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Every company should have a set of growth metrics to track and use for data-driven decisions. But you probably already know this. The question is what are the metrics that actually do matter?
So, I've done some research online, and all I found was "essential lists" of metrics every company should be tracking.
While I'm sure this is helpful, it barely gives any guidance on why these metrics are important and in what situations they should be applied.
Therefore, instead of providing a general list, I think it will be more helpful for you to learn about our story of testing different metrics, so you can pick up some ideas relevant to your business.
In the end, I will share the five metrics that really made a difference and helped us collectively boost our MRR by 35%.
How did we select 100 metrics?
This process didn't happen overnight. In fact, it took us several months to test them all. Being part of a growth team, I was constantly testing different revenue growth ideas. Growth analytics is an area where you can get a lot of testing ideas from, but there are just so many things you can be measuring. As a result, I ended up tracking more than 100 metrics simultaneously. Which wasn't fun at all.
To make it more manageable, each week I selected metrics and analyzed their performance over the last quarter to see if I could extract any useful information to drive future experiments.
The reality I encountered was that the majority of these metrics were not meaningful at all. They were either unhelpful or failed to tell the story of what to do with all that data.
Take Average Recurring Revenue, for example. This overhyped metric doesn't make much sense to me. It's usually calculated by taking your best month (or any other period of time that makes sense) in terms of revenue and multiplying it by 12 to get your Average Recurring Revenue. This method doesn't account for anything before or after that month.
So, if you had a spike in orders one month due to seasonality or other time-specific reasons and earned $500,000, compared to an average revenue of $125,000 over the last six months, you're now a '6 million ARR company.'
Another example is Customer Acquisition Costs. Many claim that Customer Acquisition Costs (CAC) is the golden metric as it shows how much it costs to acquire a new customer. However, every customer is different. Every click is different.
You can't just rely on the average cost to acquire people from some channel. What's really important is the value of these customers.
I wouldn't mind spending $5,000 on a single customer that will bring me $15,000 in average revenue. But similarly, $100 can be too much for someone who will ask for a refund and never return
That’s why I decided to structure my approach to evaluate all these metrics on an equal scale:
Relevance to Business Goals: How does each metric align with specific business goals? Does tracking this metric directly contribute to achieving our objectives? Metrics should be relevant to what we want to achieve as a company.
Actionability: Consider whether the metric provides actionable insights. Can we take specific steps based on the data gathered from this metric to improve the business? Metrics that lead to actionable decisions are more valuable.
Customer-Centricity: For customer-related metrics, assess whether they offer insights into customer behavior and preferences. Customer-centric metrics are valuable for understanding and catering to our audience.
Impact on Key Results: Evaluate the potential impact of each metric on key results and North Star metrics, like average revenue per user, customer acquisition, or retention. Metrics that significantly affect these areas should be prioritized.
Scalability: Consider whether the metric remains relevant and useful as the company scales. Good growth metrics should continue to provide valuable insights as the company grows. Keep in mind that sometimes metrics for a startup and for an established company might differ, and that’s fine. There is no one-size-fits-all solution.
Here’s an example of how it looked like in a spreadsheet I created:
As you can see, I’ve highlighted in green the metrics that really stood out and drove our most successful experiments. And now, we're getting to the part you're probably most interested in: the metrics that worked exceptionally well.
The Top 5 Growth Metrics
PQL CR (Product Qualified Lead Conversion Rate)
This is my favorite metric, and it might sound a bit complex at first. Sometimes people call it Activation Rate. Let’s start by defining Product-Led Growth (PLG).
PLG is a business methodology where user acquisition, expansion, conversion, and retention are primarily driven by the product itself. It's like a highly efficient self-serve funnel.
PQLs, or Product Qualified Leads, are leads that have experienced significant value through your product. In a PLG model, a high number of PQLs often leads to a noticeable revenue per user lift.
How Calculated & Implementation Tips
Identifying Key Product Actions. To calculate the Product Qualified Lead Conversion Rate (PQL CR), first identify key product actions that indicate high user engagement and potential for long-term value. This might include specific features used, time spent, or certain milestones achieved within the product.
Cohort Analysis for PQL Identification. We used cohort analysis to monitor user behavior and Customer Lifetime Value (LTV) across different user segments. The goal was to find behaviors that correlate with high LTV. For example, in a tech company, a simple lead might be a signup or demo booking, but a PQL might be a user who has used a key feature multiple times within a specific period of time.
Calculating Conversion Rate. In the end, we measured the conversion from a general lead to a PQL by tracking how many users moved from initial engagement (like sign-up) to key product actions. This metric will vary based on your business model and user journey but is pivotal for understanding the effectiveness of your PLG strategy.
Results and Impact
Implementation and Improvement: After identifying our golden cohort and the defining PQL action, we channeled our efforts into guiding users towards this experience. This involved improving the design, leveraging sales and support teams for user education, and optimizing the overall product experience.
Increase in Activation and Revenue: By focusing on these strategies, we boosted our activation rate from 35% to 64%. This significant increase in engagement directly impacted our average revenue per user, almost doubling it.
Key Takeaways
Actionability for Every Team: PQL CR should be a metric that every customer-facing department can act upon. It's not just a number but a guidepost for strategic decision-making across the company.
Reflects Customer Engagement and Value: This metric is a powerful indicator of how well your product resonates with your users and their journey towards becoming high-value customers.
Drives Cross-Functional Alignment: Perhaps most importantly, PQL CR aligns everyone towards a common goal of enhancing customer experience and driving growth through the product.
By focusing on PQL CR, you're essentially investing in a metric that directly influences your revenue stream and customer churn by enhancing customer engagement and value realization from your product.
ROI (Return on Investment)
Okay, you probably don’t need me to tell you why ROI is important. What I want to stress is HOW IMPORTANT it is. ROI is essentially the godfather of all metrics. Whenever you're evaluating any aspect of your business – be it the traffic someone promises to bring, the buzz of a social media engagement, or the number of signups – always, and I mean always, prioritize ROI.
Your team generated 50 new signups? That's great, but the critical question is, how much money did these signups bring in compared to the money spent? Your social media post exploded? Fantastic – but what did we get out of it value-wise? This is the mindset you need to adopt.
It’s not just about the numbers or the apparent successes - it’s about understanding how many dollars you are getting back for each dollar spent. This perspective changes the game – it shifts the focus from mere metrics to the value and returns of your investments and efforts.
How Calculated & Implementation Tips
Basic Calculation: At its core, ROI calculation is straightforward: (Value Gained from an Activity - Cost of the Activity) / Cost of the Activity.
Beyond Monetary Value: the 'value' gained isn't always directly monetary. It could be time saved, increased customer satisfaction, or improved brand perception.
Segmentation: Always segment ROI calculations. This means not just looking at overall ROI but breaking it down by customer segments, channels, or campaigns. You’ll find many patterns and insights about your customers and what really resonates with them.
Results and Impact
Strategic Resource Allocation: The primary impact of focusing on ROI was the reallocation of resources. We cut down on activities with low ROI, focusing on those with high returns.
Application of the Bullseye Framework: We adopted the Bullseye Framework, concentrating on a few high-return activities and doubling down on them. For us, outbound sales and content marketing showed the highest ROI. By focusing our marketing efforts and budget on these channels, we enhanced their effectiveness instead of diluting our efforts across multiple fronts.
Scalability with Company Size: It’s important to note that as a company grows, the number of high-ROI channels can increase, allowing for diversification without sacrificing focus.
Key Takeaways
ROI as a Decision-Maker: ROI should be the driving force behind where and how resources are allocated. It ensures that every investment, be it time, money, or effort, is accountable for a return.
Value-Driven Focus: Shifting focus to ROI encourages a value-driven approach, moving beyond vanity metrics to what genuinely contributes to the company’s growth.
Adaptability and Learning: By segmenting ROI, we gained deeper insights into our customers and market, allowing for more targeted and effective strategies.
Incorporating a rigorous ROI analysis into your business strategy not only spurs you to use resources efficiently but also aligns your team’s efforts with the most impactful and value-generating activities.
Account Activity Flags
Account Activity Flags is an anomaly detection metric that serves as an early warning system. It's a model that analyzes the volume of orders (or any other unit) coming from your customer base. It's like knowing about a hurricane before it hits the town, rather than just reacting when it's already here. This is precisely why I prefer it over simple revenue metrics.
Revenue metrics often tell you what has happened, not what's about to happen. With Account Activity Flags, you get the chance to be proactive, to prepare and respond before issues escalate into significant problems.
How Calculated & Implementation Tips
Implementing Account Activity Flags isn't straightforward. It usually requires the expertise of a data scientist or an engineer adept in handling data.
Set up ARMA. The core idea revolves around applying a time series forecasting model, like ARMA (Autoregressive Moving Average), to monitor the purchasing behavior of your accounts. The goal is to identify accounts in your customer base that exhibit unusual yet significant shifts in performance—be it drops or spikes—and flag them for attention.
Track Individual Users. One tip could be to track individual user activities within an organization. If some active users became non-active, but the overall account remains active, you can still win them back as you didn’t lose all the trust, preventing the revenue churn.
Why Chosen
Doubling Down on Negative Revenue Churn: Our analysis showed that we had a negative revenue churn rate, which is a golden scenario for any SaaS business. This meant that our existing customers were expanding their usage and spending more over time. By focusing on Account Activity Flags, we wanted to double down on this. Our goal was to keep accounts more active and engaged, thereby extending the customer lifetime value (LTV) even further.
Improving Board Reporting and Revenue Prediction: Having a reliable predictive metric is invaluable for high-level reporting and forecasting. It allows for more accurate revenue predictions, which makes your life way easier when it comes to board meetings and strategic planning. By understanding potential account fluctuations in advance, we could provide more accurate and confident forecasts to our stakeholders.
Setting More Accurate Sales Targets: With a clear understanding of account behaviors and potential customer churn rate (adjusted), we could set more realistic and achievable sales goals. This helped our team focus their marketing efforts on accounts that needed attention, ensuring better resource allocation and maximizing their chances of success.
Results and Impact
We leveraged this metric in two main ways:
Direct Engagement: Our sales team would proactively reach out to accounts flagged by the system. This direct contact allowed us to address potential issues before they led to churn.
Targeted Communication: We also launched "win back" email campaigns, asking flagged accounts for feedback and then offering tailored solutions to their specific problems with our product.
This proactive approach led to a solid 10% boost in our customer retention rates. We were able to win a decent percentage of customers that were about to churn and it provided a wealth of feedback for our product team, which we used to optimize our product.
Key Takeaways
Sometimes you need to go beyond easy, surface-level metrics. By diving into more complex, predictive analytics, you can foresee and mitigate issues before they escalate.
This metric is not just about crunching numbers, but about understanding and preemptively responding to the nuances of customer behavior and satisfaction.
First Week Retention Rate
Did you know that the first week retention rate often mirrors the overall retention rate? We didn't know either. Its correlation with MRR turned out to be also significant. While optimizing for PQL CR can require a month or more to observe performance changes, the First Week Retention Rate offers immediate feedback. This immediacy was really useful when onboarding new customers, allowing us to quickly gauge and monitor their initial engagement levels.
Calculation & Implementation Tips
Basically you just track the percentage of active users in their first week after signing up for your product. However, keep in mind that this metric's applicability can vary depending on the product and industry.
For instance, in a SaaS context, a one-week frame is ideal, but for retail or industries with longer sales cycles, this timeframe might not be as relevant. The key is to adapt the timeframe to fit your business model and industry.
Results and Impact
We found that a specific customer segment had almost no retention after the first day. Despite a good number of initial signups, there was a significant drop in activity on the second day.
To understand the reasons behind this, we conducted user tests and customer interviews. It didn't take long to spot that a critical feature for this segment was hidden in our setting. Once we made this feature more accessible, the first week retention rate for this segment soared by 56%, contributing to a 12% increase in MRR.
Key Takeaways
The First Week Retention Rate is not a one-size-fits-all metric by any means. It requires customization to align with your specific business context and industry.
The timeframe for measuring retention should be adapted to reflect the nature of your product and customer behavior. Once appropriately tailored, this metric will allow you to quickly assess and improve customer experience, leading to significant growth.
Time to Value to Revenue
Time to Value (TTV) is the duration it takes for a new customer to realize the core value of your product - the 'Aha!' moment when they first experience the primary benefit. Very often the immediate perceived value can significantly influence customer lifetime value and retention.
Why Chosen
We zeroed in on TTV as a key metric after realizing from customer feedback that a major hurdle for new users was the time and effort required to understand and experience the key value of our product.
Our hypothesis was straightforward: by reducing the TTV, we could significantly boost both the First Week Retention Rate and revenue.
Calculation & Implementation Tips
To track it we dissected the user journey into incremental steps and calculated the average time different user segments take to progress from one step to the next.
The pivotal 'Value' step is when the user experiences their 'Aha!' moment. For example, in a fast-food context, it's the moment of tasting the burger and fries and realizing how little you paid for it.
The trick here is to lay out every step in the process, no matter how small, so you can identify opportunities for optimization or elimination. A detailed map of the user journey, complete with conversion rates at each step, will guide you on which steps need optimization.
Results and Impact
Our initial approach was to streamline the user journey to the extreme – we removed the signup form and delayed payment to offer the value as quickly as possible. This as expected led to a significant increase in signups and activation rate.
However, we soon found that many of these new users didn't retain beyond the first week and contributed little to our revenue. Despite the initial spike in engagement, the overall revenue impact wasn’t this high, keeping the revenue churn on it's old level.
So, we iteratively reintroduced some relevant steps back into the signup process. This slight increase in Time to Value helped filter out unqualified signups, attracting active users, that are more serious about using our product.
Key takeaways
This story teaches a valuable lesson: while it's super important to deliver value quickly, it's equally important to qualify users. Speeding up the user journey can attract more users, but without the right qualification steps, it might not translate into sustainable revenue or long-term engagement.
A balanced approach, where you optimize the user journey without compromising on user qualification, can lead to more qualified signups, better retention, and ultimately, a positive impact on revenue.
Understanding and optimizing these and other key performance indicators and measurable metrics has allowed us to boost monthly recurring revenue substantially. We’ll continue refining which ones provide the most valuable insights over time.
Other Basic But Popular Metrics
As our business has evolved over the past few years, we've tested tracking a variety of other metrics to gauge the performance and make data-driven decisions about where to focus our efforts.
And even though we talked about the 5 most helpful metrics that worked for us - here are some of the key metrics you want to make sure you track as well, to better understand your business growth and sales performance:
Monthly Recurring Revenue (MRR) and Annual Recurring Revenue (ARR): These metrics show the predictable revenue we can count on from ongoing subscriptions. Tracking the growth in MRR and ARR monthly and annually is critical for forecasting, and positive trends show we are clearly moving in the right direction.
Number of New Customers: Understanding both the total number of new customers in a given period as well as the number that came from specific products or pricing models indicates how well our customer acquisition efforts are working and where to concentrate marketing. A high churn rate can offset gains though, so customer loyalty is worth tracking too through metrics like Net Promoter Score.
Gross Margin: By breaking out the total amount of revenue minus the cost of goods sold, gross margin percentage shows how much money we retain as profit from our products and services. It's an important metric for business decisions around pricing and production costs.
Lead Conversion: While the total number of leads measures our pipeline, tracking what percentage convert to new business shows how we are performing at actually winning new customers. Together these highlight growth trajectory.
Conclusion
The journey through these top growth metrics revealed one important aspect of growth analytics: interdependence. Each metric, while powerful on its own, often influences and is influenced by others. This means that focusing on one metric often creates a ripple effect, improving others in the process.
Another vital thing is segmentation in every analysis. Treating all users or customers as a homogenous group can obscure the insights. By segmenting users based on behavior, demographics, or other relevant criteria, we can uncover hidden gems.
And by any means this list of growth metrics is exhaustive. There are countless other valuable metrics that could be more relevant to different business models or industries. What worked for us might not work for everyone in the same way. You should consider your unique context, goals, and challenges when selecting and prioritizing metrics.
The end goal is to find the right levers to pull and often, these levers are the metrics that you choose to focus on. By understanding and optimizing these growth metrics, you can create a foundation for sustainable growth and continuous improvement.
Every company should have a set of growth metrics to track and use for data-driven decisions. But you probably already know this. The question is what are the metrics that actually do matter?
So, I've done some research online, and all I found was "essential lists" of metrics every company should be tracking.
While I'm sure this is helpful, it barely gives any guidance on why these metrics are important and in what situations they should be applied.
Therefore, instead of providing a general list, I think it will be more helpful for you to learn about our story of testing different metrics, so you can pick up some ideas relevant to your business.
In the end, I will share the five metrics that really made a difference and helped us collectively boost our MRR by 35%.
How did we select 100 metrics?
This process didn't happen overnight. In fact, it took us several months to test them all. Being part of a growth team, I was constantly testing different revenue growth ideas. Growth analytics is an area where you can get a lot of testing ideas from, but there are just so many things you can be measuring. As a result, I ended up tracking more than 100 metrics simultaneously. Which wasn't fun at all.
To make it more manageable, each week I selected metrics and analyzed their performance over the last quarter to see if I could extract any useful information to drive future experiments.
The reality I encountered was that the majority of these metrics were not meaningful at all. They were either unhelpful or failed to tell the story of what to do with all that data.
Take Average Recurring Revenue, for example. This overhyped metric doesn't make much sense to me. It's usually calculated by taking your best month (or any other period of time that makes sense) in terms of revenue and multiplying it by 12 to get your Average Recurring Revenue. This method doesn't account for anything before or after that month.
So, if you had a spike in orders one month due to seasonality or other time-specific reasons and earned $500,000, compared to an average revenue of $125,000 over the last six months, you're now a '6 million ARR company.'
Another example is Customer Acquisition Costs. Many claim that Customer Acquisition Costs (CAC) is the golden metric as it shows how much it costs to acquire a new customer. However, every customer is different. Every click is different.
You can't just rely on the average cost to acquire people from some channel. What's really important is the value of these customers.
I wouldn't mind spending $5,000 on a single customer that will bring me $15,000 in average revenue. But similarly, $100 can be too much for someone who will ask for a refund and never return
That’s why I decided to structure my approach to evaluate all these metrics on an equal scale:
Relevance to Business Goals: How does each metric align with specific business goals? Does tracking this metric directly contribute to achieving our objectives? Metrics should be relevant to what we want to achieve as a company.
Actionability: Consider whether the metric provides actionable insights. Can we take specific steps based on the data gathered from this metric to improve the business? Metrics that lead to actionable decisions are more valuable.
Customer-Centricity: For customer-related metrics, assess whether they offer insights into customer behavior and preferences. Customer-centric metrics are valuable for understanding and catering to our audience.
Impact on Key Results: Evaluate the potential impact of each metric on key results and North Star metrics, like average revenue per user, customer acquisition, or retention. Metrics that significantly affect these areas should be prioritized.
Scalability: Consider whether the metric remains relevant and useful as the company scales. Good growth metrics should continue to provide valuable insights as the company grows. Keep in mind that sometimes metrics for a startup and for an established company might differ, and that’s fine. There is no one-size-fits-all solution.
Here’s an example of how it looked like in a spreadsheet I created:
As you can see, I’ve highlighted in green the metrics that really stood out and drove our most successful experiments. And now, we're getting to the part you're probably most interested in: the metrics that worked exceptionally well.
The Top 5 Growth Metrics
PQL CR (Product Qualified Lead Conversion Rate)
This is my favorite metric, and it might sound a bit complex at first. Sometimes people call it Activation Rate. Let’s start by defining Product-Led Growth (PLG).
PLG is a business methodology where user acquisition, expansion, conversion, and retention are primarily driven by the product itself. It's like a highly efficient self-serve funnel.
PQLs, or Product Qualified Leads, are leads that have experienced significant value through your product. In a PLG model, a high number of PQLs often leads to a noticeable revenue per user lift.
How Calculated & Implementation Tips
Identifying Key Product Actions. To calculate the Product Qualified Lead Conversion Rate (PQL CR), first identify key product actions that indicate high user engagement and potential for long-term value. This might include specific features used, time spent, or certain milestones achieved within the product.
Cohort Analysis for PQL Identification. We used cohort analysis to monitor user behavior and Customer Lifetime Value (LTV) across different user segments. The goal was to find behaviors that correlate with high LTV. For example, in a tech company, a simple lead might be a signup or demo booking, but a PQL might be a user who has used a key feature multiple times within a specific period of time.
Calculating Conversion Rate. In the end, we measured the conversion from a general lead to a PQL by tracking how many users moved from initial engagement (like sign-up) to key product actions. This metric will vary based on your business model and user journey but is pivotal for understanding the effectiveness of your PLG strategy.
Results and Impact
Implementation and Improvement: After identifying our golden cohort and the defining PQL action, we channeled our efforts into guiding users towards this experience. This involved improving the design, leveraging sales and support teams for user education, and optimizing the overall product experience.
Increase in Activation and Revenue: By focusing on these strategies, we boosted our activation rate from 35% to 64%. This significant increase in engagement directly impacted our average revenue per user, almost doubling it.
Key Takeaways
Actionability for Every Team: PQL CR should be a metric that every customer-facing department can act upon. It's not just a number but a guidepost for strategic decision-making across the company.
Reflects Customer Engagement and Value: This metric is a powerful indicator of how well your product resonates with your users and their journey towards becoming high-value customers.
Drives Cross-Functional Alignment: Perhaps most importantly, PQL CR aligns everyone towards a common goal of enhancing customer experience and driving growth through the product.
By focusing on PQL CR, you're essentially investing in a metric that directly influences your revenue stream and customer churn by enhancing customer engagement and value realization from your product.
ROI (Return on Investment)
Okay, you probably don’t need me to tell you why ROI is important. What I want to stress is HOW IMPORTANT it is. ROI is essentially the godfather of all metrics. Whenever you're evaluating any aspect of your business – be it the traffic someone promises to bring, the buzz of a social media engagement, or the number of signups – always, and I mean always, prioritize ROI.
Your team generated 50 new signups? That's great, but the critical question is, how much money did these signups bring in compared to the money spent? Your social media post exploded? Fantastic – but what did we get out of it value-wise? This is the mindset you need to adopt.
It’s not just about the numbers or the apparent successes - it’s about understanding how many dollars you are getting back for each dollar spent. This perspective changes the game – it shifts the focus from mere metrics to the value and returns of your investments and efforts.
How Calculated & Implementation Tips
Basic Calculation: At its core, ROI calculation is straightforward: (Value Gained from an Activity - Cost of the Activity) / Cost of the Activity.
Beyond Monetary Value: the 'value' gained isn't always directly monetary. It could be time saved, increased customer satisfaction, or improved brand perception.
Segmentation: Always segment ROI calculations. This means not just looking at overall ROI but breaking it down by customer segments, channels, or campaigns. You’ll find many patterns and insights about your customers and what really resonates with them.
Results and Impact
Strategic Resource Allocation: The primary impact of focusing on ROI was the reallocation of resources. We cut down on activities with low ROI, focusing on those with high returns.
Application of the Bullseye Framework: We adopted the Bullseye Framework, concentrating on a few high-return activities and doubling down on them. For us, outbound sales and content marketing showed the highest ROI. By focusing our marketing efforts and budget on these channels, we enhanced their effectiveness instead of diluting our efforts across multiple fronts.
Scalability with Company Size: It’s important to note that as a company grows, the number of high-ROI channels can increase, allowing for diversification without sacrificing focus.
Key Takeaways
ROI as a Decision-Maker: ROI should be the driving force behind where and how resources are allocated. It ensures that every investment, be it time, money, or effort, is accountable for a return.
Value-Driven Focus: Shifting focus to ROI encourages a value-driven approach, moving beyond vanity metrics to what genuinely contributes to the company’s growth.
Adaptability and Learning: By segmenting ROI, we gained deeper insights into our customers and market, allowing for more targeted and effective strategies.
Incorporating a rigorous ROI analysis into your business strategy not only spurs you to use resources efficiently but also aligns your team’s efforts with the most impactful and value-generating activities.
Account Activity Flags
Account Activity Flags is an anomaly detection metric that serves as an early warning system. It's a model that analyzes the volume of orders (or any other unit) coming from your customer base. It's like knowing about a hurricane before it hits the town, rather than just reacting when it's already here. This is precisely why I prefer it over simple revenue metrics.
Revenue metrics often tell you what has happened, not what's about to happen. With Account Activity Flags, you get the chance to be proactive, to prepare and respond before issues escalate into significant problems.
How Calculated & Implementation Tips
Implementing Account Activity Flags isn't straightforward. It usually requires the expertise of a data scientist or an engineer adept in handling data.
Set up ARMA. The core idea revolves around applying a time series forecasting model, like ARMA (Autoregressive Moving Average), to monitor the purchasing behavior of your accounts. The goal is to identify accounts in your customer base that exhibit unusual yet significant shifts in performance—be it drops or spikes—and flag them for attention.
Track Individual Users. One tip could be to track individual user activities within an organization. If some active users became non-active, but the overall account remains active, you can still win them back as you didn’t lose all the trust, preventing the revenue churn.
Why Chosen
Doubling Down on Negative Revenue Churn: Our analysis showed that we had a negative revenue churn rate, which is a golden scenario for any SaaS business. This meant that our existing customers were expanding their usage and spending more over time. By focusing on Account Activity Flags, we wanted to double down on this. Our goal was to keep accounts more active and engaged, thereby extending the customer lifetime value (LTV) even further.
Improving Board Reporting and Revenue Prediction: Having a reliable predictive metric is invaluable for high-level reporting and forecasting. It allows for more accurate revenue predictions, which makes your life way easier when it comes to board meetings and strategic planning. By understanding potential account fluctuations in advance, we could provide more accurate and confident forecasts to our stakeholders.
Setting More Accurate Sales Targets: With a clear understanding of account behaviors and potential customer churn rate (adjusted), we could set more realistic and achievable sales goals. This helped our team focus their marketing efforts on accounts that needed attention, ensuring better resource allocation and maximizing their chances of success.
Results and Impact
We leveraged this metric in two main ways:
Direct Engagement: Our sales team would proactively reach out to accounts flagged by the system. This direct contact allowed us to address potential issues before they led to churn.
Targeted Communication: We also launched "win back" email campaigns, asking flagged accounts for feedback and then offering tailored solutions to their specific problems with our product.
This proactive approach led to a solid 10% boost in our customer retention rates. We were able to win a decent percentage of customers that were about to churn and it provided a wealth of feedback for our product team, which we used to optimize our product.
Key Takeaways
Sometimes you need to go beyond easy, surface-level metrics. By diving into more complex, predictive analytics, you can foresee and mitigate issues before they escalate.
This metric is not just about crunching numbers, but about understanding and preemptively responding to the nuances of customer behavior and satisfaction.
First Week Retention Rate
Did you know that the first week retention rate often mirrors the overall retention rate? We didn't know either. Its correlation with MRR turned out to be also significant. While optimizing for PQL CR can require a month or more to observe performance changes, the First Week Retention Rate offers immediate feedback. This immediacy was really useful when onboarding new customers, allowing us to quickly gauge and monitor their initial engagement levels.
Calculation & Implementation Tips
Basically you just track the percentage of active users in their first week after signing up for your product. However, keep in mind that this metric's applicability can vary depending on the product and industry.
For instance, in a SaaS context, a one-week frame is ideal, but for retail or industries with longer sales cycles, this timeframe might not be as relevant. The key is to adapt the timeframe to fit your business model and industry.
Results and Impact
We found that a specific customer segment had almost no retention after the first day. Despite a good number of initial signups, there was a significant drop in activity on the second day.
To understand the reasons behind this, we conducted user tests and customer interviews. It didn't take long to spot that a critical feature for this segment was hidden in our setting. Once we made this feature more accessible, the first week retention rate for this segment soared by 56%, contributing to a 12% increase in MRR.
Key Takeaways
The First Week Retention Rate is not a one-size-fits-all metric by any means. It requires customization to align with your specific business context and industry.
The timeframe for measuring retention should be adapted to reflect the nature of your product and customer behavior. Once appropriately tailored, this metric will allow you to quickly assess and improve customer experience, leading to significant growth.
Time to Value to Revenue
Time to Value (TTV) is the duration it takes for a new customer to realize the core value of your product - the 'Aha!' moment when they first experience the primary benefit. Very often the immediate perceived value can significantly influence customer lifetime value and retention.
Why Chosen
We zeroed in on TTV as a key metric after realizing from customer feedback that a major hurdle for new users was the time and effort required to understand and experience the key value of our product.
Our hypothesis was straightforward: by reducing the TTV, we could significantly boost both the First Week Retention Rate and revenue.
Calculation & Implementation Tips
To track it we dissected the user journey into incremental steps and calculated the average time different user segments take to progress from one step to the next.
The pivotal 'Value' step is when the user experiences their 'Aha!' moment. For example, in a fast-food context, it's the moment of tasting the burger and fries and realizing how little you paid for it.
The trick here is to lay out every step in the process, no matter how small, so you can identify opportunities for optimization or elimination. A detailed map of the user journey, complete with conversion rates at each step, will guide you on which steps need optimization.
Results and Impact
Our initial approach was to streamline the user journey to the extreme – we removed the signup form and delayed payment to offer the value as quickly as possible. This as expected led to a significant increase in signups and activation rate.
However, we soon found that many of these new users didn't retain beyond the first week and contributed little to our revenue. Despite the initial spike in engagement, the overall revenue impact wasn’t this high, keeping the revenue churn on it's old level.
So, we iteratively reintroduced some relevant steps back into the signup process. This slight increase in Time to Value helped filter out unqualified signups, attracting active users, that are more serious about using our product.
Key takeaways
This story teaches a valuable lesson: while it's super important to deliver value quickly, it's equally important to qualify users. Speeding up the user journey can attract more users, but without the right qualification steps, it might not translate into sustainable revenue or long-term engagement.
A balanced approach, where you optimize the user journey without compromising on user qualification, can lead to more qualified signups, better retention, and ultimately, a positive impact on revenue.
Understanding and optimizing these and other key performance indicators and measurable metrics has allowed us to boost monthly recurring revenue substantially. We’ll continue refining which ones provide the most valuable insights over time.
Other Basic But Popular Metrics
As our business has evolved over the past few years, we've tested tracking a variety of other metrics to gauge the performance and make data-driven decisions about where to focus our efforts.
And even though we talked about the 5 most helpful metrics that worked for us - here are some of the key metrics you want to make sure you track as well, to better understand your business growth and sales performance:
Monthly Recurring Revenue (MRR) and Annual Recurring Revenue (ARR): These metrics show the predictable revenue we can count on from ongoing subscriptions. Tracking the growth in MRR and ARR monthly and annually is critical for forecasting, and positive trends show we are clearly moving in the right direction.
Number of New Customers: Understanding both the total number of new customers in a given period as well as the number that came from specific products or pricing models indicates how well our customer acquisition efforts are working and where to concentrate marketing. A high churn rate can offset gains though, so customer loyalty is worth tracking too through metrics like Net Promoter Score.
Gross Margin: By breaking out the total amount of revenue minus the cost of goods sold, gross margin percentage shows how much money we retain as profit from our products and services. It's an important metric for business decisions around pricing and production costs.
Lead Conversion: While the total number of leads measures our pipeline, tracking what percentage convert to new business shows how we are performing at actually winning new customers. Together these highlight growth trajectory.
Conclusion
The journey through these top growth metrics revealed one important aspect of growth analytics: interdependence. Each metric, while powerful on its own, often influences and is influenced by others. This means that focusing on one metric often creates a ripple effect, improving others in the process.
Another vital thing is segmentation in every analysis. Treating all users or customers as a homogenous group can obscure the insights. By segmenting users based on behavior, demographics, or other relevant criteria, we can uncover hidden gems.
And by any means this list of growth metrics is exhaustive. There are countless other valuable metrics that could be more relevant to different business models or industries. What worked for us might not work for everyone in the same way. You should consider your unique context, goals, and challenges when selecting and prioritizing metrics.
The end goal is to find the right levers to pull and often, these levers are the metrics that you choose to focus on. By understanding and optimizing these growth metrics, you can create a foundation for sustainable growth and continuous improvement.
Every company should have a set of growth metrics to track and use for data-driven decisions. But you probably already know this. The question is what are the metrics that actually do matter?
So, I've done some research online, and all I found was "essential lists" of metrics every company should be tracking.
While I'm sure this is helpful, it barely gives any guidance on why these metrics are important and in what situations they should be applied.
Therefore, instead of providing a general list, I think it will be more helpful for you to learn about our story of testing different metrics, so you can pick up some ideas relevant to your business.
In the end, I will share the five metrics that really made a difference and helped us collectively boost our MRR by 35%.
How did we select 100 metrics?
This process didn't happen overnight. In fact, it took us several months to test them all. Being part of a growth team, I was constantly testing different revenue growth ideas. Growth analytics is an area where you can get a lot of testing ideas from, but there are just so many things you can be measuring. As a result, I ended up tracking more than 100 metrics simultaneously. Which wasn't fun at all.
To make it more manageable, each week I selected metrics and analyzed their performance over the last quarter to see if I could extract any useful information to drive future experiments.
The reality I encountered was that the majority of these metrics were not meaningful at all. They were either unhelpful or failed to tell the story of what to do with all that data.
Take Average Recurring Revenue, for example. This overhyped metric doesn't make much sense to me. It's usually calculated by taking your best month (or any other period of time that makes sense) in terms of revenue and multiplying it by 12 to get your Average Recurring Revenue. This method doesn't account for anything before or after that month.
So, if you had a spike in orders one month due to seasonality or other time-specific reasons and earned $500,000, compared to an average revenue of $125,000 over the last six months, you're now a '6 million ARR company.'
Another example is Customer Acquisition Costs. Many claim that Customer Acquisition Costs (CAC) is the golden metric as it shows how much it costs to acquire a new customer. However, every customer is different. Every click is different.
You can't just rely on the average cost to acquire people from some channel. What's really important is the value of these customers.
I wouldn't mind spending $5,000 on a single customer that will bring me $15,000 in average revenue. But similarly, $100 can be too much for someone who will ask for a refund and never return
That’s why I decided to structure my approach to evaluate all these metrics on an equal scale:
Relevance to Business Goals: How does each metric align with specific business goals? Does tracking this metric directly contribute to achieving our objectives? Metrics should be relevant to what we want to achieve as a company.
Actionability: Consider whether the metric provides actionable insights. Can we take specific steps based on the data gathered from this metric to improve the business? Metrics that lead to actionable decisions are more valuable.
Customer-Centricity: For customer-related metrics, assess whether they offer insights into customer behavior and preferences. Customer-centric metrics are valuable for understanding and catering to our audience.
Impact on Key Results: Evaluate the potential impact of each metric on key results and North Star metrics, like average revenue per user, customer acquisition, or retention. Metrics that significantly affect these areas should be prioritized.
Scalability: Consider whether the metric remains relevant and useful as the company scales. Good growth metrics should continue to provide valuable insights as the company grows. Keep in mind that sometimes metrics for a startup and for an established company might differ, and that’s fine. There is no one-size-fits-all solution.
Here’s an example of how it looked like in a spreadsheet I created:
As you can see, I’ve highlighted in green the metrics that really stood out and drove our most successful experiments. And now, we're getting to the part you're probably most interested in: the metrics that worked exceptionally well.
The Top 5 Growth Metrics
PQL CR (Product Qualified Lead Conversion Rate)
This is my favorite metric, and it might sound a bit complex at first. Sometimes people call it Activation Rate. Let’s start by defining Product-Led Growth (PLG).
PLG is a business methodology where user acquisition, expansion, conversion, and retention are primarily driven by the product itself. It's like a highly efficient self-serve funnel.
PQLs, or Product Qualified Leads, are leads that have experienced significant value through your product. In a PLG model, a high number of PQLs often leads to a noticeable revenue per user lift.
How Calculated & Implementation Tips
Identifying Key Product Actions. To calculate the Product Qualified Lead Conversion Rate (PQL CR), first identify key product actions that indicate high user engagement and potential for long-term value. This might include specific features used, time spent, or certain milestones achieved within the product.
Cohort Analysis for PQL Identification. We used cohort analysis to monitor user behavior and Customer Lifetime Value (LTV) across different user segments. The goal was to find behaviors that correlate with high LTV. For example, in a tech company, a simple lead might be a signup or demo booking, but a PQL might be a user who has used a key feature multiple times within a specific period of time.
Calculating Conversion Rate. In the end, we measured the conversion from a general lead to a PQL by tracking how many users moved from initial engagement (like sign-up) to key product actions. This metric will vary based on your business model and user journey but is pivotal for understanding the effectiveness of your PLG strategy.
Results and Impact
Implementation and Improvement: After identifying our golden cohort and the defining PQL action, we channeled our efforts into guiding users towards this experience. This involved improving the design, leveraging sales and support teams for user education, and optimizing the overall product experience.
Increase in Activation and Revenue: By focusing on these strategies, we boosted our activation rate from 35% to 64%. This significant increase in engagement directly impacted our average revenue per user, almost doubling it.
Key Takeaways
Actionability for Every Team: PQL CR should be a metric that every customer-facing department can act upon. It's not just a number but a guidepost for strategic decision-making across the company.
Reflects Customer Engagement and Value: This metric is a powerful indicator of how well your product resonates with your users and their journey towards becoming high-value customers.
Drives Cross-Functional Alignment: Perhaps most importantly, PQL CR aligns everyone towards a common goal of enhancing customer experience and driving growth through the product.
By focusing on PQL CR, you're essentially investing in a metric that directly influences your revenue stream and customer churn by enhancing customer engagement and value realization from your product.
ROI (Return on Investment)
Okay, you probably don’t need me to tell you why ROI is important. What I want to stress is HOW IMPORTANT it is. ROI is essentially the godfather of all metrics. Whenever you're evaluating any aspect of your business – be it the traffic someone promises to bring, the buzz of a social media engagement, or the number of signups – always, and I mean always, prioritize ROI.
Your team generated 50 new signups? That's great, but the critical question is, how much money did these signups bring in compared to the money spent? Your social media post exploded? Fantastic – but what did we get out of it value-wise? This is the mindset you need to adopt.
It’s not just about the numbers or the apparent successes - it’s about understanding how many dollars you are getting back for each dollar spent. This perspective changes the game – it shifts the focus from mere metrics to the value and returns of your investments and efforts.
How Calculated & Implementation Tips
Basic Calculation: At its core, ROI calculation is straightforward: (Value Gained from an Activity - Cost of the Activity) / Cost of the Activity.
Beyond Monetary Value: the 'value' gained isn't always directly monetary. It could be time saved, increased customer satisfaction, or improved brand perception.
Segmentation: Always segment ROI calculations. This means not just looking at overall ROI but breaking it down by customer segments, channels, or campaigns. You’ll find many patterns and insights about your customers and what really resonates with them.
Results and Impact
Strategic Resource Allocation: The primary impact of focusing on ROI was the reallocation of resources. We cut down on activities with low ROI, focusing on those with high returns.
Application of the Bullseye Framework: We adopted the Bullseye Framework, concentrating on a few high-return activities and doubling down on them. For us, outbound sales and content marketing showed the highest ROI. By focusing our marketing efforts and budget on these channels, we enhanced their effectiveness instead of diluting our efforts across multiple fronts.
Scalability with Company Size: It’s important to note that as a company grows, the number of high-ROI channels can increase, allowing for diversification without sacrificing focus.
Key Takeaways
ROI as a Decision-Maker: ROI should be the driving force behind where and how resources are allocated. It ensures that every investment, be it time, money, or effort, is accountable for a return.
Value-Driven Focus: Shifting focus to ROI encourages a value-driven approach, moving beyond vanity metrics to what genuinely contributes to the company’s growth.
Adaptability and Learning: By segmenting ROI, we gained deeper insights into our customers and market, allowing for more targeted and effective strategies.
Incorporating a rigorous ROI analysis into your business strategy not only spurs you to use resources efficiently but also aligns your team’s efforts with the most impactful and value-generating activities.
Account Activity Flags
Account Activity Flags is an anomaly detection metric that serves as an early warning system. It's a model that analyzes the volume of orders (or any other unit) coming from your customer base. It's like knowing about a hurricane before it hits the town, rather than just reacting when it's already here. This is precisely why I prefer it over simple revenue metrics.
Revenue metrics often tell you what has happened, not what's about to happen. With Account Activity Flags, you get the chance to be proactive, to prepare and respond before issues escalate into significant problems.
How Calculated & Implementation Tips
Implementing Account Activity Flags isn't straightforward. It usually requires the expertise of a data scientist or an engineer adept in handling data.
Set up ARMA. The core idea revolves around applying a time series forecasting model, like ARMA (Autoregressive Moving Average), to monitor the purchasing behavior of your accounts. The goal is to identify accounts in your customer base that exhibit unusual yet significant shifts in performance—be it drops or spikes—and flag them for attention.
Track Individual Users. One tip could be to track individual user activities within an organization. If some active users became non-active, but the overall account remains active, you can still win them back as you didn’t lose all the trust, preventing the revenue churn.
Why Chosen
Doubling Down on Negative Revenue Churn: Our analysis showed that we had a negative revenue churn rate, which is a golden scenario for any SaaS business. This meant that our existing customers were expanding their usage and spending more over time. By focusing on Account Activity Flags, we wanted to double down on this. Our goal was to keep accounts more active and engaged, thereby extending the customer lifetime value (LTV) even further.
Improving Board Reporting and Revenue Prediction: Having a reliable predictive metric is invaluable for high-level reporting and forecasting. It allows for more accurate revenue predictions, which makes your life way easier when it comes to board meetings and strategic planning. By understanding potential account fluctuations in advance, we could provide more accurate and confident forecasts to our stakeholders.
Setting More Accurate Sales Targets: With a clear understanding of account behaviors and potential customer churn rate (adjusted), we could set more realistic and achievable sales goals. This helped our team focus their marketing efforts on accounts that needed attention, ensuring better resource allocation and maximizing their chances of success.
Results and Impact
We leveraged this metric in two main ways:
Direct Engagement: Our sales team would proactively reach out to accounts flagged by the system. This direct contact allowed us to address potential issues before they led to churn.
Targeted Communication: We also launched "win back" email campaigns, asking flagged accounts for feedback and then offering tailored solutions to their specific problems with our product.
This proactive approach led to a solid 10% boost in our customer retention rates. We were able to win a decent percentage of customers that were about to churn and it provided a wealth of feedback for our product team, which we used to optimize our product.
Key Takeaways
Sometimes you need to go beyond easy, surface-level metrics. By diving into more complex, predictive analytics, you can foresee and mitigate issues before they escalate.
This metric is not just about crunching numbers, but about understanding and preemptively responding to the nuances of customer behavior and satisfaction.
First Week Retention Rate
Did you know that the first week retention rate often mirrors the overall retention rate? We didn't know either. Its correlation with MRR turned out to be also significant. While optimizing for PQL CR can require a month or more to observe performance changes, the First Week Retention Rate offers immediate feedback. This immediacy was really useful when onboarding new customers, allowing us to quickly gauge and monitor their initial engagement levels.
Calculation & Implementation Tips
Basically you just track the percentage of active users in their first week after signing up for your product. However, keep in mind that this metric's applicability can vary depending on the product and industry.
For instance, in a SaaS context, a one-week frame is ideal, but for retail or industries with longer sales cycles, this timeframe might not be as relevant. The key is to adapt the timeframe to fit your business model and industry.
Results and Impact
We found that a specific customer segment had almost no retention after the first day. Despite a good number of initial signups, there was a significant drop in activity on the second day.
To understand the reasons behind this, we conducted user tests and customer interviews. It didn't take long to spot that a critical feature for this segment was hidden in our setting. Once we made this feature more accessible, the first week retention rate for this segment soared by 56%, contributing to a 12% increase in MRR.
Key Takeaways
The First Week Retention Rate is not a one-size-fits-all metric by any means. It requires customization to align with your specific business context and industry.
The timeframe for measuring retention should be adapted to reflect the nature of your product and customer behavior. Once appropriately tailored, this metric will allow you to quickly assess and improve customer experience, leading to significant growth.
Time to Value to Revenue
Time to Value (TTV) is the duration it takes for a new customer to realize the core value of your product - the 'Aha!' moment when they first experience the primary benefit. Very often the immediate perceived value can significantly influence customer lifetime value and retention.
Why Chosen
We zeroed in on TTV as a key metric after realizing from customer feedback that a major hurdle for new users was the time and effort required to understand and experience the key value of our product.
Our hypothesis was straightforward: by reducing the TTV, we could significantly boost both the First Week Retention Rate and revenue.
Calculation & Implementation Tips
To track it we dissected the user journey into incremental steps and calculated the average time different user segments take to progress from one step to the next.
The pivotal 'Value' step is when the user experiences their 'Aha!' moment. For example, in a fast-food context, it's the moment of tasting the burger and fries and realizing how little you paid for it.
The trick here is to lay out every step in the process, no matter how small, so you can identify opportunities for optimization or elimination. A detailed map of the user journey, complete with conversion rates at each step, will guide you on which steps need optimization.
Results and Impact
Our initial approach was to streamline the user journey to the extreme – we removed the signup form and delayed payment to offer the value as quickly as possible. This as expected led to a significant increase in signups and activation rate.
However, we soon found that many of these new users didn't retain beyond the first week and contributed little to our revenue. Despite the initial spike in engagement, the overall revenue impact wasn’t this high, keeping the revenue churn on it's old level.
So, we iteratively reintroduced some relevant steps back into the signup process. This slight increase in Time to Value helped filter out unqualified signups, attracting active users, that are more serious about using our product.
Key takeaways
This story teaches a valuable lesson: while it's super important to deliver value quickly, it's equally important to qualify users. Speeding up the user journey can attract more users, but without the right qualification steps, it might not translate into sustainable revenue or long-term engagement.
A balanced approach, where you optimize the user journey without compromising on user qualification, can lead to more qualified signups, better retention, and ultimately, a positive impact on revenue.
Understanding and optimizing these and other key performance indicators and measurable metrics has allowed us to boost monthly recurring revenue substantially. We’ll continue refining which ones provide the most valuable insights over time.
Other Basic But Popular Metrics
As our business has evolved over the past few years, we've tested tracking a variety of other metrics to gauge the performance and make data-driven decisions about where to focus our efforts.
And even though we talked about the 5 most helpful metrics that worked for us - here are some of the key metrics you want to make sure you track as well, to better understand your business growth and sales performance:
Monthly Recurring Revenue (MRR) and Annual Recurring Revenue (ARR): These metrics show the predictable revenue we can count on from ongoing subscriptions. Tracking the growth in MRR and ARR monthly and annually is critical for forecasting, and positive trends show we are clearly moving in the right direction.
Number of New Customers: Understanding both the total number of new customers in a given period as well as the number that came from specific products or pricing models indicates how well our customer acquisition efforts are working and where to concentrate marketing. A high churn rate can offset gains though, so customer loyalty is worth tracking too through metrics like Net Promoter Score.
Gross Margin: By breaking out the total amount of revenue minus the cost of goods sold, gross margin percentage shows how much money we retain as profit from our products and services. It's an important metric for business decisions around pricing and production costs.
Lead Conversion: While the total number of leads measures our pipeline, tracking what percentage convert to new business shows how we are performing at actually winning new customers. Together these highlight growth trajectory.
Conclusion
The journey through these top growth metrics revealed one important aspect of growth analytics: interdependence. Each metric, while powerful on its own, often influences and is influenced by others. This means that focusing on one metric often creates a ripple effect, improving others in the process.
Another vital thing is segmentation in every analysis. Treating all users or customers as a homogenous group can obscure the insights. By segmenting users based on behavior, demographics, or other relevant criteria, we can uncover hidden gems.
And by any means this list of growth metrics is exhaustive. There are countless other valuable metrics that could be more relevant to different business models or industries. What worked for us might not work for everyone in the same way. You should consider your unique context, goals, and challenges when selecting and prioritizing metrics.
The end goal is to find the right levers to pull and often, these levers are the metrics that you choose to focus on. By understanding and optimizing these growth metrics, you can create a foundation for sustainable growth and continuous improvement.
Start retrieving the insights in your own language
Think about the last time you had a question about your data. How long did it take to answer it?
Start retrieving the insights in your own language
Think about the last time you had a question about your data. How long did it take to answer it?
Start retrieving the insights in your own language
Think about the last time you had a question about your data. How long did it take to answer it?