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Product-led growth has proven to be one of the most effective strategies for building and growing digital products. One of the core reasons behind its success is the heavy reliance on data analytics to make smart decisions.

So, if you're currently exploring better ways to win at product-led growth, this article is about helping you take advantage of its data-driven qualities by tracking and monitoring the right metrics.

The Importance of PLG For SaaS Companies

There is a reason product-led growth has taken over, especially in the SaaS product space. With a PLG strategy, you can acquire new customers and grow your revenue in a very sustainable and affordable manner.

PLG is sustainable because people do not rely on your marketing campaigns only to like and buy your product. Instead, the strategy relies on letting users use your product in real life, (often without paying, depending on your product pricing strategy), test drive your most compelling features, and (hopefully) solve their problems with it.

As soon as they realize that your product can solve their problems, the scales tip in your favor, and the user starts paying for your product.

PLG is affordable because your product acts as a self-serve marketing channel. By creating exceptional user experiences, you can convince your users that your product is exactly what they're looking for, turning them into product-qualified leads (PQLs).

Since your product is doing most of the marketing for you, you are not spending nearly as much on your sales team and marketing efforts to acquire customers. This leads to a significant decrease in the average cost of acquiring a new user.

Now that we have refreshed our memories with the benefits of PLG, let’s examine the key SaaS metrics that you need to measure to make sure you're headed in the right direction with your product-led growth strategy.

Product-Led Growth Metrics: Measuring Activation

Every user’s journey in your PLG product starts with activation. It’s the process of people setting up your product, understanding how things work (thanks to your product onboarding process!), trying your core functionality, and experiencing your product’s value.

I suggest you track the following metrics to optimize your activation flow.

Note: To show you how to set up tracking for specific metrics, I will bring examples using Mixpanel, but you are free to use other product analytics tools.

Metric #1: Time to Value

Let’s begin with one of the most crucial leading indicators of good activation—time to value (TTV). There’s a good reason I use the term “leading indicator.” It means that improving this metric will improve activation overall.

TTV represents the time it takes for your users to experience your product's core value. The quicker users get to your core value, the less likely they are to get bored and ditch your product.

To calculate your TTV, you must track two key moments in your user journey:

  1. When users sign up and start using your product.
  2. When they perform the action representing your key feature.

Now, let’s see how you can create a report in Mixpanel to help you measure the TTV of an integration SaaS product similar to Zapier. In this case, the action that users perform to experience the core value of your product is creating and integrating.

So, we can make a funnel chart with two steps: “signup” and “create integration.”

TTV chart setup in Mixpanel: funnel chart with two steps: “signup” and “create integration.”

Next, we will set up this funnel's view as “time to convert,” which calculates the median time it takes users to go from step one to step two.

Chart showing "time to convert" is 24 days

Of course, 24 days, as shown in this report, is too long, and you will most definitely lose your users before they can check out your integrations.

So, there must be something very wrong with either your analytics setup (maybe you are not firing the right events) or with your activation strategy.

Metric #2: Feature or Product Adoption Rate

Another crucial metric for PLG-driven products is the rate at which people adopt your product features.

In PLG, every new feature should serve a specific purpose (e.g., improving habit formation, activation, etc.). For your feature to make an impact, people will need to adopt and start actively using it first.

Therefore, you need to measure the feature adoption rate and fix your feature or UX if it is too low.

My approach to calculating the feature adoption rate is a bit controversial and differs from others'. I calculate the percentage of users who have used the feature once (meaning they discovered it) and the percentage of users who have used it more than twice (meaning they've adopted it).

In Mixpanel, you can achieve this by creating two cohorts.

The first one I created here is for measuring discovery. Here, I have selected the measurement start date to be the date of the release for that feature.

feature or product adoption rate

For the second one, though, the timeframe is “last 30 days,” as using the feature more than twice within a couple of months would hardly be considered an adoption.

feature or product adoption rate

Afterward, you can create an Insights report with custom formulas that calculate the percentages I mentioned above by dividing these cohorts into the number of active users.

A healthy adoption and discovery chart will keep the number of discovered people stable, while the adoption rate grows steadily.

A healthy adoption and discovery chart will keep the number of discovered people stable, while the adoption rate grows steadily.

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Metric #3: AHA! Moment

Finally, we have the holy grail of all PLG activation metrics—the AHA! Moment. It represents the point in your users’ journey when they get the most value from your product and realize that your product is worthy of adopting into their day-to-day.

It sounds a bit similar to TTV, right? Well, they are both about the usage of your core feature. But time to value focuses on showing the delay between your users signing up and using that feature, while AHA! Moment shows you the number or percentage of all users who have used it (among those who have signed up).

Now, to build the AHA! Moment report in Mixpanel will start the same way as with TTV and create a funnel with “Sign Up” and “Create Integration” as our steps.

To build the AHA! Moment report in Mixpanel will start the same way as with TTV and create a funnel with “Sign Up” and “Create Integration” as our steps.

Unlike TTV, however, we will not choose the “time to convert” visualization. Instead, we will pick the “funnel trends” option.

This option will show the Step 1 -> Step 2 conversion rate change over time.

Approximately 30% of signed-up users create an integration and reach their AHA! Moment.

As we can see from this chart, approximately 30% of signed-up users create an integration and reach their AHA! Moment.

Moreover, it seems like this rate is neither growing nor decreasing, as the trend line for this line chart seems to be horizontal.

Engagement Metrics

A healthy activation rate is only part of the success. If you want your product to grow sustainably, you will also need to make sure that users are in love with your product experience and that user engagement and product usage are healthy as well.

Here’s what I suggest you use to keep track of your usage health.

Metric #4: Habit Moment

The next crucial milestone that you want your users to reach after experiencing their AHA! Moment is when they have successfully formed a habit around your product and will have a hard time performing their tasks without it.

In the PLG world, we call this moment in the user journey the Habit Moment.

Just like the AHA! Moment and the TTV, we care about the core features of your product. Specifically, we pay attention to the frequency of people using it.

You would claim that a person has developed a habit of using your product if the usage frequency of your core features is equal to (or at least close to) the natural frequency of the problem occurring for the user.

For instance, remote workers will usually make at least one conference call during the weekdays. So, the natural frequency of them needing a Zoom-like tool would be daily/weekdays or 5d7 (5 days out of a 7-day period).

In real life, however, people would have no-meeting days or arrange their schedule in a way that not all of their weekdays have calls. So, you would have people with established habits of using your video-conferencing tool every time they need to make a work-related call with a 4d7 frequency.

So, for your habit moment milestone, you would generally pick a frequency rate that is slightly lower than the natural frequency of the problem occurring.

In terms of the tools that you can use to measure and visualize it, I would recommend asking your data analytics team to build you a custom report on Tableau or PowerBI as event-driven analytics tools (e.g. Amplitude or Mixpanel) struggle making habit-moment charts.

Metric #5: Retention Rate

Retention is actually the main reason you want to build habits among your users. Habits are really hard to break, so, a person with a habit of solving their problems with your product will do it for a very long time (until the problem goes away or a 5x better product appears on the market).

To understand if people are staying with you in the long run, you will need to measure their retention.

Retention shows the percentage of people who have continuously used your app over an extended period of time. A 50% 7-day customer retention, for instance, says that half of the people stop using your app after 7 days.

There are three elements of retention that you need to figure out in order to understand the long-term usage trends of your product:

  • Key action: Just like with other metrics, this will be the core feature that represents the main value of your product. 
  • Frequency: Daily/Weekly/Monthly, etc. By setting this up, you are telling the analytics tool to calculate the continuous use of the feature based on whether it is used at least once a day/week/month, etc.
  • Timeframe: This is the number of consecutive days/weeks that the people have been using your product. You would traditionally keep track of D7, D28, and D90 retentions to know how many people keep using your product after a week, month, or quarter.

Now, let’s imagine that you are managing an online storage service similar to Dropbox. To calculate your retention, you choose “Upload Media” as your key action, “Daily” as your frequency, and D28 as your timeframe.

If you set this all up in Mixpanel, this is what you will see.

To calculate your retention, you choose “Upload Media” as your key action, “Daily” as your frequency, and D28 as your timeframe.

As we can see, the retention curve for our product is not flat and it nears zero at D28. It means that we lose everybody within 28 days and have no long-term users.

So, our next steps would be to figure out why people abandon us and fix the issues we identify.

Metric #6: Churn Rate

We talked about the people who stay with you (represented by retention). Now, it is time to talk about the people who didn’t (represented by the drop in the retention rate over time).

The process of your users abandoning your product is known as churn. They might either replace you with a direct competitor or start solving their problem with indirect alternative options.

In both cases, churn is something that you need to actively measure, monitor, and avoid.

The traditional formula for measuring churn is the following.

Churn rate - (numbers of users churned divided by number of users at start of period) times 100.

This looks all straightforward, right? If you want to measure a weekly churn, you will first count the total number of end users you have lost during the last 7 days and divide it by the number of users you had a week ago.

This specific type of churn is useful when you are working with your financials (e.g. measuring LTV or looking at lost revenue) or when you want to calculate the overall rate of growth of your user base (new users - churned).

This metric, however, will struggle to show you the effectiveness of your product in terms of its ability to solve peoples’ pain points. For that, you can calculate a different subtype of churn where you look at the number of people lost since the moment they signed up and started using your product.

This one is essentially the opposite of the retention rate and lets you see how many people you lose in the first couple of days (diagnosis: bad activation and setup), and how many you lose over the first couple of weeks (diagnosis: bad habit formation).

In terms of calculating your churned users in Mixpanel, you can create a cohort that looks like this.

Churn rate setup in Mixpanel: all users who did not create an integration in the last 60 days.

Here, I have used a large timeframe of 60 days, assuming that we are working with a product with either weekly or biweekly usage frequency. If your product has a daily frequency, you can use a 14-day timeframe too.

Acquisition Metrics

We talked about the product onboarding experience and product engagement so far. But a great PLG strategy is not only about those two.

You also need to implement and measure in-app acquisition using growth loops.

To do this, you can consider using these metrics.

Metric #7: Virality

One key differentiator of a product-led growth strategy from more traditional sales or marketing-led ones is its active use of growth loops.

A loop, unlike a funnel (tracking pirate metrics), is self-reinforcing. It means that the people you have acquired using the first iteration of your loop will help you enter the second iteration and acquire more people. The people acquired from the second one will help you get into the third one, and so on.

One of the most common types of growth loops is the viral loop, which occurs when people share content with their friends or colleagues using your product (e.g., a document in the case of Google Docs), and these friends sign up for your product to view it or collaborate on it.

The newly signed-up users will share their own content with their friends, keeping the loop going.

To measure the effectiveness of this referral loop, you will need to calculate the K metric, which is better known as the viral coefficient.

Formula to calculate referral loop effectiveness: K = # of invitations per user x Conversion Rate of Invitations.

K tells you how many new users your existing users can acquire using your sharing (or similar) features.

For instance, if the average Google Doc user shares their docs with five people and 60% of them sign up for the product, then the K for Google Doc’s sharing feature is 5 x 60% = 3.

This number indicates that each Google Doc user can bring in three more users by simply using your product.

It’s hard to measure this with an event-based analytics tool, so ask your data team to give you a custom BI report.

Metric #8: Growth Multiplier

Virality is a critical metric, but it does not represent the true conversion rate of your SaaS growth model and loops. The reason is that virality measures the output of a single loop iteration only.

But we know that the signed-up users of the first iteration are the ones who start the second iteration by sharing content with their friends and colleagues. The virality number will apply to all of them, so the real number of users a single existing user can bring is the sum of all the new users from all the consequential loops.

To measure this, we introduce a metric called Growth Multiplier.

Growth multiplier formula: 1 divided by 1-V. (V = viral coefficient)

In the formula above, V is the viral coefficient for a single loop. So, if you have a product with a V of 0.8 (having viral coefficients above 1 is rare in real life), your growth multiplier will be 5. This means that each of your users will bring in five more users, assuming that the users they bring will use the sharing feature and keep the loop running until it fades away.

Again, this is something to measure using a BI solution.

Metric #9: Net Promoter Score

This is not an acquisition metric per se. However, I wanted to include it here, as it represents customer satisfaction and is a leading indicator for one of PLG's core growth loops—the word-of-mouth (WOM) loop. The name of this loop is quite self-explanatory—people use your product, love it, tell their friends about it, then these friends start using it and tell their friends, and so on.

Now, you can't really measure the viral coefficient of the WOM loop, as you have little to no idea of how many people sign up based on the recommendations of a single user. Therefore, you need to rely on leading indicators to predict and optimize your loop. One of the best ones for measuring your WOM, in this case, is the net promoter score (NPS).

It is a simple questionnaire that asks how likely people are (on a scale from 1 to 10) to recommend your product to their friends and colleagues. Then, you take the results of this survey and plug them into this formula.

Net promoter score formula: NPS = number of promoters, minus number of detractors, divided by total number of respondents, times 100.

Here, promoters are those who have selected 9 or 10 on the scale, and detractors are those with a rate of 6 or less.

Now, in terms of executing this survey, you can ask customer success teams to ask that question to the people they talk to, ask your marketing team to add it to their user feedback email workflows or do a UX solution by adding them to your customer journey.

Bonus Round: Financial PLG Metrics

Before we wrap up with the KPIs of the product-led growth team, I would like to give you a quick rundown of the important financial metrics that a typical PLG company would consider.

Net New Revenue and Expansion Revenue

Whether you measure it by the month (MRR) or the year (ARR), it is a good idea to break your new revenue down into two components: expansion and new revenue.

New revenue is from newly acquired customers, while expansion revenue is from existing customers who decided to buy more seats or upgrade their plan (upsells and cross-sells).

Differentiating those two helps you do better revenue growth planning and execution, as expansion is usually easier to do, but you need a constant inflow of new customers that you can later expand on.

Net Revenue Churn

This is kind of self-explanatory. This metric shows you the amount of annual or monthly recurring revenue that you have lost due to customers leaving you during a specific time period.

Average Revenue Per User (ARPU)

This metric is essentially your ARR divided by the number of paying users. Product-led companies use this metric to divide users into specific segments by user behavior in terms of finances (e.g., consumers, prosumers, and enterprise-size customers) and monitor the quality of acquisition based on it (ARPU growth means better-quality users).

Customer Lifetime Value (CLV)

There is another use for ARPU. It serves as a basis for calculating customer lifetime value. You calculate it as ARPU divided by your churn rate and it represents the total amount of money that you are going to earn from an average customer before they churn.

Customer Acquisition Cost (CAC)

Finally, you have the cost of acquiring a single paying customer, which is the total amount you spend on acquisition divided by the number of customers you have acquired.

You use CAC and CLV to evaluate your business model's viability. You want the CLV to be more than CAC (a good benchmark is 4x) to ensure that you are not operating at a loss.

Data is great, but don’t dismiss your gut feeling!

It would seem a bit weird for me to wrap up a guide on metrics and analytics by asking you to trust your intuition. But, believe me, it is good advice.

Tracking the right metrics will make your product growth efforts much more effective. However, you should never base your decisions on data only. Don’t forget that quantitative data lacks some of the qualitative insights you have in your head.

So, data is great as long as you use it in parallel with your “product gut feeling.”

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Suren Karapetyan

Suren Karapetyan, MBA, is a senior product manager focused on AI-driven SaaS products. He thrives in the fast-paced world of early stage startups and finds the product-market fit for them. His portfolio is quite diverse, ranging from background noise cancellation tools for work-from-home folks to customs clearance software for government agencies.