With product analytics software providing insight into your product, monitoring product performance has never been easier. All you need to do is identify the most impactful metrics for your roadmap and scrap the ones that are a waste of time.
But that’s only the tip of the iceberg—interpreting and incorporating them into your product strategy is where the real challenge begins.
Thankfully, we’re here to help product teams along the way. Be sure to continue reading to learn more about:
- Key product metrics in the B2B SaaS environment
- Vanity metrics product teams should avoid
- How to incorporate product metrics into the product development process
- How to use metrics to inform product development: 3 use cases
Key product metrics in the B2B SaaS environment
As Jason Cohen of WP Engine pointed out, choosing which metrics to track depends on who you’re presenting them to:
They’re all correct, so how do we select metrics that satisfy everyone?”
In this article, we’re focusing on the metrics product teams should rely on to inform their roadmaps. Below is a list of metrics that should be included in every product dashboard.
Acquisition Metrics
You don’t have to wait around for product usage statistics to accumulate before you can evaluate the performance of your product. Acquisition metrics will give you a good idea of how an audience perceives your product, including whether it meets their needs (or whether you’ve chosen the right audience).
What acquisition metrics say about your PX: While typically used to assess the effectiveness of marketing and sales activities, these metrics are valuable assets for young product teams as they’re good indicators of product-market fit — whether or not your product is aligned with the needs and expectations of your target market.
Conversion Rate
The conversion rate is a metric that product teams, in addition to marketers and salespeople, should pay attention to. It’s an early indicator of whether your SaaS product resonates with your target audience or not.
Note: What you define as a ‘conversion’ depends on your customer journey and business strategy. Any key action to be a conversion, from a free trial sign-up to upgrading to a premium package.
How to measure it: Divide the number of conversions (e.g., sign-ups, free trials, demos, etc.) by the total number of visitors to your website or landing page, and multiply it by 100 to get a percentage.
Customer Acquisition Cost
The Customer Acquisition Cost (CAC) is a financial indication of whether your product is product/market fit.
How to measure it: Add up all sales and marketing expenses involved in acquiring new customers and divide the sum by the number of new customers acquired over a specific period.
Activation and Engagement Metrics
While valuable early in the product cycle, acquisition metrics don’t say much to product teams that have already found their product-market fit—activation and engagement metrics do.
But these metrics can be tricky as every company has its own definition of product value. This is why it’s paramount that you define your own.
As you scale, start to segment users and personalize activation paths to different audiences.
Not all of your users are trying to achieve the same thing. So why do you push them down the same onboarding path?
Folks often focus on simplifying onboarding, reducing friction, etc., but can’t crack the code on improving their activation rate. The problem: you shouldn’t have only had one definition of activation.”
What activation and engagement metrics say about your PX: These metrics show how well users engage with your product features. They usually correlate with the quality of your onboarding process and your ability to understand and meet your customers’ needs in regard to product functionality.
You should use activation and engagement metrics to identify friction points users face early in their product journey.
Activation Rate
The activation rate measures the percentage of users who take a specific action or reach a milestone early in their product journey. Depending on your product, actions can include completing a tutorial, setting up a profile, or performing a key task.
How to measure it: Divide the number of activated users by the number of registered users. It’s also a good idea to calculate the activation rate by separating users into different cohorts (e.g. by registration date, customer demographics, etc.) to gain insight into the behavior of specific segments.
1. Identify actions/events with the ability to predict when users will convert to paid members.
2. Conduct interviews to understand when users truly grasp the value of PandaDoc.
3. Summarize findings to select a metric related to a predictive event supported by user interviews.”
What it says about your PX: The activation rate measures how well your onboarding process guides users toward success.
Free-to-Paid Conversion Rate
A free-to-paid conversion rate represents the percentage of free users who transitioned to paid customers.
How to measure it: Divide the number of users who transition from the free version of your product to the paid version by the total number of free users, then multiply that figure by 100 to get a percentage.
A low free-to-paid conversion rate may indicate a range of issues:
- Your product hasn’t delivered on your marketing/sales messaging promise
- The free version doesn’t fully showcase the value of your paid features
- Users face friction or barriers during the upgrade process
Onboarding Completion Rate
The onboarding completion rate measures the percentage of people who have successfully gone through your onboarding flow (and thus are most likely to become loyal users).
How to measure it: Divide the number of users who have successfully completed the onboarding flow by the total number of new users over a specified time.
A low onboarding completion rate may indicate several issues:
- The flow is too long and time-consuming
- Your content is irrelevant to the needs of users
- It’s displayed at the wrong time in the wrong place
- Users are proficient enough to use your product without basic guidance
Time to Value
Time to Value (TTV) measures how fast users can get value from your product after registration.
How to measure it: First, define an action that indicates a user has realized the core value of your product. Next, create an event for this action in your product analytics software and start tracking it specifically for new users. Do this with Smartlook by creating a cohort of users who registered on the same day, then turning to Retention tables to see the statistics for the specified event day by day.
New Customer Churn Rate
New customer churn rate is a metric that focuses on understanding the churn rate as it relates to new SaaS customers.
How to measure it: Divide the number of customers who canceled or stopped using your product during a period by the total number of customers at the beginning of the said period, then multiply it by 100 to get a percentage.
Feature Adoption Rate
Feature adoption rate measures the percentage of users that actively engage with specific product features.
How to measure it: Divide the number of users who interacted with the feature over a specific period by the total number of users who interacted with your app over the same period.
Retention Metrics
Retention metrics help you assess the long-term health of your B2B SaaS product.
What retention metrics say about your PX: These are direct indicators of how satisfied customers are in the long term.
Customer Retention Rate
The customer retention rate gauges the ability of your product to gain long-term users.
How to measure it: To calculate the customer retention rate, you’ll need to collect data on the number of customers at the beginning of a specific period (let’s say a month) and the number of customers at the end of that period. Divide the number of customers who remained active by the total number of customers, then multiply by 100 to get a percentage.
Customer Lifetime Value
Customer Lifetime Value (CLV) is a crucial metric that quantifies the total value a customer brings to your business throughout their entire relationship with your company. It assesses the long-term revenue potential of each customer.
How to measure it: Calculate the average revenue generated from a single customer throughout their entire relationship with your company.
Customer Satisfaction Score
A Customer Satisfaction Score (CSAT) measures how satisfied your customers are with your product after interacting with it for some time.
How to measure it: Run an in-app survey asking users to rate their satisfaction with your product on a scale (typically from 1 to 5 or 1 to 10).
Tip: You can build in-app CSAT surveys with Survicate. Target various customer segments for a comprehensive understanding of customer satisfaction across the entire customer journey.
Survicate integrates with Smartlook allowing you to back customer feedback with real-time user behavior insights.
Net Promoter Score
A Net Promoter Score (NPS) measures customer loyalty, including the likelihood of customers recommending your product or service to others.
How to measure it: Ask your customers how likely they are to recommend your product or service to others on a scale from 0 to 10. Based on their response, you can segment customers into Promoters (score from 9-10), Passives (score from 7-8), and Detractors (score from 0-6).
It’s worth noting that, on their own, CSAT, NPS, and other metrics don’t offer much value to product teams. You’ll need to take these metrics in context and analyze them alongside other relevant data points to see their value.
Vanity metrics product teams should avoid
So, why not just incorporate as many metrics as possible into your dashboard? Although it may sound like a good idea, there is a risk of falling into the trap of vanity metrics.
Vanity metrics are data points that appear significant at first but fail to provide valuable insight into the actual performance/health of your product. These metrics are not directly correlated with your objectives and therefore will only divert your attention from actionable insights.
The most common examples of vanity metrics for product teams include:
- Total sign-ups
- Total conversions
- Session duration
Instead of fixating on vanity metrics, product teams should prioritize insights into user behavior and product performance.
Here are some tips to avoid falling into the vanity metrics trap:
- Identify your product’s primary objectives
- Select metrics that directly relate to those goals
- Group the metrics that relate to different stages of the customer journey to make it easier to analyze the data
- Ignore metrics that aren’t aligned with your objectives
That’s it. You’re ready to track and use your product metrics to inform your product map and optimize your product for maximum customer satisfaction. Just stick to the instruction below.
How to incorporate product metrics into the product development process
Choosing the right product metrics is only the beginning. You still need to incorporate them into your product management routine.
Set up a monitoring system
When setting up or revisiting your product tracking system, you should know what you’re looking for. As mentioned, compiling as many product metrics as possible into one dashboard won’t help you make smarter product decisions—it will only distract you from what really matters.
These are some rules that will help you keep your product data organized and aligned with your goals:
- Map out product user journeys (you may have different user journeys for different custom segments) and define key milestones throughout
- Set up events with your product analytics software to keep track of milestones. Smartlook can track any website or in-app event, from landing page visits to saving a project in your product
- Identify the key metrics that align with each dashboard’s goal
- Make sure to set up a system for tracking each metric. For instance, while you can track conversion rates and activation metrics using Smartlook, you’ll need to add Survicate into the mix to collect user feedback
- Stick to the “one goal = one dashboard” rule. With Smartlook, you can create granular dashboards that focus on specific user segments, product aspects, and teams
Understanding metric hierarchy
Understanding metric hierarchy will help you allocate your resources and efforts effectively so you can focus on the metrics that will have the most impact.
To make sense of the metrics you’re tracking, try splitting them into three levels:
- North Star—this is the ultimate measure of product success. It can be MRR, CLV, or any other revenue-related metric
- Key influencers of North Star—these are the secondary metrics that have a direct impact on the North Star metric
- Levers—these are the individual initiatives you can employ to improve Key Influencer metrics
By organizing your metrics into the levels described, you’ll create a clear hierarchy to guide your decision-making process. Now, your product team can prioritize their efforts based on the impact each metric has on the North Star.
It also helps to understand the difference between leading and lagging indicators. Leading indicators, like activation and product adoption, predict future success. Lagging indicators, such as revenue or CLV, measure the outcome of past actions.
So, even though it is the ultimate measure of success for a product, you have to put it in context with other metrics to run your product properly.”
Compare the metrics against your goals/benchmarks
All these metrics only make sense when viewed in the context of your business goals and benchmarks.
Once you have your North Star, you’ll need to set goals for Key Influencer metrics and levers—the milestones you’ll need to reach to achieve your primary goal. These are the milestones you’ll be comparing your actual product performance against.
Combine quantitative and qualitative insights
You’ll need to go beyond the numbers. While quantitative data provides numerical metrics and statistics, qualitative insights will show you the user behavior and experiences behind the numbers.
Use quantitative product metrics to spot potential issues in the customer experience. Instead of coming up with hypotheses regarding what may have caused an issue, turn to qualitative insights like funnel reports and session recordings to detect real customer struggles.
Here are a few examples of quantitative and qualitative data working together:
- Activation. Your TTV metric is too high, and you don’t know why. You proceed to analyze other activation metrics only to find out that your onboarding completion rate is low. In Smartlook funnels, you review session replays from individuals who drop from the onboarding flow. It turns out they were closing guidance pop-ups and then struggling to find help content within your UI.
Session recordings in Smartlook
- Feature adoption. Quantitative data shows that a certain feature has a low adoption rate. To understand why users aren’t engaging with the feature, you perform a funnel analysis and discover that your users aren’t following the flow you’ve laid out for them. With help from in-app surveys, you ask users about their experience with the feature only to learn they’re unaware of it.
Funnel view in Smartlook
- Customer satisfaction. Recent surveys indicate poor customer satisfaction scores within a customer segment. Through contextual micro surveys, you discover that customers in this segment are experiencing issues with a new product update.
Survicate’s in-product surveys
Revisit your product development cycle regularly
Revisiting your product development cycle regularly is a critical practice that allows product teams to stay agile, responsive, and aligned with the ever-changing needs and expectations of their customers.
Create a schedule for periodic reviews and updates to your product roadmap. Depending on the complexity of your product and the pace of the market, you may choose to conduct these reviews monthly, quarterly, or on a more frequent basis.
Prioritize friction points
Friction points are areas where users encounter difficulties, obstacles, or frustrations while using your product. While developing new features is important, focusing on resolving existing issues will lead to more immediate and tangible results.
Here’s what you need to do:
- After identifying friction points using quantitative and qualitative product data, assess the impact of each issue on the UX
- Connect the identified friction points with your overall business goals and product strategy
- Evaluate the effort and resources required to address each friction point
- Prioritize the friction points that have a negative impact on the UX but are feasible to address quickly
Use the insights to forecast future performance
Product metrics not only help product teams address issues but they also allow them to gauge a product’s future performance and anticipate potential challenges.
Use product metrics to spot early signs of UX issues and address them proactively. The most insightful metrics in this instance are customer satisfaction scores and Net Promoter Score (NPS) over time. If these begin to decline, you can still proactively investigate and address the root cause before it impacts other product performance metrics.
Collecting historic product data will help you understand cause-and-effect relationships and forecast future performance with accuracy. By analyzing past trends and patterns in product metrics, you can identify the factors that have historically influenced your product’s success and failures and address them more effectively in the future.
Search for continuous opportunities for growth
Nothing is worse for product development than sticking to a static strategy. To achieve sustainable growth, you should not only focus on addressing immediate issues but also proactively identify and capitalize on growth opportunities.
Keep a direct line of communication open with your users. Customer feedback is the best way to unearth growth prospects aligned with user needs.
Use historic product insights to make hypotheses and develop experimentation plans. These insights unveil trends, behaviors, and patterns that lay the foundation for hypotheses.
What worked before? What didn’t?
Use this knowledge to predict how proposed changes might impact user experiences.
Most importantly, before pursuing growth opportunities, be sure to check whether they align with your long-term business objectives. While it might be tempting to aim for quick victories, it’s smart to ensure your efforts guide your product toward long-term success.
How to use metrics to inform product development: 3 use cases
Product metrics serve a greater purpose than simply displaying team performance during routine reviews. They should inform product team decisions throughout the entire development lifecycle.
Here are the most common use cases for incorporating product metrics:
Validating hypotheses
When considering product changes, product teams speculate about how these updates will impact UX and key performance indicators (KPIs).
By setting up clear success criteria and defining the right product metrics, product teams can test hypotheses and determine whether proposed changes are likely to yield desired outcomes.
Prioritizing feature enhancements
In a dynamic product development environment, teams are often bombarded with ideas for new features and improvements. However, it’s essential to prioritize these ideas based on their potential impact on the user experience.
Product metrics provide objective data to evaluate the potential impact of feature adjustments. By analyzing metrics like customer feedback, feature adoption rate, and customer retention, product teams can identify which enhancements are likely to foster the most user value.
Addressing product friction
By analyzing quantitative data like activation and engagement metrics and retention rates, as well as qualitative data from user feedback and support tickets, teams can pinpoint the specific pain points users face.
With this data in hand, product teams can develop targeted solutions to alleviate them. Whether it’s improving the onboarding process, simplifying complex features, or streamlining the checkout flow, data-driven insights help teams prioritize the most critical friction points and focus on improving the UX.
Back your product metrics with PX insights
With Smartlook, you’ll bridge the gap between quantitative product metrics and qualitative PX insights so you can:
- Collect the most important product metrics including conversions, retention, and feature adoption stats
- Analyze funnels and review session recordings to understand user behavior, including the “why” behind the numbers
- Dig deeper into CSAT and NPS scores with Survicate & Smartlook integration
- Accumulate enough historic data to make informed decisions regarding product performance and anticipate potential challenges
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