The KPIs of product-led customer success teams

Product-led organizations are data-driven organizations. And one of their greatest strengths is that they share and democratize data so that teams across the company feel empowered to take action, too. While all of this data—product analytics, in-app messaging engagement, user journeys, feedback—is relevant for stakeholders across the organization, it’s important to examine it through the lens of how it helps specific teams drive business success and better product experiences. 

Here are the top eight KPIs the best customer success teams monitor and measure, why they matter to customer success managers (CSMs), and how they contribute to building a strong, product-led organization.

1. Time to value

Time to value measures the time it takes for a customer to derive value from your product, from the moment they start using it. This is often called the “aha” moment—when the user grasps why they need the product and understands how it will benefit them. Time to value can be measured using product analytics—specifically how long it takes users to engage with the most important features of your product. It’s a useful measure of customer satisfaction and a great early indicator of retention (or conversely, churn) for CS teams.

How to take action on time to value data

• For users with high time to value:
  If users don’t have a good first impression of a product and are struggling to use its key features, it’s unlikely that they will return. Dig into the behaviors of the users who are taking the longest time to engage with your product’s key features to see where they’re getting stuck. You might be able to take those findings to the product team and work with them to improve the product’s user interface (UI) or functionality, or perhaps deploy a tooltip or walkthrough to direct these users to the areas of the product they’re currently missing but should be using.
• For users with low time to value:
  The users getting to that “aha” moment the fastest are a goldmine of good behaviors. Take a look at their journey through your product and see how you might replicate their workflows across your other customers. Create in-app onboarding programs to guide struggling users through the steps your most successful users are taking.

2. Adoption

At its core, the most important function of CS is to help customers get what they need out of the products they’ve invested in, so that they can use them to achieve their goals. A clear way to assess whether customers and users are getting value from the product is to track their adoption, or the number of users or accounts who are interacting with the product (product adoption) or specific features within it (feature adoption).

Product adoption can be expressed over time by the number of monthly active users (MAU), weekly active users (WAU), or daily active users (DAU); or as a rate relative to new user signups for a given period of time. Feature adoption tracks specific features within the product so you can understand who is making use of them, and when. Additionally, closely monitoring adoption at both the user and account level helps CSMs understand which customers are seeing value from the product, which might be struggling or need a little extra help, or which might be at risk of churning—so they can intervene as appropriate. Adoption is also an effective early indicator of overall brand satisfaction and a customer’s intent to renew.

How to take action on adoption data

• For accounts with low feature adoption:
  Target users who have not yet adopted key features with in-app guides featuring videos or GIFs that demonstrate how to use them. This will help address enablement gaps and encourage those users to utilize the product for its intended purpose.
• For accounts with lagging or diminishing product adoption:
  Proactively reach out to customers whose adoption is trending downward to understand their challenges, inform them of new or soon-to-be released features with in-app guides, or set up a meeting to realign on how the product can help them achieve their goals.
• For users with excellent product and feature adoption:
  Launch a congratulatory in-app guide to celebrate the progress they’ve made, and ask for a review or feedback on what they love most about the product.

3. Stickiness

Stickiness measures how many users return to the product on a regular basis, and can be tracked in three different ways: Monthly users who return daily (DAU/MAU), weekly users who return daily (DAU/WAU), or monthly users who return weekly (WAU/MAU). The ratio you choose should reflect what ideal engagement with your product looks like—for example, a platform that users need to leverage on a daily basis (like an electronic records management platform for physicians) vs. on a less frequent or even seasonal basis (like a tax planning and filing app for individuals). 

Stickiness is valuable for CS teams because, like adoption, it indicates how many users are finding value in the product and incorporating it into their expected workflows. CSMs can use stickiness as a clue to help monitor and encourage the same behaviors of their “power users” across their entire book of business.

How to take action on stickiness data

• For accounts with low stickiness:
  This could be a signal that additional enablement or guidance is needed, because users aren’t using the product at a frequency or in ways that deliver the most value. Target these users with emails to encourage them to revisit the product, then serve them onboarding-style walkthroughs that guide them through key functionality when they do return the product.

• For accounts with high stickiness:
  Examine the behaviors of the users and customers leveraging the product at the highest frequencies, then build resources (like training sessions or in-app FAQs) to help other users achieve the same success.

4. Growth

Growth measures the net effect of your user acquisition and retention efforts. It signals both the addition of new accounts and increased usage of existing accounts by capturing how many users are joining, staying, and leaving your product in a given period. 

In general, CS teams are responsible for customer retention and expansion within their organization. Growth is a “north star” metric that gives CSMs a clear line of sight into how customers and users perceive the value and efficacy of the product. And when it’s evaluated alongside other product analytics data, feature releases, or qualitative feedback, it can be a valuable starting point for understanding what customers and users really want or need. CSMs can then take this information back to the product team to help inform the roadmap and guide the future direction of the product.

How to take action on growth data

• If user growth increases following the announcement or release of a key feature:
  You could infer that the feature—and potentially others related to it—are highly desired, and thus more likely to be broadly adopted. This is valuable information for the product team, and also arms CSMs with valuable proof points to bring into conversations with at-risk or low-adopting customers, in an effort to encourage them to try the new feature for themselves.
• If growth is stagnant following a release:
  This could signal a lack of awareness about the new feature. Work with the marketing team to deploy promotional in-app guides encouraging users who haven’t engaged with the feature to explore it for themselves, or to ask them what other features they may want to see in the future.

5. Product Engagement Score (PES)

The Product Engagement Score (PES) is a composite metric, representing the product’s average adoption, stickiness, and growth rates. PES is intended to serve as a single, overarching metric to measure how users or customers are engaging with the product, and can be used to measure success at either the visitor or account level. If your product is only used by individuals (and not teams), then you’ll likely only want to measure each PES component at the visitor level. But if your company is focused on new logo acquisition, measuring growth (and overall PES) at the account level is best. 

For CS teams, tracking PES over time and comparing it against things like company news and product changes is a good way to compare customer satisfaction and feature efficacy. And similar to more generally used metrics like Net Promoter Score (NPS) or Customer Satisfaction (CSAT), PES is another tool CSMs can have in their arsenal to measure and monitor overall account health over time.

How to take action on PES data

• Monitor PES over time and report on it regularly (for example, during monthly account check-ins) to identify trends and areas for improvement. Drill down into each of the three measures to explore how and why they may have impacted the overall PES score month-over-month.
• Report on PES during quarterly business reviews (QBRs) or other executive meetings to provide an at-a-glance view into the product’s performance.

6. Retention and net revenue retention (NRR)

Retention measures the percentage of users (or customer accounts) still using your product after they initially install or start using it, at both the app and feature level. This metric tells you how many users or customers continue using a product or feature during a given time period, and is a good indicator of churn risk. It’s an important measure for CS teams, in particular, because it’s generally operationally less expensive to retain current customers than it is to acquire new ones. In other words, low retention can cause an organization to lose money when they sign a new customer—so it’s important to work hard to drive reliable retention by delivering a great product experience, and to understand (and mitigate) the factors that contribute to a poor product experience.

Net revenue retention (NRR) is related. It’s the percentage of revenue retained from your existing customers over a given period of time. As the primary owners of customer retention and expansion opportunities, NRR is particularly important to CS teams because it helps signal the product’s ability to continually meet the needs of customers and grow its reach while minimizing churn.

How to take action on retention and NRR data

• Retention:
  In general, the fewer features a user interacts with on a regular basis, the less likely they are to glean value from your product. A steady decline in feature retention could be an early indicator of churn. Segment your retention data (e.g. by role, company size, or free users vs. paying customers) to compare feature retention across different subsets of users and determine how their behaviors differ.
• NRR:
  Examine the accounts who contributed the greatest dips or increases in NRR. Dig into the behaviors of those users to identify what they did well (contributing to increased NRR) or where they were struggling (contributing to decreased NRR). This can help you replicate positive workflows or onboarding processes from your customers who expanded; or get ahead of product issues, gaps in enablement, or other factors that contributed to a negative experience for your customers who churned.

7. Net Promoter Score (NPS)

Net Promoter Score (NPS) is one of the most common ways companies gauge customer loyalty. It is delivered via a single question that asks, “How likely are you to recommend this brand to a friend or colleague?”, with customers answering on a 0-10 scale. Customers who answer 9 or 10 are your “Promoters,” those who answer 7 or 8 are “Passives,” and those who answer 0 through 6 are “Detractors.” 

NPS is highly pervasive, and is thus a valuable way to benchmark your organization against others in your industry. It’s also an easy metric to collect and track at regular increments of time. CS teams are particularly well-equipped to field and respond to customer NPS scores because they work with customers every day and are in a unique position to capture the qualitative context behind each score. NPS is also a good signal of overall expansion and success—your customers’ propensity to advocate for your brand serves as a proxy for their happiness and, eventually, business growth.

How to take action on NPS data

• Measure NPS at the account and individual user level. Account-level scores will help you understand each customer’s average sentiment, whereas user scores will help uncover if there are any key personas driving your score up or down (so you can investigate their behaviors further and replicate or intervene as necessary).
• Always ask respondents for written comments in addition to their numerical scores. This qualitative information can help add valuable context, and gives you a natural segue for following up with customers as needed.

8. Feedback and feature requests

Customer feedback is information provided by customers about their experience with a product or service. Its purpose is to reveal their level of satisfaction and help product, customer success, and marketing teams understand where there might be room for improvement. Feature requests are a subset of customer feedback—specifically related to the features or optimizations customers and users want to see from the product in the future. CS teams are in an optimal position to hear both feedback and feature requests first-hand from customers, and help other teams across the organization—like product and engineering—gather these insights in a single place to be triaged and considered for the roadmap. 

It’s impossible (and inadvisable) for the product team to act on every feature request, so having an idea of the top-most requested features from customers and users is extremely useful. This information benefits CSMs, too. To help prevent churn from your biggest customers, it’s important to analyze top requests by account to see if there are improvements worth investing in. CSMs can also proactively bring these top requests as talking points during customer meetings and get more context or explain why particular features are (or aren’t) being considered for future releases.

How to take action on feedback and feature request data

• Feedback:
  Use product analytics to correlate how customers are using the product with their feedback to get a complete picture of their journey. For example, if a customer provides feedback that they think a particular workflow feels overly complicated, you can look into their analytics data to see the path they typically take to complete the workflow, as well as which features they engage with along the way. This could help you uncover whether a change to the product’s functionality or UI might be needed, or whether the issue actually stems from an enablement gap that could be solved with an in-app guide.
• Feature requests:
  Take a look at requests made at the account level before each QBR with your customers. This will help you feel prepared to answer their questions and gives you the opportunity to proactively get more context about the challenges they’re hoping to solve. And ultimately, it also builds trust and helps your customers feel heard and valued.

Keep learning