing preference for unified data solutions. Specialized tools like June.so, designed for B2B SaaS businesses, are adapting to this trend as well. This evolution points towards a more streamlined approach in handling and analyzing customer data, with product analytics software at the forefront of this change.
Data warehouse native
Cloud-based data warehouses (DWHs) are increasingly favored for their straightforward deployment, large-scale capacity, and superior performance. They are now a common feature in the technology stacks of many enterprises, including marketing technology stacks.
In these DWHs, tools like DBT are used to organize raw data – think transaction details, table attributes, and time-series data – into a standardized model. This makes the data warehouse both the primary location for unprocessed customer data and the computing power needed to analyze it.
However, a DWH on its own doesn't offer all the functionalities of a Customer Data Platform (CDP). This is where reverse-ETL (Extract, Transform, Load) or DWH-native applications come into play, bridging the gap between the two. With reverse ETL, you can create universal customer segments right in the DWH and apply them across various platforms. Marketers can also assign labels and structure data for different endpoints. A key feature of reverse-ETL is that it doesn't duplicate data; instead, it dynamically generates segments and audiences at the time of the query and then sends this information to the target platforms.
Have a clear owner
Making data work for a business means getting five main things right: setting up the data, organizing it, setting up signals and alerts, running tests, and getting the data where it needs to go. Here's how different teams can work together:
- The Sales team can take charge of testing out different ways to score leads and then tell the product teams what they need. The product and data folks usually handle setting up the data, but Sales will use that data to make things happen.
- The Product team looks after the product-led growth strategy and comes up with ideas for tests. Sometimes, if the Product and Sales teams are really in sync, they might both handle some tasks together.
- A specialized team, like Revenue Operations (RevOps) or Product Operations (ProductOps), can help keep everything moving smoothly. The Product team might set up the data tools, but the Ops team will use the data. They'll both need extra hands to keep things under control.
- The Data Team acts as a resource center for everyone else, giving insights on what's valuable for the business, what the outcomes are, what can be done, and what's most important. They're in charge of keeping an eye on events, but they leave the sales strategy to others.
No matter who does what, all the teams need to share the responsibility for how well the sales pipeline works. Product leaders and sales heads should come together to set common goals.
Why you need B2B Product Analytics
If you have multiple users per account - for example, your product is collaborative - then ensuring that the system you choose allows for group-based filtering to perform analytics beyond individual user analysis is essential.
You can look at the data in two ways: by company (often called the "Logo level") or by team. For example, Slack lets teams set up their own spaces, while Miro lets people from the same company join different spaces. When you analyze how groups of users interact with your account or workspace, you can figure out things like which teams keep using your product or how they get started.
It's really important to track what actions users take within their accounts, not just which account they belong to. A user might be part of several teams, so you need to know which team they were working with for each thing they did.
Tools designed for product analytics and CDPs are good at this group-level view. They process the data so you can see what groups are doing. If you're using a data warehouse, you'll have to set up the connections between users and teams yourself using tools like dbt. Some solutions, like June, go a step further and set up these group profiles for you without any extra work.
Conclusion
Incorporating Product-Led Sales (PLS) into a Product-Led Growth (PLG) approach can be a game-changer for selling B2B software. By carefully analyzing how your product is used over time, pinpointing which customers are ready to buy, and using scoring systems, you can turn user activity into actual sales.
When it comes to putting PLS into action, you've got options. Customer Data Platforms (CDPs), product analytics tools, and data warehouses all play a key role. To really make this work, sales, product, and data teams need to know who's responsible for what and work together closely.
As the world of B2B software keeps changing, having strong product analytics isn't just a nice-to-have. It's becoming essential, especially for products that multiple people use together.
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Danny Chepenko is the founder of the immersive video communication app SpatialChat and the Data Advisor to B2B SaaS companies. You can engage with Danny through Linkedin and his Substack.
