Chapter 13: SaaS Metrics: Measuring Success with a Product-led GTM

Peter Drucker, one of the most famous management consultants of the last century, once said: “You can’t manage what you can’t measure.”

There is plenty of material available on SaaS metrics; we will reference a few valuable resources in this section. However, it’s important to keep in mind that a product-led GTM strategy, as we have shown throughout this book, changes the traditional sales funnel, replacing MQLs and SQLs with PQLs. Moreover, a product-led approach enables teams to track and measure in-product customer behaviors and correlate them with crucial SaaS metrics, such as CLV,CAC, and others.

Simply put, a product-led approach slightly changes the focus of what and how we measure success and effectiveness. SaaS companies are unique in how they acquire and retain customers. They invest significant resources in acquiring customers up front, and then recover the costs and realize revenue through the lifetime relationship with each customer. First, let’s look closely at how to track customer progression through a lifecycle.

13.1 Step 1: Tracking Customer Progression Through a Lifecycle

A product-led strategy changes how companies track the effectiveness of customers moving through different lifecycle stages: from signup to PQL, from PQL to customer, and from customer to active customer. Each ratio helps identify bottlenecks in the lifecycle where customers experience a value gap and fall off the process.

With a product-led customer acquisition strategy, companies start gathering valuable behavior data early in the customer lifecycle, which increases team effectiveness in re-engaging customers with the product. By understanding conversion processes along the customer lifecycle, companies can design timely engagement campaigns to encourage customers to return to, and actively use, their product.

Let’s quickly review the seven user states in the customer lifecycle:

The initial goal for SaaS companies is to convert visitors to signups. At signup stage, the team can collect profile, company, and in-product behavioral data. Using this data allows teams to deliver timely engagements with the customer through in-product, mobile, or e-mail messages. Then companies can nurture prospects until they show enough interest in the product based on their product usage. Prospects that reach PQL stage are engaged by the sales team to assist in buying or in helping them increase product usage. Tracking customer behavior inside the product enables companies to predict and mitigate customer churn, and forecast CLV.

But let’s take a step back and explore how tracking conversion rates is an effective way to understand how prospects and customers progress through the customer lifecycle.

Measuring Effectiveness with Conversion Rates

Conversion rate from one state to the next in the lifecycle helps companies understand what part of the lifecycle can be improved. At the same time, sales can more accurately forecast when they understand conversion rates.

Let’s review the basic conversion rates that companies have to track to understand how successful they are in progressing a prospect through the customer lifecycle.

Visitor-to-Signup Rate is the percentage of visitors that visit your page and then sign up. It is calculated by dividing the number of product signups by the number of visitors to a signup page. The visitor-to-signup rate shows how effectively your company convinces visitors to sign up for free trials or a freemium.

Signup-to-PQL Rate is the percentage of prospects that complete profile and in-product engagement requirements to become PQLs. It is calculated by dividing the number of PQLs by the number of signups. The signup-to-PQL rate provides insights into how effectively your company engages prospects in the early stages of reaching initial value.

Note: In some cases, it makes sense to analyze signup-to-PQL rate in terms of individual and account signups.

PQL-to-Customer Rate is the percentage of PQLs that convert to customers. It is calculated by dividing the number of customers by the number of PQLs. The PQL-to-customer rate shows how effectively your company converts PQLs to customers.

Signup-to-Customer Rate is the percentage of signups that become paying customers. It is calculated by dividing the number of customers by the number of signups. The signup-to-customer rate shows how your company, on average, converts signups to customers.

Customer Churn Rate is the percentage of customers lost due to churn (i.e., cancellation or failure to renew). It is calculated by dividing the number of churned customers by the number of customers at the start of the period for which the churn rate is calculated.

Monthly Recurring Revenue (MRR) Churn Rate (a.k.a. Gross Revenue Churn) is the percentage of MRR revenue lost from existing customers at the start of the period for which MRR churn is calculated. MRR churn rate is always positive (positive churn rate means the company loses money), because it does not include upsell and cross-sell.

MRR Expansion Rate (aka Net Revenue Retention Rate) is the percentage of MRR that is gained due to upsells and cross-sells to existing customers for the period for which MRR expansion is calculated. It is calculated by dividing the amount of expansion MRR by the starting MRR.

Net MRR Churn (a.k.a. Net Revenue Churn) is the percentage of MRR change based on churned and expansion MRR for the same period. It is calculated by subtracting expansion MRR from churned MRR and dividing that by MRR at the start of the period. Net MRR churn is the percentage of MRR lost from existing customers in a period.

When measuring churn, we need to remember that positive churn means that the company is losing customers and revenue, and negative churn means the opposite — the company generates more revenue from upsell and cross-sell than it loses to churn.

The table below summarizes these conversion and effectiveness metrics.

Differentiating between customer and revenue churn rates is important. It is possible for a company to experience both a positive customer churn rate (i.e., the company lost customers) and a negative net MRR churn rate (i.e., the company generated more revenues from new customers than the revenues it lost from churned customers). This situation can be caused by the company deliberately moving up-market, selling to larger companies at a higher average selling price while losing smaller and less profitable customers. Another common cause for achieving both a positive customer churn rate and negative net MRR churn rate is a rise in prices that drives smaller customers to churn, but extracts more revenue from larger accounts.

As Tom Tunguz, partner at Redpoint Ventures, explains, negative churn can be a powerful growth mechanism.

Breaking down churn metrics

Churn and retention metrics can be calculated in multiple ways; we have provided the most common measurements. While many in the SaaS industry are aware of the importance of tracking churn rate, let’s highlight a few reasons why.

A high churn rate can drive a SaaS company out of business. Acquiring a new customer is always more expensive than retaining an existing one. For SaaS businesses, keeping existing customers is even more critical, because if a customer leaves before the break-even point, the company loses money. The longer a customer stays with the company, the higher revenue margins the company realizes.

In the traditional GTM model, the customer success team is in charge of managing churn. Often, customer success teams use Net Promoter Scores (NPS) and product analytics to plan how to follow up with customers. The problem with this approach is that customer success solutions only show forensics data on customer behavior, piecing the data puzzle together after the fact. On the other hand, a product-led customer acquisition process allows teams to monitor customer engagement as it occurs and proactively deliver engagements that reduce the probability of churn.

Let’s look at a simple example of how a fictional SaaS company calculates its churn rate.

You can find a more detailed analysis of churn metrics in the iconic post “SaaS Metrics 2.0 — A Guide to Measuring and Improving what Matters” by David Skok, general partner with Matrix Partners.

Measuring the velocity of customer lifecycle

Velocity metrics describe how long it takes a company to achieve certain milestones. This includes the average number of days it takes a prospect to go from signup to becoming a PQL, and the average number of months it takes a company to break even (when CLV>CAC).

Understanding how long it takes for prospects and customers to move from one customer lifecycle state to another helps companies in two ways. They can better forecast revenue, and also understand what part of the lifecycle is the longest and how to shorten it.

Customer lifecycle length is the average number of days that takes a prospect or customer to advance to the next stage. Let’s review a few critical metrics within this lifecycle:

Days from signup to PQL measures the average number of days it takes a prospect to become a PQL.

Days from PQL to customer measures the average number of days it takes a PQL to become a customer.

Days from signup to customer measures the average number of days it takes a prospect to become a customer.

Days to break-even measures the average number of days it takes a customer to generate enough revenue to cover the CAC. In other words, it shows how quickly a company recovers its CAC.

Average customer life is the total time (in days) of a relationship between a customer and a company. It measures the average number of days (or months) between the day a prospect becomes a customer and when that customer churns (or cancels).

The table below provides examples of customer acquisition metrics.

13.2 Step 2: Defining PQLs

In the traditional customer acquisition process, marketing is in charge of generating and nurturing leads until they are ready to buy. This qualification process includes understanding a prospect’s profile and company data, as well as responses to marketing such as downloading a whitepaper, opening an e-mail, or attending a webinar. As we highlighted earlier, such engagement is minimally correlated with buying intent, which is why we see a low MQL-to-customer conversion rate.

In additional to a prospect’s profile and company data, PQL also includes in-product behavioral data to quantify buying intent. There are three categories of data that define PQL state: profile, company data, and CBI. CBI is an essential aspect of a product-led strategy, as it provides insights into customer intent and predicts how likely a user is to progress to the next stage of the customer lifecycle. So, essentially:

PQL = profile + company + CBI

In some cases, companies can break down PQLs into more granular milestones. As we showed in Chapter 9, at Aptrinsic, the process of getting a prospect to the PQL stage includes four milestones. Each milestone represents the completion of a certain onboarding journey.

1. Profile Data
   Profile data includes a prospect’s name, e-mail, title, function, and sometimes other information.
2. Company (or firmographic data)
   Company data includes company name, size, industry, revenue, location, news, and other relevant company-related details.
3. CBI
   Companies are familiar with profile and company data since both are a part of the traditional sales funnel. The CBI introduces a new concept for measuring and understanding how engaged prospects and customers are with the product.

Step 3: Understanding the CBI

The CBI is a metric (often normalized) that measures how engaged your prospects and customers are, based on their in-product activity and usage.

CBI measures user engagement through the whole customer lifecycle. By incorporating a CBI into their product-led strategy, companies can predict adoption, retention, and growth of a customer. A CBI calculation includes all the usage and interactions that a user has with the product. These are weighted by how much a specific interaction is correlated with the likelihood of a customer advancing to the next stage of the customer lifecycle. Ultimately, they are correlated with CLV.

CBI includes, but is not limited to, the following metrics:

• DAU/MAU, or the number of logins over a period of time
• Average time spent in-product per session
• Number of completed core use cases
• Number of features interacted with
• Engagements with in-product notifications

CBI enables teams to anticipate issues that customers may run into, and use that insight to design behavior-based triggers that automate engagement for appropriate situations.

13.4 Step 4: Monitoring Customer Satisfaction

Customer satisfaction metrics show how engaged customers are, how much value they derived from your product, and whether they will recommend your solution to peers. Customer satisfaction reflects how a customer perceives the value and experience with your company, brand, and product. A few examples of metrics in this category include NPS, CBI, and CLV.

• CBI helps you figure out how, and how often, customers interact with your product.
• NPS helps you determine how likely a customer is to recommend your product.
• CLV helps you understand the potential net profit over the entire future relationship with a customer.

There are a few reasons why customers can use your product and not be satisfied. Remember: The decision to sign on to use your product wasn’t necessarily unanimous. Perhaps it was pushed through because of an executive’s ties to the leadership of your company. Regardless, your goal is to make all the users happy with the decision so you reduce the likelihood of churn.

Even though your product is a SaaS offering, there are real costs associated with migration, integration, and training. While sales teams are typically focused on minimizing those costs to get new accounts signed on, the reality is that customer success teams should emphasize those switching costs. Remember: the goal is to retain the customer by providing as much product value as possible, and making a switch seem unappealing.

Let’s not forget that your customers will likely be judging their experience of your product based on their experience with best-in-class apps and leading consumer companies (i.e., the industry standard-bearers). It’s critical to understand which of these aspects and features your customers most value and appreciate so you understand how the customer experience with your product is being measured.

A product-led strategy enables companies to measure customer satisfaction more accurately through both self-reported surveys and behavioral data. Furthermore, personalizing the customer experience based on in-product behaviors gives users more reasons to stay with your product.

Your organization needs to keep a finger on the pulse of customer experience and satisfaction, and identify the customers who are using your product but aren’t happy with it. Customers who get stuck with a bad experience when using your product can negatively impact your brand. Even if they haven’t churned yet, they might share their dissatisfaction on social media or through other venues, and influence buying decisions in their extended networks. If you’ve spent time on LinkedIn, you’ve probably seen someone asking for a product review or complaining about the product they are using; and you know such discussions can quickly catch fire and go viral.

Asking customers why they feel a certain way may not yield real answers. In fact, people are shown to lie quite frequently when surveyed. The reasons for their lies can include a desire for self-preservation (for example, wanting to appear better than they are), and to be helpful by giving what they think are the desirable answers; or they can believe their answers are correct, but it’s hard to be certain when trying to recall a moment from long ago or envision a future scenario. Plus, the outcomes and perceptions of many interactions can’t be easily understood and analyzed by conducting surveys.

The NPS is a great tool to get an idea of overall customer satisfaction, but it’s not good at diagnosing the problem. That said, it is still valuable for assessing the customer experience. Even experienced teams sometimes overlook this metric, but it can be an early diagnostic tool to predict how churn rate will change in the future.

Another option is to observe users in their “natural habitat” (i.e., their work environment) as they use your product. Watching how a sample of users interact with your product can quickly yield valuable insights. However, this may not be very practical or scalable to do with each new feature release.

Tips:

• Beware of written surveys. Occasionally conduct phone or in-person interviews.
• Never ask “What don’t you like?” about your product or competitor, because people don’t want to admit that they made a mistake.

Customer health (or customer satisfaction) should be evaluated based not only on self-reported surveys such as the NPS score, but also based on customer behaviors. Customer behavior is a more reliable metric than answers provided on self-reported surveys. That’s because people can’t always remember — or remember accurately — what their experience was like once they’re outside of it. In many instances, customer behavior is all that matters. A properly designed and executed product-led strategy should enable the company to calculate CBI to predict customer satisfaction and retention.

Customer Health (Satisfaction) = CBI + NPS

13.5 Step 5: Analyzing Product Engagement and Adoption Metrics

What has been missing from the traditional customer acquisition process is product usage and feature adoption metrics. Customer satisfaction metrics assess how effective companies are in engaging and delivering value to customers. The product-led strategy adds a new dimension by analyzing product features, core use cases, and other product usage metrics to decide what to build next, what feature to discontinue, or what customer journey needs to be redesigned.

Here are a few examples of product engagement and adoption metrics that a company should consider:

• Percentage of users that use a particular product feature or channel (for example, InVision can track the percentage of customers that use a mobile app on a daily or monthly basis)
• Number of steps or clicks it takes a customer to complete core product use cases (for example, for Expensify, it could be the number of steps or clicks it takes to file and approve an expense report)
• Average time it takes for a user to complete core product journeys (for example, at Aptrinsic, we track how long it takes prospects to implement our JS code in their products)
• Percentage of users or sessions where the number of steps or clicks exceed the optimal number (for example, MailChimp can track the percentage of customers that exceed the optimal — smallest — number of clicks it takes to create an e-mail marketing campaign)
• Average number of days it takes a newly signed-up prospect to fully onboard with the product (for example, Asana can track the average number of days it takes for a new user to create the project, assign tasks, and complete 10 tasks)

The successful product-led strategy enables teams to understand what product features are driving adoption and engagement, and for which customer segment. This data helps evaluate the product on a deeper level when used in combination with customer satisfaction metrics (NPS and CBI). Contrast this with the traditional GTM strategy, where the granular breakdown of product usage data is missing from the customer acquisition process. As a result, marketing teams rarely segment customers based on product usage and features and instead use outside-of-the-product interaction data to optimize lead generation campaigns. In other words, with the traditional model, the best the organization can do is use demographics and outside of the product data to impact marketing campaigns.

13.6 Step 6: Measuring Core Business Metrics

A few vital SaaS metrics are used to assess the health of a SaaS company. Aside from the basic revenue growth metrics, such as MRR and annual recurring revenue (ARR), SaaS teams need to closely monitor CLV, CAC, average selling price, and break-even.

Let’s review these core SaaS metrics and how they are influenced by a product-led approach.

Customer Lifetime Value (CLV) is a prediction of the net profit attributable to the entire future relationship with a customer. It is the revenue generated from customers between the time the company reaches the break-even point with them and the end of the relationship with them.

Customer Acquisition Cost (CAC) is the average amount that a company spends to acquire a single customer. CAC is the sum of all customer acquisition costs, including sales & marketing expenses and salaries divided by the number of customers acquired during the same period.

Average Selling Price (ASP) is the amount of revenue per customer generated.

Break-even is a point when a company generates enough revenue from a customer to cover all the costs and expenses it took to acquire this customer.

Customer Lifetime Value as a core metric

Every SaaS organization should strive toward increasing CLV, because it closely correlates to the value a company provides to a customer. It also correlates to the company’s profitability. The longer a SaaS company can keep a subscribed customer by providing value, the more profitable it is.

SaaS businesses can calculate CLV in a few ways. The most common way is to multiply average revenue per account (ARPA) by customer lifespan. (You can find more details in this infographic by Kissmetrics.) However, this calculation doesn’t consider the average CAC. Another common CLV calculation is average MRR x gross margin x customer lifetime in months. (Check out this great guide to SaaS metrics by Eckhard Ortwein for more details).

What is common among all traditional CLV calculations is that they use grossly simplified average numbers and, because of this, are not useful for predicting CLV trends early. Since it takes time for companies to evaluate new gross margins or ARPA, they cannot respond in a timely way to trends around changing CLV values.

The properly executed product-led strategy can help teams not only segment customers and forecast CLV for each segment, but also predict the rise and fall of CLV value based on in-product customer behaviors. For example, let’s say last month your marketing team generated 1,000 product signups. However, after the first month, the CBI for this group is below average, which will result in a lower than expected signup-to-customer conversion rate. Your team can forecast how your CLV will change based on this data and can adjust current campaigns to improve the quality of signups and likelihood of conversion.

A successful product-led GTM strategy is not just about providing a freemium or free trial; it is a strategic way of increasing virality and reducing CAC.

Monitoring CLV vs CAC ratio and breakeven analysis

The ratio between CLV and CAC shows how effective and efficient a company is in acquiring, retaining, and growing its customers. No business can sustain a CLV-to-CAC ratio below one in the long term. Such a ratio means the company spends more money to acquire a customer than the revenue it generates from the customer. As David Skok pointed out in his article “Startup Killer: The Cost of Customer Acquisition”, an unhealthy CLV-to-CAC ratio could very well signal the end of your company. A healthy CLV-to-CAC ratio, Skok explains, is when CLV is three times or more greater than CAC.

If the CAC is higher than CLV, the company is either paying too much for customers or doesn’t provide enough value to retain them — or both. Calculating expected CAC and CLV — as Tomasz Tunguz explains how to do in his article, “The Math Behind SaaS Startup Customer Lifetime Value” — can provide a more accurate view.

Before a prospect becomes a customer, a company accumulates costs. The purchasing event then triggers the process of recovering this customer acquisition investment. At this stage, the customer is moving toward the break-even point, which means generating revenue to cover all CAC. With a product-led strategy, companies can reduce the customer acquisition cost by building virality into freemium and free trial offerings. When existing users invite others to try your product, you avoid the need to spend money trying to acquire a new customer through traditional channels.

The value doesn’t end there. A product-led strategy also helps companies understand what customer journeys, product features, and usage patterns influence average selling prices and the time it takes to reach the break-even point. With these insights, they can design their products, campaigns and communications to shorten the time to reach break-even, while also commanding higher average selling prices.

That said, companies should not only think about optimizing CLV to minimize their costs. This is according to Michael Schrage, the author of Who Do You Want Your Customers to Become? As he highlighted in his article “What Most Companies Miss About Customer Lifetime Value”, by focusing on how to provide and extract maximum value to customers, companies can improve CLV in a way that benefits them in more and more meaningful, ways. For example, customers can suggest new product features and evangelize about companies and products on social media channels. They can even introduce your product to new customers and provide early feedback on a new product release or feature. In other words, by asking who you want your customers to become and what makes your customers more valuable, you can take a different perspective of CLV. In fact, you can measure it differently.

The product-led strategy fits the notion, described by Michael Schrage, of evaluating innovation and product investments based on what behaviors you want your customers to exhibit. Done successfully, this enables your company to design shorter and more agile feedback loops to build a product that your customers will love.

The table below summarizes the core metrics for a SaaS company using a product-led GTM strategy.

The diagram below summarizes customer states in a lifecycle, and critical metrics to evaluate the performance of a SaaS company using a product-led GTM approach.

13.7 Key Takeaways

• A product-led strategy changes how companies track the effectiveness of customers moving through different lifecycle stages.
• Measuring the conversion rate from one state to the next helps companies understand what part of the lifecycle can be improved.
• With a product-led approach, there are seven states in the customer lifecycle:

1. Visitor
2. Signup/Trial
3. PQL
4. Customer
5. Active Customer
6. Renewed/Retained
7. Churned

• A high churn rate can put a SaaS company out of business.
• Velocity metrics describe how long it takes a company to achieve certain milestones.
• The CBI is the essential (often normalized) metric for a product-led strategy. It measures how engaged your prospects and customers are based on their in-product activity and usage.
• Customer satisfaction metrics show how engaged customers are, how much value they derive from your product, and whether they recommend your solution to peers.
• Customer health (or customer satisfaction) should be evaluated based not only on self-reported surveys such as NPS scores, but also analyzed based on customer behaviors. A product-led strategy enables companies to measure in this way.
• Customers that are stuck using your product can negatively impact your brand.
• A product-led strategy brings a new dimension by analyzing product features, core use cases, and other product usage metrics to decide what to build next, what feature to discontinue, and/or what customer journey needs a redesign.
• Aside from basic revenue growth metrics, such as MRR and ARR, SaaS teams need to closely monitor CLV, CAC, ASP, and break-even.
• Focus on maximizing CLV both by delivering maximum value to customers and extracting maximum value from them.